Learning Launch Toll for fairs 

 Challenge : 

 I studied Mechanical Engineering , and our final assignment for one course was to create a device   that could help the people that works at neighbourhood fairs with a problem that we could detect .   The main constraints of the project were that we had a little under three months , and the solution   had to be affordable for the customers . 

 My team found that the volume of organic waste produced at the fair was a problem for many of   the stakeholders because it was difficult to handle it , and if they left the street with trash , it could   result in a fine that would hurt them financially . 

 Selection 

 By using design thinking tools , such as an ethnographic approach and visualization , we came to   the concept of " reducing the volume " of the waste as a solution to their problem . So we   developed different alternatives on how to reduce the size of this type of garbage . 

 Through the use of the learning launch tool , we expected to get the feedback on the early   prototypes we manufactured , which focused on different things , like size , weight , cost , reduction   mechanism and user interaction . 

 Application 

 To test our different prototypes , created different prototypes that we grouped considering the   feature we wanted to test . The first time we did a test with the user , the difference between the   models was substantial . For example , the mechanisms for the reduction of the volume would be   a type of shredder with cylinders that you would put over the trash can , or a machine that   smashed the trash with pistons and makes blocks or even a rotating axis that would cut the trash . 

 We invited people from various fairs to use the devices , and we recorded the interactions , the   commentaries and the suggestions they had . Because the designs focused on different aspects of   the machine , each person needed to experiment with as many models as possible . This way , we   could check our assumptions , such as the weight the users tolerated , the more intuitive   mechanism for them , and if it was within their budget or willingness to pay . 

 We did a total of three iterations with different groups of people ( around 20 each time ) . The first   test helped us to discover the traits that the customers prefered , for the second one we did other   cheap prototypes , with small changes ( mostly on the shape and the weight ) on the base design of   the selected traits of the first iteration . Finally , in the third iteration , we combined the different   features in a single prototype and tested it again to see if the sum of the selected parts of the   previous tests was well received . 

 Insight 

 The results were pleasing for the customer because they were participants in the process for the   creation of the device . They choose each iteration , what was the type of solution that was more   comfortable for them , and by doing that , they saved us a lot of time and effort . Because if we had   not tested the devices so often , we would have developed prototypes that were too complex , had   too many attributes to rate at the same time , and probably would have failed to fulfil the   requirements of the stakeholders . 

 Having a broad look at the problem , helped us to deliver many different alternatives for the first   iteration . This way , we were able to show vastly different alternatives and make them compete   with the final user as the centre of the next iterations . I think this also was key to the success of   this assignment . 

 Finally , the use of low - cost and fast - building models was important when creating a product . The   idea of narrowing the designs , to what your customer feels comfortable with was essential to   deliver the product . 

 Approach 

 The main problem that we faced was that because the groups were so large , getting people to   agree on some of the different features that they liked was no easy task , and many times there   was not a total agreement at all . 

 In the future , when we do more field tests with the learning launch tool , I think it would be   beneficial to have smaller groups for the tests . This way , people are more comfortable   investigating the devices and feel free to speak what they think , and the data we can collect   would have a higher quality and diversity . 


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: Learning Launch Toll for fairs 

 Challenge : 

 I studied Mechanical Engineering , and our final assignment for one course was to create a device   that could help the people that works at neighbourhood fairs with a problem that we could detect .   The main constraints of the project were that we had a little under three months , and the solution   had to be affordable for the customers . 

 My team found that the volume of organic waste produced at the fair was a problem for many of   the stakeholders because it was difficult to handle it , and if they left the street with trash , it could   result in a fine that would hurt them financially . 

 Selection 

 By using design thinking tools , such as an ethnographic approach and visualization , we came to   the concept of " reducing the volume " of the waste as a solution to their problem . So we   developed different alternatives on how to reduce the size of this type of garbage . 

 Through the use of the learning launch tool , we expected to get the feedback on the early   prototypes we manufactured , which focused on different things , like size , weight , cost , reduction   mechanism and user interaction . 

 Application 

 To test our different prototypes , created different prototypes that we grouped considering the   feature we wanted to test . The first time we did a test with the user , the difference between the   models was substantial . For example , the mechanisms for the reduction of the volume would be   a type of shredder with cylinders that you would put over the trash can , or a machine that   smashed the trash with pistons and makes blocks or even a rotating axis that would cut the trash . 

 We invited people from various fairs to use the devices , and we recorded the interactions , the   commentaries and the suggestions they had . Because the designs focused on different aspects of   the machine , each person needed to experiment with as many models as possible . This way , we   could check our assumptions , such as the weight the users tolerated , the more intuitive   mechanism for them , and if it was within their budget or willingness to pay . 

 We did a total of three iterations with different groups of people ( around 20 each time ) . The first   test helped us to discover the traits that the customers prefered , for the second one we did other   cheap prototypes , with small changes ( mostly on the shape and the weight ) on the base design of   the selected traits of the first iteration . Finally , in the third iteration , we combined the different   features in a single prototype and tested it again to see if the sum of the selected parts of the   previous tests was well received . 

 Insight 

 The results were pleasing for the customer because they were participants in the process for the   creation of the device . They choose each iteration , what was the type of solution that was more   comfortable for them , and by doing that , they saved us a lot of time and effort . Because if we had   not tested the devices so often , we would have developed prototypes that were too complex , had   too many attributes to rate at the same time , and probably would have failed to fulfil the   requirements of the stakeholders . 

 Having a broad look at the problem , helped us to deliver many different alternatives for the first   iteration . This way , we were able to show vastly different alternatives and make them compete   with the final user as the centre of the next iterations . I think this also was key to the success of   this assignment . 

 Finally , the use of low - cost and fast - building models was important when creating a product . The   idea of narrowing the designs , to what your customer feels comfortable with was essential to   deliver the product . 

 Approach 

 The main problem that we faced was that because the groups were so large , getting people to   agree on some of the different features that they liked was no easy task , and many times there   was not a total agreement at all . 

 In the future , when we do more field tests with the learning launch tool , I think it would be   beneficial to have smaller groups for the tests . This way , people are more comfortable   investigating the devices and feel free to speak what they think , and the data we can collect   would have a higher quality and diversity .
### Output: 