Coursera – Learning Launch for ‘ Wollega ’     Challenge :   Through market research including competitor analysis , empathy mapping , government law   review and stakeholder interviews a market opportunity has been identified for a Medical   app , a Chabot helping people with diagnosis and support treatment .      The app named ‘ Wollega ’ supplies highly accurate and free medical advice to in need   patients through accessing and   interrogating open source medical   information . The product is in the format   of a ChatBot which plugs into open   source government data supplied by the   UK National Health Service and provides   patients with accurate diagnosis and   treatment recommendations for mild   conditions .     The main challenge was to launch and validate the product with a select group of people   which will enable the product to : 

 -   Gain credibility within the medical community 

 -   Gain funding for future iterations 

 -   Uncover new medical data with private institutions who are impressed with the   product 

 -   Gain trust and references with users who may use this product in the future 

 -   Provide quantitative feedback on the product which can be used for future iterations 

 -   Gather as much information as possible which can be stored and used for the   products accuracy . 

 Selection :   To successfully validate the prototype , we conducted a live ‘ Learning Launch ’ at the   Automation & Robotics medical conference in San Diego . The conference was an specialised   event to showcase new tech within the medical and pharmaceutical industry which is the   marketplace our product Wollega sits .      The audience included members of the public , medical practitioners , insurance companies   and financial institutions . A combination of support , buy in , trust and testimonials were   needed from all four attendee types in order to deem the product validated and viable for   market entry .     I selected a Learning Launch at this conference as it provided a platform for interacting with   the key audiences that are needed to validate and scrutinise the prototype . As conferences   are high in traffic and fast in pace it provides an opportunity to speak to a high number of   relevant persons which will give me high quality and a good quantity of data . 

 I previously have used a learning   launch with an event management   platform at a Tech Conference , what   I have already learnt from using   Learning Launches is the need to run   the product with over 30 people , for   this reason we had 8 people running   conference site store , with an   expectation that each person would   engage 20 people throughout the   day ensuring the day provides   extensive feedback and statistically   significant data for any interactions   needed for launch .      The reference material provided in week 3 of the Design Thinking Course , ‘ The IBM Story ’   provided me with knowledge on the environment and space needed to better interact with   participants vs other stalls at the conference    ( https://www.coursera.org/learn/uva-darden-design-thinking- innovation / lecture / iq0kE / the - ibm - story ) 

 Application :   The Learning Launch consisted of a stall in the middle of the conference where users came   through and interacted with a product demonstrator . Key features of the stall included : 

 -   No walls , meaning many people can see through and feel invited to pass through 

 -   Soft flooring , meaning people felt comfortable and rested within the store 

 -   Light music , creating a relaxing environment 

 -   Positive imagery , helping people realise this is a human based product 

 -   Medical professionals have been hired ( but wo n’t be openly advertising they are this   role until after simulations ) to manage the product demonstrations . 

 -   8 demonstrators to maximise data collection 

 The participant who entered into the stall at the conference will run through the following   experience : 

 -   Demonstrators will ask persons to sit and talk about the last time they were ill . 

 -   The demonstrator will take down key information and enter it into the chatbot   Wollega 

 -   The Chabot Wollega will then run these criteria and make a diagnosis 

 -   The participant will confirm if accurate 

 -   The Chabot Wollega will then recommend a treatment 

 -   The participant will confirm if the treatment is the same or differs from the course   taken . 

 -   Post the simulation the demonstrator will reveal they are a medical practitioner and   will speak with the participant to either confirm or challenge the diagnosis provided   and insight into how the product works . 

 -   A testimonial will then be provided along with feedback and a request for us to store   their test information . 

 The learning launch provided a scenario where people hopefully have their Wollega   diagnosis confirmed by two sources , providing trust and credibility with the product . The   learning launch and it ’s part was implemented successfully however some elements could   be improved for future learning launches as will be described in the Insight section .       Insight :   The biggest takeout / insight from the Learning Launch was the need to reduce noise and   traffic within the conference stall . The high noise volume from the conference meant that   people would have to speak loudly to compete with the background noise , this created a   secondary problem where people felt uncomfortable to speak about their previous medical   history , as they felt they were communicating to a wider audience and not just the   practitioner . While the amount of data collected was sufficient the trust element from a   relationship perspective was hard to establish .      I am satisfied with the buy in from stakeholders but additional PR will need to be completed   in order to prove it ’s success and market entry . I think this challenged my perception on   environments in which is use Learning Launches in the design thinking process as the   environment can be just as significant as the audience being tested , it also helped me   rethink the timing of a learning launch vs the audience m trying to engage . For a future   Learning Launch I would focus on one industry group / audience type at a time .       Approach :   For an alternate approach , I would still maintain the use of a learning launch as it did   provide a successful scenario for engaging key industry professionals , however the key   difference would be in its application in order to engage members of the public and build   trust and word of mouth amplification of the product .     I feel if post the learning launch public trust was my biggest barrier so I would need to apply   the learning launch in two scenarios , a private ( one already conducted at the conference ) , in   addition to a public one where I can engage and wow members of the public .      In this public scenario , I would include walls on three of the four activation stall sides to   reduce noise , I would select a lower foot traffic area so time spent with participants could   be extended and more personalised and provide seating . This would help build comfort and   security when discussing personal matters , it would also allow for more time to collect   qualitative data on each patient and in turn provide a higher success rate of patients   allowing us to store and use their data for future product improvement ( as the product   accuracy is reliant on vast collection ) . 


