Contextual AR Guidance Rendering for Remote Expert Assistance
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Solution Overview
Problem
Current augmented reality (AR) technologies face challenges in providing effective remote expert assistance in industrial settings, particularly due to high employee turnover and the need for inexperienced workers, where existing solutions lack efficiency and accuracy in guiding tasks across varying environments and worker contexts.
Innovation Solution
An automated AR rendering platform utilizing trainable machine learning models to generate contextualized guidance, integrating real-time context data from the work environment, worker data, and task data, allowing for real-time feedback to improve guidance and rendering processes, and leveraging 5G connectivity and edge compute architecture for efficient bandwidth utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional user interfaces (keyboards, displays) are used for remote assistance, then device complexity is low, but ease of operation and information delivery are insufficient for complex industrial tasks
Solution Approach 1:
The patent replaces conventional mechanical user interfaces (keyboards, physical displays) with augmented reality overlays that present information directly in the worker's field of view. This substitution eliminates the need for manual typing or navigating complex display menus, allowing workers to receive guidance hands-free while maintaining contextual awareness of their work environment.
Solution Approach 2:
The system introduces an augmented reality overlay as an intermediary between the worker and the expert system. This intermediary presents contextualized guidance information directly in the worker's visual field, bridging the gap between complex industrial tasks and the worker's need for simple, actionable instructions without requiring complex interface interactions.
2Manufacturing precision
If AR content is customized for each worker and task context, then task accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and contextualizing AR content before it reaches the worker. The server prepares personalized guidance overlays based on worker profiles, task requirements, and environmental context in advance, so that when the worker needs assistance, the customized content is already ready for immediate display, minimizing delivery time while maintaining high accuracy.
Solution Approach 2:
The system applies local quality by customizing AR content specifically for each worker's context, task, and skill level rather than using generic guidance. The content is tailored to the specific work environment, equipment being serviced, and individual worker's experience, delivering precise information exactly where and when it is needed without unnecessary processing delays.
3Measurement precision
If real-time contextual data is collected and processed, then guidance accuracy improves, but bandwidth consumption and system complexity increase
Solution Approach 1:
The system extracts only the essential contextual data needed for accurate guidance from the vast amount of available information. Instead of transmitting all collected sensor data and environmental information, the server identifies and processes only the critical parameters relevant to the current task and worker context, reducing bandwidth consumption while maintaining guidance accuracy.
Solution Approach 2:
The system changes parameters by transforming raw contextual data into optimized AR content parameters. The server processes contextual information and converts it into condensed, visually-efficient overlay elements that convey maximum guidance value with minimum data transmission, reducing bandwidth requirements while preserving measurement precision.
4Adaptability or versatility
If machine learning models are trained continuously with feedback, then system intelligence improves, but computational load and training time increase
Solution Approach 1:
The system implements periodic action by training machine learning models at scheduled intervals rather than continuously. Feedback from worker interactions and task outcomes is accumulated and processed in periodic training cycles, allowing the system to improve its intelligence and adaptability while managing computational load through batch processing rather than constant resource consumption.
Data Source
AI summary
A scheme (300) for facilitating automated AR-based rendering with respect to expert guidance or assistance provided in a connected work environment based on contextualization is disclosed. In one aspect, a method comprises a worker (102) requiring assistance with respect to a given task generating (302) one or more suitable queries. Responsive thereto, real-time context data is gathered (304), which may comprise data relating to the work environment. A remote expert (118) may generate (306) appropriate guidance relevant to the task and assistance query, which may be rendered as AR content (308) for worker consumption by using an AR rendering module (112). The worker (102) consumes or uses the AR content for performing (310) an action with respect to the task. The performance of the worker (102) may be used as a measure to improve the AR rendering in automated fashion (312, 314) using one or more machine learning modules (114).