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.


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 .