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1. A method for searching a structured data table T with m attributes and n records, where A={a 1 ; a 2 , : : : ; a m } denotes an attribute set, R={r 1 ; r 2 , : : : , r n } denotes the record set, and W={w 1 ; w 2 , : : : ; w p } denotes a distinct word set in T, where given two words, w i and w i , “w i ≦w j †denotes that w i is a prefix string of w j , where a query consists of a set of prefixes Q={p 1 , p 2 , . . . , p l }, where a predicted-word set is W k l ={w|w is a member of W and k l ≦w}, the method comprising for each prefix p i finding the set of prefixes from the data set that are similar to p i , by: determining the predicted-record set R Q ={r|r is a member of R, for every i; 1≦i≦·l−1, p i appears in r, and there exists a w included in W k l , w appears in r}; and for a keystroke that invokes query Q, returning the top-t records in R Q for a given value t, ranked by their relevancy to the query, treating every keyword as a partial keyword, namely given an input Q={k 1 ; k 2 ; : : : ; k l for each predicted record r, for each 1≦i≦·l, there exists at least one predicted word w i for k i in r, since k i must be a prefix of w i ,quantifying their similarity as:
sim =( k i ;w i )=| k i |/|w i | if there are multiple predicted words in r for a partial keyword k j , selecting the predicted word w i with the maximal similarity to k i and quantifying a weight of a predicted word to capture the importance of a predicted word, and taking into account the number of attributes that the l predicted words appear in, denoted as n a , to combine similarity, weight and number of attributes to generate a ranking function to score r for the query Q as follows: SCORE ⡠( r , Q ) = α * ∑ l = 1 1 ⢠⢠idf w i * sim ⡠( k i , w i ) + ( 1 - α ) * 1 n a , where α is a tuning parameter between 0 and 1.
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1. A method for searching a structured data table T with m attributes and n records, where A={a 1 ; a 2 , : : : ; a m } denotes an attribute set, R={r 1 ; r 2 , : : : , r n } denotes the record set, and W={w 1 ; w 2 , : : : ; w p } denotes a distinct word set in T, where given two words, w i and w i , “w i ≦w j †denotes that w i is a prefix string of w j , where a query consists of a set of prefixes Q={p 1 , p 2 , . . . , p l }, where a predicted-word set is W k l ={w|w is a member of W and k l ≦w}, the method comprising for each prefix p i finding the set of prefixes from the data set that are similar to p i , by: determining the predicted-record set R Q ={r|r is a member of R, for every i; 1≦i≦·l−1, p i appears in r, and there exists a w included in W k l , w appears in r}; and for a keystroke that invokes query Q, returning the top-t records in R Q for a given value t, ranked by their relevancy to the query, treating every keyword as a partial keyword, namely given an input Q={k 1 ; k 2 ; : : : ; k l for each predicted record r, for each 1≦i≦·l, there exists at least one predicted word w i for k i in r, since k i must be a prefix of w i ,quantifying their similarity as:
sim =( k i ;w i )=| k i |/|w i | if there are multiple predicted words in r for a partial keyword k j , selecting the predicted word w i with the maximal similarity to k i and quantifying a weight of a predicted word to capture the importance of a predicted word, and taking into account the number of attributes that the l predicted words appear in, denoted as n a , to combine similarity, weight and number of attributes to generate a ranking function to score r for the query Q as follows: SCORE ⡠( r , Q ) = α * ∑ l = 1 1 ⢠⢠idf w i * sim ⡠( k i , w i ) + ( 1 - α ) * 1 n a , where α is a tuning parameter between 0 and 1. 21. The method of claim 1 where the data table T is structured into a trie and further comprising linking each node on the trie corresponding to a word, w i , to each node corresponding to the synonyms of the word, w i , in the trie and vise versa to return both w i and its synonyms using the link when the word, w i is retrieved.
| 0.579007 |
8. The method of claim 7 , wherein the second graphical user interface comprises three percentage scales.
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8. The method of claim 7 , wherein the second graphical user interface comprises three percentage scales. 9. The method of claim 8 , wherein one percentage scale is to control a granularity of subject topics generated by said portioning.
| 0.924485 |
3. A barcoded quality indicator according to claim 2 , wherein said first set of colorable areas continues to be colored following exceedance of said first threshold.
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3. A barcoded quality indicator according to claim 2 , wherein said first set of colorable areas continues to be colored following exceedance of said first threshold. 4. A barcoded indicator according to claim 3 , wherein colorable areas forming part of at least one of said first set of colorable areas and said second set of colorable areas become colored sequentially.
| 0.950435 |
1. A method of loading a web page, comprising: providing, via a computer network and to a computing device having one or more processors, a script configured for loading with a web page, the web page configured for display on the computing device, the script having a plurality of function definitions and configured for asynchronous loading such that the web page is operable while the script is loaded; receiving an indication of a user interaction with the web page prior to complete loading of the plurality of function definitions on the web page; determining that the user interaction corresponds to a function definition of the plurality of function definitions that has not been loaded; subsequent to determining that the user interaction corresponds to the function definition that has not been loaded: instructing, using a variable, the computing device to queue a command string corresponding to the function definition; determining that the function has been loaded and instructing the computing device to retrieve the command string from the variable; and instructing the computing device to execute the function definition corresponding to the command string.
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1. A method of loading a web page, comprising: providing, via a computer network and to a computing device having one or more processors, a script configured for loading with a web page, the web page configured for display on the computing device, the script having a plurality of function definitions and configured for asynchronous loading such that the web page is operable while the script is loaded; receiving an indication of a user interaction with the web page prior to complete loading of the plurality of function definitions on the web page; determining that the user interaction corresponds to a function definition of the plurality of function definitions that has not been loaded; subsequent to determining that the user interaction corresponds to the function definition that has not been loaded: instructing, using a variable, the computing device to queue a command string corresponding to the function definition; determining that the function has been loaded and instructing the computing device to retrieve the command string from the variable; and instructing the computing device to execute the function definition corresponding to the command string. 10. The method of claim 1 , further comprising: providing, by a server having one or more processors, the script prior to the web page loading.
| 0.540047 |
10. A system comprising: a processor; and computer-readable medium coupled to the processor and having instructions stored thereon, which, when executed by the processor, cause the processor to perform operations comprising: obtaining a plurality of instances and a plurality of attributes, wherein each instance has one or more attributes of the plurality of attributes as attributes of the instance; for each attribute of an instance: identifying a plurality documents from an unstructured document collection that are relevant to the instance, where each of the documents include at least a value for the attribute of the instance; grouping values of the attribute of the instance into two or more groups; and establishing a subset of the one or more values of the attribute as characterizing the instance including selecting one group of values from the two or more groups; and adding each instance, the respective attributes of each instance, and the respective subset of values for the corresponding attributes to a structured data collection.
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10. A system comprising: a processor; and computer-readable medium coupled to the processor and having instructions stored thereon, which, when executed by the processor, cause the processor to perform operations comprising: obtaining a plurality of instances and a plurality of attributes, wherein each instance has one or more attributes of the plurality of attributes as attributes of the instance; for each attribute of an instance: identifying a plurality documents from an unstructured document collection that are relevant to the instance, where each of the documents include at least a value for the attribute of the instance; grouping values of the attribute of the instance into two or more groups; and establishing a subset of the one or more values of the attribute as characterizing the instance including selecting one group of values from the two or more groups; and adding each instance, the respective attributes of each instance, and the respective subset of values for the corresponding attributes to a structured data collection. 16. The system of claim 10 , further comprising: extracting a first candidate value of a first attribute of a first instance from a first electronic document; extracting a second candidate value of the first attribute of the first instance from a second electronic document; determining a first likelihood that the first candidate value correctly characterizes the first attribute of the first instance; determining a second likelihood that the second candidate value correctly characterizes the first attribute of the first instance; determining that the first likelihood is higher than the second likelihood; and establishing, in response to determining that the first likelihood is higher than the second likelihood, the first candidate value rather than the second candidate value as characterizing the first instance in the structured data collection.
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14. A computing device comprising: a memory to store instructions for performing machine learning; and a processing device, coupled to the memory, to execute the instructions, wherein the processing device is to: receive a training data set that comprises a plurality of sensitive documents and a plurality of non-sensitive documents; determine a quality of the training data set, wherein determining the quality of the training data set comprises performing at least one of k-fold cross validation or latent semantic indexing using the training data set; in response to determining that the training data set has a satisfactory quality, analyze the training data set using machine learning to generate a machine learning based detection (MLD) profile, the MLD profile to be used by a data loss prevention (DLP) system to classify new documents as sensitive documents or as non-sensitive documents; and in response to determining that the training data set does not have satisfactory quality, identify at least one document from the training data set that caused the quality of the training data set to be reduced.
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14. A computing device comprising: a memory to store instructions for performing machine learning; and a processing device, coupled to the memory, to execute the instructions, wherein the processing device is to: receive a training data set that comprises a plurality of sensitive documents and a plurality of non-sensitive documents; determine a quality of the training data set, wherein determining the quality of the training data set comprises performing at least one of k-fold cross validation or latent semantic indexing using the training data set; in response to determining that the training data set has a satisfactory quality, analyze the training data set using machine learning to generate a machine learning based detection (MLD) profile, the MLD profile to be used by a data loss prevention (DLP) system to classify new documents as sensitive documents or as non-sensitive documents; and in response to determining that the training data set does not have satisfactory quality, identify at least one document from the training data set that caused the quality of the training data set to be reduced. 15. The computing device of claim 14 , wherein the processing device is further configured to: for each document in the training data set, determine whether the document is a sensitive document or a non-sensitive document based on performing local weighted latent semantic indexing.
| 0.708734 |
11. A machine-readable storage medium storing an instruction that, when executed by a processor, causes the processor to perform a method of ranking search results in an electronic environment in response to a search query received from a searching party located in a geographic region, the method comprising: conducting a search and generating search results based on the search query received from a computing device used by the searching party to access an online marketplace: retrieving an IP address or a browser language setting of the computing device of the searching party; identifying the geographic region of the searching party based at least in part on the retrieved IP address or the retrieved browser language setting of the computing device; identifying a language associated with the identified geographic region of the searching party based on the identified geographic region of the searching party; for a seller associated with at least one of the search results, determining a proficiency in the language associated with the geographic region of the searching party; ranking the search results based at least on the identified language and the determined proficiency, wherein search results associated with a higher proficiency in the identified language are ranked higher relative to search results associated with a lower proficiency in the identified language: and providing the ranked search results to the searching party at the online marketplace.
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11. A machine-readable storage medium storing an instruction that, when executed by a processor, causes the processor to perform a method of ranking search results in an electronic environment in response to a search query received from a searching party located in a geographic region, the method comprising: conducting a search and generating search results based on the search query received from a computing device used by the searching party to access an online marketplace: retrieving an IP address or a browser language setting of the computing device of the searching party; identifying the geographic region of the searching party based at least in part on the retrieved IP address or the retrieved browser language setting of the computing device; identifying a language associated with the identified geographic region of the searching party based on the identified geographic region of the searching party; for a seller associated with at least one of the search results, determining a proficiency in the language associated with the geographic region of the searching party; ranking the search results based at least on the identified language and the determined proficiency, wherein search results associated with a higher proficiency in the identified language are ranked higher relative to search results associated with a lower proficiency in the identified language: and providing the ranked search results to the searching party at the online marketplace. 13. The machine-readable storage medium of claim 11 wherein ranking the search results includes: assigning a relevance weighting to an item listed in the search results based on a degree of matching or similarity between the language of the geographic region of the searching party and the language of the geographic region of the seller.
| 0.727113 |
10. The computer storage medium of claim 8 , wherein identifying, by one or more processors of an entity management system, a cluster of immutable observations that represents the entity using the context comprises: generating a query from one or more of the plurality of attribute values; identifying a plurality of candidate clusters responsive to the query; calculating a respective score for each of the plurality of candidate clusters based on comparison of at least one of the plurality of attribute values to an attribute value of respective candidate cluster; and identify the candidate cluster with the highest score as the cluster that represents the entity.
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10. The computer storage medium of claim 8 , wherein identifying, by one or more processors of an entity management system, a cluster of immutable observations that represents the entity using the context comprises: generating a query from one or more of the plurality of attribute values; identifying a plurality of candidate clusters responsive to the query; calculating a respective score for each of the plurality of candidate clusters based on comparison of at least one of the plurality of attribute values to an attribute value of respective candidate cluster; and identify the candidate cluster with the highest score as the cluster that represents the entity. 12. The computer storage medium of claim 10 , wherein calculating a respective score for each of the plurality of candidate clusters comprises assigning a zero score if a geographic location indicated in the context and the geographic location of a respective candidate cluster differ by greater than a threshold distance.
| 0.896075 |
3. The computer-implemented method of claim 2 , wherein the step of selecting the case having the case antecedents that best match the contextual antecedents comprises selecting the case from the case base having the highest case sum.
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3. The computer-implemented method of claim 2 , wherein the step of selecting the case having the case antecedents that best match the contextual antecedents comprises selecting the case from the case base having the highest case sum. 4. The computer-implemented method of claim 3 , wherein if two or more cases have the same case sum, the most-recently used case is selected.
| 0.951954 |
18. The computing device of claim 15 , wherein the instructions further configure the system to: detecting second contact on the touch-screen; determine that the second contact corresponds to a second arc gesture, the second contact extending along both a horizontal axis and a vertical axis from a third point to a fourth point, a difference between a third horizontal coordinate associated with the third point and a fourth horizontal coordinate associated with the fourth point exceeding a horizontal threshold in a second direction relative to the third point, and a difference between a third vertical coordinate associated with the third point and a fourth vertical coordinate associated with a midpoint of the second input exceeding a vertical threshold.
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18. The computing device of claim 15 , wherein the instructions further configure the system to: detecting second contact on the touch-screen; determine that the second contact corresponds to a second arc gesture, the second contact extending along both a horizontal axis and a vertical axis from a third point to a fourth point, a difference between a third horizontal coordinate associated with the third point and a fourth horizontal coordinate associated with the fourth point exceeding a horizontal threshold in a second direction relative to the third point, and a difference between a third vertical coordinate associated with the third point and a fourth vertical coordinate associated with a midpoint of the second input exceeding a vertical threshold. 19. The computing device of claim 18 , wherein the instructions further configure the device to: identify second secondary content in response to the horizontal difference between the third point and the fourth point being in a second direction relative to the third point; and output third audio corresponding to the second secondary content in response to the second arc gesture.
| 0.891433 |
1. A method for reducing a set of strings to approximately match to a first string by determining an edit distance between the first string and the set of strings is within a predetermined threshold, the method comprising: (a) receiving, by a device, a request to approximately match a first string with a set of strings using a predetermined edit distance; (b) generating, by a device, a difference histogram comprising a distribution of a difference in a first number of occurrences of each character of a character set in the first string of the request and a second number of occurrences of each character of the character set in a second string of the set of strings, by incrementing each cell in the difference histogram corresponding to each character in the first string by a positive value and decrementing each cell in the difference histogram corresponding to each character set in the second string by a negative value; (c) determining, by a device, via the difference histogram that a first sum of values across a plurality of cells of the difference histogram is greater than a predetermined threshold and that a second sum of negative values across a second plurality of cells of the difference histogram is less than a negative of the predetermined threshold; and (d) identifying, by the device, the second string as having an edit distance from the first string greater than the predetermined edit distance in response to the determination.
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1. A method for reducing a set of strings to approximately match to a first string by determining an edit distance between the first string and the set of strings is within a predetermined threshold, the method comprising: (a) receiving, by a device, a request to approximately match a first string with a set of strings using a predetermined edit distance; (b) generating, by a device, a difference histogram comprising a distribution of a difference in a first number of occurrences of each character of a character set in the first string of the request and a second number of occurrences of each character of the character set in a second string of the set of strings, by incrementing each cell in the difference histogram corresponding to each character in the first string by a positive value and decrementing each cell in the difference histogram corresponding to each character set in the second string by a negative value; (c) determining, by a device, via the difference histogram that a first sum of values across a plurality of cells of the difference histogram is greater than a predetermined threshold and that a second sum of negative values across a second plurality of cells of the difference histogram is less than a negative of the predetermined threshold; and (d) identifying, by the device, the second string as having an edit distance from the first string greater than the predetermined edit distance in response to the determination. 9. The method of claim 1 , comprising setting one of the predetermined edit distance or the predetermined threshold based on a percentage of number of characters of the first string.
| 0.747934 |
1. A method comprising: (i) for each of a plurality of client devices, storing a corresponding client profile at a server, each corresponding client profile comprising a font capabilities list for a respective one of the plurality of client devices, each font capabilities list comprising a list of fonts for which the respective client device has font structure data stored in a client font data store of the respective client device, the font structure data defining the structure in which text formatted with the respective font is to be rendered on the respective client device; (ii) receiving an electronic data transfer at the server, addressed to a designated one of the plurality of client devices, the electronic data transfer comprising text data and one or more font identifiers identifying one or more fonts to use to render the text data on the designated one of the plurality of client devices; (iii) comparing the one or more font identifiers in the electronic data transfer with a current version of the fonts in the capabilities list stored by the server for the designated device, to determine fonts in the electronic data transfer for which the designated device lacks font structure data; (iv) transferring, from the server to the designated device, the font structure data of the fonts determined to be lacked by the designated device and the text data, wherein the font structure data lacked by the designated device and the text data are included in the same electronic data transfer, and wherein the designated device stores the received font structure data in the client font data store; and (v) updating the font capabilities list stored by the server for the designated device to include, in a new version of the font capabilities list, the fonts whose font structure data are transferred to the designated device.
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1. A method comprising: (i) for each of a plurality of client devices, storing a corresponding client profile at a server, each corresponding client profile comprising a font capabilities list for a respective one of the plurality of client devices, each font capabilities list comprising a list of fonts for which the respective client device has font structure data stored in a client font data store of the respective client device, the font structure data defining the structure in which text formatted with the respective font is to be rendered on the respective client device; (ii) receiving an electronic data transfer at the server, addressed to a designated one of the plurality of client devices, the electronic data transfer comprising text data and one or more font identifiers identifying one or more fonts to use to render the text data on the designated one of the plurality of client devices; (iii) comparing the one or more font identifiers in the electronic data transfer with a current version of the fonts in the capabilities list stored by the server for the designated device, to determine fonts in the electronic data transfer for which the designated device lacks font structure data; (iv) transferring, from the server to the designated device, the font structure data of the fonts determined to be lacked by the designated device and the text data, wherein the font structure data lacked by the designated device and the text data are included in the same electronic data transfer, and wherein the designated device stores the received font structure data in the client font data store; and (v) updating the font capabilities list stored by the server for the designated device to include, in a new version of the font capabilities list, the fonts whose font structure data are transferred to the designated device. 8. The method of claim 1 wherein each of the plurality of client devices are wireless mobile communication devices.
| 0.918768 |
1. A computerized teaching system providing a teaching tool for presenting and teaching step by step solutions to STEM (science, technology, engineering and mathematics) questions, the system comprising: a communications network; at least one teacher computer operable by a respective teacher; at least one student computer operable by a respective student; and at least one computer-readable storage medium; wherein each of the at least one teacher computer and the at least one student computer includes an input device and a touch sensitive screen for receiving handwritten input via the input device; wherein the at least one student computer is operably connected to the at least one teacher computer via the communications network; and wherein the at least one teacher computer and the at least one student computer are operatively linked to the at least one computer-readable storage medium containing program instructions for implementing an application of the teaching system comprising one or more program instructions for performing the steps of: (a) receiving at least one question description being handwritten in algebraic math notation by the teacher on the touch sensitive screen of the at least one teacher computer and being displayed thereon; (b) highlighting the math notation of the at least one question description defined in step (a) using a first highlighting color to provide a highlighted math notation of the at least one question description; (c) displaying the highlighted math notation of the at least one question description of step (b) on the screen of the at least one student computer; (d) receiving at least one step of a step by step solution to the at least one question description, the at least one step being handwritten by the teacher in algebraic math notation on the screen of the at least one teacher computer and being displayed thereon; (e) highlighting the math notation of the at least one step in step (d) by either using the first highlighting color in step (b) if the math notation of the at least one step of step (d) is the algebraic equivalent of the math notation of the at least one question description of step (b) or using a second highlighting color if the math notation of the at least one step of step (d) is not the algebraic equivalent of the math notation of the at least one question description of step (b) to provide a highlighted math notation of the at least one step, the first highlighting color being different from the second highlighting color; (f) displaying the highlighted math notation of the at least one step of step (e) in one of the first highlighting color and the second highlighting color on the screen of the at least one student computer; and (g) repeating steps (d), (e) and (f), if necessary, to provide and display on the screen of the at least one student computer a completely color coded step by step solution to the at least one question description.
