User Intent Decisioning for Automated Transaction Management
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Solution Overview
Problem
Information related to transactions remains siloed at individual entities, leading to inefficient and cumbersome transaction management for individuals, potentially resulting in forgotten transactions.
Innovation Solution
A system utilizing trained machine learning models to analyze historical transaction data, identify user intent, and generate customized transaction recommendations, which can be automatically initiated, processed, or completed based on user confirmation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If transaction information remains siloed at individual entities, then each entity maintains control over its own data, but transaction management becomes inefficient and cumbersome for users
Solution Approach 1:
The patent combines transaction information from multiple siloed entities into a centralized user profile. The system aggregates transaction data across different merchants and service providers, creating a unified view that improves transaction management efficiency while maintaining data control through user authorization and encryption.
Solution Approach 2:
The patent introduces a centralized transaction management system as an intermediary between users and multiple entities. This mediator collects, processes, and analyzes transaction data from various sources, enabling efficient transaction patterns recognition and automation without requiring users to manually manage each entity's data separately.
2Ease of operation
If users manually manage periodic transactions, then they maintain control over each transaction, but the process becomes time-consuming and error-prone
Solution Approach 1:
The patent implements preliminary action by automatically recognizing periodic transaction patterns from historical data and pre-configuring future transactions. The system learns user spending patterns and automatically schedules recurring transactions, eliminating the need for manual repetition while maintaining user control through confirmation and modification capabilities.
Solution Approach 2:
The patent enables self-service by allowing the system to automatically manage periodic transactions based on learned patterns. The transaction management system autonomously identifies patterns, generates recommendations, and executes transactions without requiring continuous user intervention, freeing users from time-consuming manual management while preserving control through opt-in and modification options.
3Ease of operation
If users forget periodic transactions, then individual transaction control is maintained, but problems occur and user experience deteriorates
Solution Approach 1:
The patent implements feedback mechanisms that continuously monitor transaction patterns and provide users with insights about their spending habits. The system analyzes historical transaction data, identifies periodic patterns, and provides feedback through recommendations and notifications, ensuring users remain informed about upcoming transactions while the system handles the operational burden of remembering and scheduling them.
Data Source
AI summary
A system for automated account interaction receives historical information associated with an account corresponding to a user. The historical information identifies a transaction involving the account. The system uses one or more trained machine learning models to identify an intent for the transaction at least in part by inputting the historical information to the trained machine learning models. The system uses the trained machine learning models to generate a recommended transaction at least in part by inputting the intent for the transaction to the trained machine learning models. The system outputs the recommended transaction and receives a confirmation regarding the recommended transaction.