Challenge​ :    My company provides Human Resource Management software , and the customers are small   businesses who use the platform to buy health insurance for their employees . 

 As part of buying health insurance , the small businesses have to upload documents which verify the   company and the employees . We were seeing a large number of 1 ) incorrect documents there were   uploaded ( ex . instead of state certified payroll proof they were uploading an excel sheet ) 2 ) wrong   quarter or year for the documents 3 ) not all employees and owners were in the documents . This led   to back - and - forth between us and the customer , which overall delayed the process and made it a   bad customer experience . This also led to higher operations cost for us . 

 Selection :   Given that we had over 87 different types of documents , we wanted to experiment with a few   different solutions before implementing a larger solution . I decided to use ​Learning Launch​ to   experiment with a few different solutions the cross - functional team identified as part of the “ what if ”   state of the design thinking process . The goal was to find the best self - service solution which would   enable the customer to upload the correct document with all the information required . This would   decrease the time to get the health insurance and decrease our operations cost . 

 Application :   Our hypothesis was that if we were to give a sample document , the timeframe of the document and   a confirmation modal to ensure all employees are listed , users will upload the correct document . 

 I decided to experiment with just 2 different documents ( instead of 87 types ) to learn quickly . The   design team quickly put together a mini - document - upload flow . This had a sample document type ,   instead of saying last 2 years or last quarter , we clearly displayed the years . Example : we displayed   April - June 2017 . We also had a confirmation box that prompted the user to confirm all employees   are listed . 

 This was all developed in less than 2 weeks and launched as an A / B test . This was the quickest way   to ensure our hypothesis was tested . I also identified that if this hypothesis would work , we would   work on building the mini flow for all the different types of documents . 

 After running the experiment for a week , we saw a 93 % decrease in incorrect documents that were   uploaded . It was evident that our hypothesis was validated . We then expanded the solution to the   remaining document types . 

 Insight :   The insight here was that the Learning Launch is a power tool to quickly experiment with different   hypothesis without investing a lot in design and engineering . A clear hypothesis really helps guide 

 the experiment . It is also important to identify the next steps in case the hypothesis fails or is   confirmed to align the teams . 

 Approach :   Overall Learning Launch was a powerful tool for us to test hypothesis . As a retrospective , I would   have broken the test further and tested sample document , displaying the dates and the employee   confirmation modal separately . This would have clearly signaled which solution had the biggest   impact . 


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​ :    My company provides Human Resource Management software , and the customers are small   businesses who use the platform to buy health insurance for their employees . 

 As part of buying health insurance , the small businesses have to upload documents which verify the   company and the employees . We were seeing a large number of 1 ) incorrect documents there were   uploaded ( ex . instead of state certified payroll proof they were uploading an excel sheet ) 2 ) wrong   quarter or year for the documents 3 ) not all employees and owners were in the documents . This led   to back - and - forth between us and the customer , which overall delayed the process and made it a   bad customer experience . This also led to higher operations cost for us . 

 Selection :   Given that we had over 87 different types of documents , we wanted to experiment with a few   different solutions before implementing a larger solution . I decided to use ​Learning Launch​ to   experiment with a few different solutions the cross - functional team identified as part of the “ what if ”   state of the design thinking process . The goal was to find the best self - service solution which would   enable the customer to upload the correct document with all the information required . This would   decrease the time to get the health insurance and decrease our operations cost . 

 Application :   Our hypothesis was that if we were to give a sample document , the timeframe of the document and   a confirmation modal to ensure all employees are listed , users will upload the correct document . 

 I decided to experiment with just 2 different documents ( instead of 87 types ) to learn quickly . The   design team quickly put together a mini - document - upload flow . This had a sample document type ,   instead of saying last 2 years or last quarter , we clearly displayed the years . Example : we displayed   April - June 2017 . We also had a confirmation box that prompted the user to confirm all employees   are listed . 

 This was all developed in less than 2 weeks and launched as an A / B test . This was the quickest way   to ensure our hypothesis was tested . I also identified that if this hypothesis would work , we would   work on building the mini flow for all the different types of documents . 

 After running the experiment for a week , we saw a 93 % decrease in incorrect documents that were   uploaded . It was evident that our hypothesis was validated . We then expanded the solution to the   remaining document types . 

 Insight :   The insight here was that the Learning Launch is a power tool to quickly experiment with different   hypothesis without investing a lot in design and engineering . A clear hypothesis really helps guide 

 the experiment . It is also important to identify the next steps in case the hypothesis fails or is   confirmed to align the teams . 

 Approach :   Overall Learning Launch was a powerful tool for us to test hypothesis . As a retrospective , I would   have broken the test further and tested sample document , displaying the dates and the employee   confirmation modal separately . This would have clearly signaled which solution had the biggest   impact .
### Output: 