Interface-Based Decision Analysis for Unstructured Data
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Solution Overview
Problem
Existing machine learning models struggle to effectively analyze and process large volumes of unstructured natural language data, particularly in environments where human judgment and subtle reasoning are required, such as in medical record analysis.
Innovation Solution
A system and method that utilize a user interface to capture user interactions with data records, converting these interactions into training data that reflects the complex assessments and discernments made by human reviewers, thereby enabling machine learning models to develop comparable data discernment and analysis capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models are used to process large volumes of data, then data-processing throughput is improved, but the effectiveness and reliability deteriorate when subtle human judgment is required
Solution Approach 1:
The patent introduces an interface as an intermediary between human reviewers and machine learning models. The interface captures human interactions with data records and converts them into training data, allowing the model to learn from human judgment patterns while maintaining high processing throughput
Solution Approach 2:
The system performs preliminary action by collecting and processing human interaction data through the interface before training the machine learning model. This preliminary data collection phase enables the model to be pre-trained with nuanced human judgment patterns, improving its effectiveness for complex analysis tasks
2Extent of automation
If conventional machine learning systems are used, then automation is improved, but the ability to handle unstructured natural language data deteriorates
Solution Approach 1:
The system enables self-service by allowing the machine learning model to automatically learn from captured human interactions without manual annotation. The interface automatically records user actions and converts them into training data, reducing manual effort while improving the model's ability to handle unstructured natural language data
Solution Approach 2:
The patent changes the parameter of training data representation by capturing detailed interaction data (clicks, selections, navigation patterns) rather than simple labeled data. This parameter change enables the model to learn from the nuanced ways humans interact with unstructured data, improving adaptability
Data Source
AI summary
An apparatus, method, and computer program product for the improved development of training data sets for use in connection with machine learning models capable of operating on natural language data records and other unstructured data in a network environment. Some example implementations provide for the generation and presentation of record images in a user interface that allows captures user actions reflecting higher-order data analysis and discernment for incorporation into training protocols used for machine learning models.