Challenge 

 I develop software for a measurement device ( the “ Device ” ) which is installed on large , complex   semiconductor processing equipment ( the “ Tool ” ) .   The device requires hardware “ tuning ” by installers   who turn calibration screws until the Device signals are maximized .   Currently , the installation team uses   a vendor app running on a laptop to view the Device signals .   The laptop must be abandoned at   customer sites , as customers wish to ensure that none of their Intellectual Property can be stolen .   The   install team wants to make similar software available through the Tool software / GUI .   However , the   Device produces a substantial volume of data , at a high data rate , and must be transferred through   three to four servers before it can be plotted on the GUI .   The data is likely to have a time lag or be a   drag on the local network . 

 Selection 

 I chose the Learning Launch tool .   In particular , I wanted to apply the Assumption Worksheet , as there   were considerable assumptions on the part of the installers requesting the software and on my part in   being wary of the complexity of the data . 

 Application 

 Hypothesis : we need a new utility on the Tool software to replace the Vendor software running on the   discardable laptop . 

 Key assumption 1 :   Data volume for the transferring all of the data from the Device to the Tool ’s GUI is   not feasible . 

 Test : Calculate the data volume and find out who is the network expert for the Tool ’s software ,   and ask if the data transfer is doable . 

 Results of test : Data transfer would be acceptable . 

 Key assumption 2 :   The computer can pre - do some of the analysis that the installers do by eyeball , and   present the data to the installers in a superior way to the Vendor software . 

 Test : I interviewed the installers to determine exactly how they use their eyeballs to evaluate   the continuous stream of data . 

 Results of test :    I determined that the computer can indeed do that analysis faster and better .    In addition , the computer can display the resulting data as a time series , which enable the   installers to tell if the data are growing , which makes it easier to maximize the Device signals   while turning the calibration screws . 

 Learning Launch :   I created a mock - up of the data display for the new utility , and demonstrated to the   installers how it would be used . 

 Disconfirming data : The installers hated the proposed utility .   They want exactly what they had with the   Vendor software , and in fact they added a requirement that the new software must be run from a   remote Tablet computer ( which will also have to be left at customer sites for IP reasons ! )   Since the data   transfer is possible , I have no excuse not to implement a duplicate of the Vendor app . 

 Insight 

 1 . Users be crazy ! 

 2 . As the software developer for the team , my job is mostly attending to all the details .   None of the   Design Thinking tools seems suitable for determining HOW to achieve what needs to be achieved .   The   tools seem to be for determining WHAT needs to be achieved .   Those things are usually determined well   before the software developers get involved . 

 Approach 

 I found that listing my knee - jerk objections to the project as a list of assumptions , with methods for   testing each assumption was very useful for documenting both “ what is ” and “ what if ” .   Evaluating the   key assumptions , in a systematic way , was very useful as the tests disproved one of my major objections   to the project .   In hindsight , I should have stopped there , rather than trying to improve on Utility that   the users requested and were used to .   My proposal did n’t “ wow ” , and I ended up going with “ what   works ” . 


EMAIL: 
ID_NUM: 
NAME_STUDENT: 
PHONE_NUM: 
STREET_ADDRESS: 
URL_PERSONAL: 
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: Challenge 

 I develop software for a measurement device ( the “ Device ” ) which is installed on large , complex   semiconductor processing equipment ( the “ Tool ” ) .   The device requires hardware “ tuning ” by installers   who turn calibration screws until the Device signals are maximized .   Currently , the installation team uses   a vendor app running on a laptop to view the Device signals .   The laptop must be abandoned at   customer sites , as customers wish to ensure that none of their Intellectual Property can be stolen .   The   install team wants to make similar software available through the Tool software / GUI .   However , the   Device produces a substantial volume of data , at a high data rate , and must be transferred through   three to four servers before it can be plotted on the GUI .   The data is likely to have a time lag or be a   drag on the local network . 

 Selection 

 I chose the Learning Launch tool .   In particular , I wanted to apply the Assumption Worksheet , as there   were considerable assumptions on the part of the installers requesting the software and on my part in   being wary of the complexity of the data . 

 Application 

 Hypothesis : we need a new utility on the Tool software to replace the Vendor software running on the   discardable laptop . 

 Key assumption 1 :   Data volume for the transferring all of the data from the Device to the Tool ’s GUI is   not feasible . 

 Test : Calculate the data volume and find out who is the network expert for the Tool ’s software ,   and ask if the data transfer is doable . 

 Results of test : Data transfer would be acceptable . 

 Key assumption 2 :   The computer can pre - do some of the analysis that the installers do by eyeball , and   present the data to the installers in a superior way to the Vendor software . 

 Test : I interviewed the installers to determine exactly how they use their eyeballs to evaluate   the continuous stream of data . 

 Results of test :    I determined that the computer can indeed do that analysis faster and better .    In addition , the computer can display the resulting data as a time series , which enable the   installers to tell if the data are growing , which makes it easier to maximize the Device signals   while turning the calibration screws . 

 Learning Launch :   I created a mock - up of the data display for the new utility , and demonstrated to the   installers how it would be used . 

 Disconfirming data : The installers hated the proposed utility .   They want exactly what they had with the   Vendor software , and in fact they added a requirement that the new software must be run from a   remote Tablet computer ( which will also have to be left at customer sites for IP reasons ! )   Since the data   transfer is possible , I have no excuse not to implement a duplicate of the Vendor app . 

 Insight 

 1 . Users be crazy ! 

 2 . As the software developer for the team , my job is mostly attending to all the details .   None of the   Design Thinking tools seems suitable for determining HOW to achieve what needs to be achieved .   The   tools seem to be for determining WHAT needs to be achieved .   Those things are usually determined well   before the software developers get involved . 

 Approach 

 I found that listing my knee - jerk objections to the project as a list of assumptions , with methods for   testing each assumption was very useful for documenting both “ what is ” and “ what if ” .   Evaluating the   key assumptions , in a systematic way , was very useful as the tests disproved one of my major objections   to the project .   In hindsight , I should have stopped there , rather than trying to improve on Utility that   the users requested and were used to .   My proposal did n’t “ wow ” , and I ended up going with “ what   works ” .
### Output: 