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1. A computerized teaching system providing a teaching tool for presenting and teaching step by step solutions to STEM (science, technology, engineering and mathematics) questions, the system comprising: a communications network; at least one teacher computer operable by a respective teacher; at least one student computer operable by a respective student; and at least one computer-readable storage medium; wherein each of the at least one teacher computer and the at least one student computer includes an input device and a touch sensitive screen for receiving handwritten input via the input device; wherein the at least one student computer is operably connected to the at least one teacher computer via the communications network; and wherein the at least one teacher computer and the at least one student computer are operatively linked to the at least one computer-readable storage medium containing program instructions for implementing an application of the teaching system comprising one or more program instructions for performing the steps of: (a) receiving at least one question description being handwritten in algebraic math notation by the teacher on the touch sensitive screen of the at least one teacher computer and being displayed thereon; (b) highlighting the math notation of the at least one question description defined in step (a) using a first highlighting color to provide a highlighted math notation of the at least one question description; (c) displaying the highlighted math notation of the at least one question description of step (b) on the screen of the at least one student computer; (d) receiving at least one step of a step by step solution to the at least one question description, the at least one step being handwritten by the teacher in algebraic math notation on the screen of the at least one teacher computer and being displayed thereon; (e) highlighting the math notation of the at least one step in step (d) by either using the first highlighting color in step (b) if the math notation of the at least one step of step (d) is the algebraic equivalent of the math notation of the at least one question description of step (b) or using a second highlighting color if the math notation of the at least one step of step (d) is not the algebraic equivalent of the math notation of the at least one question description of step (b) to provide a highlighted math notation of the at least one step, the first highlighting color being different from the second highlighting color; (f) displaying the highlighted math notation of the at least one step of step (e) in one of the first highlighting color and the second highlighting color on the screen of the at least one student computer; and (g) repeating steps (d), (e) and (f), if necessary, to provide and display on the screen of the at least one student computer a completely color coded step by step solution to the at least one question description. 6. The system according to claim 1 , wherein each of the highlighted math notations is color coded by displaying each of the math notations within a box having a perimeter, the perimeter of the box being highlighted in one of the first highlighting color and the second highlighting color.
| 0.54134 |
9. A non-transitory computer-readable medium comprising program code, the program code being operable, when executed by a computer system, to cause the computer system to perform a method comprising: acquiring a plurality of eigenvectors, each having a corresponding eigenvalue, wherein the plurality of eigenvectors are based on a plurality of tokenized electronic documents having unstructured text, the plurality of tokenized electronic documents forming a data matrix, and the unstructured text includes background terms and nonbackground terms; and classifying the plurality of eigenvectors and their corresponding eigenvalues into one or more background eigenvectors and background eigenvalues, and one or more nonbackground eigenvectors and nonbackground eigenvalues, wherein the background eigenvectors correspond to the background terms and the nonbackground eigenvectors correspond to nonbackground terms; acquiring a threshold; comparing the nonbackground eigenvalues with the threshold; and providing the nonbackground eigenvectors whose corresponding nonbackground eigenvalues exceed the threshold, wherein the provided nonbackground eigenvectors are used for clustering the plurality of documents.
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9. A non-transitory computer-readable medium comprising program code, the program code being operable, when executed by a computer system, to cause the computer system to perform a method comprising: acquiring a plurality of eigenvectors, each having a corresponding eigenvalue, wherein the plurality of eigenvectors are based on a plurality of tokenized electronic documents having unstructured text, the plurality of tokenized electronic documents forming a data matrix, and the unstructured text includes background terms and nonbackground terms; and classifying the plurality of eigenvectors and their corresponding eigenvalues into one or more background eigenvectors and background eigenvalues, and one or more nonbackground eigenvectors and nonbackground eigenvalues, wherein the background eigenvectors correspond to the background terms and the nonbackground eigenvectors correspond to nonbackground terms; acquiring a threshold; comparing the nonbackground eigenvalues with the threshold; and providing the nonbackground eigenvectors whose corresponding nonbackground eigenvalues exceed the threshold, wherein the provided nonbackground eigenvectors are used for clustering the plurality of documents. 10. The computer readable medium of claim 9 , wherein the threshold is set by a user, by a human operator other than a user, or from a data storage.
| 0.882241 |
11. A system comprising: a user device; and one or more computers configured to interact with the user device and to perform operations comprising: receiving a search query from a user of the user device during a user session; obtaining a plurality of prior search queries by the user received during the user session; generating a plurality of candidate query rewrites, wherein the candidate query rewrites are derived from the search query and the plurality of prior search queries by the user; scoring each candidate query rewrite, wherein scoring each candidate rewrite includes applying a plurality of scoring factors including a quality measure for each candidate query rewrite, and wherein the quality measure is based on an analysis of search results responsive to the candidate query rewrite; selecting a top scoring candidate query rewrite in response to determining that the score satisfies a threshold value; and providing search results responsive to the selected candidate query rewrite to the user device.
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11. A system comprising: a user device; and one or more computers configured to interact with the user device and to perform operations comprising: receiving a search query from a user of the user device during a user session; obtaining a plurality of prior search queries by the user received during the user session; generating a plurality of candidate query rewrites, wherein the candidate query rewrites are derived from the search query and the plurality of prior search queries by the user; scoring each candidate query rewrite, wherein scoring each candidate rewrite includes applying a plurality of scoring factors including a quality measure for each candidate query rewrite, and wherein the quality measure is based on an analysis of search results responsive to the candidate query rewrite; selecting a top scoring candidate query rewrite in response to determining that the score satisfies a threshold value; and providing search results responsive to the selected candidate query rewrite to the user device. 16. The system of claim 11 , wherein applying the plurality of scoring factors for each candidate query rewrite further comprises determining whether the search query includes a referential term of a particular type.
| 0.536258 |
1. A method of forming a target error model to facilitate spell checking input text related to a target data collection comprising steps of: a) providing a source query log containing user queries to at least one source data collection; b) generating target relational data based on the source query log including corrective substring suggestions that relate to the target data collection and corresponding misspelled substrings for the corrective substring suggestions extracted from the source query log, by applying a source error model to the source query log to thereby generate source relational data including corrective substring suggestions for misspelled substrings of the source query log, and selecting a subset of the source relational data that relate to the target data collection as the target relational data; c) building a target error model using the target relational data including target statistical occurrence data for the substrings of the target relational data derived from the source query log; and d) storing the target error model on a computer readable medium.
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1. A method of forming a target error model to facilitate spell checking input text related to a target data collection comprising steps of: a) providing a source query log containing user queries to at least one source data collection; b) generating target relational data based on the source query log including corrective substring suggestions that relate to the target data collection and corresponding misspelled substrings for the corrective substring suggestions extracted from the source query log, by applying a source error model to the source query log to thereby generate source relational data including corrective substring suggestions for misspelled substrings of the source query log, and selecting a subset of the source relational data that relate to the target data collection as the target relational data; c) building a target error model using the target relational data including target statistical occurrence data for the substrings of the target relational data derived from the source query log; and d) storing the target error model on a computer readable medium. 7. The method of claim 1 , wherein the target statistical occurrence data includes at least one of occurrence statistics and co-occurrence statistics for the substrings of the target relational data.
| 0.548528 |
10. A computer system for merging taxonomies comprising one or more processors and one or more memory devices coupled to the one or more processors, the one or more memory devices storing executable code effective to cause the one or more processors to: initialize a merged taxonomy by merging at least one node of a plurality of nodes of a second taxonomy to at least one node of a plurality of nodes of a first taxonomy, the merged taxonomy comprising the first taxonomy and the second taxonomy; merge the second taxonomy into the merged taxonomy by, traversing the second taxonomy from below at least one top level of the second taxonomy toward a bottom of the second taxonomy and performing: comparing, by the computer system, one or more identifiers or titles of the at least one node of the plurality of nodes of the second taxonomy to one or more identifiers or titles of the at least one node of the plurality of nodes of the first taxonomy; comparing, by the computer system, one or more lineages of one or more unmerged nodes of the plurality of nodes of the second taxonomy in the merged taxonomy to one or more lineages of the plurality of nodes of the first taxonomy; and merging, by the computer system, the at least one node of the plurality of nodes of the second taxonomy and the at least one node of the plurality of nodes of the first taxonomy in the merged taxonomy if the comparison of the one or more identifiers or titles of the at least one node of the second taxonomy to the one or more identifiers or titles of the at least one node of the plurality of nodes of the first taxonomy and the comparison of the one or more lineages of the one or more unmerged nodes of the plurality of nodes of the plurality of nodes of the second taxonomy in the merged taxonomy to the one or more lineages of the one or more nodes of the plurality of nodes of the first taxonomy satisfy a threshold condition; and for each unmerged node of the plurality of nodes of the second taxonomy, hereinafter a current node of the plurality of nodes of the second taxonomy: filtering the plurality of nodes of the first taxonomy according to a Jaccard distance of titles thereof with respect to a title of the current node of the plurality of nodes of the second taxonomy to define a filtered set of nodes; calculating an edit distance between the current node of the plurality of nodes of the second taxonomy and each node of the filtered set of nodes; calculating a lineage score for each node of the filtered set of nodes according to a comparison of a lineage of the current node of the plurality of nodes of the second taxonomy in the merged taxonomy to lineages of nodes of the filtered set of nodes; and merging the current node of the plurality of nodes of the second taxonomy with a selected node of the filtered set of nodes if a combined score of the edit distance and the lineage score for the selected node is both: a greatest combined score for the nodes of the filtered set of nodes; and the combined score for the selected node of the filtered set of nodes satisfies a combined score threshold condition.
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10. A computer system for merging taxonomies comprising one or more processors and one or more memory devices coupled to the one or more processors, the one or more memory devices storing executable code effective to cause the one or more processors to: initialize a merged taxonomy by merging at least one node of a plurality of nodes of a second taxonomy to at least one node of a plurality of nodes of a first taxonomy, the merged taxonomy comprising the first taxonomy and the second taxonomy; merge the second taxonomy into the merged taxonomy by, traversing the second taxonomy from below at least one top level of the second taxonomy toward a bottom of the second taxonomy and performing: comparing, by the computer system, one or more identifiers or titles of the at least one node of the plurality of nodes of the second taxonomy to one or more identifiers or titles of the at least one node of the plurality of nodes of the first taxonomy; comparing, by the computer system, one or more lineages of one or more unmerged nodes of the plurality of nodes of the second taxonomy in the merged taxonomy to one or more lineages of the plurality of nodes of the first taxonomy; and merging, by the computer system, the at least one node of the plurality of nodes of the second taxonomy and the at least one node of the plurality of nodes of the first taxonomy in the merged taxonomy if the comparison of the one or more identifiers or titles of the at least one node of the second taxonomy to the one or more identifiers or titles of the at least one node of the plurality of nodes of the first taxonomy and the comparison of the one or more lineages of the one or more unmerged nodes of the plurality of nodes of the plurality of nodes of the second taxonomy in the merged taxonomy to the one or more lineages of the one or more nodes of the plurality of nodes of the first taxonomy satisfy a threshold condition; and for each unmerged node of the plurality of nodes of the second taxonomy, hereinafter a current node of the plurality of nodes of the second taxonomy: filtering the plurality of nodes of the first taxonomy according to a Jaccard distance of titles thereof with respect to a title of the current node of the plurality of nodes of the second taxonomy to define a filtered set of nodes; calculating an edit distance between the current node of the plurality of nodes of the second taxonomy and each node of the filtered set of nodes; calculating a lineage score for each node of the filtered set of nodes according to a comparison of a lineage of the current node of the plurality of nodes of the second taxonomy in the merged taxonomy to lineages of nodes of the filtered set of nodes; and merging the current node of the plurality of nodes of the second taxonomy with a selected node of the filtered set of nodes if a combined score of the edit distance and the lineage score for the selected node is both: a greatest combined score for the nodes of the filtered set of nodes; and the combined score for the selected node of the filtered set of nodes satisfies a combined score threshold condition. 12. The computer system of claim 10 , wherein the executable code are further effective to cause the one or more processors to repeatedly merge the second taxonomy and the first taxonomy until no comparisons of the one or more identifiers or titles of the at least one node of the plurality of nodes of the second taxonomy to the one or more identifiers the plurality of nodes of the first taxonomy and no comparisons of the one or more lineages of the one or more unmerged nodes of the plurality of nodes of the second taxonomy in the merged taxonomy to the one or more lineages of the one or more unmerged nodes of the plurality of nodes of the first taxonomy satisfy the threshold condition.
| 0.56894 |
24. A method comprising: identifying a plurality of statistically interesting phrases occurring within a plurality of documents of a corpus of documents, the statistically interesting phrases having a statistical property that distinguishes them from other phrases in the documents; identifying locations referenced within the identified statistically interesting phrases; determining to display a representation of a domain, the domain encompassing at least a subset of the identified locations; determining to display representations of the identified locations; and determining to display the identified statistically interesting phrases, each of the displayed phrases visually associated with a corresponding one of the representations of the identified locations.
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24. A method comprising: identifying a plurality of statistically interesting phrases occurring within a plurality of documents of a corpus of documents, the statistically interesting phrases having a statistical property that distinguishes them from other phrases in the documents; identifying locations referenced within the identified statistically interesting phrases; determining to display a representation of a domain, the domain encompassing at least a subset of the identified locations; determining to display representations of the identified locations; and determining to display the identified statistically interesting phrases, each of the displayed phrases visually associated with a corresponding one of the representations of the identified locations. 25. The method of claim 24 , further comprising computing a relevance score for at least one of the identified statistically interesting phrases, and determining to display phrases having a relevance score exceeding a predetermined threshold.
| 0.673091 |
14. The apparatus of claim 13 , wherein the computer program is further configured to process by: (a) parsing output from the extracting; (b) for each of the mapping categories extracted: (i) retrieving a corresponding canonical category; (ii) inserting the canonical category into the data; and (iii) if there are any canonical attributes for the mapping category, inserting the canonical attributes into the data.
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14. The apparatus of claim 13 , wherein the computer program is further configured to process by: (a) parsing output from the extracting; (b) for each of the mapping categories extracted: (i) retrieving a corresponding canonical category; (ii) inserting the canonical category into the data; and (iii) if there are any canonical attributes for the mapping category, inserting the canonical attributes into the data. 15. The apparatus of claim 14 , wherein the retrieving of the corresponding canonical category comprises selecting a canonical category based on how well defined the mapping category is.
| 0.928904 |
8. The method of claim 1 , further comprising: automatically generating the search customization profile by analysis of attributes of the third party website.
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8. The method of claim 1 , further comprising: automatically generating the search customization profile by analysis of attributes of the third party website. 14. The method of claim 8 , wherein automatically generating the search customization profile comprises: determining from a plurality of users, domain access information from user accesses to multiple domains; determining for a domain including the third party website, at least one of other domains most frequently accessed from the third party website, or other domains from which the third party website is most frequently accessed; and including the determined domains in the search customization profile.
| 0.84846 |
9. A system, comprising: one or more processors and memory, the memory storing computer-executable instructions that, when executed, cause the one or more processors to perform a method comprising: selecting an object user; obtaining content objects associated with the object user; determining a ranking of the content objects; determining a rotation value; presenting the content objects based on the ranking of the content objects and the rotation value; obtaining user feedback associated with the presented content objects, the user feedback comprising one or more user interface inputs; determining a content object score for each of the content objects based on the user feedback; updating the ranking of the content objects based on the one or more content object scores; and presenting the content objects based on the updated ranking and the rotation value.
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9. A system, comprising: one or more processors and memory, the memory storing computer-executable instructions that, when executed, cause the one or more processors to perform a method comprising: selecting an object user; obtaining content objects associated with the object user; determining a ranking of the content objects; determining a rotation value; presenting the content objects based on the ranking of the content objects and the rotation value; obtaining user feedback associated with the presented content objects, the user feedback comprising one or more user interface inputs; determining a content object score for each of the content objects based on the user feedback; updating the ranking of the content objects based on the one or more content object scores; and presenting the content objects based on the updated ranking and the rotation value. 14. The system of claim 9 , wherein the content objects are obtained from a remote third-party content object datastore.
| 0.776029 |
33. A speech recognition system comprising: a first speech signal preprocessor to receive first input data representing a speech input signal and having first speech input signal preclassifying output data; a second speech signal preprocessor to receive second input data representing the speech input signal and having second speech input signal preclassifying output data; a mixer to receive the first and second speech input signal preclassifying output data and having output data represented by a selected mix of the first and second speech input signal preclassifying output data; a selection control circuit coupled to the mixer to determine when to include the first speech input signal, the second speech input signal, and a combination of the first and second speech input signals in the selected mix; a speech classifier to receive the selected mix and having output data to classify the speech input signal as recognized speech; and a noise level detector to provide a noise level parameter output signal to the selection control circuit.
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33. A speech recognition system comprising: a first speech signal preprocessor to receive first input data representing a speech input signal and having first speech input signal preclassifying output data; a second speech signal preprocessor to receive second input data representing the speech input signal and having second speech input signal preclassifying output data; a mixer to receive the first and second speech input signal preclassifying output data and having output data represented by a selected mix of the first and second speech input signal preclassifying output data; a selection control circuit coupled to the mixer to determine when to include the first speech input signal, the second speech input signal, and a combination of the first and second speech input signals in the selected mix; a speech classifier to receive the selected mix and having output data to classify the speech input signal as recognized speech; and a noise level detector to provide a noise level parameter output signal to the selection control circuit. 35. The speech recognition system of claim 33 wherein the noise level detector comprises: a database of noise level information corresponding to noise levels at different traveling speeds of a vehicle; and a data retriever to retrieve noise level information from the database of noise level information corresponding to a traveling speed of the vehicle.
| 0.74375 |
7. An apparatus, comprising: one or more computer processors that execute: using an input sentence to extract an extracted example sentence from a set of example sentences according to a matching evaluation on a character block basis between the input sentence and the set of example sentences; selecting, as a reevaluation portion, a portion of the input sentence other than a portion that contributed to the matching evaluation for the extracting of the extracted example sentence; re-extracting another example sentence from the set of example sentences according to a re-evaluation of the matching using the reevaluation portion of the input sentence; and identifying example sentence segments for segments of the input sentence based upon candidate sentence segments according to the extracted and re-extracted example sentences.
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7. An apparatus, comprising: one or more computer processors that execute: using an input sentence to extract an extracted example sentence from a set of example sentences according to a matching evaluation on a character block basis between the input sentence and the set of example sentences; selecting, as a reevaluation portion, a portion of the input sentence other than a portion that contributed to the matching evaluation for the extracting of the extracted example sentence; re-extracting another example sentence from the set of example sentences according to a re-evaluation of the matching using the reevaluation portion of the input sentence; and identifying example sentence segments for segments of the input sentence based upon candidate sentence segments according to the extracted and re-extracted example sentences. 11. The apparatus according to claim 7 , wherein the re-evaluating includes reevaluating degrees of matching on a character block basis between the set of example sentences and the reevaluation portion of the input sentence.
| 0.768041 |
8. The apparatus according to claim 1 , wherein the substring extractor transforms a corresponding substring to a numeral values using a one-to-one transformation method that transforms the corresponding substring to an unsigned short value when the length of the corresponding substring is shorter than about two bytes or using a many-to-one transformation method that transforms the corresponding substring by applying a hashing algorithm when the corresponding substring is longer than two bytes, and updates the substring frequency table according to the modified information.
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8. The apparatus according to claim 1 , wherein the substring extractor transforms a corresponding substring to a numeral values using a one-to-one transformation method that transforms the corresponding substring to an unsigned short value when the length of the corresponding substring is shorter than about two bytes or using a many-to-one transformation method that transforms the corresponding substring by applying a hashing algorithm when the corresponding substring is longer than two bytes, and updates the substring frequency table according to the modified information. 9. The apparatus according to claim 8 , wherein the substring frequency table includes a field denoting an observation period of each entry for performing an initialization operation per each entry, and the substring extractor increases substring generation frequency value of the entry when an observation period of a corresponding entry is not elapsed, or initializes a substring generation frequency value of the entry and then increases the substring generation frequency value from a corresponding initialization value when an observation period of a corresponding entry is elapsed.
| 0.688976 |
9. A computer-implemented method, comprising: under control of a device comprising one or more processors configured with executable instructions, receiving at a graphical user interface of the device user selection of a sample electronic text; identifying multiple sample n-grams of the sample electronic text; for a first language: calculating a first probability based at least in part on a frequency of occurrence, in the first language, of a first sample n-gram of the multiple n-grams; calculating a second probability based at least in part on a frequency of occurrence, in the first language, of a second sample n-gram of the multiple n-grams; generating a first average based at least in part on the first probability and the second probability; for a second language: calculating a third probability based at least in part on a frequency of occurrence, in the second language, of the first sample n-gram of the multiple sample n-grams; calculating a fourth probability based at least in part on a frequency of occurrence, in the second language, of the second sample n-gram of the multiple n-grams; generating a second average based at least in part on the third probability and the fourth probability; determining a language of the sample electronic text based at least in part on comparing at least the first average and the second average; displaying, via the graphical user interface, an indication of the language; performing, via the device, a language-dependent operation based at least in part on the language of the sample electronic text; and displaying, via the graphical user interface, information associated with the language-dependent operation.
