AI Image State Verification for Semantic Workflow Guidance
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Solution Overview
Problem
Existing systems struggle to accurately determine the desired state of a system using digital images, as computers face challenges in identifying semantically meaningful differences and providing actionable guidance for transitioning between states, even with AI and ML models requiring extensive training.
Innovation Solution
A system that uses agentic workflows leveraging AI and ML to process digital images, evaluate objects against a semantic description of the desired state, and provide feedback on necessary modifications to achieve the desired state, utilizing grounded computer vision models, vision-language models, and large language models with chain-of-thought reasoning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional SOP materials are produced and distributed, then workers can access procedural information, but the materials require extensive time and labor to produce and may contain errors that reduce reliability
Solution Approach 1:
The patent replaces manual SOP creation and distribution mechanisms with an automated computer vision system that captures, processes, and delivers procedural information through images and AI analysis, eliminating the time-consuming manual documentation process while maintaining information accuracy
Solution Approach 2:
The system enables self-service by allowing workers to capture images with their devices and receive automated analysis and guidance without requiring manual SOP creation or updates by specialists, making the information delivery process autonomous and efficient
2Measurement precision
If AI and ML models are trained extensively to accurately evaluate system states from images, then measurement precision improves, but the complexity of the system increases due to extensive training requirements
Solution Approach 1:
The system performs preliminary actions by capturing reference images of the desired state during system setup, which are then used to train the AI model specifically for that system configuration, reducing the need for extensive general training while maintaining high accuracy for the specific application
Solution Approach 2:
The patent applies local quality by training the AI model with system-specific reference images that capture the particular characteristics, lighting conditions, and configurations of the target system, rather than using generic training data, thereby achieving high precision for the specific application with reduced training complexity
3Measurement precision
If computer vision systems compare images at pixel level to detect differences, then measurement precision improves, but the system cannot determine which differences are semantically meaningful
Solution Approach 1:
The patent introduces an intermediary layer of object detection and semantic analysis between pixel-level comparison and state evaluation. The system identifies objects, their properties, and relationships in the image, then uses this semantic understanding to determine which visual differences are meaningful for assessing system state, filtering out irrelevant pixel variations
4Productivity
If guided workflows are implemented to assist workers, then productivity and quality improve, but the workflows require extensive setup and configuration time
Solution Approach 1:
The system enables self-service guided workflows by automatically generating task guidance and state verification requirements based on the captured reference images and AI analysis, eliminating the need for manual workflow configuration while providing context-specific instructions to workers during task execution
Data Source
AI summary
Aspects of the present disclosure are directed to using AI tools such as large language models, grounded computer vision models, and visual-language models to verify that the state of a target is the desired state of the target. An image of the target and a semantic textual description of the desired state of the target may be used to determine whether the state of the target is the desired state.