ML Plan Platform Automating Expense Tracking and Adherence
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Solution Overview
Problem
Maintaining personal plans is challenging due to unrealistic thresholds, lack of flexibility, and cumbersome accounting for expenses during events like business trips, which often require manual tracking and justification of expenditures.
Innovation Solution
A plan platform utilizing machine learning models to receive and process plan information and transaction data, identifying relevant transactions and providing recommendations to maintain plan thresholds, automate actions, and simplify expense tracking and justification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual tracking and justification of expenditures is required, then plan adherence can be monitored, but the process becomes cumbersome and time-consuming
Solution Approach 1:
The system automatically performs expense tracking and plan adherence monitoring without requiring manual user intervention. The machine learning model processes transaction data autonomously to determine plan compliance, eliminating the need for users to manually track and justify expenditures while maintaining reliable monitoring.
Solution Approach 2:
The patent replaces the manual mechanical process of tracking and justifying expenses with an automated machine learning system. The ML model analyzes transaction data and automatically determines plan adherence, substituting human effort with computational processing to reduce time loss while maintaining monitoring reliability.
2Adaptability or versatility
If realistic thresholds are used in plans, then plan feasibility improves, but the system requires more complex processing to maintain flexibility
Solution Approach 1:
The system dynamically adjusts plan thresholds based on processed transaction data and event context. The machine learning model modifies threshold parameters in real-time to maintain realism while adapting to changing conditions, allowing flexible plan adjustment without requiring complex manual processing or multiple rigid threshold sets.
3Loss of energy
If automated actions are performed based on machine learning recommendations, then resource conservation improves, but the system requires advanced machine learning processing
Solution Approach 1:
The system implements a feedback loop where the machine learning model continuously processes transaction data, generates recommendations, and automatically executes actions that conserve resources. The feedback mechanism allows the system to learn from outcomes and improve resource conservation automatically, managing the complexity through iterative optimization rather than static complex processing.
Data Source
AI summary
A device receives, from a user device, plan information that identifies a plan for an event and includes information identifying an account associated with the plan, plan items of the plan, and priorities and preferences associated with the plan items, where the user device is associated with a user of the account and the plan. The device receives transaction information identifying transactions associated with the account, and processes the plan information and the transaction information, with a first model, to identify transactions related to the plan. The device processes information associated with the particular plan item, the plan information, and the transaction information, with a second model, to determine recommendations for the plan, where the information associated with the particular plan item includes information identifying a priority and a preference associated with the particular plan item. The device provides information indicating the recommendations to the user device.