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9. A computer-implemented method, comprising: under control of a device comprising one or more processors configured with executable instructions, receiving at a graphical user interface of the device user selection of a sample electronic text; identifying multiple sample n-grams of the sample electronic text; for a first language: calculating a first probability based at least in part on a frequency of occurrence, in the first language, of a first sample n-gram of the multiple n-grams; calculating a second probability based at least in part on a frequency of occurrence, in the first language, of a second sample n-gram of the multiple n-grams; generating a first average based at least in part on the first probability and the second probability; for a second language: calculating a third probability based at least in part on a frequency of occurrence, in the second language, of the first sample n-gram of the multiple sample n-grams; calculating a fourth probability based at least in part on a frequency of occurrence, in the second language, of the second sample n-gram of the multiple n-grams; generating a second average based at least in part on the third probability and the fourth probability; determining a language of the sample electronic text based at least in part on comparing at least the first average and the second average; displaying, via the graphical user interface, an indication of the language; performing, via the device, a language-dependent operation based at least in part on the language of the sample electronic text; and displaying, via the graphical user interface, information associated with the language-dependent operation. 15. The computer-implemented method of claim 9 , wherein calculating the first probability comprises calculating a Bayesian probability P(A|B) that the first sample n-gram corresponds to the language based at least in part on: P ( B ❘ A ) P ( A ) P ( B ) where: P(B|A) is a first frequency with which the first sample n-gram occurs within the language, relative to other n-grams that occur within the first language; P(B) is a second frequency with which the first sample n-gram occurs within multiple languages, relative to other n-grams that occur within the multiple languages; and P(A) is a number of short words of the first language that occur in the sample electronic text.
| 0.540216 |
12. A non-transitory computer-readable storage device having instructions stored thereon, execution of which by a computing device, cause said computing device to perform operations comprising: determining a presence of a disjunction expression in a query expression, the disjunction expression including a disjunctive operator, a disjunctive condition, and one or more disjunctive conditions that precede the disjunctive condition in the complex query expression; receiving, in response to the determining, a satisfaction bitmap that represents a set of rows that have satisfied the one or more preceding disjunctive conditions, wherein a bit from the satisfaction bitmap represents whether a respective row has satisfied the one or more preceding disjunctive conditions; determining whether respective cost savings justify restricting evaluation of each disjunctive condition within the disjunction expression using the satisfaction bitmap; responsive to determining that the disjunctive condition has a respective cost saving that justifies restricting evaluation, restricting scope of evaluation of the disjunctive condition to a set of rows that are not within the satisfaction bitmap; and providing, by the at least one processor, a bitmap result of evaluating the disjunction expression, the bitmap result determined, in part, using the restricted evaluation of the disjunctive condition.
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12. A non-transitory computer-readable storage device having instructions stored thereon, execution of which by a computing device, cause said computing device to perform operations comprising: determining a presence of a disjunction expression in a query expression, the disjunction expression including a disjunctive operator, a disjunctive condition, and one or more disjunctive conditions that precede the disjunctive condition in the complex query expression; receiving, in response to the determining, a satisfaction bitmap that represents a set of rows that have satisfied the one or more preceding disjunctive conditions, wherein a bit from the satisfaction bitmap represents whether a respective row has satisfied the one or more preceding disjunctive conditions; determining whether respective cost savings justify restricting evaluation of each disjunctive condition within the disjunction expression using the satisfaction bitmap; responsive to determining that the disjunctive condition has a respective cost saving that justifies restricting evaluation, restricting scope of evaluation of the disjunctive condition to a set of rows that are not within the satisfaction bitmap; and providing, by the at least one processor, a bitmap result of evaluating the disjunction expression, the bitmap result determined, in part, using the restricted evaluation of the disjunctive condition. 13. The non-transitory computer-readable storage device of claim 12 , said operations further comprising: reordering, based on selectivities of respective disjunction conditions, the disjunctive conditions in the disjunction expression from the least selective disjunctive condition to the most selective condition; and evaluating the reordered disjunctive conditions.
| 0.745752 |
1. A non-transitory computer-readable medium storing computer executable instructions, the instructions comprising: one or more instructions that, when executed by a processor, cause the processor to: provide a handle base class; define one or more handle subclasses, where the one or more handle subclasses have a class hierarchy that includes the handle base class; construct handle objects from the one or more handle subclasses; use the handle objects, where the handle objects are used exclusively through references to the handle objects; define one or more non-handle base classes and one or more non-handle subclasses, where the one or more non-handle subclasses have a class hierarchy that includes at least one of the one or more non-handle base classes and does not include the handle base class; construct non-handle objects from the one or more non-handle subclasses; use the non-handle objects, where the non-handle objects are used exclusively by value; store the handle objects and the non-handle objects in a memory coupled to the processor; provide a first syntax for constructing both the handle objects and the non-handle objects; and provide a second syntax for using both the handle objects and the non-handle objects.
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1. A non-transitory computer-readable medium storing computer executable instructions, the instructions comprising: one or more instructions that, when executed by a processor, cause the processor to: provide a handle base class; define one or more handle subclasses, where the one or more handle subclasses have a class hierarchy that includes the handle base class; construct handle objects from the one or more handle subclasses; use the handle objects, where the handle objects are used exclusively through references to the handle objects; define one or more non-handle base classes and one or more non-handle subclasses, where the one or more non-handle subclasses have a class hierarchy that includes at least one of the one or more non-handle base classes and does not include the handle base class; construct non-handle objects from the one or more non-handle subclasses; use the non-handle objects, where the non-handle objects are used exclusively by value; store the handle objects and the non-handle objects in a memory coupled to the processor; provide a first syntax for constructing both the handle objects and the non-handle objects; and provide a second syntax for using both the handle objects and the non-handle objects. 14. The medium of claim 1 , wherein the instructions further comprise: one or more instructions that, when executed by the processor, cause the processor to: receive a definition of a new handle subclass created by subclassing at least one of the one or more handle subclasses.
| 0.547254 |
1. A method for person re-identification, the method comprising: mapping color values separately for different regions from first and second images to first and second probability distributions over a plurality of colors, the plurality of colors in a first color space different than a second color space of the color values; calculating separately, with a processor, for the different regions a similarity score between the first and second probability distributions; determining an affinity score as a function of the similarity scores from the different regions, and different weights applied to different similarity scores, the weight being a rank-boosted machine-learnt value; and identifying a person in the second image as a person in the first image, the identifying being a function of the affinity scores.
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1. A method for person re-identification, the method comprising: mapping color values separately for different regions from first and second images to first and second probability distributions over a plurality of colors, the plurality of colors in a first color space different than a second color space of the color values; calculating separately, with a processor, for the different regions a similarity score between the first and second probability distributions; determining an affinity score as a function of the similarity scores from the different regions, and different weights applied to different similarity scores, the weight being a rank-boosted machine-learnt value; and identifying a person in the second image as a person in the first image, the identifying being a function of the affinity scores. 8. The method of claim 1 wherein identifying the person comprises comparing the affinity score to a threshold.
| 0.902116 |
8. The system of claim 1 , wherein the annotation database is configured to store the at least one annotation and the object identifier for the at least one object.
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8. The system of claim 1 , wherein the annotation database is configured to store the at least one annotation and the object identifier for the at least one object. 9. The system of claim 8 , wherein the annotation database is located on a distributed network.
| 0.961677 |
9. A method of teaching the pronounciation and spelling and distinguishing between the written and spoken form of any language, the method comprising utilizing three sets of tiles, the first set denoting alphabet letters, the second set denoting phonetic vowels, the third set denoting phonetic consonants, the first set comprising all geometrically uniform tiles, each tile having an alphabet letter on one surface thereof, in black or white, and a background of opposite white or black colour to the letter colours, there being separate tiles for each upper case alphabet letter and separate tiles for each lower case alphabet letter, the second set of tiles comprising geometrically uniform tiles wherein one surface of each tile is blank and individually and distinctively coloured to represent a phonetic vowel sound and vowel spelling where there are differences in spelling the same vowel sound, while the other surface of each tile contains an individual International Phonetic Alphabet Symbol to represent the vowel sound on said one surface, the second set of tiles also including tiles individually distinctively coloured to represent single letter vowel sounds and tiles individually distinctively coloured to represent double letter vowel sounds, the third set of tiles comprising single letter consonants and two letter digraphs of two-letter combinations of consonants having a single phonetic sound, selecting the first, second and third sets to be distinct, and teaching how to distinguish between pronounciation and spelling of words of the language by interposing the alphabet tiles and the phonetic tiles.
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9. A method of teaching the pronounciation and spelling and distinguishing between the written and spoken form of any language, the method comprising utilizing three sets of tiles, the first set denoting alphabet letters, the second set denoting phonetic vowels, the third set denoting phonetic consonants, the first set comprising all geometrically uniform tiles, each tile having an alphabet letter on one surface thereof, in black or white, and a background of opposite white or black colour to the letter colours, there being separate tiles for each upper case alphabet letter and separate tiles for each lower case alphabet letter, the second set of tiles comprising geometrically uniform tiles wherein one surface of each tile is blank and individually and distinctively coloured to represent a phonetic vowel sound and vowel spelling where there are differences in spelling the same vowel sound, while the other surface of each tile contains an individual International Phonetic Alphabet Symbol to represent the vowel sound on said one surface, the second set of tiles also including tiles individually distinctively coloured to represent single letter vowel sounds and tiles individually distinctively coloured to represent double letter vowel sounds, the third set of tiles comprising single letter consonants and two letter digraphs of two-letter combinations of consonants having a single phonetic sound, selecting the first, second and third sets to be distinct, and teaching how to distinguish between pronounciation and spelling of words of the language by interposing the alphabet tiles and the phonetic tiles. 11. The method of claim 9, comprising teaching both the pronounciation and spelling of said any language by interposing tiles of said second set between tiles of said first set to represent both the alphabetical spelling of a word and any combination of the alphabet letters and the vowels of said word.
| 0.5 |
15. A computer-readable storage device having instructions stored which, when executed by a computing device, cause the computing device to perform operations comprising: generating a semantic and syntactic graph associated with a first call type; converting the semantic and syntactic graph into a first finite state transducer; composing the first finite state transducer with a second finite state transducer to form a third finite state transducer, wherein the second finite state transducer comprises all possible n-grams, and wherein the third finite state transducer comprises a subset of the all-possible n-grams; extracting the subset of the all-possible n-grams as features from the third finite state transducer, to yield extracted n-grams; associating an utterance with a second call type based on the extracted n-grams, to yield a classified utterance, wherein the second call type is determined based on semantic and syntactic features in the extracted n-grams; and responding to a user in a natural language dialog based on the classified utterance.
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15. A computer-readable storage device having instructions stored which, when executed by a computing device, cause the computing device to perform operations comprising: generating a semantic and syntactic graph associated with a first call type; converting the semantic and syntactic graph into a first finite state transducer; composing the first finite state transducer with a second finite state transducer to form a third finite state transducer, wherein the second finite state transducer comprises all possible n-grams, and wherein the third finite state transducer comprises a subset of the all-possible n-grams; extracting the subset of the all-possible n-grams as features from the third finite state transducer, to yield extracted n-grams; associating an utterance with a second call type based on the extracted n-grams, to yield a classified utterance, wherein the second call type is determined based on semantic and syntactic features in the extracted n-grams; and responding to a user in a natural language dialog based on the classified utterance. 16. The computer-readable storage device of claim 15 , wherein the semantic and syntactic graph further comprises lexical information.
| 0.5628 |
21. A method for predicting a data value, said method comprising: receiving a first data set; predicting a predicted data value of the first data set at a predicted dimension value utilizing a focus topic profile; analyzing a second set of data using a latent variable method; the latent variable method using at least one focus feature from a third data set to create at least one focus topic from the second data set; and the focus topic profile comprises at least one focus topic value of the at least one focus topic from the second set of data.
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21. A method for predicting a data value, said method comprising: receiving a first data set; predicting a predicted data value of the first data set at a predicted dimension value utilizing a focus topic profile; analyzing a second set of data using a latent variable method; the latent variable method using at least one focus feature from a third data set to create at least one focus topic from the second data set; and the focus topic profile comprises at least one focus topic value of the at least one focus topic from the second set of data. 25. The method of claim 21 wherein the first data set comprises financial data.
| 0.955006 |
1. A computer implemented method, comprising: receiving a query from a client device; identifying search results responsive to the received query, the search results including one or more first search results about an entity and one or more additional search results about an additional entity; providing the search results to the client device; identifying that the entity is associated with the query; providing, to the client device, an entity summary for the entity to display along with the search results in response to the query, the entity summary including an image related to the entity, a name of the entity, and a plurality of additional properties for the entity; identifying that the additional entity is associated with the search query; providing, to the client device, an additional entity summary for the additional entity for display along with the search results and the entity summary in response to the query, the additional entity summary including an additional image related to the additional entity, an additional name of the additional entity, and including an option to perform additional searching related to the additional entity; identifying an additional entity search query for the additional entity based on a database mapping between the additional entity and the additional entity search query; associating the additional entity search query with the option, of the additional entity summary, to perform additional searching related to the additional entity; wherein selection of the option of the additional entity summary via the client device issues a search for the additional entity search query; and wherein a quantity of the additional properties for the entity that are included in the entity summary is greater than a quantity of any additional properties for the additional entity that are included in the additional entity summary.
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1. A computer implemented method, comprising: receiving a query from a client device; identifying search results responsive to the received query, the search results including one or more first search results about an entity and one or more additional search results about an additional entity; providing the search results to the client device; identifying that the entity is associated with the query; providing, to the client device, an entity summary for the entity to display along with the search results in response to the query, the entity summary including an image related to the entity, a name of the entity, and a plurality of additional properties for the entity; identifying that the additional entity is associated with the search query; providing, to the client device, an additional entity summary for the additional entity for display along with the search results and the entity summary in response to the query, the additional entity summary including an additional image related to the additional entity, an additional name of the additional entity, and including an option to perform additional searching related to the additional entity; identifying an additional entity search query for the additional entity based on a database mapping between the additional entity and the additional entity search query; associating the additional entity search query with the option, of the additional entity summary, to perform additional searching related to the additional entity; wherein selection of the option of the additional entity summary via the client device issues a search for the additional entity search query; and wherein a quantity of the additional properties for the entity that are included in the entity summary is greater than a quantity of any additional properties for the additional entity that are included in the additional entity summary. 2. The method of claim 1 , further comprising: identifying a score for the entity for the query; and including the greater quantity of the additional properties for the entity in the entity summary based on the score.
| 0.58337 |
1. An input recognition system comprising: a keypad comprising a set of keys that includes a predefined hotkey; a hotkey detection component that identifies a single actuation of the predefined hotkey as indicating the start of a first sequence of key activations corresponding to a first pattern that is being input into the keypad for recognizing a first character, and two actuations of the predefined hotkey as indicating an end of the first sequence of key activations; a timer configured to cooperate with the hotkey detection component for detecting a pattern boundary that distinguishes the first sequence of key activations corresponding to the first pattern from a second sequence of key activations corresponding to a second pattern when a user fails to indicate a completion of the first sequence of key activations; an input component that acquires input data corresponding to said first sequence of key activations; and an analysis component that receives the input data from the input component and recognizes the first character based on interpreting said first pattern as corresponding to a visual representation of the first character superimposed over the set of keys.
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1. An input recognition system comprising: a keypad comprising a set of keys that includes a predefined hotkey; a hotkey detection component that identifies a single actuation of the predefined hotkey as indicating the start of a first sequence of key activations corresponding to a first pattern that is being input into the keypad for recognizing a first character, and two actuations of the predefined hotkey as indicating an end of the first sequence of key activations; a timer configured to cooperate with the hotkey detection component for detecting a pattern boundary that distinguishes the first sequence of key activations corresponding to the first pattern from a second sequence of key activations corresponding to a second pattern when a user fails to indicate a completion of the first sequence of key activations; an input component that acquires input data corresponding to said first sequence of key activations; and an analysis component that receives the input data from the input component and recognizes the first character based on interpreting said first pattern as corresponding to a visual representation of the first character superimposed over the set of keys. 2. The system of claim 1 , wherein the keypad is a numeric keypad.
| 0.720257 |
14. A device for language processing that represents both sematic and orientation content within a text, the device comprising: circuitry configured to receive ordered data elements representing respective words in a text, the ordered data elements including a first data element and a second data element, which is sequential with the first data element; generate, based on a plurality of semantic classes corresponding respectively to blades of a graded vector space representing clusters of semantic meaning, a first subspace formed within the graded vector space by the first data element, a second subspace within the graded vector space by the second data element, a first set of data elements corresponding to one or more first semantic classes that include the first data element, and a second set of data elements corresponding to one or more second semantic classes that include the second data element; encode, when the one or more first semantic classes is non-degenerate with the one or more second semantic classes, the first data element with respect to the second data element such that the an encoded subspace formed by encoding the first data element with respect to the second data element is oriented with respect to an order of the first data element with respect to the second data element, the encoding being performed by computing one of a left contraction and a right contraction of the first set of data elements with respect to the second set of data elements; compute respective components of a weight distribution of the first data element with respect to the second data element, the components of the weight distribution corresponding to respective ordered pairs for each of the one or more first semantic classes with respect to the one or more second semantic classes; and determine a dominant semantic class of to an ordered sequence of the first data element and the second data element, the dominant semantic class representing an oriented semantic context of the ordered sequence of the first and second data elements, and the dominant semantic class being determined as a semantic class corresponding to a component having a maximum magnitude of the weight distribution, wherein the left contraction is one of a Clifford Algebra left contraction and a Geometric Algebra left contraction, and the right contraction is one of a Clifford Algebra right contraction and a Geometric Algebra right contraction.
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14. A device for language processing that represents both sematic and orientation content within a text, the device comprising: circuitry configured to receive ordered data elements representing respective words in a text, the ordered data elements including a first data element and a second data element, which is sequential with the first data element; generate, based on a plurality of semantic classes corresponding respectively to blades of a graded vector space representing clusters of semantic meaning, a first subspace formed within the graded vector space by the first data element, a second subspace within the graded vector space by the second data element, a first set of data elements corresponding to one or more first semantic classes that include the first data element, and a second set of data elements corresponding to one or more second semantic classes that include the second data element; encode, when the one or more first semantic classes is non-degenerate with the one or more second semantic classes, the first data element with respect to the second data element such that the an encoded subspace formed by encoding the first data element with respect to the second data element is oriented with respect to an order of the first data element with respect to the second data element, the encoding being performed by computing one of a left contraction and a right contraction of the first set of data elements with respect to the second set of data elements; compute respective components of a weight distribution of the first data element with respect to the second data element, the components of the weight distribution corresponding to respective ordered pairs for each of the one or more first semantic classes with respect to the one or more second semantic classes; and determine a dominant semantic class of to an ordered sequence of the first data element and the second data element, the dominant semantic class representing an oriented semantic context of the ordered sequence of the first and second data elements, and the dominant semantic class being determined as a semantic class corresponding to a component having a maximum magnitude of the weight distribution, wherein the left contraction is one of a Clifford Algebra left contraction and a Geometric Algebra left contraction, and the right contraction is one of a Clifford Algebra right contraction and a Geometric Algebra right contraction. 25. The device of claim 14 , wherein each semantic class belonging to a data cluster of the plurality of data clusters corresponds to a blade of the blades, which is a heterogeneous space formed by the plurality of data clusters.
| 0.568852 |
21. The method as claimed in claim 20 , wherein the managing data loading comprises applying predefined rules defining one or more data movement or transformations for moving or transforming data extracted from the data source systems.
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21. The method as claimed in claim 20 , wherein the managing data loading comprises applying predefined rules defining one or more data movement or transformations for moving or transforming data extracted from the data source systems. 22. The method as claimed in claim 21 , wherein the applying predefined rules applies predefined rules including one or more of pivot transformation, hierarchy flattening transformation, aggregation transformation and combining data transformation.
| 0.928878 |
1. A computer-implemented method, comprising: identifying image content for a plurality of distinct book content items, the image content defining images that appear in the plurality of distinct book content items; identifying descriptor points from the image content, the descriptor points defining localized features of the image content of the distinct book content items, the localized features being characteristics of a portion of the image content; representing the distinct book content items as a weighted graph in computer memory, where each of the distinct book content items is represented as a distinct node in the weighted graph and where an edge exists in the weighted graph between each pair of distinct nodes that represent distinct book content items that both include a matching descriptor point, matching descriptor points being identified based on similarities between pairs of descriptor points, and each edge in the weighted graph being weighted based on a relative importance of the distinct node from which the edge originates, wherein representing the distinct book content items includes: for each distinct node: identifying matching image content in the image content of other distinct book content items that are represented by other distinct nodes, each instance of matching image content being image content that has descriptor points that match the corresponding descriptor points of image content in the distinct book content item corresponding to the distinct node; and generating edges in the weighted graph connecting the distinct node to other distinct nodes corresponding to the other distinct book content items, each edge representing one or more matches of image content in the distinct book content item to matching image content in another distinct book content item; and determining a rank score for each distinct book content item based on the edges between the distinct nodes representing the distinct book content items, the rank score being a score indicative of the importance of each distinct book content item relative to other distinct book content items.
