Payment Request Ranking via Social and Location Data
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Solution Overview
Problem
Conventional peer-to-peer transaction systems require excessive data input and do not provide a ranked list of likely transactions, making it cumbersome for users to conduct transactions, especially in group settings like dining at a restaurant where one person pays for the group.
Innovation Solution
A method that utilizes social networking content and location data to rank potential payors by their proximity, social network connections, and previous transaction history, allowing users to easily identify and request payment from suitable individuals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional peer-to-peer transaction systems are used to identify the other party and conduct transactions, then transactions can be completed, but an overwhelming amount of data input is required making the process cumbersome
Solution Approach 1:
The system automatically identifies potential payors using the payor's own social network data, location history, and transaction history stored in their device. The ranking algorithm self-generates the list of likely transactions without requiring manual data entry by the user, making the system serve itself by utilizing its own stored information
Solution Approach 2:
The system pre-collects and stores social network data, location history, and transaction history in advance before the actual payment request is made. This preliminary data gathering and processing eliminates the need for excessive data input at the moment of transaction, as the information is already available in the device's memory
2Productivity
If conventional transaction systems are used without ranking, then all transactions can be processed, but users cannot easily identify the most likely transactions among multiple options
Solution Approach 1:
The system changes the parameter of transaction presentation by introducing a ranking system that orders payment requests based on multiple weighted factors including social network strength, location proximity, and transaction frequency. This parameter transformation converts an unsorted list into a prioritized sequence, making the most likely transactions immediately visible at the top of the list
3Reliability
If manual data entry is required for each transaction, then accurate transaction information can be captured, but the process becomes time-consuming and burdensome for users
Solution Approach 1:
The system copies existing data from the payor's social network profile, location history, and transaction history into the payment request interface. Instead of requiring the user to manually enter information, the system automatically populates fields with copied data from previously stored sources, maintaining accuracy while eliminating time-consuming manual input
Data Source
AI summary
Ranking payment requests includes a peer-to-peer payment system that employs a server configured for receiving a payment request from a requester computing device; receiving location data of the requester network device; receiving a request for a ranking of payment requests; searching social network information of the payor for occurrences of the requester of the payment request; receiving location data of the payor network device, the location data comprising a location of the payor computing device and a location history of the payor computing device; searching a transaction history of the payor; ranking the payment requests based at least in part on one or more of the location of the requester, a strength of social network connections to the payor for each of the payment requesters, and number of previous transactions between the payor and the requester; and providing the ranking of the payment requests to the payor.