AR Repair Guidance for Home Device Update Decisions
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Solution Overview
Problem
Homeowners and other structure occupants often overlook the need for device repairs or replacements, leading to potential losses and increased insurance premiums, as conventional instructions are ineffective in educating them about necessary updates.
Innovation Solution
An augmented reality (AR) system utilizing machine learning models and AR displays to visualize recommended updates for existing devices, providing users with real-time recommendations for repairs or replacements that improve a home score.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional instructions accompany devices, then device information is provided, but the information is ineffective in educating people about the need to repair or replace devices
Solution Approach 1:
The patent replaces conventional static printed instructions with an augmented reality system that uses computer vision, machine learning models, and AR displays to dynamically identify devices and provide personalized maintenance recommendations. This substitution of information delivery mechanisms transforms ineffective static text into interactive, context-aware guidance that directly addresses device conditions and user needs.
Solution Approach 2:
The system incorporates feedback loops where the AR device captures images of existing devices, the machine learning model analyzes device conditions and provides recommendations, and the AR display presents actionable information to users. This feedback mechanism ensures that information is not only provided but also tailored to actual device states and user situations, significantly improving effectiveness.
2Loss of time
If people rely on conventional instructions, then device information is accessible, but people may forget or be too busy to investigate and maintain existing devices
Solution Approach 1:
The system performs preliminary actions by automatically analyzing device conditions through computer vision and machine learning before users need to take maintenance action. The AR display proactively presents maintenance recommendations and priority assessments, allowing users to understand what needs to be done without having to investigate device conditions themselves, thus preventing time loss while maintaining reliability.
Solution Approach 2:
The AR system enables self-service maintenance guidance by automatically identifying devices, analyzing their conditions, and providing personalized maintenance recommendations. Users simply point the AR device at the equipment, and the system handles the complex analysis and information delivery, freeing users from the burden of investigating device maintenance needs while ensuring reliable maintenance decisions.
3Ease of manufacture
If existing devices are not updated, then current devices remain in place, but potential losses occur and insurance premiums increase
Solution Approach 1:
The AR display uses visual indicators and color-coded information to highlight device conditions, maintenance priorities, and potential risks. By transforming abstract device data into visual color changes and highlighted elements in the augmented reality view, the system makes harmful factors like device failures and insurance risks immediately visible and understandable to users, facilitating informed update decisions.
Solution Approach 2:
The machine learning model acts as an intermediary between device conditions and user decisions. It analyzes device data, assesses risks, and translates complex information into actionable recommendations that balance update necessity with cost considerations. This intermediary function helps users understand the relationship between device maintenance and loss prevention, making the implementation of updates more straightforward.
Data Source
AI summary
Systems and methods disclosed herein relates generally to using augmented reality (AR) for visualizing recommended updates to devices proximate a structure. In some examples, underlay layer data may be received (e.g., from a camera of an AR viewer device); and overlay layer data may be received (e.g., from a different camera or other overlay layer device). An AR display may be created by correlating the underlay layer data with the overlay layer data. An improved home score indicia may be displayed based upon the recommended update.