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1. A computer-implemented method, comprising: identifying image content for a plurality of distinct book content items, the image content defining images that appear in the plurality of distinct book content items; identifying descriptor points from the image content, the descriptor points defining localized features of the image content of the distinct book content items, the localized features being characteristics of a portion of the image content; representing the distinct book content items as a weighted graph in computer memory, where each of the distinct book content items is represented as a distinct node in the weighted graph and where an edge exists in the weighted graph between each pair of distinct nodes that represent distinct book content items that both include a matching descriptor point, matching descriptor points being identified based on similarities between pairs of descriptor points, and each edge in the weighted graph being weighted based on a relative importance of the distinct node from which the edge originates, wherein representing the distinct book content items includes: for each distinct node: identifying matching image content in the image content of other distinct book content items that are represented by other distinct nodes, each instance of matching image content being image content that has descriptor points that match the corresponding descriptor points of image content in the distinct book content item corresponding to the distinct node; and generating edges in the weighted graph connecting the distinct node to other distinct nodes corresponding to the other distinct book content items, each edge representing one or more matches of image content in the distinct book content item to matching image content in another distinct book content item; and determining a rank score for each distinct book content item based on the edges between the distinct nodes representing the distinct book content items, the rank score being a score indicative of the importance of each distinct book content item relative to other distinct book content items. 10. The method of claim 1 , wherein identifying descriptor points comprises identifying feature vectors that represent the localized features of the image content.
| 0.568543 |
1. A method performed by an electronic device for selecting a gesture recognition system, the method comprising: determining a context of operation for the electronic device that affects a gesture recognition function performed by the electronic device, wherein determining the context of operation further comprises determining an ambient light level for the electronic device; and selecting, based on the ambient light level, one of a plurality of gesture recognition systems in the electronic device as an active gesture recognition system for receiving gesturing input to perform the gesture recognition function, wherein the plurality of gesture recognition systems comprises an image-based gesture recognition system and a non-image-based gesture recognition system.
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1. A method performed by an electronic device for selecting a gesture recognition system, the method comprising: determining a context of operation for the electronic device that affects a gesture recognition function performed by the electronic device, wherein determining the context of operation further comprises determining an ambient light level for the electronic device; and selecting, based on the ambient light level, one of a plurality of gesture recognition systems in the electronic device as an active gesture recognition system for receiving gesturing input to perform the gesture recognition function, wherein the plurality of gesture recognition systems comprises an image-based gesture recognition system and a non-image-based gesture recognition system. 7. The method of claim 1 , wherein determining the context of operation comprises determining that an imager of the image-based gesture recognition system is in use, and wherein the non-image-based gesture recognition system is selected as the active gesture recognition system.
| 0.73101 |
1. A method for detecting the number and/or location of particles, the method comprising: imaging a plurality of particles using an imaging system having a point spread function, wherein the imaging returns an image; estimating a plurality of point spread function dictionary coefficients of the image using a point spread function dictionary, wherein the point spread function dictionary includes a plurality of point spread function responses that each correspond with a different particle position; and determining the number and/or location of the particles in the image from the point spread function dictionary coefficients.
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1. A method for detecting the number and/or location of particles, the method comprising: imaging a plurality of particles using an imaging system having a point spread function, wherein the imaging returns an image; estimating a plurality of point spread function dictionary coefficients of the image using a point spread function dictionary, wherein the point spread function dictionary includes a plurality of point spread function responses that each correspond with a different particle position; and determining the number and/or location of the particles in the image from the point spread function dictionary coefficients. 2. The method according to claim 1 , further comprising: imaging the plurality of particles using a second imaging system having a second point spread function, wherein the imaging returns a second image; estimating a plurality of second point spread function dictionary coefficients of the image using a second point spread function dictionary, wherein the second point spread function dictionary includes a plurality of second point spread function responses that each correspond with a different particle position; and determining the number and/or location of the particles in the image from the second point spread function dictionary coefficients.
| 0.530172 |
9. A computer-based system, comprising: a) a markup language based document including a plurality of values, each value representative of a corresponding input control of a set of input controls associated with an expansion control; b) a script associated with the markup language based document, the script configured to perform actions on a client device including: i) in response to loading or rendering the markup language based document, detaching the set of input controls from a document object model (DOM) representative of the markup language document; ii) attaching the set of input controls to the DOM; iii) inserting a substitute control in the DOM and displaying the substitute control in response to a command to display the expansion control, a command to display at least a portion of the set of input controls is invoked by a user selection of the substitute control; and iv) maintaining an input control value component in memory, the input control value component including a value of each input control of the set of input controls; and c) an application server configured to perform actions including: i) transmitting the markup language based document and the script to the client device; and ii) receiving, from the client device, the value of each input control of the set of input controls.
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9. A computer-based system, comprising: a) a markup language based document including a plurality of values, each value representative of a corresponding input control of a set of input controls associated with an expansion control; b) a script associated with the markup language based document, the script configured to perform actions on a client device including: i) in response to loading or rendering the markup language based document, detaching the set of input controls from a document object model (DOM) representative of the markup language document; ii) attaching the set of input controls to the DOM; iii) inserting a substitute control in the DOM and displaying the substitute control in response to a command to display the expansion control, a command to display at least a portion of the set of input controls is invoked by a user selection of the substitute control; and iv) maintaining an input control value component in memory, the input control value component including a value of each input control of the set of input controls; and c) an application server configured to perform actions including: i) transmitting the markup language based document and the script to the client device; and ii) receiving, from the client device, the value of each input control of the set of input controls. 11. The computer-based system of claim 9 , the script actions further including inserting a hidden field into the DOM, the hidden field including a value of each input control of at least a portion of the set of input controls.
| 0.548697 |
1. A method for extending markup supported by a browser comprising: identifying a Web browser that presents information written in a markup language, wherein said Web browser comprises a markup rendering processor that is a runtime processor that renders or interprets markup documents, and wherein said Web browser comprises a syntax processor that is a Document Type Definition (DTD) processor; identifying an extender comprising an extension module and linking instructions, wherein the extension module defines a new language tag that was not previously part of a markup language supported by the Web browser; loading the extender; and extending the markup language supported by the Web browser to include the new language tag, wherein the extending step further comprises: the syntax processor using the extender to dynamically produce an operational DTD based upon the linking instructions, wherein said operational DTD is used by the Web browser in parsing a markup document that includes the new language tag; wherein the identified extender further comprises a plurality of attributes, wherein said Web browser further comprises an extension loader for loading the extender at runtime, wherein the attributes of the extender define times at which the extension loader is to load the extender, wherein specifiable times via the attributes comprise at least two of “when the Web browser loads,” “when a markup document is parsed,” when a markup document is rendered,” and when specified Document Object Model (DOM) events occur.
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1. A method for extending markup supported by a browser comprising: identifying a Web browser that presents information written in a markup language, wherein said Web browser comprises a markup rendering processor that is a runtime processor that renders or interprets markup documents, and wherein said Web browser comprises a syntax processor that is a Document Type Definition (DTD) processor; identifying an extender comprising an extension module and linking instructions, wherein the extension module defines a new language tag that was not previously part of a markup language supported by the Web browser; loading the extender; and extending the markup language supported by the Web browser to include the new language tag, wherein the extending step further comprises: the syntax processor using the extender to dynamically produce an operational DTD based upon the linking instructions, wherein said operational DTD is used by the Web browser in parsing a markup document that includes the new language tag; wherein the identified extender further comprises a plurality of attributes, wherein said Web browser further comprises an extension loader for loading the extender at runtime, wherein the attributes of the extender define times at which the extension loader is to load the extender, wherein specifiable times via the attributes comprise at least two of “when the Web browser loads,” “when a markup document is parsed,” when a markup document is rendered,” and when specified Document Object Model (DOM) events occur. 12. The method of claim 1 , further comprising: adding an extension point defined by said extender as a Document Object Model (DOM) event, said extension point being used to execute code for the extension; and said Web browser triggering the DOM event.
| 0.547489 |
1. A method to process a document, the method comprising: employing a processor executing computer-executable instructions stored on a computer-readable storage medium to perform the following acts: determining a presence of keyword in a document based on features of the document and a pre-set vocabulary list, wherein the keyword in the document matches a keyword appearing in the pre-set vocabulary list; searching the document for one or more additional keywords related to the matching keyword, to determine a context for the matching keyword; generating a set of domain models that represent the document, wherein the set of domain models that represent the document is a function of the matching keyword and the one or more additional keywords, and wherein the set of domain models comprises properties relevant to the matching keyword; populating properties of the set of domain models with corresponding data extracted from the document; populating the properties of a set of other domain models representing other documents with corresponding data extracted from the other documents; storing the set of domain models with the set of other domain models; structuring the stored domain models so as to be searchable by a querying system; retrieving a collection of domain models, from among the stored domain models, in response to a search performed on the document for further analysis of specific domain model properties; and applying an algorithm to the properties of the retrieved collection of domain models to compute a data value relating to the collection of domain models.
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1. A method to process a document, the method comprising: employing a processor executing computer-executable instructions stored on a computer-readable storage medium to perform the following acts: determining a presence of keyword in a document based on features of the document and a pre-set vocabulary list, wherein the keyword in the document matches a keyword appearing in the pre-set vocabulary list; searching the document for one or more additional keywords related to the matching keyword, to determine a context for the matching keyword; generating a set of domain models that represent the document, wherein the set of domain models that represent the document is a function of the matching keyword and the one or more additional keywords, and wherein the set of domain models comprises properties relevant to the matching keyword; populating properties of the set of domain models with corresponding data extracted from the document; populating the properties of a set of other domain models representing other documents with corresponding data extracted from the other documents; storing the set of domain models with the set of other domain models; structuring the stored domain models so as to be searchable by a querying system; retrieving a collection of domain models, from among the stored domain models, in response to a search performed on the document for further analysis of specific domain model properties; and applying an algorithm to the properties of the retrieved collection of domain models to compute a data value relating to the collection of domain models. 10. The method of claim 1 , wherein a hierarchy of domain models is generated as a function of respective analyzed features.
| 0.58638 |
6. Apparatus for the modeling and query of an information system, the apparatus using natural-language like constructs and further comprising: a programmable computer including memory; a display device coupled to the computer; a data entry device, further coupled to the computer; a graphical user interface implemented on the computer; a repository further implemented on the computer; an edit window displayed on the display device for entering text therein, the edit window utilizing the textual form of a computer language having both textual and graphical forms for translating the natural language-like constructs into object-role modeling symbology; conceptual schema diagram formation means for forming a conceptual schema diagram representing the information system on the display device, the conceptual schema diagram utilizing the graphical form of the computer language having both textual and graphical forms; drag-and-drop means further implemented on the computer and in operative combination with the display device and the data entry device, for dragging the text from the edit window and for dropping the text item onto the conceptual schema diagram; parsing means for parsing the text into at least one of object, fact and constraint; list means, responsive to the parsing means, for creating an object list, a fact list and a constraint list in the repository; a compiler, in operative combination with the parsing means and the list means, for compiling the text into at least an appropriate one of the object list, the fact list and the constraint list; drawing means, responsive to the drag-and-drop means and the parsing means for drawing a graphic representation of the text on the conceptual schema diagram using the graphical form of the computer language; database mapping means, for mapping the conceptual schema to a database; the edit window, conceptual schema diagram, drag-and-drop means, parsing means, list means, compiler, drawing means, and database mapping means forming in operative combination a drain-and-drop fact compiler for specifying the information system represented in the conceptual schema; and query means for specifying a query to the information system.
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6. Apparatus for the modeling and query of an information system, the apparatus using natural-language like constructs and further comprising: a programmable computer including memory; a display device coupled to the computer; a data entry device, further coupled to the computer; a graphical user interface implemented on the computer; a repository further implemented on the computer; an edit window displayed on the display device for entering text therein, the edit window utilizing the textual form of a computer language having both textual and graphical forms for translating the natural language-like constructs into object-role modeling symbology; conceptual schema diagram formation means for forming a conceptual schema diagram representing the information system on the display device, the conceptual schema diagram utilizing the graphical form of the computer language having both textual and graphical forms; drag-and-drop means further implemented on the computer and in operative combination with the display device and the data entry device, for dragging the text from the edit window and for dropping the text item onto the conceptual schema diagram; parsing means for parsing the text into at least one of object, fact and constraint; list means, responsive to the parsing means, for creating an object list, a fact list and a constraint list in the repository; a compiler, in operative combination with the parsing means and the list means, for compiling the text into at least an appropriate one of the object list, the fact list and the constraint list; drawing means, responsive to the drag-and-drop means and the parsing means for drawing a graphic representation of the text on the conceptual schema diagram using the graphical form of the computer language; database mapping means, for mapping the conceptual schema to a database; the edit window, conceptual schema diagram, drag-and-drop means, parsing means, list means, compiler, drawing means, and database mapping means forming in operative combination a drain-and-drop fact compiler for specifying the information system represented in the conceptual schema; and query means for specifying a query to the information system. 7. The apparatus of claim 6 wherein the query means further comprises: query mapping means for generating a fact tree representing a query; and query generation means for generating the query represented by the fact tree to the database.
| 0.614294 |
1. A computer implemented method for inferring a probability of a first inference, the computer implemented method comprising: receiving a query at a database regarding a fact, wherein the first inference is absent from the database, wherein the database comprises a plurality of divergent data, wherein the plurality of divergent data includes a plurality of cohort data, wherein each datum of the plurality of divergent data in the database is conformed to dimensions of the database, wherein each datum of the plurality of divergent data has associated metadata and an associated key, wherein the associated metadata comprises data regarding cohorts associated with corresponding datum, data regarding hierarchies associated with corresponding datum, data regarding a corresponding source of each datum, and data regarding probabilities associated with integrity, reliability, and importance of each datum; establishing the fact as a frame of reference for the query, wherein the frame of reference is established to determine rules for limiting the plurality of divergent data that is searched, and wherein the frame of reference is established to generate associations among cohorts of the plurality of cohort data, wherein the cohorts are a group of associated individuals or objects sharing a common characteristic, and the cohorts are automatically generated using data stored at an atomic level; applying a first set of rules to the query, wherein the first set of rules are determined for the query according to a second set of rules, wherein the first set of rules determine how the plurality of divergent data are to be compared to the fact, and wherein the first set of rules determine a search space for the query; executing the query to create the probability of the first inference, wherein the probability of the first inference is determined from comparing the plurality of data divergent according to the first set of rules; responsive to at least one of a) the probability of the first inference exceeding a first pre-selected value, b) a significance of the first inference exceeding a second pre-selected value, c) a rate of change in the probability of the first inference exceeding a third pre-selected value, d) an amount of change in the probability of the first inference exceeding a fourth pre-selected value, and e) combinations thereof, executing an action trigger; and storing at least one of the probability of the first inference and the action trigger.
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1. A computer implemented method for inferring a probability of a first inference, the computer implemented method comprising: receiving a query at a database regarding a fact, wherein the first inference is absent from the database, wherein the database comprises a plurality of divergent data, wherein the plurality of divergent data includes a plurality of cohort data, wherein each datum of the plurality of divergent data in the database is conformed to dimensions of the database, wherein each datum of the plurality of divergent data has associated metadata and an associated key, wherein the associated metadata comprises data regarding cohorts associated with corresponding datum, data regarding hierarchies associated with corresponding datum, data regarding a corresponding source of each datum, and data regarding probabilities associated with integrity, reliability, and importance of each datum; establishing the fact as a frame of reference for the query, wherein the frame of reference is established to determine rules for limiting the plurality of divergent data that is searched, and wherein the frame of reference is established to generate associations among cohorts of the plurality of cohort data, wherein the cohorts are a group of associated individuals or objects sharing a common characteristic, and the cohorts are automatically generated using data stored at an atomic level; applying a first set of rules to the query, wherein the first set of rules are determined for the query according to a second set of rules, wherein the first set of rules determine how the plurality of divergent data are to be compared to the fact, and wherein the first set of rules determine a search space for the query; executing the query to create the probability of the first inference, wherein the probability of the first inference is determined from comparing the plurality of data divergent according to the first set of rules; responsive to at least one of a) the probability of the first inference exceeding a first pre-selected value, b) a significance of the first inference exceeding a second pre-selected value, c) a rate of change in the probability of the first inference exceeding a third pre-selected value, d) an amount of change in the probability of the first inference exceeding a fourth pre-selected value, and e) combinations thereof, executing an action trigger; and storing at least one of the probability of the first inference and the action trigger. 2. The computer implemented method of claim 1 wherein the action trigger comprises a message presented to a user.
| 0.5169 |
14. A system comprising user equipment for implementing an interactive media guidance application, the user equipment comprising a processor for: receiving a first user input having a common attribute of video content; storing a plurality of media assets including a subset stored based on the common attribute; subsequent to storing the plurality of media assets, receiving a second user input requesting playback of the subset of the plurality of stored media assets having the common attribute of video content; identifying, from the plurality of stored media assets, the subset of the plurality of stored media assets having the common attribute of video content; generating, for simultaneous display, a first list of the plurality of stored media assets, a second list of the subset of the plurality of stored media assets having the common attribute of video content and an indication of the common attribute corresponding to the second list, wherein the second list of the subset includes all media assets having the common attribute from the first list of the plurality of stored media assets; and automatically selecting for playback the subset of the plurality of stored media assets having the common attribute of video content in response to the second user input.
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14. A system comprising user equipment for implementing an interactive media guidance application, the user equipment comprising a processor for: receiving a first user input having a common attribute of video content; storing a plurality of media assets including a subset stored based on the common attribute; subsequent to storing the plurality of media assets, receiving a second user input requesting playback of the subset of the plurality of stored media assets having the common attribute of video content; identifying, from the plurality of stored media assets, the subset of the plurality of stored media assets having the common attribute of video content; generating, for simultaneous display, a first list of the plurality of stored media assets, a second list of the subset of the plurality of stored media assets having the common attribute of video content and an indication of the common attribute corresponding to the second list, wherein the second list of the subset includes all media assets having the common attribute from the first list of the plurality of stored media assets; and automatically selecting for playback the subset of the plurality of stored media assets having the common attribute of video content in response to the second user input. 25. The system of claim 14 , further comprising the processor for generating for display a playlist transport bar that provides information regarding playback progress of the subset of the plurality of stored media assets.
| 0.528464 |
12. A server for interacting with a collection of documents and a set of language-specific indices, with the server configured: to receive a query from a user, with the query associated with a set of one or more target languages; to parse the query into one or more terms, using at least one processor, with each term associated with a corresponding language identifier and a stemmed version of the term; to translate the original and stemmed versions of each term, using at least one processor, into each of the target languages and thus define respective sets of one or more equivalent query terms; and to identify a set of documents from the collection of documents for each of the target languages, with each set identified based on the equivalent query terms for the corresponding target language.
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12. A server for interacting with a collection of documents and a set of language-specific indices, with the server configured: to receive a query from a user, with the query associated with a set of one or more target languages; to parse the query into one or more terms, using at least one processor, with each term associated with a corresponding language identifier and a stemmed version of the term; to translate the original and stemmed versions of each term, using at least one processor, into each of the target languages and thus define respective sets of one or more equivalent query terms; and to identify a set of documents from the collection of documents for each of the target languages, with each set identified based on the equivalent query terms for the corresponding target language. 14. The server of claim 12 , wherein the server is further configured to define a graphical user interface for enabling a user to submit a query, the interface having a query submission screen having a control region for entering terms of the query; a control region for selecting one or more of the target language; and a control region for submitting the query.
| 0.73508 |
15. A system for monitoring a conversation between a pair of speakers for detecting an emotion of at least one of the speakers using voice analysis comprising: (a) logic that receives a voice signal representing voices of speakers in a conversation; (b) logic that extracts at least one feature of the voice signal selected from a group of features consisting of a maximum value of a fundamental frequency, a standard deviation of the fundamental frequency, a range of the fundamental frequency, a mean of the fundamental frequency, a mean of a bandwidth of a first formant, a mean of a bandwidth of a second formant, a standard deviation of energy, a speaking rate, a slope of the fundamental frequency, a maximum value of the first formant, a maximum value of the energy, a range of the energy, a range of the second formant, and a range of the first formant; (c) logic that determines an emotion associated with the voice signal based on the extracted feature; (d) a code segment that determines whether the emotion matches a negative emotion selected from a predefined group of negative emotions consisting of anger, sadness and fear; and (e) logic that outputs the determined emotion to a third party during the conversation if the emotion matches one of the negative emotions.
