Challenge 

 In South - East Asia , especially in the Indian subcontinent , the economy is largely dependent on   agriculture . It ’s been this way since ancient times because of the abundance of fertile land and   weather pattern . Though in recent times the economy significantly moved towards   manufacturing and heavy industry . But the rural economy is largely dependent on agriculture .   Bangladesh is a small riverine country in this region . The country has a subtropical monsoon   climate and the majority of its population is living in rural areas . Dairying is a good source of   income to small and marginal farmers . The feeds required for milk production can be met from   their limited land resources as most of the milch animals are ruminants and the majority of their   food can be derived from forages , coarse roughages , and byproducts not utilized by human   beings , without incurring much additional cost . The demand for milk and milk products is   increasing because of the rapid increase in population , the spread of education , and growing   nutrition awareness . But the inefficient and age - old farming methodology is the root cause of   millions of tons of annual deficiency of milk in our country . So I and my team , which consist of   four other members wanted to capitalize on this opportunity . We wanted to incorporate modern   technology in dairy farming for efficiency and we also wanted to deliver it to the rural farmers in   a very affordable rate . So after research , existing data analysis we came up with an integrated   idea and three major prototypes to test . A health tracker - a wearable IoT device for the cow ,   which monitors the health and predicts heat . An RFID Tracker- an ear clip - on tag to track a cow 's   location and to store breed information . And a digital app for a one - stop digital platform for   training , knowledge , and access to resources . And now in this module , I want to apply the   “ Learning Launch ” design tool to verify the primary assumptions about the prototypes .     Selection 

 A learning launch is an experiment conducted in the marketplace quickly and inexpensively . It   forms a bridge between customer co - creation and commercial rollout . As the previous steps like   idea generation and prototyping are already done in this case learning launch is the perfect tool   to capitalize at this point in time . As we need to verify our assumptions through real - time data   and customer feedback . And it is required to get the answer to the questions like 1.what do we   know ? 2 . What do we not know ? And 3 . What we need to know . And as the hypothesis is done   and the key assumptions are surfaced , now only real - time testing in the market place can prove   the worth of the idea . 

 Application 

 Since this is a learning launch and not a pilot , it is important to plan for it to end . Set concrete   limits on key variables , such as time , geography , number of customers , features , and partner   firms . So for our first learning launch , we focused on the structure of the existing market and   current constraints in it . We delivered one hundred pieces of health tracker and RFID tracker to   three different dairy firms in three different regions of the country . Selecting three different 

 regions is necessary to bring diversity in the test . And we also provide the farmers our digital app   and primary training to get along with it . We delivered the product for a fraction of the   production cost and the testing period was scheduled to be one month long . During this one   month , the dairy farmers will use the technology properly . And after the month of usage , we   hoped that we would find enough data to verify our assumption . If the assumptions are verified   then we could go to the mass production and marketing stage . Or if otherwise happened we can   go to the drawing board and run our second learning launch in some time . 

 Insight 

 Our main assumption was that this tools will help the farmers to become more organized than   ever before . So the efficiency will increase and with efficiency total production and profit margin   will also increase . The wearable health tracker will constantly update the temperature of the   animals in the mobile app of the farmer . And immediate action can be taken if there is any sick   animal in the firm . And the RFID tracker will help the farmers to store the relative necessary   information more conveniently . And the final outcome was largely positive . In three firms   production was increased moderately and sick animals were identified and treated much faster   than usual time . And the overall customer feedback was positive . But as usual there was some   isolated cases of device malfunction and disintegration due to extreme environment . As we used   leather strap for health tracker , in some case the leather was disintegrated due to exposure in   constant moisture . In this regard , we need to go back to the design board and do some   modification in the product . But otherwise , it was a very successful experiment . 

 Approach 

 The learning launch is when your growth project first makes contact with reality . As our team has   already gone through several design thinking activities / tools and are at this stage in the timeline ,   the learning launch is the most appropriate tool . So there is no confusion in the choice of tool .   But to successfully implement the learning launch tool we have to beware of four important   things , confirmation bias , quantity of data , quality of data , and group thinking . In these case   quality of was good and quantity was sufficient . But as it was a startup and the all the team   members are from the same field so there might be some confirmation bias due to lack of   diversity in the group . And that should be modified in the long run . 


