Istanbul Mobile Application   – Learning Launch   Challenge     As an automobile industry employee living in Istanbul where has many transportation   issues   to   be   improved or solved , I was a team member for providing mobile apps for city ’s Dolmuş System . Dolmuş is   a microtransit solution created by the city itself as a self - improvisation decades ago . Those are public   minibuses   operates in pre - designated and planned routes , leaves the station when it is full . The full price   is equal to a taxi price yet it is divided into 8 as the vehicle has 8 passengers . It is faster and more   comfortable than other public transportation solutions .      As the vehicle starts travelling when it is full , there is no chance to get in the vehicle in case you are not   on the departing station , especially for long routes . We thought creating a mobile app would help people   to make reservations , embrace the utilization of Dolmuş , and dispose the obligation to go to the   departing station by making a seat reservation and pre - payment .     Selection     We have conducted very intensive design thinking activities which ended nowhere as we received   multiple and various information that contradicts each other . Thus , we have created a clickable   prototype and provide social media and personal advertisements on the stations . Learning Launch then   commenced as a step .     Application     Our learning launch was to identify the potential customers that do not use Dolmuş as the vehicles   departs with no empty seats . Those potential passengers are the dwellers living at mid - roads , thus unable   to utilize the Dolmuş vehicles unless they arrive at the first station .   Thus , team prioritized the longest routes that has no passenger circulation on the road . Those routes also   are expensive ones , and drivers do not want to risk any rooms on the vehicle . We organized the drivers to   reserve seats for the applications made via clickable prototype application . Apart from the application ,   every step was manual and human driven , as we were looking for the customer demand and operability   before creating expensive complete mobile application .   Surprisingly there were not even one demand for the seat reservation via mobile application . To be   honest , we were close to be confirmation bias in case there would be little sign of customer demand .     Actually , before the learning launch , we asked the dwellers of target areas if they had problems with   Dolmuş usages or would they open for alternative ways of public transportation via mobile apps ? We   somehow interpreted the key parts of the application to the persons we thought to be right customer .   Thus , we set a minimum number , a percentage of   the people we talked to , because we needed   a big   enough sample of data   and we needed the right quality of data . Personally , we still think we had right   quality of data but , we could not distinguish behavior and attitude deferral .       Insight   The outcomes were a bit different from what we had expected . Despite , Customers are tend to utilize   mobile apps and made online reservations for ride hailing applications such as Uber and Bitaksi , the   demand for public transportation reservation was not that obvious . Furthermore , we kept customer   interviews , and found out that the target customers call the Dolmuş station and made empty seats if they   really desire to get on the Dolmuş . There was somehow an improvisation and our solution did not add   enough value to change the business model .   We avoided big investments by failing fast and saved our resources and kept studying for the mobility   solutions . 

 Approach     Team conducted two trials as Learning Launch and no customer demand reflected as an outcome . Team   maintained customer interviews and observations during the launches . The approach intended to   measure the possibility of attitude to utilize mobile application for Dolmuş travels via clickable   prototypes only . We did not put the idea into trash can as the timing would not be sufficient for the   model . 


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: Istanbul Mobile Application   – Learning Launch   Challenge     As an automobile industry employee living in Istanbul where has many transportation   issues   to   be   improved or solved , I was a team member for providing mobile apps for city ’s Dolmuş System . Dolmuş is   a microtransit solution created by the city itself as a self - improvisation decades ago . Those are public   minibuses   operates in pre - designated and planned routes , leaves the station when it is full . The full price   is equal to a taxi price yet it is divided into 8 as the vehicle has 8 passengers . It is faster and more   comfortable than other public transportation solutions .      As the vehicle starts travelling when it is full , there is no chance to get in the vehicle in case you are not   on the departing station , especially for long routes . We thought creating a mobile app would help people   to make reservations , embrace the utilization of Dolmuş , and dispose the obligation to go to the   departing station by making a seat reservation and pre - payment .     Selection     We have conducted very intensive design thinking activities which ended nowhere as we received   multiple and various information that contradicts each other . Thus , we have created a clickable   prototype and provide social media and personal advertisements on the stations . Learning Launch then   commenced as a step .     Application     Our learning launch was to identify the potential customers that do not use Dolmuş as the vehicles   departs with no empty seats . Those potential passengers are the dwellers living at mid - roads , thus unable   to utilize the Dolmuş vehicles unless they arrive at the first station .   Thus , team prioritized the longest routes that has no passenger circulation on the road . Those routes also   are expensive ones , and drivers do not want to risk any rooms on the vehicle . We organized the drivers to   reserve seats for the applications made via clickable prototype application . Apart from the application ,   every step was manual and human driven , as we were looking for the customer demand and operability   before creating expensive complete mobile application .   Surprisingly there were not even one demand for the seat reservation via mobile application . To be   honest , we were close to be confirmation bias in case there would be little sign of customer demand .     Actually , before the learning launch , we asked the dwellers of target areas if they had problems with   Dolmuş usages or would they open for alternative ways of public transportation via mobile apps ? We   somehow interpreted the key parts of the application to the persons we thought to be right customer .   Thus , we set a minimum number , a percentage of   the people we talked to , because we needed   a big   enough sample of data   and we needed the right quality of data . Personally , we still think we had right   quality of data but , we could not distinguish behavior and attitude deferral .       Insight   The outcomes were a bit different from what we had expected . Despite , Customers are tend to utilize   mobile apps and made online reservations for ride hailing applications such as Uber and Bitaksi , the   demand for public transportation reservation was not that obvious . Furthermore , we kept customer   interviews , and found out that the target customers call the Dolmuş station and made empty seats if they   really desire to get on the Dolmuş . There was somehow an improvisation and our solution did not add   enough value to change the business model .   We avoided big investments by failing fast and saved our resources and kept studying for the mobility   solutions . 

 Approach     Team conducted two trials as Learning Launch and no customer demand reflected as an outcome . Team   maintained customer interviews and observations during the launches . The approach intended to   measure the possibility of attitude to utilize mobile application for Dolmuş travels via clickable   prototypes only . We did not put the idea into trash can as the timing would not be sufficient for the   model .
### Output: 