Learning Launch Tool 

 1 . Challenge : 

 Working as a leader in R&D organization of machining software company , I am never short of   ideas in our group . Hundreds are generated when we get team together to brainstorm on any   topic . Challenge is always selecting right few to proceed with and doing that selection without   technology , execution or individual biases . 

 In this specific case , we had gathered ideas on simplifying steps in providing tolerance values on   the part , so that machining allowance / errors wo nt exceed the limits even after considering worst   possible scenario . Let me take help of below figure to explain the process . Each plate has   tolerance as mentioned below . If you take worst case , i.e. each tolerance limit staying on positive   max , then overall tolerance for X will be summation of all positive worst - case tolerances . In   assembly , typically you have overall tolerance and then need to reverse calculate permissible   tolerance for each part to stay within limits . 

 Now imagine complex assembly with multiple parts which have holes , fillets , or shapes which are   not rectangular . Tolerance calculation keeps becoming complex and it becomes very   cumbersome process for user of our software to input it meaningfully . In machining , you have to   compute and adjust for these tolerances to meet overall fitment of the part within assembly for   given functionality . 

 2 . Selection : 

 Team came out with multiple approaches that can possibly simplify this interaction and we   shortlisted three of them as most promising based on our internal criteria . These approaches   varied on controls it provided to user to finetune their inputs for various scenario ( maximum   functionality ) versus asking minimum intervention from user and product making assumptions on   their behalf ( minimum UI ) . 

 Then we decided to select Learning Launch tool to experiment with these three approaches to   come out with best possible one for final implementation . 

 3 . Application : 

 We build quick and dirty simulated prototype of these approaches in less than 2 weeks of efforts   and exposed them to ~18 customers / users who had volunteered to work with us to improve our   productline . 

 We developed ‘ customer satisfaction index ’ ( CSI ) survey for evaluation with criteria like if   proposed solutions meets their need , is it easier to use , what they most liked about this particular   approach . We exposed our quick and dirty simulated prototypes to them through cloud VM and   also observed their usage through video calls . We asked them to fill these CSI survey after   roughly allowing 20 mins play time for them . Each user had to spend roughly 30 min of its total   time for the same including documenting their feedback for ~10min . 

 4 . Insight : 

 This exercise resulted in surprise findings for us . None of the three was clear winner in terms of   distinct leader in CSI index . Users like simplicity of the approach where we did n’t ask them too   many inputs , but wanted deeper control over functionality for their real world parts and their   variations . Due to insight gained from our learning launch experiment , we developed new   approach altogether where we provided simplicity of making assumptions on behalf of users to   generate default output for most cases , but then allowing to go back and launch detailed controls   for few cases . This detailed UI was required to be invoked only in rare cases to provide different   values than what software has calculated . 

 This approach was like combining best of all worlds – no UI and default output for 80 % of   scenarios , but ability to go back provide details for 20 % cases where unique inputs are required ,   and more functionality needs to be exposed . Our users loved this approach when it was launched   finally after few more confirmation trials . 

 5 . Approach : 

 Clearly keeping open mind in Learning Launch phase and allowing customers / users provide all   possible feedback is important . We quickly learned none of our shortlisted ideas were great , but   could develop new one based on feedback from these experiment that allowed us to provide   innovative solution that could help our customer . 

 References : 

 1 . Above figure , examples taken from real work done during project and publicly published material 

 related to this product at : http://hogan.com/app/categoriesindex.php 


EMAIL: 
ID_NUM: 
NAME_STUDENT: 
PHONE_NUM: 
STREET_ADDRESS: 
URL_PERSONAL: http://hogan.com/app/categoriesindex.php
USERNAME: 

### Task: You are a PII (Personal Identity Information) Detection. Given a essays written by students, you have to identify ONLY the following:
NAME_STUDENT - The full or partial name of a student that is not necessarily the author of the essay. This excludes instructors, authors, and other person names.
EMAIL - A student’s email address.
USERNAME - A student's username on any platform.
ID_NUM - A number or sequence of characters that could be used to identify a student, such as a student ID or a social security number.
PHONE_NUM - A phone number associated with a student.
URL_PERSONAL - A URL that might be used to identify a student.
STREET_ADDRESS - A full or partial street address that is associated with the student, such as their home address.
### Input: Learning Launch Tool 

 1 . Challenge : 

 Working as a leader in R&D organization of machining software company , I am never short of   ideas in our group . Hundreds are generated when we get team together to brainstorm on any   topic . Challenge is always selecting right few to proceed with and doing that selection without   technology , execution or individual biases . 

 In this specific case , we had gathered ideas on simplifying steps in providing tolerance values on   the part , so that machining allowance / errors wo nt exceed the limits even after considering worst   possible scenario . Let me take help of below figure to explain the process . Each plate has   tolerance as mentioned below . If you take worst case , i.e. each tolerance limit staying on positive   max , then overall tolerance for X will be summation of all positive worst - case tolerances . In   assembly , typically you have overall tolerance and then need to reverse calculate permissible   tolerance for each part to stay within limits . 

 Now imagine complex assembly with multiple parts which have holes , fillets , or shapes which are   not rectangular . Tolerance calculation keeps becoming complex and it becomes very   cumbersome process for user of our software to input it meaningfully . In machining , you have to   compute and adjust for these tolerances to meet overall fitment of the part within assembly for   given functionality . 

 2 . Selection : 

 Team came out with multiple approaches that can possibly simplify this interaction and we   shortlisted three of them as most promising based on our internal criteria . These approaches   varied on controls it provided to user to finetune their inputs for various scenario ( maximum   functionality ) versus asking minimum intervention from user and product making assumptions on   their behalf ( minimum UI ) . 

 Then we decided to select Learning Launch tool to experiment with these three approaches to   come out with best possible one for final implementation . 

 3 . Application : 

 We build quick and dirty simulated prototype of these approaches in less than 2 weeks of efforts   and exposed them to ~18 customers / users who had volunteered to work with us to improve our   productline . 

 We developed ‘ customer satisfaction index ’ ( CSI ) survey for evaluation with criteria like if   proposed solutions meets their need , is it easier to use , what they most liked about this particular   approach . We exposed our quick and dirty simulated prototypes to them through cloud VM and   also observed their usage through video calls . We asked them to fill these CSI survey after   roughly allowing 20 mins play time for them . Each user had to spend roughly 30 min of its total   time for the same including documenting their feedback for ~10min . 

 4 . Insight : 

 This exercise resulted in surprise findings for us . None of the three was clear winner in terms of   distinct leader in CSI index . Users like simplicity of the approach where we did n’t ask them too   many inputs , but wanted deeper control over functionality for their real world parts and their   variations . Due to insight gained from our learning launch experiment , we developed new   approach altogether where we provided simplicity of making assumptions on behalf of users to   generate default output for most cases , but then allowing to go back and launch detailed controls   for few cases . This detailed UI was required to be invoked only in rare cases to provide different   values than what software has calculated . 

 This approach was like combining best of all worlds – no UI and default output for 80 % of   scenarios , but ability to go back provide details for 20 % cases where unique inputs are required ,   and more functionality needs to be exposed . Our users loved this approach when it was launched   finally after few more confirmation trials . 

 5 . Approach : 

 Clearly keeping open mind in Learning Launch phase and allowing customers / users provide all   possible feedback is important . We quickly learned none of our shortlisted ideas were great , but   could develop new one based on feedback from these experiment that allowed us to provide   innovative solution that could help our customer . 

 References : 

 1 . Above figure , examples taken from real work done during project and publicly published material 

 related to this product at : http://hogan.com/app/categoriesindex.php
### Output: 