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 

 In South - East Asia , especially in the Indian subcontinent , the economy is largely dependent on   agriculture . It ’s been this way since ancient times because of the abundance of fertile land and   weather pattern . Though in recent times the economy significantly moved towards   manufacturing and heavy industry . But the rural economy is largely dependent on agriculture .   Bangladesh is a small riverine country in this region . The country has a subtropical monsoon   climate and the majority of its population is living in rural areas . Dairying is a good source of   income to small and marginal farmers . The feeds required for milk production can be met from   their limited land resources as most of the milch animals are ruminants and the majority of their   food can be derived from forages , coarse roughages , and byproducts not utilized by human   beings , without incurring much additional cost . The demand for milk and milk products is   increasing because of the rapid increase in population , the spread of education , and growing   nutrition awareness . But the inefficient and age - old farming methodology is the root cause of   millions of tons of annual deficiency of milk in our country . So I and my team , which consist of   four other members wanted to capitalize on this opportunity . We wanted to incorporate modern   technology in dairy farming for efficiency and we also wanted to deliver it to the rural farmers in   a very affordable rate . So after research , existing data analysis we came up with an integrated   idea and three major prototypes to test . A health tracker - a wearable IoT device for the cow ,   which monitors the health and predicts heat . An RFID Tracker- an ear clip - on tag to track a cow 's   location and to store breed information . And a digital app for a one - stop digital platform for   training , knowledge , and access to resources . And now in this module , I want to apply the   “ Learning Launch ” design tool to verify the primary assumptions about the prototypes .     Selection 

 A learning launch is an experiment conducted in the marketplace quickly and inexpensively . It   forms a bridge between customer co - creation and commercial rollout . As the previous steps like   idea generation and prototyping are already done in this case learning launch is the perfect tool   to capitalize at this point in time . As we need to verify our assumptions through real - time data   and customer feedback . And it is required to get the answer to the questions like 1.what do we   know ? 2 . What do we not know ? And 3 . What we need to know . And as the hypothesis is done   and the key assumptions are surfaced , now only real - time testing in the market place can prove   the worth of the idea . 

 Application 

 Since this is a learning launch and not a pilot , it is important to plan for it to end . Set concrete   limits on key variables , such as time , geography , number of customers , features , and partner   firms . So for our first learning launch , we focused on the structure of the existing market and   current constraints in it . We delivered one hundred pieces of health tracker and RFID tracker to   three different dairy firms in three different regions of the country . Selecting three different 

 regions is necessary to bring diversity in the test . And we also provide the farmers our digital app   and primary training to get along with it . We delivered the product for a fraction of the   production cost and the testing period was scheduled to be one month long . During this one   month , the dairy farmers will use the technology properly . And after the month of usage , we   hoped that we would find enough data to verify our assumption . If the assumptions are verified   then we could go to the mass production and marketing stage . Or if otherwise happened we can   go to the drawing board and run our second learning launch in some time . 

 Insight 

 Our main assumption was that this tools will help the farmers to become more organized than   ever before . So the efficiency will increase and with efficiency total production and profit margin   will also increase . The wearable health tracker will constantly update the temperature of the   animals in the mobile app of the farmer . And immediate action can be taken if there is any sick   animal in the firm . And the RFID tracker will help the farmers to store the relative necessary   information more conveniently . And the final outcome was largely positive . In three firms   production was increased moderately and sick animals were identified and treated much faster   than usual time . And the overall customer feedback was positive . But as usual there was some   isolated cases of device malfunction and disintegration due to extreme environment . As we used   leather strap for health tracker , in some case the leather was disintegrated due to exposure in   constant moisture . In this regard , we need to go back to the design board and do some   modification in the product . But otherwise , it was a very successful experiment . 

 Approach 

 The learning launch is when your growth project first makes contact with reality . As our team has   already gone through several design thinking activities / tools and are at this stage in the timeline ,   the learning launch is the most appropriate tool . So there is no confusion in the choice of tool .   But to successfully implement the learning launch tool we have to beware of four important   things , confirmation bias , quantity of data , quality of data , and group thinking . In these case   quality of was good and quantity was sufficient . But as it was a startup and the all the team   members are from the same field so there might be some confirmation bias due to lack of   diversity in the group . And that should be modified in the long run .
### Output: 