Wearable Operation Guidance Using Real-Time Task Recognition
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Solution Overview
Problem
Inexperienced workers may inefficiently perform operations due to static training materials that do not account for rare problems or efficient procedures, leading to wasted resources and time, especially when experts cannot provide in-person training.
Innovation Solution
An operation management system using a wearable device and machine learning model that identifies tasks and physical objects in real-time, providing dynamic training by processing video streams from wearable devices to facilitate more efficient task performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If static training materials are used for inexperienced workers, then training can be provided without expert assistance, but the training efficiency and operation performance are poor due to inability to account for rare problems or efficient procedures
Solution Approach 1:
The patent transforms static training materials into a dynamic system that adapts to real-time worker actions and environmental context. The wearable device continuously captures video streams and the system dynamically generates and updates AR instructions based on the worker's current state, creating a responsive training system that evolves during operation performance.
Solution Approach 2:
The system implements continuous feedback loops by monitoring the worker's performance through video analysis, comparing it against the operation model, and providing real-time corrective instructions. The system observes the worker's actions, determines deviations from optimal procedures, and feeds back targeted guidance to correct inefficiencies dynamically during task execution.
2Productivity
If experts provide in-person training, then training quality and operation performance improve, but resource allocation becomes inefficient and scalability is limited
Solution Approach 1:
The patent creates a virtual copy of expert knowledge embedded in the operation model, which is trained on historical expert performance data. This digital replica captures expert procedures and decision-making patterns, allowing the system to provide expert-level guidance without requiring physical expert presence, thereby scaling training capabilities while preserving performance quality.
Solution Approach 2:
The system introduces an AI-based intermediary that mediates between the worker and expert knowledge. Rather than direct expert-worker interaction, the operation model serves as an intelligent intermediary that translates expert procedures into context-specific instructions, enabling asynchronous and scalable knowledge transfer without time constraints on expert availability.
3Productivity
If dynamic real-time analysis is implemented using wearable devices and machine learning models, then training efficiency and operation performance improve, but device complexity and processing requirements increase
Solution Approach 1:
The patent divides the complex analysis system into modular components: the wearable device handles video capture and initial processing, the edge device or server performs operation model inference and AR content generation, and the AR display presents processed information. This segmentation distributes computational complexity across multiple devices, reducing the burden on any single component while maintaining overall system capability.
Data Source
AI summary
An operation management system is disclosed. The operation management system may receive a video stream from a wearable device of a user that is performing an operation in a physical environment. The operation management system may process, using an operation performance model, a set of frames of the video stream that indicates a state of a performance of the operation by the user. The operation management system may determine, based on the state of the performance by the user, a next task of the operation. The operation management system may configure display data that is associated with a physical object that is associated with the next task. The display data may be associated with an indicator that identifies the physical object and/or task information associated with performing the next task. The operation management system may provide the display data to the wearable device.