Zero to a Patent : A Launch Learning Story 

 Challenge & Selection 

 As part of my Chemical Engineering undergraduate program , I participated in a year long   research adventure that consisted in developing a self - heating patch that could rapidly   increase its temperature and last longer than those available in the market . Although many   versions already existed , the available products lacked the complete set of prerequisites we   were looking for . 

 At ﬁrst our research seemed straight forward . There was information , data , and analytics   readily available that helped us narrow down our product options from the start . We had the   materials , patch shape and list of experiments we wanted to run before building an initial   prototype . Our ﬁrst mistake was exactly that , a lengthy list of assumptions and no prototype .   We started our research based on an initial list , which only yielded poor results . 

 This is where the Launch Learning technique came into the picture . By using Launch Learning   and continuously use it to build prototypes , we were able to hypothesize , test and implement   solutions to validate our ideas . 

 Application 

 When the Launch Learning process began , we focused on execution . The ﬁrst idea we tested   was the heart of the product , the chemicals . We tried new combinations of chemicals that we   thought could still get us to the required temperature . After trying a couple of options , one   ﬁnally worked . This validated our theory that there were many ways to generate heat using   diﬀerent products from those originally prescribed . Then , we chose the composition of   materials that reached the desired temperature . This ﬁrst outcome conﬁrmed that Launch   Learning worked and that we should continue to apply it to further reﬁne our product . 

 Even though we had successfully chosen the base chemicals , we still could not get the heat to   last as long as we needed it to . Therefore , we decided to focus on a new idea ; changing the   structure . We created multiple prototypes to determine how structure impacted the   performance of the product . The prototypes had various geometric ﬁgures and sizes to ensure   a comprehensive evaluation . The tests proved that shape indeed inﬂuenced how heat was   transmitted and how it limited the speed . In the end , we were able to select the most optimal   structure for our product . 

 Insight & Approach 

 Results speak . We were able to develop a new product that surpassed market standards and   patent it . In hindsight , we would have saved a lot of time if we had leveraged Launch Learning   earlier instead of leaning so heavily on our original assumptions . We thought data was going to   be our ally , but instead it limited ourselves to think narrowly . 

 In this scenario , the Launch Learning technique is appropriate and necessary . In other   instances we could run multiple experiments in parallel to test ideas of diﬀerent nature ( e.g. ,   Prototype A testing chemicals and Prototype B testing structure ) instead of executing them   sequentially . That way we can validate multiple theories simultaneously and then review the   outcomes , gain insight and use this new information to create products that are much closer to   the desired solution . 

 Applying Launch Learning strategies saves time . We focused on testing small changes to yield   fast results and pivoted our ideas as we disproved concepts . By changing our mentality and   approach , our team learned how to tackle similar issues in other projects . In fact , it continues   to impact the way that I personally tackle problems today . Even though I work in a corporate   environment , I believe the best outcomes have been achieved by launching products in a   smaller scale and proving if they indeed satisfy our clients ’ needs . 


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: Zero to a Patent : A Launch Learning Story 

 Challenge & Selection 

 As part of my Chemical Engineering undergraduate program , I participated in a year long   research adventure that consisted in developing a self - heating patch that could rapidly   increase its temperature and last longer than those available in the market . Although many   versions already existed , the available products lacked the complete set of prerequisites we   were looking for . 

 At ﬁrst our research seemed straight forward . There was information , data , and analytics   readily available that helped us narrow down our product options from the start . We had the   materials , patch shape and list of experiments we wanted to run before building an initial   prototype . Our ﬁrst mistake was exactly that , a lengthy list of assumptions and no prototype .   We started our research based on an initial list , which only yielded poor results . 

 This is where the Launch Learning technique came into the picture . By using Launch Learning   and continuously use it to build prototypes , we were able to hypothesize , test and implement   solutions to validate our ideas . 

 Application 

 When the Launch Learning process began , we focused on execution . The ﬁrst idea we tested   was the heart of the product , the chemicals . We tried new combinations of chemicals that we   thought could still get us to the required temperature . After trying a couple of options , one   ﬁnally worked . This validated our theory that there were many ways to generate heat using   diﬀerent products from those originally prescribed . Then , we chose the composition of   materials that reached the desired temperature . This ﬁrst outcome conﬁrmed that Launch   Learning worked and that we should continue to apply it to further reﬁne our product . 

 Even though we had successfully chosen the base chemicals , we still could not get the heat to   last as long as we needed it to . Therefore , we decided to focus on a new idea ; changing the   structure . We created multiple prototypes to determine how structure impacted the   performance of the product . The prototypes had various geometric ﬁgures and sizes to ensure   a comprehensive evaluation . The tests proved that shape indeed inﬂuenced how heat was   transmitted and how it limited the speed . In the end , we were able to select the most optimal   structure for our product . 

 Insight & Approach 

 Results speak . We were able to develop a new product that surpassed market standards and   patent it . In hindsight , we would have saved a lot of time if we had leveraged Launch Learning   earlier instead of leaning so heavily on our original assumptions . We thought data was going to   be our ally , but instead it limited ourselves to think narrowly . 

 In this scenario , the Launch Learning technique is appropriate and necessary . In other   instances we could run multiple experiments in parallel to test ideas of diﬀerent nature ( e.g. ,   Prototype A testing chemicals and Prototype B testing structure ) instead of executing them   sequentially . That way we can validate multiple theories simultaneously and then review the   outcomes , gain insight and use this new information to create products that are much closer to   the desired solution . 

 Applying Launch Learning strategies saves time . We focused on testing small changes to yield   fast results and pivoted our ideas as we disproved concepts . By changing our mentality and   approach , our team learned how to tackle similar issues in other projects . In fact , it continues   to impact the way that I personally tackle problems today . Even though I work in a corporate   environment , I believe the best outcomes have been achieved by launching products in a   smaller scale and proving if they indeed satisfy our clients ’ needs .
### Output: 