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15. A system for monitoring a conversation between a pair of speakers for detecting an emotion of at least one of the speakers using voice analysis comprising: (a) logic that receives a voice signal representing voices of speakers in a conversation; (b) logic that extracts at least one feature of the voice signal selected from a group of features consisting of a maximum value of a fundamental frequency, a standard deviation of the fundamental frequency, a range of the fundamental frequency, a mean of the fundamental frequency, a mean of a bandwidth of a first formant, a mean of a bandwidth of a second formant, a standard deviation of energy, a speaking rate, a slope of the fundamental frequency, a maximum value of the first formant, a maximum value of the energy, a range of the energy, a range of the second formant, and a range of the first formant; (c) logic that determines an emotion associated with the voice signal based on the extracted feature; (d) a code segment that determines whether the emotion matches a negative emotion selected from a predefined group of negative emotions consisting of anger, sadness and fear; and (e) logic that outputs the determined emotion to a third party during the conversation if the emotion matches one of the negative emotions. 16. A system as recited in claim 15, wherein at least two features of the voice signal selected from the group of features are extracted.
| 0.650903 |
7. A method for rendering an object model in terms of XML, the XML comprising a plurality of XML tags, the method comprising: for each XML tag in the XML defining translators, each translator including common, parser-independent mappings for the XML tag of said XML to an associated object model feature, whereby the parser-independent mappings are each useable with multiple types of parser mechanisms including at least parser mechanisms that use a document object model and parser mechanisms that are event driven; parsing the XML using a parser-specific implementation, including creating a parser specific adapter for each XML tag of the XML; and translating the results of said parsing with the parser specific adapter to render the object model, wherein each parser specific adapter uses the common, parser-independent mappings of the associated translator for each XML tag to produce the object model.
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7. A method for rendering an object model in terms of XML, the XML comprising a plurality of XML tags, the method comprising: for each XML tag in the XML defining translators, each translator including common, parser-independent mappings for the XML tag of said XML to an associated object model feature, whereby the parser-independent mappings are each useable with multiple types of parser mechanisms including at least parser mechanisms that use a document object model and parser mechanisms that are event driven; parsing the XML using a parser-specific implementation, including creating a parser specific adapter for each XML tag of the XML; and translating the results of said parsing with the parser specific adapter to render the object model, wherein each parser specific adapter uses the common, parser-independent mappings of the associated translator for each XML tag to produce the object model. 8. The method of claim 7 , wherein the defining step includes: mapping each XML tag to a feature of a meta model describing each associated object model feature.
| 0.572917 |
2. The computer-implemented method of claim 1 , wherein generating the search results page comprises generating a web page that contains a graphical feature that appears to be a paper note that contains one or more search results from the second set of search results.
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2. The computer-implemented method of claim 1 , wherein generating the search results page comprises generating a web page that contains a graphical feature that appears to be a paper note that contains one or more search results from the second set of search results. 6. The computer-implemented method of claim 2 , wherein the step of generating the web page that contains the graphical feature that appears to be the paper note comprises generating a graphical feature that appears to be a paper note that contains a user-selectable control which, when selected by a user, will cause one or more search results contained within the graphical feature, but no search results that are not contained within the graphical feature, to be printed on one or more sheets of physical media.
| 0.839728 |
17. The system of claim 14 , further comprising a recording module, communicatively coupled to the data store, configured to create the electronic representation of the narration by recording a narrator reading a subset of the text aloud.
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17. The system of claim 14 , further comprising a recording module, communicatively coupled to the data store, configured to create the electronic representation of the narration by recording a narrator reading a subset of the text aloud. 18. The system of claim 17 , further comprising a display module, communicatively coupled to the recording module, configured to prompt the narrator to record additional narration for a specific component of the book that is not correlated with a portion of the electronic representation of the narration.
| 0.90886 |
1. A computationally-implemented method, comprising: managing adaptation data that is stored at a reference location, wherein the adaptation data is at least partly based on at least one speech interaction of a particular party; determining an availability of the adaptation data by comparing a property of the adaptation data located at the referenced location with an expected value of the property of the adaptation data; facilitating transmission of the adaptation data to a target device when there is an indication of a speech-facilitated transaction between the target device and the particular party, wherein the adaptation data is configured to be applied to the target device to assist in execution of the speech-facilitated transaction; and facilitating acquisition of adaptation result data that is based on at least one aspect of the speech-facilitated transaction and configured to be used in determining whether to modify the adaptation data, upon receipt of an indication from the target device of a status of the speech-facilitated transaction between the target device and the particular party, wherein said status includes an indicator of a success in determining speech of the speech-facilitated transaction.
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1. A computationally-implemented method, comprising: managing adaptation data that is stored at a reference location, wherein the adaptation data is at least partly based on at least one speech interaction of a particular party; determining an availability of the adaptation data by comparing a property of the adaptation data located at the referenced location with an expected value of the property of the adaptation data; facilitating transmission of the adaptation data to a target device when there is an indication of a speech-facilitated transaction between the target device and the particular party, wherein the adaptation data is configured to be applied to the target device to assist in execution of the speech-facilitated transaction; and facilitating acquisition of adaptation result data that is based on at least one aspect of the speech-facilitated transaction and configured to be used in determining whether to modify the adaptation data, upon receipt of an indication from the target device of a status of the speech-facilitated transaction between the target device and the particular party, wherein said status includes an indicator of a success in determining speech of the speech-facilitated transaction. 8. The computationally-implemented method of claim 1 , wherein said facilitating transmission of the adaptation data to a target device when there is an indication of a speech-facilitated transaction between the target device and the particular party, wherein the adaptation data is configured to be applied to the target device to assist in execution of the speech-facilitated transaction comprises: facilitating transmission of the adaptation data to a target device when there is an indication of a speech-facilitated transaction between the target device and the particular party, wherein the adaptation data is configured to be applied to the target device to improve performance in processing speech received during execution of the speech-facilitated transaction.
| 0.756472 |
1. A computer-implementable method comprising: creating, using multiple prioritized naming rules in response to determining that more than one set of naming attributes is provided by the multiple prioritized naming rules, all valid names for a resource that is managed by a configuration management database (CMDB), where the resource belongs to a class of resources that comprises the multiple prioritized naming rules, and where the valid names are assigned by the CMDB to have a validity priority order based upon a prioritized naming rule used to create each valid name; and assigning one of the valid names a status of being a master name for the resource.
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1. A computer-implementable method comprising: creating, using multiple prioritized naming rules in response to determining that more than one set of naming attributes is provided by the multiple prioritized naming rules, all valid names for a resource that is managed by a configuration management database (CMDB), where the resource belongs to a class of resources that comprises the multiple prioritized naming rules, and where the valid names are assigned by the CMDB to have a validity priority order based upon a prioritized naming rule used to create each valid name; and assigning one of the valid names a status of being a master name for the resource. 31. The computer-implementable method of claim 1 , where assigning one of the valid names the status of being the master name for the resource comprises assigning the valid name with a highest priority based upon the validity priority order as the master name for the resource.
| 0.680242 |
1. A computer implemented method comprising: receiving, via an application programming interface (API) of a calculation engine including a model optimizer having an optimizer framework, data comprising at least one optimization rule, the calculation engine executing on a calculation engine layer that interacts with a physical table pool and a logical layer, the physical table pool comprising physical tables containing data to be queried, and the logical layer defining a logical metamodel joining at least a portion of the physical tables in the physical table pool; registering, by the calculation engine, the at least one optimization rule for execution by a rules engine of the optimizer framework, wherein the optimizer framework assigns cost functions to at least a portion of optimization rules, the cost functions being used to determine when to execute a particular optimization rule; receiving, by the calculation engine, subsequently each query; generating, by the calculation engine, an initial data flow graph comprising a plurality of nodes specifying operations for executing the query; optimizing, by the calculation engine, the initial data flow graph using the rules engine, wherein at least one rule is applied to only a single node of the initial data flow graph, wherein a first subset of the operations are executed directly by the calculation engine and a second, different subset of the operations are transformed into a set of logical database execution plans, wherein each rule of the rules engine identifies one to N predecessors of corresponding start node from which optimization can be initiated, wherein N is an integer number, wherein said each rule specifies read and write operations to perform on corresponding nodes; and initiating executing of the query using the optimized data flow graph.
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1. A computer implemented method comprising: receiving, via an application programming interface (API) of a calculation engine including a model optimizer having an optimizer framework, data comprising at least one optimization rule, the calculation engine executing on a calculation engine layer that interacts with a physical table pool and a logical layer, the physical table pool comprising physical tables containing data to be queried, and the logical layer defining a logical metamodel joining at least a portion of the physical tables in the physical table pool; registering, by the calculation engine, the at least one optimization rule for execution by a rules engine of the optimizer framework, wherein the optimizer framework assigns cost functions to at least a portion of optimization rules, the cost functions being used to determine when to execute a particular optimization rule; receiving, by the calculation engine, subsequently each query; generating, by the calculation engine, an initial data flow graph comprising a plurality of nodes specifying operations for executing the query; optimizing, by the calculation engine, the initial data flow graph using the rules engine, wherein at least one rule is applied to only a single node of the initial data flow graph, wherein a first subset of the operations are executed directly by the calculation engine and a second, different subset of the operations are transformed into a set of logical database execution plans, wherein each rule of the rules engine identifies one to N predecessors of corresponding start node from which optimization can be initiated, wherein N is an integer number, wherein said each rule specifies read and write operations to perform on corresponding nodes; and initiating executing of the query using the optimized data flow graph. 5. The method as in claim 1 , wherein each rule identifies a start node in the initial data flow graph from which optimization can be initiated.
| 0.816919 |
8. A computer program product comprising at least one computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions comprising: a first executable portion for extracting a feature indicative of a property of a vocal tract of a speaker from each of training source speech and training target speech; a second executable portion for defining sub-feature units with respect to the feature for both the training source speech and the training target speech to generate training source speech sub-feature units and training target speech sub-feature units, respectively; and a third executable portion for performing voice conversion of source speech to target speech based on the conversion of the sub-feature units to corresponding target speech sub-feature units using a conversion model trained with respect to converting the training source speech sub-feature units to the training target speech sub-feature units.
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8. A computer program product comprising at least one computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions comprising: a first executable portion for extracting a feature indicative of a property of a vocal tract of a speaker from each of training source speech and training target speech; a second executable portion for defining sub-feature units with respect to the feature for both the training source speech and the training target speech to generate training source speech sub-feature units and training target speech sub-feature units, respectively; and a third executable portion for performing voice conversion of source speech to target speech based on the conversion of the sub-feature units to corresponding target speech sub-feature units using a conversion model trained with respect to converting the training source speech sub-feature units to the training target speech sub-feature units. 9. A computer program product according to claim 8 , further comprising a fourth executable portion for an initial operation of training the conversion model using parallel source and target utterances that have been aligned at a sub-feature level.
| 0.544723 |
22. The non-transitory machine readable medium of claim 17 , wherein the JS file comprises a set of behavioral descriptions of the JSON-based model and the JSON file comprises a set of property descriptions of the JSON-based model.
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22. The non-transitory machine readable medium of claim 17 , wherein the JS file comprises a set of behavioral descriptions of the JSON-based model and the JSON file comprises a set of property descriptions of the JSON-based model. 24. The non-transitory machine readable medium of claim 22 , wherein the set of property descriptions of the JSON-based model is associated with a set of properties of a table in the database.
| 0.858814 |
19. The method of claim 18 wherein said select image portion identified by said mouse-over gesture is expanded in response to a passage of time.
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19. The method of claim 18 wherein said select image portion identified by said mouse-over gesture is expanded in response to a passage of time. 20. The method of claim 19 wherein said expansion includes additional elements within proximity to the mouse-over gesture.
| 0.949267 |
10. A system for providing text flow around a non-rectangular graphic, comprising: a processor configured to find the intersection, if any, between a proposed text rectangle and the graphic; and identify as a valid text area within the proposed text rectangle a valid rectangle, if any, that is not within the bounds in the x-direction of an area of intersection between the proposed text rectangle and the graphic; and in the event no valid text area is found, to look ahead by: extending the bottom of the proposed rectangle to the bottom of the graphic to form an extended rectangle; uniting the graphic with the extended rectangle; subtracting the graphic and the proposed rectangle from the union of the graphic with the extended rectangle; skipping ahead in the y-direction by a distance equal to the bottom of the extended rectangle minus the bottom of the proposed rectangle if the result of subtracting the graphic and the proposed rectangle from the union of the graphic with the extended rectangle is empty; and skipping ahead in the y-direction by a distance equal to the top of the result of subtracting the graphic and the proposed rectangle from the union of the graphic with the extended rectangle minus the bottom of the proposed rectangle if the result of subtracting the graphic and the proposed rectangle from the union of the graphic with the extended rectangle is not empty; and a memory configured to provide instructions to the processor.
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10. A system for providing text flow around a non-rectangular graphic, comprising: a processor configured to find the intersection, if any, between a proposed text rectangle and the graphic; and identify as a valid text area within the proposed text rectangle a valid rectangle, if any, that is not within the bounds in the x-direction of an area of intersection between the proposed text rectangle and the graphic; and in the event no valid text area is found, to look ahead by: extending the bottom of the proposed rectangle to the bottom of the graphic to form an extended rectangle; uniting the graphic with the extended rectangle; subtracting the graphic and the proposed rectangle from the union of the graphic with the extended rectangle; skipping ahead in the y-direction by a distance equal to the bottom of the extended rectangle minus the bottom of the proposed rectangle if the result of subtracting the graphic and the proposed rectangle from the union of the graphic with the extended rectangle is empty; and skipping ahead in the y-direction by a distance equal to the top of the result of subtracting the graphic and the proposed rectangle from the union of the graphic with the extended rectangle minus the bottom of the proposed rectangle if the result of subtracting the graphic and the proposed rectangle from the union of the graphic with the extended rectangle is not empty; and a memory configured to provide instructions to the processor. 12. A system as recited in claim 10 , wherein the processor is configured to identify as a valid text area within the proposed text rectangle a valid rectangle, if any, that is not within the bounds in the x-direction of an area of intersection between the proposed text rectangle and the graphic at least in part by determining the bounds in the x-direction of each area of intersection and considering each such area of intersection to be a hole within which text may not be included.
| 0.506603 |
1. A method of optimizing a sequence of operations adapted for execution by a processor, wherein the processor executes an operation set and has a set of registers, and wherein the method comprises: associating symbolic expressions with each of at least a subset of the set of registers, thereby creating a plurality of associated symbolic expressions, wherein each of the plurality of associated symbolic expressions may be emitted as one or more operations; holding a set of dependency indicators that specify, for each particular associated symbolic expression from the plurality of associated symbolic expressions, which of the other associated symbolic expressions must be emitted prior to emitting the particular associated symbolic expression; locating an operation, if any, that is next within the sequence of operations and setting that operation to be a working operation; processing the working operation as follows: a) handling the working operation by a combination of updating, if necessary, any of the associated symbolic expressions and emitting any of the associated symbolic expressions; b) identifying any of the updated associated symbolic expressions that must be emitted prior to emitting any of the other associated symbolic expressions, and then updating the set of dependency indicators to include any such dependencies; and c) identifying any of the updated associated symbolic expressions that no longer need to be emitted prior to emitting any of the other associated symbolic expressions, and then updating the set of dependency indicators to remove any such dependencies; wherein the set of dependency indicators includes a matrix of dependency indicators; wherein each column of the matrix of dependency indicators is associated with a particular register from the set of registers; wherein each row of the matrix of dependency indicators is associated with a particular register from the set of registers; wherein each dependency indicator found in a non-diagonal element of the matrix of dependency indicators indicates that the associated symbolic expression associated with the particular register associated with the row is to be emitted after the emission of the associated symbolic expression associated with the particular register associated with the column; and when any row of the matrix of dependency indicators is read the diagonal element of the row of the matrix of dependency indicators is read normally; and when any column of the matrix of dependency indicators is read, the diagonal element of the column is read as not indicating dependency regardless of the state of the dependency indicator in the diagonal element.
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1. A method of optimizing a sequence of operations adapted for execution by a processor, wherein the processor executes an operation set and has a set of registers, and wherein the method comprises: associating symbolic expressions with each of at least a subset of the set of registers, thereby creating a plurality of associated symbolic expressions, wherein each of the plurality of associated symbolic expressions may be emitted as one or more operations; holding a set of dependency indicators that specify, for each particular associated symbolic expression from the plurality of associated symbolic expressions, which of the other associated symbolic expressions must be emitted prior to emitting the particular associated symbolic expression; locating an operation, if any, that is next within the sequence of operations and setting that operation to be a working operation; processing the working operation as follows: a) handling the working operation by a combination of updating, if necessary, any of the associated symbolic expressions and emitting any of the associated symbolic expressions; b) identifying any of the updated associated symbolic expressions that must be emitted prior to emitting any of the other associated symbolic expressions, and then updating the set of dependency indicators to include any such dependencies; and c) identifying any of the updated associated symbolic expressions that no longer need to be emitted prior to emitting any of the other associated symbolic expressions, and then updating the set of dependency indicators to remove any such dependencies; wherein the set of dependency indicators includes a matrix of dependency indicators; wherein each column of the matrix of dependency indicators is associated with a particular register from the set of registers; wherein each row of the matrix of dependency indicators is associated with a particular register from the set of registers; wherein each dependency indicator found in a non-diagonal element of the matrix of dependency indicators indicates that the associated symbolic expression associated with the particular register associated with the row is to be emitted after the emission of the associated symbolic expression associated with the particular register associated with the column; and when any row of the matrix of dependency indicators is read the diagonal element of the row of the matrix of dependency indicators is read normally; and when any column of the matrix of dependency indicators is read, the diagonal element of the column is read as not indicating dependency regardless of the state of the dependency indicator in the diagonal element. 5. The method of claim 1 wherein: the working operation has associated therewith a destination register and one or more source registers and the dependency indicators show that a particular one of the one or more source registers must be emitted before the destination register; process (a) further includes emitting the associated symbolic expression associated with the particular source register prior to the handling of the working operation.
| 0.743103 |
79. Apparatus according to claim 64, wherein the document is stored in the memory means as a plurality of records and further including means for associating control information with each record relating to the points in which a name associated with a set of data presentation characteristics begins or ends, said control information including an offset defining the bytes of the record relative to the first record byte that the characteristics begin and end, and an identifier that identifies aid name.
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79. Apparatus according to claim 64, wherein the document is stored in the memory means as a plurality of records and further including means for associating control information with each record relating to the points in which a name associated with a set of data presentation characteristics begins or ends, said control information including an offset defining the bytes of the record relative to the first record byte that the characteristics begin and end, and an identifier that identifies aid name. 82. Apparatus according to claim 79, further including manager means for keeping track of all points in the record which relate to a range for an assigned name representative of a set of data presentation characteristics.
| 0.896646 |
1. A content item retrieval method, the method comprising: determining multiple base locations, wherein determining each base location is based on GPS information or user entry, each base location being a location from which to apply a corresponding criterion distance-determined granularity thresholding for setting a threshold for location similarity in selecting or rejecting target items for content item retrieval, wherein criterion distance-determined granularity thresholding is applied for each base location of the multiple base locations separately based on differences in distance between farther locations being less important than between equally distant closer locations, further wherein the farther in distance moved from a corresponding base location of the multiple base locations, the less important, in terms of determining similarity, are differences in distance between locations of different content items at the corresponding further distances from the corresponding base location; extracting, as a first anchor item location, location data for a first identified anchor content item, the first identified anchor content item for designating which candidate content items for which a content type is not known or specified by a user are to be retrieved; setting a first threshold based on a criterion distance that candidate content items must meet to be selected, wherein the first threshold comprises an assigned value on a scale of 1 to 10, where a value of 1 indicates a very small distance between a corresponding base location and candidate content item and a value of 10 indicates a great distance between the corresponding base location and candidate content item, and wherein the criterion distance is determined, using criterion distance-determined granularity thresholding, as a distance between the corresponding base location of the multiple base locations and the first anchor item location, further wherein the distance from the corresponding base location is ranked on the scale and as the distance from the corresponding base location increases, then longer distances are encompassed by fewer gradations of the scale, such that distance granularity on the scale is higher for locations geographically closer to the corresponding base location than for locations further away from the corresponding base location; extracting, as a first candidate location, the location data for a first candidate content item, and determining, as a first candidate distance, the distance between the corresponding base location of the multiple base locations and the first candidate location; selecting the first candidate content item as similar for content item retrieval based on (i) the first candidate distance that corresponds to the distance between the corresponding base location of the multiple base locations and the first candidate location and (ii) the first threshold that is based upon the criterion distance, wherein the first candidate content item is selected as being similar to the first identified content item in response to the determined first candidate distance, when compared to the first threshold, being within or with the first threshold; and outputting a selection signal for indicating retrieval of the first candidate content item when the first candidate location of the candidate content item is selected as being similar to the first identified content item for content item retrieval.
