Automated Shared Recurring Bill Detection and P2P Mapping
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Solution Overview
Problem
Existing systems lack an efficient and automated method for identifying and managing shared recurrent transactions, such as utility bills and subscription services, which leads to cumbersome and time-consuming processes for calculating and reimbursing shared expenses.
Innovation Solution
An automated process that predicts transactional patterns indicative of shared recurrent transactions by parsing transaction records, identifying recurrent payments, and mapping corresponding person-to-person transactions, thereby facilitating the automation of bill sharing with minimal user intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If manual calculation and collection of reimbursements is performed for shared recurrent bills, then accuracy of expense allocation can be maintained, but time consumption and operational complexity increase significantly
Solution Approach 1:
The system enables self-service automation by having the platform automatically detect recurrent transactions, identify shared payment patterns, and generate reimbursement requests without requiring manual intervention. The system monitors financial accounts, recognizes recurring bill patterns, and autonomously manages the entire reimbursement process for shared expenses.
Solution Approach 2:
The patent replaces manual mechanical processes (hand calculations, physical note-taking, manual tracking) with an automated electronic system that uses software algorithms to detect transaction patterns, calculate reimbursements, and communicate with financial institutions. This substitution eliminates repetitive manual operations while maintaining accuracy.
2Productivity
If automated detection of shared recurrent transactions is implemented, then productivity and efficiency improve, but system complexity and initial setup requirements increase
Solution Approach 1:
The system provides universal functionality by handling multiple types of recurrent transactions (utilities, subscriptions, insurance) through a single unified platform. It can detect various payment patterns, work with different financial institutions, and accommodate multiple users and accounts, reducing the need for separate specialized tools for each function.
Solution Approach 2:
The patent introduces an intermediary system layer between users and financial institutions that handles the complexity of transaction monitoring, pattern recognition, and automated communication. This intermediary platform absorbs the system complexity while presenting a simple user interface, shielding end users from the underlying technical complexity.
3Measurement precision
If transaction pattern detection algorithms are applied to identify shared recurrent transactions, then accuracy of transaction classification improves, but computational resources and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-defining common transaction patterns and bill types before actual detection occurs. It maintains databases of known recurrent transaction patterns, allowing rapid matching against new transactions without requiring complex real-time analysis, thus reducing computational burden while maintaining high accuracy.
Solution Approach 2:
The patent applies partial action by focusing computational resources only on transactions that require analysis rather than processing all transactions uniformly. It uses heuristic filters to pre-screen transactions and applies detailed pattern matching only to suspicious or ambiguous cases, reducing overall computational resource consumption while maintaining classification accuracy.
Data Source
AI summary
Systems and methods for implementing an automated process for detection of a shared recurring bills and identification of a set of corresponding person-to-person transactions associated with shared portions of the recurring bill. The identification process is prediction-based and contingent upon an analysis of a user's transaction records. A key aspect of the analysis is directed towards transaction amount data (fraction test) and occurrence patterns, derived from the transaction records, in order to identify a shared bill and a set of person-to-person (P2P) transfer transactions associated therewith. Upon identification of a set of P2P transfer transactions associated with a recurrent shared bill, an automation option may be provided to the user.