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: Coursera – Learning Launch for ‘ Wollega ’     Challenge :   Through market research including competitor analysis , empathy mapping , government law   review and stakeholder interviews a market opportunity has been identified for a Medical   app , a Chabot helping people with diagnosis and support treatment .      The app named ‘ Wollega ’ supplies highly accurate and free medical advice to in need   patients through accessing and   interrogating open source medical   information . The product is in the format   of a ChatBot which plugs into open   source government data supplied by the   UK National Health Service and provides   patients with accurate diagnosis and   treatment recommendations for mild   conditions .     The main challenge was to launch and validate the product with a select group of people   which will enable the product to : 

 -   Gain credibility within the medical community 

 -   Gain funding for future iterations 

 -   Uncover new medical data with private institutions who are impressed with the   product 

 -   Gain trust and references with users who may use this product in the future 

 -   Provide quantitative feedback on the product which can be used for future iterations 

 -   Gather as much information as possible which can be stored and used for the   products accuracy . 

 Selection :   To successfully validate the prototype , we conducted a live ‘ Learning Launch ’ at the   Automation & Robotics medical conference in San Diego . The conference was an specialised   event to showcase new tech within the medical and pharmaceutical industry which is the   marketplace our product Wollega sits .      The audience included members of the public , medical practitioners , insurance companies   and financial institutions . A combination of support , buy in , trust and testimonials were   needed from all four attendee types in order to deem the product validated and viable for   market entry .     I selected a Learning Launch at this conference as it provided a platform for interacting with   the key audiences that are needed to validate and scrutinise the prototype . As conferences   are high in traffic and fast in pace it provides an opportunity to speak to a high number of   relevant persons which will give me high quality and a good quantity of data . 

 I previously have used a learning   launch with an event management   platform at a Tech Conference , what   I have already learnt from using   Learning Launches is the need to run   the product with over 30 people , for   this reason we had 8 people running   conference site store , with an   expectation that each person would   engage 20 people throughout the   day ensuring the day provides   extensive feedback and statistically   significant data for any interactions   needed for launch .      The reference material provided in week 3 of the Design Thinking Course , ‘ The IBM Story ’   provided me with knowledge on the environment and space needed to better interact with   participants vs other stalls at the conference    ( https://www.coursera.org/learn/uva-darden-design-thinking- innovation / lecture / iq0kE / the - ibm - story ) 

 Application :   The Learning Launch consisted of a stall in the middle of the conference where users came   through and interacted with a product demonstrator . Key features of the stall included : 

 -   No walls , meaning many people can see through and feel invited to pass through 

 -   Soft flooring , meaning people felt comfortable and rested within the store 

 -   Light music , creating a relaxing environment 

 -   Positive imagery , helping people realise this is a human based product 

 -   Medical professionals have been hired ( but wo n’t be openly advertising they are this   role until after simulations ) to manage the product demonstrations . 

 -   8 demonstrators to maximise data collection 

 The participant who entered into the stall at the conference will run through the following   experience : 

 -   Demonstrators will ask persons to sit and talk about the last time they were ill . 

 -   The demonstrator will take down key information and enter it into the chatbot   Wollega 

 -   The Chabot Wollega will then run these criteria and make a diagnosis 

 -   The participant will confirm if accurate 

 -   The Chabot Wollega will then recommend a treatment 

 -   The participant will confirm if the treatment is the same or differs from the course   taken . 

 -   Post the simulation the demonstrator will reveal they are a medical practitioner and   will speak with the participant to either confirm or challenge the diagnosis provided   and insight into how the product works . 

 -   A testimonial will then be provided along with feedback and a request for us to store   their test information . 

 The learning launch provided a scenario where people hopefully have their Wollega   diagnosis confirmed by two sources , providing trust and credibility with the product . The   learning launch and it ’s part was implemented successfully however some elements could   be improved for future learning launches as will be described in the Insight section .       Insight :   The biggest takeout / insight from the Learning Launch was the need to reduce noise and   traffic within the conference stall . The high noise volume from the conference meant that   people would have to speak loudly to compete with the background noise , this created a   secondary problem where people felt uncomfortable to speak about their previous medical   history , as they felt they were communicating to a wider audience and not just the   practitioner . While the amount of data collected was sufficient the trust element from a   relationship perspective was hard to establish .      I am satisfied with the buy in from stakeholders but additional PR will need to be completed   in order to prove it ’s success and market entry . I think this challenged my perception on   environments in which is use Learning Launches in the design thinking process as the   environment can be just as significant as the audience being tested , it also helped me   rethink the timing of a learning launch vs the audience m trying to engage . For a future   Learning Launch I would focus on one industry group / audience type at a time .       Approach :   For an alternate approach , I would still maintain the use of a learning launch as it did   provide a successful scenario for engaging key industry professionals , however the key   difference would be in its application in order to engage members of the public and build   trust and word of mouth amplification of the product .     I feel if post the learning launch public trust was my biggest barrier so I would need to apply   the learning launch in two scenarios , a private ( one already conducted at the conference ) , in   addition to a public one where I can engage and wow members of the public .      In this public scenario , I would include walls on three of the four activation stall sides to   reduce noise , I would select a lower foot traffic area so time spent with participants could   be extended and more personalised and provide seating . This would help build comfort and   security when discussing personal matters , it would also allow for more time to collect   qualitative data on each patient and in turn provide a higher success rate of patients   allowing us to store and use their data for future product improvement ( as the product   accuracy is reliant on vast collection ) .
### Output: 