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1. A content item retrieval method, the method comprising: determining multiple base locations, wherein determining each base location is based on GPS information or user entry, each base location being a location from which to apply a corresponding criterion distance-determined granularity thresholding for setting a threshold for location similarity in selecting or rejecting target items for content item retrieval, wherein criterion distance-determined granularity thresholding is applied for each base location of the multiple base locations separately based on differences in distance between farther locations being less important than between equally distant closer locations, further wherein the farther in distance moved from a corresponding base location of the multiple base locations, the less important, in terms of determining similarity, are differences in distance between locations of different content items at the corresponding further distances from the corresponding base location; extracting, as a first anchor item location, location data for a first identified anchor content item, the first identified anchor content item for designating which candidate content items for which a content type is not known or specified by a user are to be retrieved; setting a first threshold based on a criterion distance that candidate content items must meet to be selected, wherein the first threshold comprises an assigned value on a scale of 1 to 10, where a value of 1 indicates a very small distance between a corresponding base location and candidate content item and a value of 10 indicates a great distance between the corresponding base location and candidate content item, and wherein the criterion distance is determined, using criterion distance-determined granularity thresholding, as a distance between the corresponding base location of the multiple base locations and the first anchor item location, further wherein the distance from the corresponding base location is ranked on the scale and as the distance from the corresponding base location increases, then longer distances are encompassed by fewer gradations of the scale, such that distance granularity on the scale is higher for locations geographically closer to the corresponding base location than for locations further away from the corresponding base location; extracting, as a first candidate location, the location data for a first candidate content item, and determining, as a first candidate distance, the distance between the corresponding base location of the multiple base locations and the first candidate location; selecting the first candidate content item as similar for content item retrieval based on (i) the first candidate distance that corresponds to the distance between the corresponding base location of the multiple base locations and the first candidate location and (ii) the first threshold that is based upon the criterion distance, wherein the first candidate content item is selected as being similar to the first identified content item in response to the determined first candidate distance, when compared to the first threshold, being within or with the first threshold; and outputting a selection signal for indicating retrieval of the first candidate content item when the first candidate location of the candidate content item is selected as being similar to the first identified content item for content item retrieval. 7. The method of claim 1 , wherein at least one of the base location and the location data is determined based on GPS information.
| 0.95836 |
17. A non-transitory computer readable medium including program code for providing a message-based interface for performing a service part order history service, the medium comprising: program code for receiving via a message-based interface derived from a common business object model, where the common business object model includes business objects having relationships that enable derivation of message-based interfaces and message packages, the message-based interface exposing at least one service as defined in a service registry and from a heterogeneous application executing in an environment of computer systems providing message-based services, a first message for querying supply chain management for service part order histories satisfying a set of selection criteria specified by a set of query elements, the first message including a first message package derived from the common business object model and hierarchically organized as: a service part order history supply chain management by elements query message entity; and at a first hierarchical level within the first message package, a selection package and a processing conditions package, where the selection package includes, at a second hierarchical level within the first message package, a service part order history selection by elements entity, and where the service part order history selection by elements entity includes, at a third hierarchical level within the first message package, a planning version identifier (ID), and where the processing conditions package includes, at the second hierarchical level within the first message package, a processing conditions entity, where the processing conditions entity includes, at the third second hierarchical level within the first message package, an unlimited hits indicator; program code for processing the first message according to the hierarchical organization of the first message package, where processing the first message includes unpacking the first message package based on the common business object model; and program code for sending a second message to the heterogeneous application responsive to the first message, where the second message includes a second message package derived from the common business object model to provide consistent semantics with the first message package.
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17. A non-transitory computer readable medium including program code for providing a message-based interface for performing a service part order history service, the medium comprising: program code for receiving via a message-based interface derived from a common business object model, where the common business object model includes business objects having relationships that enable derivation of message-based interfaces and message packages, the message-based interface exposing at least one service as defined in a service registry and from a heterogeneous application executing in an environment of computer systems providing message-based services, a first message for querying supply chain management for service part order histories satisfying a set of selection criteria specified by a set of query elements, the first message including a first message package derived from the common business object model and hierarchically organized as: a service part order history supply chain management by elements query message entity; and at a first hierarchical level within the first message package, a selection package and a processing conditions package, where the selection package includes, at a second hierarchical level within the first message package, a service part order history selection by elements entity, and where the service part order history selection by elements entity includes, at a third hierarchical level within the first message package, a planning version identifier (ID), and where the processing conditions package includes, at the second hierarchical level within the first message package, a processing conditions entity, where the processing conditions entity includes, at the third second hierarchical level within the first message package, an unlimited hits indicator; program code for processing the first message according to the hierarchical organization of the first message package, where processing the first message includes unpacking the first message package based on the common business object model; and program code for sending a second message to the heterogeneous application responsive to the first message, where the second message includes a second message package derived from the common business object model to provide consistent semantics with the first message package. 19. The medium of claim 17 , where the processing conditions entity further includes at least one of a query hits maximum number value and a last provided business transaction document reference item ID.
| 0.730233 |
1. A method for sending information to a user, the method comprising: retrieving a plurality of terms from a memory descriptive of an upcoming event for the user; selecting at least one information source from a plurality of information sources depending on at least one of the plurality of terms retrieved, the at least one information source being less than all of the plurality of information sources; selecting a query generation algorithm from among a plurality of query generation algorithms based on which of the at least one information source is selected from the plurality of information sources; executing, using at least one processor, the selected query generation algorithm to generate a query based on the terms, a different query being generated based on which of the plurality of query generation algorithms is selected and executed; querying, using the at least one processor, the at least one information source utilizing the query to generate information for the upcoming event; and sending at least a portion of the information generated to the user, wherein the information is generated at a predetermined time before the event.
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1. A method for sending information to a user, the method comprising: retrieving a plurality of terms from a memory descriptive of an upcoming event for the user; selecting at least one information source from a plurality of information sources depending on at least one of the plurality of terms retrieved, the at least one information source being less than all of the plurality of information sources; selecting a query generation algorithm from among a plurality of query generation algorithms based on which of the at least one information source is selected from the plurality of information sources; executing, using at least one processor, the selected query generation algorithm to generate a query based on the terms, a different query being generated based on which of the plurality of query generation algorithms is selected and executed; querying, using the at least one processor, the at least one information source utilizing the query to generate information for the upcoming event; and sending at least a portion of the information generated to the user, wherein the information is generated at a predetermined time before the event. 5. The method of claim 1 , wherein the information source selected comprises a website.
| 0.678571 |
1. A method for detecting collusion in online poker involving a plurality of players, comprising the steps of: collecting data on every action in every game for every player and sending the data to an online poker database; storing hidden game information data on every action in every game for every player in the online poker database; storing game action data on every action in every game for every player in the online poker database; performing a player action analysis of correlated actions between a pair of online poker players and building a user action database with data obtained in the player action analysis; creating two or more Bayesian Network graphical models including at least one collusional model to represent forms of collusional behavior and at least one non-collusional model to represent forms of non-collusional behavior; employing the created Bayesian Network graphical models to determine a likelihood of conditional behavior between the pair of online poker players; after determining the likelihood of conditional behavior, computing individual scores for the Bayesian Network graphical models using a Bayesian statistical technique; comparing the computed score of a collusional model with that of a non-collusional model; after comparing the computed scores, performing a collusion threshold analysis using a thresholding scheme to determine whether the collusional model indicates collusion for the pair of online poker players; and notifying an appropriate party of unfair collusional play if it is determined that the collusional model is appropriate for the pair of online poker players.
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1. A method for detecting collusion in online poker involving a plurality of players, comprising the steps of: collecting data on every action in every game for every player and sending the data to an online poker database; storing hidden game information data on every action in every game for every player in the online poker database; storing game action data on every action in every game for every player in the online poker database; performing a player action analysis of correlated actions between a pair of online poker players and building a user action database with data obtained in the player action analysis; creating two or more Bayesian Network graphical models including at least one collusional model to represent forms of collusional behavior and at least one non-collusional model to represent forms of non-collusional behavior; employing the created Bayesian Network graphical models to determine a likelihood of conditional behavior between the pair of online poker players; after determining the likelihood of conditional behavior, computing individual scores for the Bayesian Network graphical models using a Bayesian statistical technique; comparing the computed score of a collusional model with that of a non-collusional model; after comparing the computed scores, performing a collusion threshold analysis using a thresholding scheme to determine whether the collusional model indicates collusion for the pair of online poker players; and notifying an appropriate party of unfair collusional play if it is determined that the collusional model is appropriate for the pair of online poker players. 5. The method of claim 1 , further comprising the step of determining a likelihood of fairness of a particular poker game.
| 0.663347 |
14. A non-transitory computer-readable storage medium storing a plurality of instructions for providing online support to a user of a client application executing on a client computing device, the plurality of instructions comprising functionality to: initiate a client application wizard on the client computing device comprising a plurality of dialogs for completing a task; display a dialog of the plurality of dialogs to the user of the client application, wherein the client application is one selected from a group consisting of a tax preparation application, a payroll application, and a financial management application; determine by a user support module of the client application that a network connection is available on the client computing device; enable by the user support module of the client application, after determining that the network connection is available on the client computing device, a help threshold associated with the dialog and comprising a help threshold percentage; calculate, by the user support module of the client application, a result based on a dollar amount in a first field of the client application wizard populated by the user; determine by the user support module executing on a computer processor of the client computing device, after enabling the help threshold and based on a comparison of the result and the help threshold percentage, that the help threshold is exceeded, wherein exceeding the help threshold indicates that the user of the client application requires assistance in completing the task; send via the network connection of the client computing device, in response to the help threshold being exceeded, an availability request to an external support server; receive via the network connection of the computing device, after sending the availability request, a confirmation of an available support specialist specializing in completing the task; display, in response to receiving the confirmation, an indication to the user of the client computing device that the support specialist is available over the network connection; receive, in response to displaying the indication to the user of the client application, a support request from the user of the client application; open a chat dialog over the network connection of the client computing device in response to receiving the support request from the user of the client application; and initiate, using the chat dialog, a chat session over the network connection of the client computing device between the user and the available support specialist.
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14. A non-transitory computer-readable storage medium storing a plurality of instructions for providing online support to a user of a client application executing on a client computing device, the plurality of instructions comprising functionality to: initiate a client application wizard on the client computing device comprising a plurality of dialogs for completing a task; display a dialog of the plurality of dialogs to the user of the client application, wherein the client application is one selected from a group consisting of a tax preparation application, a payroll application, and a financial management application; determine by a user support module of the client application that a network connection is available on the client computing device; enable by the user support module of the client application, after determining that the network connection is available on the client computing device, a help threshold associated with the dialog and comprising a help threshold percentage; calculate, by the user support module of the client application, a result based on a dollar amount in a first field of the client application wizard populated by the user; determine by the user support module executing on a computer processor of the client computing device, after enabling the help threshold and based on a comparison of the result and the help threshold percentage, that the help threshold is exceeded, wherein exceeding the help threshold indicates that the user of the client application requires assistance in completing the task; send via the network connection of the client computing device, in response to the help threshold being exceeded, an availability request to an external support server; receive via the network connection of the computing device, after sending the availability request, a confirmation of an available support specialist specializing in completing the task; display, in response to receiving the confirmation, an indication to the user of the client computing device that the support specialist is available over the network connection; receive, in response to displaying the indication to the user of the client application, a support request from the user of the client application; open a chat dialog over the network connection of the client computing device in response to receiving the support request from the user of the client application; and initiate, using the chat dialog, a chat session over the network connection of the client computing device between the user and the available support specialist. 15. The non-transitory computer-readable storage medium of claim 14 , wherein the plurality of instructions further comprise functionality to: set focus to the first field in the dialog, wherein the help threshold further comprises a time interval corresponding to the first field, and wherein determining that the help threshold is exceeded further comprises determining that the time interval is elapsed.
| 0.525357 |
11. A system for generating a multi-voice font from a plurality of source voice fonts, the system comprising: a phoneme sequencer for parsing input text into a sequence of phonemes; a predictor operable to predict values of voice font characteristics for the phonemes for each source voice font of the plurality of source voice fonts using at least one characteristic model associated with each source voice font; a weight selector operable to assign a duration weight, a f0 weight, and a spectrum weight to each source voice font, the duration weight, the f0 weight, and the spectrum weight determining the relative contribution of the voice font characteristics predicted for the corresponding source voice font to the multi-voice font; an interpolator operable to merge the predicted voice font characteristics with the weights to produce the multi-voice font having voice font characteristics derived from the source voice fonts; and a voice encoder operable to render the input text as computer-generated speech using the multi-voice font, the computer-generated speech having the voice font characteristics derived from the source voice fonts.
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11. A system for generating a multi-voice font from a plurality of source voice fonts, the system comprising: a phoneme sequencer for parsing input text into a sequence of phonemes; a predictor operable to predict values of voice font characteristics for the phonemes for each source voice font of the plurality of source voice fonts using at least one characteristic model associated with each source voice font; a weight selector operable to assign a duration weight, a f0 weight, and a spectrum weight to each source voice font, the duration weight, the f0 weight, and the spectrum weight determining the relative contribution of the voice font characteristics predicted for the corresponding source voice font to the multi-voice font; an interpolator operable to merge the predicted voice font characteristics with the weights to produce the multi-voice font having voice font characteristics derived from the source voice fonts; and a voice encoder operable to render the input text as computer-generated speech using the multi-voice font, the computer-generated speech having the voice font characteristics derived from the source voice fonts. 13. The system of claim 11 wherein the interpolator further comprises: a duration interpolator operable to merge the predicted duration values for each phoneme with the duration weight for the predicting source voice font to produce an interpolated duration value for each phoneme; a f0 interpolator operable to merge the predicted f0 values for each phoneme with the f0 weight for the predicting source voice font to produce an interpolated f0 value for each frame of the phoneme; a spectral trajectory interpolator operable to merge the predicted spectral trajectory values for each phoneme with the spectrum weight for the predicting source voice font to produce an interpolated spectrum trajectory value for each frame of the phoneme; and a voiced/unvoiced decision interpolator operable to merge the predicted voiced/unvoiced probability values for each phoneme with the spectrum weight for the predicting source voice font to produce an interpolated voiced/unvoiced probability value for each phoneme and compare the interpolated voiced/unvoiced probability value to a threshold to determine an interpolated voiced/unvoiced decision value for each phoneme.
| 0.692476 |
3. A computer implemented method for automating integration of terminological information into a knowledge base, said method comprising the steps of: receiving, into a computer, input terminology information comprising a plurality of input terms and at least one relationship indicator from a set of predetermined relationship indicators, each relationship indicator specifying an ontological relationship among at least two of said input terms; storing, in said computer, a knowledge base comprising a plurality of ontologies, each one of said ontologies comprising a plurality of nodes, each node representing a term, and comprising associations among said nodes that depict ontological relationships among respective terms; generating a logical structure of said input terms from said input terminology information using a mapping table comprising a mapping entry for each relationship indicator in said set of predetermined relationship indicators, each mapping entry comprising a mapping from a relationship indicator to a particular ontological relationship that is in a format compatible with said ontological relationships depicted in said knowledge base; and integrating said logical structure of said input terms into said knowledge base, said integrating comprising: determining whether at least one input term matches a node in said knowledge base; if so, extending said knowledge base by storing data that logically couples said logical structure of said input terms to a node that matches an input term; and if not, generating a new and independent ontology for said knowledge base comprising said logical structure of said input terms.
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3. A computer implemented method for automating integration of terminological information into a knowledge base, said method comprising the steps of: receiving, into a computer, input terminology information comprising a plurality of input terms and at least one relationship indicator from a set of predetermined relationship indicators, each relationship indicator specifying an ontological relationship among at least two of said input terms; storing, in said computer, a knowledge base comprising a plurality of ontologies, each one of said ontologies comprising a plurality of nodes, each node representing a term, and comprising associations among said nodes that depict ontological relationships among respective terms; generating a logical structure of said input terms from said input terminology information using a mapping table comprising a mapping entry for each relationship indicator in said set of predetermined relationship indicators, each mapping entry comprising a mapping from a relationship indicator to a particular ontological relationship that is in a format compatible with said ontological relationships depicted in said knowledge base; and integrating said logical structure of said input terms into said knowledge base, said integrating comprising: determining whether at least one input term matches a node in said knowledge base; if so, extending said knowledge base by storing data that logically couples said logical structure of said input terms to a node that matches an input term; and if not, generating a new and independent ontology for said knowledge base comprising said logical structure of said input terms. 4. The method as set forth in claim 3 , further comprising: determining whether an input term that matches a node in said knowledge base connotes a different meaning than said term associated with a node; if so, then: deleting said node from its existing one or more associations; logically coupling any hierarchical associations, if any, with said node so as to by pass said node deleted; generating a new node for said input term; and integrating said new node into said knowledge base based on ontological relationships with associated nodes.
| 0.668624 |
15. A processor-implemented speech recognition method, the method comprising: receiving input of first speech to be recognized; extracting some frames from all frames of the first speech; generating a second speech by using the extracted frames; calculating an acoustic score of the second speech by using a Deep Neural Network (DNN)-based acoustic model; calculating an acoustic score for one or more frames, other than the extracted frames, of the first speech based on the calculated acoustic score of the second speech; and recognizing the first speech based on the calculated acoustic score of the second speech and the calculated acoustic score for the one or more frames of the first speech.
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15. A processor-implemented speech recognition method, the method comprising: receiving input of first speech to be recognized; extracting some frames from all frames of the first speech; generating a second speech by using the extracted frames; calculating an acoustic score of the second speech by using a Deep Neural Network (DNN)-based acoustic model; calculating an acoustic score for one or more frames, other than the extracted frames, of the first speech based on the calculated acoustic score of the second speech; and recognizing the first speech based on the calculated acoustic score of the second speech and the calculated acoustic score for the one or more frames of the first speech. 18. The method of claim 15 , wherein the calculating of the acoustic score of the first speech comprises using two acoustic scores of frames of the second speech as acoustic scores of two frames of the first speech that correspond to the two frames of the second speech and using at least one acoustic score of the frames of the second speech for an acoustic score of an adjacent frame, of the first speech, that is adjacent to the two frames of the first speech.
| 0.673554 |
1. A method comprising: receiving automatic speech recognition output from a media presentation; receiving a transcription of the media presentation; determining an anchor word time duration requirement; selecting, via a processor, a pair of anchor words in the media presentation based on the automatic speech recognition output and the transcription, to yield a selected pair of anchor words, wherein the selected pair of anchor words are separated from one another within the media presentation by a time less than the anchor word time duration requirement; and generating captions by aligning the transcription with the automatic speech recognition output between the selected pair of anchor words.
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1. A method comprising: receiving automatic speech recognition output from a media presentation; receiving a transcription of the media presentation; determining an anchor word time duration requirement; selecting, via a processor, a pair of anchor words in the media presentation based on the automatic speech recognition output and the transcription, to yield a selected pair of anchor words, wherein the selected pair of anchor words are separated from one another within the media presentation by a time less than the anchor word time duration requirement; and generating captions by aligning the transcription with the automatic speech recognition output between the selected pair of anchor words. 3. The method of claim 1 , wherein selecting the pair of anchor words is based on a similarity threshold between the automatic speech recognition output and the transcription.
| 0.669674 |
37. The system of claim 31 , wherein the instructions further cause the one or more processors to: determine a relevant subset of the shared pronunciation lexicon based on the context of the third user device, wherein the speech-to-text conversion on the audio data is performed using the determined relevant subset of the shared pronunciation lexicon to generate the textual representation of the user speech.
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37. The system of claim 31 , wherein the instructions further cause the one or more processors to: determine a relevant subset of the shared pronunciation lexicon based on the context of the third user device, wherein the speech-to-text conversion on the audio data is performed using the determined relevant subset of the shared pronunciation lexicon to generate the textual representation of the user speech. 38. The system of claim 37 , wherein performing speech-to-text conversion on the audio data using the determined relevant subset of the shared pronunciation lexicon excludes the use of portions of the shared pronunciation lexicon not included in the determined relevant subset of the shared pronunciation lexicon.
| 0.865474 |
3. In a computer graphics system for enabling an operator to create a phenomenon, the phenomenon comprising an encapsulated shader DAG comprising at least one shader node, the computer graphics system comprising (A) a base shader node database configured to store a plurality of base shader nodes, each base shader node including a shader, and (B) a phenomenon creator configured to enable the operator to interconnect the base shader nodes from the base shader node database into a DAG, the phenomenon creator verifying that interconnections among the base shader nodes as provided by the operator comprise a DAG, the improvement wherein the phenomenon creator comprises: a metanode environment operable for the creation of metanodes, the metanodes comprising component shaders that can be combined to build more complex shaders, and a graphical user interface (GUI) in communication with the metanode environment and operable to manage the metanode environment to enable a user to construct shader graphs and phenomena using the metanode environment, and a software language useable by the operator and operable to manage the metanode environment, implement shaders and unify discrete shading applications, the software language configurable as a superset of a plurality of selected shader languages for selected hardware platforms, and operable to enable a compiler function to generate, from a single, re-usable description of a phenomenon expressed in the software language, optimized software code for a selected hardware platform in a selected shader language.
