Spend Memory Records Linking Transactions to Geotagged Social Data
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Solution Overview
Problem
Financial statements lack social context for transactions, making it difficult for users to remember which purchases are associated with specific social events.
Innovation Solution
A system that integrates transaction data with geotagged social media data and local data to create a spend memory record, linking transactions with photos, status updates, and location information based on timestamp and location similarities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If financial statements provide only transaction lists, then the statement is simple and easy to generate, but the user cannot recall which transactions are associated with which social events
Solution Approach 1:
The patent merges transaction data from financial statements with social media data (photos, status updates, location information) by combining multiple data sources into a unified enhanced financial statement that displays both transaction details and associated social context information together
Solution Approach 2:
The system uses timestamp and location data as intermediary elements to match and associate social media activities with financial transactions. The timestamp and location act as common keys that link unrelated data sources (financial transactions and social media posts) to each other
2Reliability
If the financial statement includes additional context information, then the user can better recall transactions, but the statement becomes more complex and requires additional data processing
Solution Approach 1:
The system performs preliminary actions by collecting and storing social media data (photos, status updates, location information) in advance before generating the enhanced financial statement. This pre-collection of data allows for easier integration and matching when creating the final statement
Solution Approach 2:
The system uses feedback mechanisms by comparing timestamp and location data from both financial transactions and social media activities to automatically match and associate relevant social context with each transaction, ensuring accurate recall information is provided
3Loss of information
If the system integrates multiple data sources, then the spend memory record is more comprehensive, but the data processing time and computational resources increase
Solution Approach 1:
The system extracts only the essential matching criteria (timestamp and location data) from the multiple data sources rather than processing and comparing all available information. This selective extraction reduces computational complexity while still achieving accurate matching between transactions and social activities
Data Source
AI summary
Systems and methods include a database maintained by a financial institution that stores transaction data associated with a previous financial transaction performed via a financial account of a respective account holder, wherein the transaction data comprises a transaction location and a transaction timestamp, a spend memory processor of the financial institution that retrieves the transaction data from the database, interacts with a social linking application programming interface (API) to receive, via a network, social data from a social networking site, wherein the social data comprises a social location and a social timestamp, compares the social data to the transaction data, and creates a spend memory record based on one or more similarities between the social data and the transaction data, and a communication interface of the financial institution that provides the spend memory record to a mobile device associated with the account holder.