Tabular Policy Action Models for Interpretable Clinical Automation
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Solution Overview
Problem
Existing action models for computer control, particularly in clinical applications, face challenges such as data inefficiency, lack of interpretability, hallucinations, and model regression, making them unsuitable for reliable and safe use in environments requiring high precision.
Innovation Solution
Implementing a tabular policy action model that maps states to actions using a table-based approach, avoiding neural networks to address data inefficiency, enhance interpretability, prevent hallucinations, and mitigate model regression, ensuring deterministic and predictable performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If neural networks are used for action models, then automation and adaptability improve, but data efficiency deteriorates and model regression occurs
Solution Approach 1:
The patent segments the action model into distinct modular components: task determination module, state determination module, and action determination module. Each module operates independently with clearly defined inputs and outputs, allowing the system to maintain automation while improving reliability through modular error isolation and preventing model regression through structured computation paths.
Solution Approach 2:
The patent introduces an intermediary structured representation of the computer screen state that mediates between raw visual input and neural network processing. This structured intermediary includes identified UI elements, their properties, and spatial relationships, which stabilizes the input to the neural network and prevents performance degradation over time.
2Adaptability or versatility
If neural networks are used for action models, then adaptability improves, but interpretability deteriorates
Solution Approach 1:
The patent segments the action model into distinct modular components: task determination module, state determination module, and action determination module. Each module operates independently with clearly defined inputs and outputs, allowing the system to maintain automation while improving reliability through modular error isolation and preventing model regression through structured computation paths.
Solution Approach 2:
The patent introduces an intermediary structured representation of the computer screen state that mediates between raw visual input and neural network processing. This structured intermediary includes identified UI elements, their properties, and spatial relationships, which stabilizes the input to the neural network and prevents performance degradation over time.
3Extent of automation
If neural networks are used for action models, then automation improves, but data efficiency deteriorates
Solution Approach 1:
The patent performs preliminary actions by structuring and preprocessing the computer screen state before neural network processing. The state determination module identifies UI elements, extracts their properties, and organizes spatial relationships in advance, which reduces the complexity and quantity of data that the neural network must process, thereby improving data efficiency while maintaining automation.
4Adaptability or versatility
If neural networks are used for action models, then adaptability improves, but hallucinations increase
Solution Approach 1:
The patent introduces an intermediary structured representation of the computer screen state that mediates between raw visual input and neural network processing. This structured intermediary includes identified UI elements, their properties, and spatial relationships, which stabilizes the input to the neural network and prevents performance degradation over time.
Solution Approach 2:
The patent implements feedback mechanisms where the determined state and task are validated against the original expression and screen state. The action determination module cross-references the structured state representation with the identified task to ensure actions are grounded in actual screen content, reducing hallucinations while maintaining adaptability.
Data Source
AI summary
In some aspects, the present disclosure provides a computer-implemented system comprising: a digital processing device comprising: at least one processor, an operating system configured to perform executable instructions, a memory, and a computer program including instructions executable by the digital processing device to perform a computer task by a user expressing to the computer-implemented system, and wherein the computer-implemented system is configured to perform the task by generating and executing a computer-executable program.