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3. In a computer graphics system for enabling an operator to create a phenomenon, the phenomenon comprising an encapsulated shader DAG comprising at least one shader node, the computer graphics system comprising (A) a base shader node database configured to store a plurality of base shader nodes, each base shader node including a shader, and (B) a phenomenon creator configured to enable the operator to interconnect the base shader nodes from the base shader node database into a DAG, the phenomenon creator verifying that interconnections among the base shader nodes as provided by the operator comprise a DAG, the improvement wherein the phenomenon creator comprises: a metanode environment operable for the creation of metanodes, the metanodes comprising component shaders that can be combined to build more complex shaders, and a graphical user interface (GUI) in communication with the metanode environment and operable to manage the metanode environment to enable a user to construct shader graphs and phenomena using the metanode environment, and a software language useable by the operator and operable to manage the metanode environment, implement shaders and unify discrete shading applications, the software language configurable as a superset of a plurality of selected shader languages for selected hardware platforms, and operable to enable a compiler function to generate, from a single, re-usable description of a phenomenon expressed in the software language, optimized software code for a selected hardware platform in a selected shader language. 4. In the computer graphics system of claim 3 , the further improvement in which the phenomenon creator stores the phenomenon created by the operator in a phenomenon database.
| 0.584156 |
1. A computer-implemented method, comprising: associating, by a computer system, a set of content related to a plurality of items with a catalog describing the plurality of items, the plurality of items offered at an electronic marketplace based at least in part on the catalog; receiving, by the computer system from a computing device of a user, a query for an item offered at the electronic marketplace, the query comprising keywords; determining, by the computer system, a context indicating a specificity of the query, the context determined based at least in part on an abandonment rate associated with the keywords; identifying, by the computer system, information about the item from the catalog based at least in part on the query; generating, by the computer system, a query result comprising the information about the item; identifying, by the computer system, content from the set of content based at least in part on a rule, the rule specifying a content type and a presentation location based at least in part on the specificity of the context, the content identified based at least in part on a determination that the content is of the content type and on the associating with the catalog; inserting, by the computer system, the content from the set of content in the query result; and providing, by the computer system to the computing device of the user, the query result, the providing causing the computing device to present the information about the item in a first location of a space in a user interface and to present the content related to the one or more items at the presentation location of the space, the space configured to present the query result.
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1. A computer-implemented method, comprising: associating, by a computer system, a set of content related to a plurality of items with a catalog describing the plurality of items, the plurality of items offered at an electronic marketplace based at least in part on the catalog; receiving, by the computer system from a computing device of a user, a query for an item offered at the electronic marketplace, the query comprising keywords; determining, by the computer system, a context indicating a specificity of the query, the context determined based at least in part on an abandonment rate associated with the keywords; identifying, by the computer system, information about the item from the catalog based at least in part on the query; generating, by the computer system, a query result comprising the information about the item; identifying, by the computer system, content from the set of content based at least in part on a rule, the rule specifying a content type and a presentation location based at least in part on the specificity of the context, the content identified based at least in part on a determination that the content is of the content type and on the associating with the catalog; inserting, by the computer system, the content from the set of content in the query result; and providing, by the computer system to the computing device of the user, the query result, the providing causing the computing device to present the information about the item in a first location of a space in a user interface and to present the content related to the one or more items at the presentation location of the space, the space configured to present the query result. 5. The computer-implemented method of claim 1 , wherein the rule specifying that the higher the specificity of the query is, the earlier the presentation location of the content is in the space, the rule further specifying that the higher the specificity of the query is, the more specific the content type is to the item.
| 0.575573 |
36. A method of indexing and searching timed media files, as recited in claim 35 , wherein said processing of language that is spoken within the timed media file includes analyzing the logical structure of the language.
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36. A method of indexing and searching timed media files, as recited in claim 35 , wherein said processing of language that is spoken within the timed media file includes analyzing the logical structure of the language. 52. A method of indexing and searching timed media files, as recited in claim 36 , wherein said rule-based system for identifying sentences that contain indications of logical structure further comprises the step of labeling sentences according to the logical structure indications.
| 0.845258 |
3. The method of claim 2 wherein if the second threshold is not exceeded, conducting further dialog with the user using an adapted dialog strategy.
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3. The method of claim 2 wherein if the second threshold is not exceeded, conducting further dialog with the user using an adapted dialog strategy. 4. The method of claim 3 wherein the adapted dialog strategy includes one of prompting the user with choices and prompting the user to confirm the recognition of NLU errors.
| 0.884961 |
3. The method of claim 1 , wherein identifying the one or more embedded coding fragments comprises: identifying, in the structured document, a pair of structured document tags that enclose at least one term of the seed query; and identifying a structure for the pair of structured document tags, the structure including the pair of structured document tags and at least a portion of content enclosed by the pair of structured document tags.
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3. The method of claim 1 , wherein identifying the one or more embedded coding fragments comprises: identifying, in the structured document, a pair of structured document tags that enclose at least one term of the seed query; and identifying a structure for the pair of structured document tags, the structure including the pair of structured document tags and at least a portion of content enclosed by the pair of structured document tags. 4. The method of claim 3 , wherein generating one or more query templates comprises generating a query template that includes: the pair of structured document tags; a wild card for the at least one term of the seed query that is enclosed by the pair of structured document tags; and a portion of the content enclosed by the pair of structured document tags that does not match a term of the seed query.
| 0.872063 |
5. The method of claim 4 , further comprising determining position of the hand-written annotation in the camera view.
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5. The method of claim 4 , further comprising determining position of the hand-written annotation in the camera view. 6. The method of claim 5 , wherein the position of the hand-written annotation is determined using one or more context sensitive methods.
| 0.959674 |
1. A method implemented on a processor, the method comprising: receiving, on a computer host, an electronic document representing a pharmaceutical prescription; identifying constituent regions that include at least a first portion and a second portion within the electronic document; identifying first spatial frequencies for the first portion within the electronic document; identifying second spatial frequencies for the second portion within the electronic document; identifying a header based upon the first spatial frequencies, wherein identifying the first and second spatial frequencies includes performing a fast Fourier Transform on a portion of a document and translating spatial information into frequency information such that (i) the header identifying the prescriber is identified from the first spatial frequencies; and (ii) the second spatial frequencies are analyzed using the profile of the identified prescriber; using the header to identify a prescriber; retrieving a profile specific for the prescriber; analyzing the second spatial frequencies using the profile specific for the prescriber such that the second spatial frequencies are compared to spatial frequency domain information from the profile specific for the prescriber, the prescriber-specific profile constructed from the prescriber's past records and including isolated handwriting of the prescriber in a spatial frequency domain; and creating, based upon results from analyzing the second spatial frequencies using the profile for the prescriber, a transaction record on the computer host for a medical transaction associated with the pharmaceutical prescription.
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1. A method implemented on a processor, the method comprising: receiving, on a computer host, an electronic document representing a pharmaceutical prescription; identifying constituent regions that include at least a first portion and a second portion within the electronic document; identifying first spatial frequencies for the first portion within the electronic document; identifying second spatial frequencies for the second portion within the electronic document; identifying a header based upon the first spatial frequencies, wherein identifying the first and second spatial frequencies includes performing a fast Fourier Transform on a portion of a document and translating spatial information into frequency information such that (i) the header identifying the prescriber is identified from the first spatial frequencies; and (ii) the second spatial frequencies are analyzed using the profile of the identified prescriber; using the header to identify a prescriber; retrieving a profile specific for the prescriber; analyzing the second spatial frequencies using the profile specific for the prescriber such that the second spatial frequencies are compared to spatial frequency domain information from the profile specific for the prescriber, the prescriber-specific profile constructed from the prescriber's past records and including isolated handwriting of the prescriber in a spatial frequency domain; and creating, based upon results from analyzing the second spatial frequencies using the profile for the prescriber, a transaction record on the computer host for a medical transaction associated with the pharmaceutical prescription. 9. The method of claim 1 , wherein retrieving the profile for the prescriber includes retrieving a computer data structure that describes different spatial frequency representations for a term appearing in a dictionary.
| 0.779559 |
15. A computer-readable storage memory comprising computer program instructions for debugging a debuggee computer program based on a plurality of logged call trace events that result from an execution of at least a portion of the debuggee program, the program instructions executable by a processor to perform actions including: a) receiving an ordered set of the logged call trace events; b) determining whether to cluster together a subset of the set of the logged call trace events based on a machine address and a corresponding source language statement of each call trace event, the subset including at least two call trace events; and c) performing a step operation that includes the subset of logged call trace events based on the determining whether to cluster together the subset.
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15. A computer-readable storage memory comprising computer program instructions for debugging a debuggee computer program based on a plurality of logged call trace events that result from an execution of at least a portion of the debuggee program, the program instructions executable by a processor to perform actions including: a) receiving an ordered set of the logged call trace events; b) determining whether to cluster together a subset of the set of the logged call trace events based on a machine address and a corresponding source language statement of each call trace event, the subset including at least two call trace events; and c) performing a step operation that includes the subset of logged call trace events based on the determining whether to cluster together the subset. 16. The computer-readable storage memory of claim 15 , determining whether to cluster comprises if each call trace event of the subset corresponds to a matching source language statement and the ordering of the subset of the logged call trace events matches an ordering of the respective machine address of each call trace event, determining to cluster together the subset of the logged call trace events.
| 0.795775 |
2. A method as described in claim 1, further including: calculating one or more such slope parameters for each of a plurality frames from the sequence of frames; and comparing the slope parameters which have been calculated for the sequence of frame against each of a plurality of acoustic models representing speech units, where each such speech-unit model has a model for the slope parameters associated with frame that correspond to the speech unit it represents.
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2. A method as described in claim 1, further including: calculating one or more such slope parameters for each of a plurality frames from the sequence of frames; and comparing the slope parameters which have been calculated for the sequence of frame against each of a plurality of acoustic models representing speech units, where each such speech-unit model has a model for the slope parameters associated with frame that correspond to the speech unit it represents. 3. A method as described in claim 2, wherein: each such speech-unit model has a model of the spectral parameters, as well as the slope parameters, associated with frames that correspond to the speech unit it represents; and said comparing includes comparing the spectral parameters, as well as the slope parameters, from individual frames against each of said plurality of speech-unit models.
| 0.888732 |
1. A computer implemented method of producing personalized documents comprising: inputting handwritten alphanumeric characters; using the computer to map the inputted characters into at least one set of text characters, wherein the mapping is done by converting the inputted alphanumeric characters into mathematical functions approximating a shape of each inputted alphanumeric character; entering a textural document into the computer; transcribing the textural document into a set of text characters corresponding to the inputted alphanumeric characters; and printing the transcribed textural document.
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1. A computer implemented method of producing personalized documents comprising: inputting handwritten alphanumeric characters; using the computer to map the inputted characters into at least one set of text characters, wherein the mapping is done by converting the inputted alphanumeric characters into mathematical functions approximating a shape of each inputted alphanumeric character; entering a textural document into the computer; transcribing the textural document into a set of text characters corresponding to the inputted alphanumeric characters; and printing the transcribed textural document. 2. The method of claim 1 wherein the step of inputting includes scanning.
| 0.779817 |
11. A system for predicting user behaviors based on term taxonomies, comprising: a processing circuitry; and a memory, wherein the memory contains instructions that, when executed by the processing circuitry, configure the system to: analyze textual content to detect a plurality of phrases in the textual content; identify each of the detected phrases as any of: a sentiment phrase, and a non-sentiment phrase, wherein each sentiment phrase includes at least one word describing a sentiment; associate each identified non-sentiment phrase with at least one of the identified sentiment phrases to create at least one term taxonomy; analyze the at least one term taxonomy to determine whether each identified non-sentiment phrase is associated with any of: a highly positive sentiment, and a highly negative sentiment; and generate an alert, when it is determined that one of the non-sentiment phrases is associated with any of: a highly positive sentiment, and a highly negative sentiment.
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11. A system for predicting user behaviors based on term taxonomies, comprising: a processing circuitry; and a memory, wherein the memory contains instructions that, when executed by the processing circuitry, configure the system to: analyze textual content to detect a plurality of phrases in the textual content; identify each of the detected phrases as any of: a sentiment phrase, and a non-sentiment phrase, wherein each sentiment phrase includes at least one word describing a sentiment; associate each identified non-sentiment phrase with at least one of the identified sentiment phrases to create at least one term taxonomy; analyze the at least one term taxonomy to determine whether each identified non-sentiment phrase is associated with any of: a highly positive sentiment, and a highly negative sentiment; and generate an alert, when it is determined that one of the non-sentiment phrases is associated with any of: a highly positive sentiment, and a highly negative sentiment. 18. The system of claim 11 , wherein the system is further configured to: identify at least one connection between at least two of the detected phrases, wherein the at least one term taxonomy is created further based on the identified at least one connection.
| 0.627206 |
9. The computer system of claim 7 , wherein the operations for identifying the mathematical statement further comprise: identifying the relationship based on one of a mathematical term and a change between the numbers.
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9. The computer system of claim 7 , wherein the operations for identifying the mathematical statement further comprise: identifying the relationship based on one of a mathematical term and a change between the numbers. 10. The computer system of claim 9 , wherein the operations for identifying the mathematical solution further comprise: evaluating the text to determine which mathematical terms indicate the mathematical solution based on the one or more predefined characteristics.
| 0.903343 |
25. A speech recognition and control system for controlling a medical device in an operating room, comprising: a medical device; one or more devices receiving an audio input including one or more speech commands and converting the audio input into digital data; an event detector identifying at least one event of the device or the audio input; a database including a plurality of rules, the plurality of rules comprising one or more of a rule to implement an action, a rule to stop an action, or a rule to issue a warning, and said plurality of rules being dynamically generated during operation of the system, a first one of the plurality of rules immediately stopping a system or device activity if the one or more speech commands is begun within a predetermined period of time after the activity was commenced, and a second one of the plurality of rules alerting if a disconnection with a microphone is detected; and a controller adapted to determine a system status including actions currently being performed by devices; the controller further adapted to determine whether or not hazard mitigation is necessary based on a comparison of the at least one event, at least one rule and the system status; if having determined that hazard mitigation is necessary, the controller sending a control command to the medical device instructing it to perform; wherein said plurality of rules mitigate conflicting commands with the system status.
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25. A speech recognition and control system for controlling a medical device in an operating room, comprising: a medical device; one or more devices receiving an audio input including one or more speech commands and converting the audio input into digital data; an event detector identifying at least one event of the device or the audio input; a database including a plurality of rules, the plurality of rules comprising one or more of a rule to implement an action, a rule to stop an action, or a rule to issue a warning, and said plurality of rules being dynamically generated during operation of the system, a first one of the plurality of rules immediately stopping a system or device activity if the one or more speech commands is begun within a predetermined period of time after the activity was commenced, and a second one of the plurality of rules alerting if a disconnection with a microphone is detected; and a controller adapted to determine a system status including actions currently being performed by devices; the controller further adapted to determine whether or not hazard mitigation is necessary based on a comparison of the at least one event, at least one rule and the system status; if having determined that hazard mitigation is necessary, the controller sending a control command to the medical device instructing it to perform; wherein said plurality of rules mitigate conflicting commands with the system status. 26. The system of claim 25 , wherein the at least one event includes a disconnection of said one or more devices.
| 0.559597 |
4. A computer implemented method for managing taxonomies comprising: retrieving a first taxonomy comprising at least one first category and one or more second taxonomies, at least one second category being associated with at least one of the one or more second taxonomies; analyzing a set of content profiles for content of the first taxonomy using evidence terms from content items; generating, using a computer processor, a category profile for the at least one first category of the first taxonomy based at least in part on the analysis of the set of content profiles for content of the first taxonomy; and creating a new taxonomy by merging the first taxonomy with the second taxonomy based on a comparison of a first category profile of the at least one first category with a second category profile of the at least one second category, wherein the comparing comprises applying a comparator function that identifies a match when a comparison between category profiles is above a threshold value, wherein the first taxonomy, the one or more second taxonomies, and the new taxonomy each comprise a controlled vocabulary organized hierarchically to represent relationships between terms in the controlled vocabulary, and wherein categories of the first taxonomy, the one or more second taxonomies, and the new taxonomy each comprise at least one labeled vocabulary term, wherein the creation of the new taxonomy comprises a taxonomy workflow process including review and approval of a plurality of tasks in a specified order.
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4. A computer implemented method for managing taxonomies comprising: retrieving a first taxonomy comprising at least one first category and one or more second taxonomies, at least one second category being associated with at least one of the one or more second taxonomies; analyzing a set of content profiles for content of the first taxonomy using evidence terms from content items; generating, using a computer processor, a category profile for the at least one first category of the first taxonomy based at least in part on the analysis of the set of content profiles for content of the first taxonomy; and creating a new taxonomy by merging the first taxonomy with the second taxonomy based on a comparison of a first category profile of the at least one first category with a second category profile of the at least one second category, wherein the comparing comprises applying a comparator function that identifies a match when a comparison between category profiles is above a threshold value, wherein the first taxonomy, the one or more second taxonomies, and the new taxonomy each comprise a controlled vocabulary organized hierarchically to represent relationships between terms in the controlled vocabulary, and wherein categories of the first taxonomy, the one or more second taxonomies, and the new taxonomy each comprise at least one labeled vocabulary term, wherein the creation of the new taxonomy comprises a taxonomy workflow process including review and approval of a plurality of tasks in a specified order. 6. The method of claim 4 , where in the event that the comparison is not above the threshold, further comprising adding both the at least one first category and the at least one second category to the new taxonomy.
| 0.556308 |
2. The method according to claim 1 wherein performing the at least one input evaluation function comprises performing a subject evaluation function for extracting subject information from at least one of keywords; phrases; sentences; concepts; compound, complex or orthogonal inputs; and a simple web query.
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2. The method according to claim 1 wherein performing the at least one input evaluation function comprises performing a subject evaluation function for extracting subject information from at least one of keywords; phrases; sentences; concepts; compound, complex or orthogonal inputs; and a simple web query. 3. The method according to claim 2 wherein performing the subject evaluation function for at least one of keywords and phrases comprises performing a pass through function.
| 0.899745 |
17. An apparatus comprising: a processor; a storage device coupled to the processor and configurable for storing instructions, which, when executed by the processor, cause the processor to perform operations comprising: searching a document for a typesetting placeholder, wherein a typesetting placeholder is a recognized non-alphabetic character that formats the document; determining that the typesetting placeholder is ambiguous, and consequently creating a set of candidate solutions from a string of characters including the ambiguous typesetting placeholder, wherein each solution in the set of candidate solutions comprises one or more character sub-strings created by uniquely resolving the ambiguous typesetting placeholder in the string of characters; searching a dictionary stored on a computer storage device for the one or more character sub-strings in each solution in the set of candidate solutions; and using the dictionary search result to resolve the ambiguous typesetting placeholder in the string of characters.
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17. An apparatus comprising: a processor; a storage device coupled to the processor and configurable for storing instructions, which, when executed by the processor, cause the processor to perform operations comprising: searching a document for a typesetting placeholder, wherein a typesetting placeholder is a recognized non-alphabetic character that formats the document; determining that the typesetting placeholder is ambiguous, and consequently creating a set of candidate solutions from a string of characters including the ambiguous typesetting placeholder, wherein each solution in the set of candidate solutions comprises one or more character sub-strings created by uniquely resolving the ambiguous typesetting placeholder in the string of characters; searching a dictionary stored on a computer storage device for the one or more character sub-strings in each solution in the set of candidate solutions; and using the dictionary search result to resolve the ambiguous typesetting placeholder in the string of characters. 21. The apparatus of claim 17 , wherein the string of characters including the ambiguous typesetting placeholder comprises a string that begins with a first character that follows the first blank space preceding the ambiguous typesetting placeholder and ends with a final character that precedes the first blank space that follows the ambiguous typesetting placeholder.
| 0.624851 |
8. A system, comprising: a plurality of input devices that provide data corresponding to input modalities; one or more processors programmed to: receive, from the input devices, sets of input data corresponding to the input modalities, the received sets of input data including a first set of input data and a second set of input data, the first set of input data being associated with a first input modality from the plurality of input modalities, the second set of input data being associated with a second input modality from the plurality of input modalities, the first input modality being a speech or text input modality and the second input modality being a gesture input modality; select the first set of input data and the second set of input data; identify, within a dictionary, speech or text input for the first set of input data and a gesture for the second set of input data to determine a meaning of a combination of the first and second set of input data; and provide output data for input by a program, the output data corresponding to the meaning of the first and second set of input data.
