Machine Learning Model for Database Record Categorization
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Solution Overview
Problem
Existing record management systems are incomplete and inaccurate for interactions like cash transactions, as they lack information to categorize purposes, leading to incomplete and inaccurate record retrieval.
Innovation Solution
A processing system that uses machine learning to categorize interactions by receiving interaction data, providing category suggestions based on location, time, and user input, and storing records with user-specified categories, enhancing the accuracy and completeness of records in a database.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional record management systems are used for cash transactions, then the system structure remains simple, but the record completeness and accuracy deteriorate due to lack of categorization information
Solution Approach 1:
The patent introduces a machine learning model as an intermediary component between the record management system and the user. This model automatically categorizes cash transactions by analyzing interaction data patterns, location, time, and merchant information, thereby recovering lost categorization information without requiring direct user intervention for each transaction.
Solution Approach 2:
The system enables self-service categorization by implementing an automated machine learning model that independently analyzes and categorizes transactions. The model learns from historical user-specified categories and automatically applies categorization to new cash transactions, allowing the system to serve itself rather than relying on manual user input for every record.
2Measurement precision
If manual categorization is required for all interactions, then record accuracy improves, but the ease of operation deteriorates due to increased user burden
Solution Approach 1:
The system implements partial action by requiring user categorization input only when the machine learning model's automatic categorization confidence is below a threshold. For high-confidence predictions, the system applies categorization automatically without user intervention. This partial approach maintains high accuracy while significantly improving ease of operation compared to requiring manual categorization for all transactions.
Solution Approach 2:
The system implements feedback mechanisms where user corrections to automatic categorizations are fed back into the training data. This allows the model to learn from user preferences and improve its categorization accuracy over time, creating a closed-loop system that progressively reduces the need for manual intervention while maintaining or improving accuracy.
3Reliability
If machine learning models are implemented to categorize interactions, then record retrieval accuracy improves, but the device complexity increases due to additional processing requirements
Solution Approach 1:
The system performs preliminary action by training the machine learning model in advance using historical interaction data and user-specified categories. Once trained, the model is deployed to automatically categorize new transactions. This preliminary training phase separates the complexity of model development from routine operation, allowing the system to achieve high retrieval reliability during normal use without requiring complex real-time processing.
Data Source
AI summary
In some implementations, a system may receive interaction data based on an interaction of a user with a terminal, wherein the interaction data indicates a location of the terminal. The system may determine a plurality of entities having a corresponding plurality of locations that are within a geographic area that includes the location of the terminal. The system may determine, based on the plurality of entities, one or more categories for categorizing a purpose associated with the interaction of the user with the terminal. The system may transmit, to a user device of the user, information that identifies the one or more categories. The system may receive, from the user device, information that identifies one or more user-specified categories of the one or more categories. The system may store, in the database, a record that includes the information that identifies the one or more user-specified categories.