AI Knowledge Graph for Dynamic AR Repair Guidance
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Solution Overview
Problem
Training technicians to be experts on a wide range of products is challenging due to the increasing product portfolio and pressure to cover multiple domains effectively, and existing augmented reality (AR) solutions are limited by the data used for self-enablement in hardware technical support.
Innovation Solution
A system and method utilizing a knowledge graph (KG) driven by artificial intelligence (AI) that integrates computer vision for dynamic content generation, enabling the recognition of physical components and their states, and dynamically generates instructions for AR guidance, reducing dependency on remote experts and enhancing technician skills.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If technicians are trained to cover multiple product domains, then service coverage is improved, but training complexity and time requirements increase
Solution Approach 1:
The system enables technicians to independently access and utilize AI-generated repair instructions without requiring extensive prior training. The computer vision model automatically identifies device components and states, and the knowledge graph generates context-specific repair guidance, allowing technicians to perform multi-domain repairs based on real-time system analysis rather than relying on their own trained expertise.
Solution Approach 2:
The patent replaces the mechanical system of human training and knowledge retention with an automated AI system. The computer vision model and knowledge graph collectively substitute for technician expertise, automatically analyzing device states and generating appropriate repair instructions, thereby eliminating the need for technicians to undergo extensive training across multiple product domains.
2Productivity
If remote support is provided without AR, then technician dependency is reduced, but support timeline increases
Solution Approach 1:
The system performs preliminary analysis of the device state using computer vision before generating repair instructions. The knowledge graph pre-processes device information and identifies relevant repair procedures in advance, allowing technicians to receive ready-to-execute guidance immediately, thereby reducing the overall support timeline while maintaining high productivity.
Solution Approach 2:
The patent introduces an intermediary AI system that bridges remote support and on-site repair. The computer vision model and knowledge graph act as intermediaries between the technician and the repair process, automatically analyzing device states and generating context-specific instructions, thereby eliminating the need for lengthy remote expert consultations while maintaining efficient support.
3Loss of time
If AR driven self-enablement is implemented, then support timeline is reduced, but data requirements and processing complexity increase
Solution Approach 1:
The system segments the complex AR support system into distinct functional modules: a computer vision model for device analysis, a knowledge graph for repair knowledge management, and an instruction generation component. This segmentation reduces processing complexity by allowing each module to specialize in specific tasks, thereby enabling fast AR-driven self-enablement without overwhelming system complexity.
Solution Approach 2:
The knowledge graph serves as a universal data structure that can represent multiple device types and repair scenarios within a single framework. This multi-functionality allows the system to handle diverse repair situations without requiring separate complex processing pipelines for each device type, thereby reducing overall system complexity while maintaining fast support timelines.
4Productivity
If knowledge articles are processed manually, then accuracy is maintained, but processing speed decreases
Solution Approach 1:
The system incorporates feedback mechanisms where the computer vision model continuously monitors device states and adjusts generated instructions accordingly. The knowledge graph is dynamically updated based on repair outcomes and new device information, ensuring that AI-generated content maintains high accuracy while being processed at automated speeds, thereby resolving the trade-off between processing speed and content accuracy.
Data Source
AI summary
Embodiments relate to an intelligent computer platform to support knowledge graph (KG) driven content generation. A KG is created from one or more knowledge articles. The created KG includes individual nodes representing individual physical object and individual edges representing a hardware state characteristic of a physical object represented in a corresponding node. A trained computer vision model is leveraged to recognize one or more physical components and localize an active state of the recognized physical components. Content is generated responsive to the localized active state and the hardware state characteristic represented in the KG, and a control signal is dynamically issued to an operatively coupled device associated with the generated content. The control signal is configured to selectively control an event injection responsive to synchronization of the recognized one or more physical components and the generated content.