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8. A system, comprising: a plurality of input devices that provide data corresponding to input modalities; one or more processors programmed to: receive, from the input devices, sets of input data corresponding to the input modalities, the received sets of input data including a first set of input data and a second set of input data, the first set of input data being associated with a first input modality from the plurality of input modalities, the second set of input data being associated with a second input modality from the plurality of input modalities, the first input modality being a speech or text input modality and the second input modality being a gesture input modality; select the first set of input data and the second set of input data; identify, within a dictionary, speech or text input for the first set of input data and a gesture for the second set of input data to determine a meaning of a combination of the first and second set of input data; and provide output data for input by a program, the output data corresponding to the meaning of the first and second set of input data. 15. The system of claim 8 further comprising a combined dictionary corresponding to the first and second input modalities, wherein the combined dictionary is selected from a plurality of combined dictionaries based on a current operating mode of the program configured to receive the meaning of a subset of input events.
| 0.5 |
1. A method comprising, by one or more computing devices: receiving from a first user of an online social network a search query; searching one or more data stores to identify one or more objects associated with the online social network that substantially match the search query, wherein each object is associated with a privacy setting, and wherein identifying the one or more objects is based in part on identifying a percentage of objects having a privacy setting likely to make the object visible to the first user; determining for each identified object a visibility of the object with respect to the first user, wherein: when the object is visible to the first user, then selecting the object; and when the object is not visible to the first user, then excluding the object; and generating one or more search results corresponding to the search query, wherein each search result corresponds to one of the selected objects having visibility that is visible to the first user; each search result comprising a reference to the identified object corresponding to the search result.
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1. A method comprising, by one or more computing devices: receiving from a first user of an online social network a search query; searching one or more data stores to identify one or more objects associated with the online social network that substantially match the search query, wherein each object is associated with a privacy setting, and wherein identifying the one or more objects is based in part on identifying a percentage of objects having a privacy setting likely to make the object visible to the first user; determining for each identified object a visibility of the object with respect to the first user, wherein: when the object is visible to the first user, then selecting the object; and when the object is not visible to the first user, then excluding the object; and generating one or more search results corresponding to the search query, wherein each search result corresponds to one of the selected objects having visibility that is visible to the first user; each search result comprising a reference to the identified object corresponding to the search result. 17. The method of claim 1 , wherein the search query is a text query comprising one or more n-grams.
| 0.646451 |
1. A method of determining consistency of training data for a machine translation system, comprising: receiving a signal indicative of a source language corpus and a target language corpus; extracting a textual string from the source language corpus; aligning the source language corpus with the target language corpus to identify a plurality of translations for the textual string from the target language corpus; utilizing a processor that is a component of a computing device to calculate a consistency index for each of the plurality of translations, the consistency index for each of the plurality of translations being based at least in part on a number of products in which the translation appears, a frequency of the translation in each of the products, and a total number of valid translations of the textual string in the products; calculating an overall consistency index for the plurality of translations; and storing indications of the consistency indexes and the overall consistency index onto a tangible medium.
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1. A method of determining consistency of training data for a machine translation system, comprising: receiving a signal indicative of a source language corpus and a target language corpus; extracting a textual string from the source language corpus; aligning the source language corpus with the target language corpus to identify a plurality of translations for the textual string from the target language corpus; utilizing a processor that is a component of a computing device to calculate a consistency index for each of the plurality of translations, the consistency index for each of the plurality of translations being based at least in part on a number of products in which the translation appears, a frequency of the translation in each of the products, and a total number of valid translations of the textual string in the products; calculating an overall consistency index for the plurality of translations; and storing indications of the consistency indexes and the overall consistency index onto a tangible medium. 11. The method of claim 1 , wherein calculating the consistency index is based on a number of occurrences of alignment between the source language corpus and each of the valid translations in the target language corpus.
| 0.619247 |
23. The system of claim 22 , wherein the training comprises: processing each of the reference responses to determine for each reference response a first numerical measure indicative of a number of words and phrases of the reference response that are included verbatim in the source text, a second numerical measure indicative of (i) an amount of the reference response that paraphrases portions of the source text, or (ii) an amount of the reference response that is semantically-similar to portions of the source text, and a third numerical measure indicative of a similarity between sentences of the reference response and sentences of the source text; and conducting a numerical machine-learning analysis based on the first, second, and third numerical measures and classification for each of the plurality of reference responses to determine the first, second, and third weighting factors.
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23. The system of claim 22 , wherein the training comprises: processing each of the reference responses to determine for each reference response a first numerical measure indicative of a number of words and phrases of the reference response that are included verbatim in the source text, a second numerical measure indicative of (i) an amount of the reference response that paraphrases portions of the source text, or (ii) an amount of the reference response that is semantically-similar to portions of the source text, and a third numerical measure indicative of a similarity between sentences of the reference response and sentences of the source text; and conducting a numerical machine-learning analysis based on the first, second, and third numerical measures and classification for each of the plurality of reference responses to determine the first, second, and third weighting factors. 24. The system of claim 23 , wherein the determining of the third numerical measure for each reference response comprises: comparing each sentence of the reference response to each sentence of the source text in a sentence-to-sentence comparison, each of the comparisons generating a value indicative of a degree of similarity between the compared sentences; determining a first metric, the first metric being a maximum value of the generated values; determining a second metric, the second metric being an average of the generated values; determining a third metric, the determining of the third metric including (i) determining, for each sentence of the reference response, a maximum sentence value, wherein the maximum sentence value is a maximum value of a subset of the values, the subset including values generated based on the comparison of the sentence to the sentences of the source text, and (ii) determining an average of the maximum sentence values, the average of the maximum sentence values being the third metric.
| 0.818134 |
8. Logic encoded in one or more non-transitory media that includes code for execution and when executed by a processor is operable to perform operations comprising: receiving data propagating in a network environment; verifying whether the data is associated with a first end user represented in a registered user list; verifying a ratification of a policy by the first end user that authorizes monitoring of e-mail traffic generated by the first end user; identifying selected words within the data based on a whitelist, wherein the whitelist includes a plurality of designated words to be tagged; and generating a resultant composite of the selected words that are tagged.
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8. Logic encoded in one or more non-transitory media that includes code for execution and when executed by a processor is operable to perform operations comprising: receiving data propagating in a network environment; verifying whether the data is associated with a first end user represented in a registered user list; verifying a ratification of a policy by the first end user that authorizes monitoring of e-mail traffic generated by the first end user; identifying selected words within the data based on a whitelist, wherein the whitelist includes a plurality of designated words to be tagged; and generating a resultant composite of the selected words that are tagged. 14. The logic of claim 8 , the processor being further operable to perform operations comprising: determining whether the data is password protected; and discarding the data if the data is password protected.
| 0.619 |
13. A method comprising: receiving data at a control module; based on the data, generating a first code word for a plurality of drives; and in response to detecting an error in a first drive of the plurality of drives subsequent to generation of the first code word, initiating replacement of the first drive with a second drive, generating a second code word for the second drive, mapping physical locations of the data in the plurality of drives to logical locations of the first code word, assigning a predetermined value to one of the logical locations corresponding to the first drive to identify an unused one of the logical locations, assigning the unused one of the logical locations to the second drive based on the predetermined value, generating a third code word based on each of the first code word and the second code word, and generating an updated code word for the plurality of drives based on each of the first code word and the third code word.
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13. A method comprising: receiving data at a control module; based on the data, generating a first code word for a plurality of drives; and in response to detecting an error in a first drive of the plurality of drives subsequent to generation of the first code word, initiating replacement of the first drive with a second drive, generating a second code word for the second drive, mapping physical locations of the data in the plurality of drives to logical locations of the first code word, assigning a predetermined value to one of the logical locations corresponding to the first drive to identify an unused one of the logical locations, assigning the unused one of the logical locations to the second drive based on the predetermined value, generating a third code word based on each of the first code word and the second code word, and generating an updated code word for the plurality of drives based on each of the first code word and the third code word. 14. The method of claim 13 , wherein: the plurality of drives are implemented as a redundant array of independent disks; and the redundant array of independent disks comprises data drives and at least one parity drive.
| 0.619943 |
15. The method as set forth in claim 13 , wherein outputting the advertisement in the virtual world comprises executing at least one ad script included in the ad definition data.
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15. The method as set forth in claim 13 , wherein outputting the advertisement in the virtual world comprises executing at least one ad script included in the ad definition data. 16. The method as set forth in claim 15 , further comprising: continuing to render the virtual world after the at least one ad script has been executed.
| 0.929752 |
24. The method of claim 22 , wherein executing instructions to obtain the information from the surrounding environments includes optically detecting a body part of the user.
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24. The method of claim 22 , wherein executing instructions to obtain the information from the surrounding environments includes optically detecting a body part of the user. 26. The method of claim 24 , wherein optically detecting the body part includes performing facial recognition.
| 0.959028 |
1. A computer-implemented method for determining topic similarity, comprising: receiving user information for one or more users, at a computer system, the information including at least one topic and a user value for each topic, where: the user value includes a user authority value representing a user expertise related to that topic and a user interest value representing a degree of user association with that topic, and the user value represents how strongly the user is associated with that topic; generating topic information for a source topic based on the user information, the topic information including at least one user and a topic value for each user, where the topic value represents how strongly the topic is associated with that user; generating similarity scores based on a topic value for each user for the source topic and a topic value for the same user for each topic in a set of topics, where each topic in the set of topics is associated with a topic value for each user; selecting one or more similar topics based on the generated similarity scores; determining and outputting one or more overlapping users and a value representing a degree of overlap for each overlapping user; and outputting one or more of the selected topics.
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1. A computer-implemented method for determining topic similarity, comprising: receiving user information for one or more users, at a computer system, the information including at least one topic and a user value for each topic, where: the user value includes a user authority value representing a user expertise related to that topic and a user interest value representing a degree of user association with that topic, and the user value represents how strongly the user is associated with that topic; generating topic information for a source topic based on the user information, the topic information including at least one user and a topic value for each user, where the topic value represents how strongly the topic is associated with that user; generating similarity scores based on a topic value for each user for the source topic and a topic value for the same user for each topic in a set of topics, where each topic in the set of topics is associated with a topic value for each user; selecting one or more similar topics based on the generated similarity scores; determining and outputting one or more overlapping users and a value representing a degree of overlap for each overlapping user; and outputting one or more of the selected topics. 6. The method of claim 1 , wherein the selecting step comprises selecting one or more similar topics associated with one or more highest generated similarity scores.
| 0.54726 |
2. The method of claim 1 , wherein respective relationship scores between two entities for a particular context, is established based on at least one of a prioritization score and a priority category of a dynamically prioritized electronic communication related to the given context, wherein the dynamically prioritized electronic communication is exchanged between the two entities in a select duration and a boundary condition.
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2. The method of claim 1 , wherein respective relationship scores between two entities for a particular context, is established based on at least one of a prioritization score and a priority category of a dynamically prioritized electronic communication related to the given context, wherein the dynamically prioritized electronic communication is exchanged between the two entities in a select duration and a boundary condition. 3. The method of claim 2 , wherein the priority score or category of the electronic communication is validated and accepted by an electronic communication recipient.
| 0.875738 |
20. A method for training an unconstrained cursive character handwritten word recognition system, comprising: processing a corpus of handwritten word images, each imaged word having one or more characters, the processing step including segmenting each of the imaged words into a set of one or more segments and determining a sequence of the segments using an over-segmentation-relabeling algorithm; extracting feature information of individual characters of the imaged words; estimating symbol probability parameters associated with each distinct character so as to allow a statistical measure that given feature information is indicative of a distinct character; and estimating state duration probabilities associated with each distinct character, wherein a state duration probability of a given distinct character represents a probability that a segmented image of the given character will have a duration of a defined number of segments, wherein the segmenting each imaged word includes locating a first segment and a last segment in the imaged word, wherein the determining a sequence of segments using an over-segmentation-relabeling algorithm includes: characterizing segments as either situated segments or unsituated segments, wherein situated segments include the first and last segments, segments having an X-coordinate or Y-coordinate coverage that exceed a threshold value, and small segments that are cursively connected to segments on each side, and wherein unsituated segments are segments not characterized as situated segments; and placing each unsituated segment having a situated segment above or below so as to either immediately precede or follow the situated segment in the sequence of segments:
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20. A method for training an unconstrained cursive character handwritten word recognition system, comprising: processing a corpus of handwritten word images, each imaged word having one or more characters, the processing step including segmenting each of the imaged words into a set of one or more segments and determining a sequence of the segments using an over-segmentation-relabeling algorithm; extracting feature information of individual characters of the imaged words; estimating symbol probability parameters associated with each distinct character so as to allow a statistical measure that given feature information is indicative of a distinct character; and estimating state duration probabilities associated with each distinct character, wherein a state duration probability of a given distinct character represents a probability that a segmented image of the given character will have a duration of a defined number of segments, wherein the segmenting each imaged word includes locating a first segment and a last segment in the imaged word, wherein the determining a sequence of segments using an over-segmentation-relabeling algorithm includes: characterizing segments as either situated segments or unsituated segments, wherein situated segments include the first and last segments, segments having an X-coordinate or Y-coordinate coverage that exceed a threshold value, and small segments that are cursively connected to segments on each side, and wherein unsituated segments are segments not characterized as situated segments; and placing each unsituated segment having a situated segment above or below so as to either immediately precede or follow the situated segment in the sequence of segments: 21. The method of claim 20 , wherein the estimating symbol probability parameters step includes calculating representative feature information for each distinct character based on the feature information extracted from character images of a like distinct character in the imaged words.
| 0.558442 |
1. A method comprising: identifying a social media identifier, the social media identifier being at least one selected from a group consisting of: a hashtag extracted from at least one social networking service and a uniform resource identifier extracted from the at least one social networking service; accessing a social graph including clustered relationships developed based on analysis of social media data extracted from at the least one social networking service; generating, using the social graph, a temporal snapshot for the social media identifier, wherein the generated temporal snapshot comprises data extracted from the social graph including: the social media identifier, a related social media link corresponding with the social media identifier, a link to a related topic for the social media identifier, and a link to a contributor for context of the social media identifier; transmitting the temporal snapshot to an application of an entry point, the application accessed by a user device to display the temporal snapshot on the user device; displaying the temporal snapshot; storing the temporal snapshot on a component of a distributed network; subsequent to storing the temporal snapshot, recalling the stored temporal snapshot in response to receiving a request for the stored temporal snapshot; and transmitting the recalled temporal snapshot to the same, or a different, application of the same or a different entry point to display the recalled temporal snapshot on the same or a different user device accessing the same or different application.
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1. A method comprising: identifying a social media identifier, the social media identifier being at least one selected from a group consisting of: a hashtag extracted from at least one social networking service and a uniform resource identifier extracted from the at least one social networking service; accessing a social graph including clustered relationships developed based on analysis of social media data extracted from at the least one social networking service; generating, using the social graph, a temporal snapshot for the social media identifier, wherein the generated temporal snapshot comprises data extracted from the social graph including: the social media identifier, a related social media link corresponding with the social media identifier, a link to a related topic for the social media identifier, and a link to a contributor for context of the social media identifier; transmitting the temporal snapshot to an application of an entry point, the application accessed by a user device to display the temporal snapshot on the user device; displaying the temporal snapshot; storing the temporal snapshot on a component of a distributed network; subsequent to storing the temporal snapshot, recalling the stored temporal snapshot in response to receiving a request for the stored temporal snapshot; and transmitting the recalled temporal snapshot to the same, or a different, application of the same or a different entry point to display the recalled temporal snapshot on the same or a different user device accessing the same or different application. 6. The method according to claim 1 , wherein the generating of the temporal snapshot further comprises generating a temporal snapshot for a first geographic location and generated a second temporal snapshot for a second geographic location, and wherein the transmitting transmit the temporal snapshot and the second temporal snapshot for the entry point to distribute the temporal snapshot and the second temporal snapshot to respective geographical locations.
| 0.5 |
20. A computer program executable stored on a computer readable medium according to the claim 17 , wherein said extracting procedure includes a first extracting procedure of extracting text image areas, graphic image areas, and photographic image areas from image data, and a second extracting procedure of extracting filled closed areas, unfilled closed areas, and line areas that do not form any closed areas from the extracted graphic image areas; wherein said procedure of recognizing attributes includes recognizing attributes concerning whether each extracted image area is a text image area, a photographic image area, a filled closed area, an unfilled closed area or a line area; and said setting up procedure includes setting up the overlaying sequence for each image area of text image areas, photographic image areas, filled closed areas, unfilled closed areas and line areas in accordance with the recognition results of the attributes.
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20. A computer program executable stored on a computer readable medium according to the claim 17 , wherein said extracting procedure includes a first extracting procedure of extracting text image areas, graphic image areas, and photographic image areas from image data, and a second extracting procedure of extracting filled closed areas, unfilled closed areas, and line areas that do not form any closed areas from the extracted graphic image areas; wherein said procedure of recognizing attributes includes recognizing attributes concerning whether each extracted image area is a text image area, a photographic image area, a filled closed area, an unfilled closed area or a line area; and said setting up procedure includes setting up the overlaying sequence for each image area of text image areas, photographic image areas, filled closed areas, unfilled closed areas and line areas in accordance with the recognition results of the attributes. 23. A computer executable program stored on a computer readable medium according to the claim 20 , wherein said second extracting procedure comprises, a procedure of transforming image data in graphic image areas into vector data; a procedure of extracting closed areas based on the connection relation of a plurality of vector data; a procedure of judging whether the color information of internal points and external points of the extracted closed areas are the same; and a procedure of detecting filled closed areas based on the judgment result of whether the compared color information is the same.
| 0.629489 |
1. A method for hosting a programming environment and processing user input, the method comprising the step of: (a) receiving, during a first session between a user and an interaction environment, via one of a plurality of media gateways, a definition of an expression type, the definition specifying an expression format and a response type; (b) storing the definition of the expression type; (c) receiving, during a second session between a second user and the interaction environment, via one of a plurality of media gateways, from the second user, an expression having a semantic structure; (d) evaluating the semantic structure of the expression; (e) identifying an expression format of the received expression as the expression format specified by the definition of the expression type, responsive to the evaluation of the semantic structure; and (f) generating a response to the expression based on the identified expression format and responsive to an execution of a computer program associated with the response type specified by the definition of the expression type.
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1. A method for hosting a programming environment and processing user input, the method comprising the step of: (a) receiving, during a first session between a user and an interaction environment, via one of a plurality of media gateways, a definition of an expression type, the definition specifying an expression format and a response type; (b) storing the definition of the expression type; (c) receiving, during a second session between a second user and the interaction environment, via one of a plurality of media gateways, from the second user, an expression having a semantic structure; (d) evaluating the semantic structure of the expression; (e) identifying an expression format of the received expression as the expression format specified by the definition of the expression type, responsive to the evaluation of the semantic structure; and (f) generating a response to the expression based on the identified expression format and responsive to an execution of a computer program associated with the response type specified by the definition of the expression type. 7. The method of claim 1 , wherein step (a) further comprises receiving, during the first session, via one of the plurality of media gateways, the definition of an expression type, the definition specifying an expression format identifying a word order of an expression having the expression type.
| 0.612277 |
1. A game delivery system for delivering a plurality of games in a training program configured to systematically drive neurological changes to overcome cognitive deficits associated with a neurological disorder, the game delivery system comprising: a computerized game manager configured to assess a game participant and, in response to the assessment, calibrate a training program comprising games for the game participant; the computerized game manager also being configured to administer the games, manipulate a plurality of game stimuli, and receive input from a game piece; and a participant portal that provides remote access to and delivers the games to game participants; wherein the game manager is configured to administer games of the training program to the game participant by: presenting a plurality of target and/or distractor stimuli; prompting the game participant to respond to the target and/or distractor stimuli; receiving the game participant's input through the game piece; and repeating the presenting through providing a signal steps over multiple repetitions while adapting one or more difficulty parameters to target maintenance of a success rate within a predetermined range; wherein the game manager is further configured to administer an assessment, using at least one of the plurality of games, by administering a brief version of the game using mid-level game difficulty parameters.
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1. A game delivery system for delivering a plurality of games in a training program configured to systematically drive neurological changes to overcome cognitive deficits associated with a neurological disorder, the game delivery system comprising: a computerized game manager configured to assess a game participant and, in response to the assessment, calibrate a training program comprising games for the game participant; the computerized game manager also being configured to administer the games, manipulate a plurality of game stimuli, and receive input from a game piece; and a participant portal that provides remote access to and delivers the games to game participants; wherein the game manager is configured to administer games of the training program to the game participant by: presenting a plurality of target and/or distractor stimuli; prompting the game participant to respond to the target and/or distractor stimuli; receiving the game participant's input through the game piece; and repeating the presenting through providing a signal steps over multiple repetitions while adapting one or more difficulty parameters to target maintenance of a success rate within a predetermined range; wherein the game manager is further configured to administer an assessment, using at least one of the plurality of games, by administering a brief version of the game using mid-level game difficulty parameters. 21. The game delivery system of claim 1 , wherein the plurality of game stimuli are images, sounds and/or haptic vibrations.
| 0.871926 |
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