Ranking P2P Counter-Parties via Social Graph and Location
Solution Overview
Problem
Conventional peer-to-peer transaction systems require excessive data input and do not provide a list or ranking of likely transaction counter-parties, making it cumbersome for users to conduct transactions with other mobile device users, such as splitting bills in a group setting.
Innovation Solution
A computer-implemented method using social networking content and location data to identify and rank likely transaction counter-parties by transmitting a user's location to a Peer-to-Peer Payment System, which searches for proximate devices and ranks them based on connection strength, then displays this list to the user for selection and transaction completion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional peer-to-peer transaction systems are used, then transactions can be conducted, but excessive data input is required and no ranking of likely counter-parties is provided
Solution Approach 1:
The system performs preliminary actions by pre-establishing social network connections and location data before transactions occur. When a transaction is needed, the system queries pre-existing social graph data and location information to automatically generate and rank potential counter-parties, eliminating the need for users to manually input account information or search through contacts.
Solution Approach 2:
The system introduces an intermediary mechanism that acts as a mediator between users and potential counter-parties. This intermediary is the automated ranking system that processes social network data and location information to generate a prioritized list of likely counter-parties, reducing the direct burden on users to manually identify and input transaction details.
2Productivity
If users manually enter account information for each counter-party, then transactions can be conducted, but the process becomes burdensome in group settings
Solution Approach 1:
The system enables self-service by automatically generating and ranking counter-party lists based on pre-existing social network data and location information. Users simply select from the automatically generated ranked list rather than manually entering account information, significantly reducing the time and effort required for transactions in group settings.
Solution Approach 2:
The system performs preliminary action by pre-organizing social network connections and location data before transactions occur. This pre-processing of data allows the system to quickly generate ranked counter-party lists during actual transactions, eliminating the need for users to spend time manually searching and entering account information.
3Measurement precision
If conventional systems are used, then transactions can be conducted, but no list or ranking of likely counter-parties is presented
Solution Approach 1:
The system implements feedback mechanisms by continuously analyzing social network data and location information to generate and update ranked lists of likely counter-parties. The system provides feedback to users in the form of a prioritized list, allowing users to quickly identify the most appropriate counter-party based on their social connections and current location, improving identification accuracy without excessive complexity.
Data Source
AI summary
A user installs a Peer-to-Peer Payment Application (“PPA”) on their mobile device. The PPA transmits the location of the user device to a server located in a Peer-to-Peer Payment System (“PPS”). The PPS may search for other devices within a certain proximity to the user that may be active. The PPS may search the social network content of the user to determine if any of the proximate devices users are likely counter-parties. If more than one likely counter-party is identified, the PPS may rank the counter-parties according to a preconfigured set of factors that define the strength of the connection to the user. These factors may include their status in the user's social networks, frequency of contact, prior transactions, or other factors that further establish a relationship. The PPS may transmit the ranked list of likely counter-parties to the PPA for display to the user on the device.