Augmented Reality Object Datastore for Change Tracking
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Solution Overview
Problem
Current augmented reality systems lack efficient methods to track and manage real-world objects within environments, failing to effectively integrate data about object changes, locations, and interactions, which limits their ability to provide accurate and useful information to users.
Innovation Solution
An augmented reality system that utilizes an object datastore to store and manage attributes of objects, including specific, physical, functional, ownership, and location attributes, allowing for querying and updating of object data, and using sensors and user input to identify and track objects, with the ability to aggregate data across local and cloud networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If augmented reality systems integrate comprehensive object data management capabilities, then information accuracy and user decision-making quality improve, but system complexity increases
Solution Approach 1:
The system divides object data management into distinct attribute categories (specific attributes, physical attributes, functional attributes, ownership attributes, location attributes) stored in a structured object datastore. This segmentation allows comprehensive tracking while maintaining organizational simplicity and reducing integration complexity.
Solution Approach 2:
The patent introduces an augmented reality module as an intermediary that manages interactions between sensors, user interface, and the object datastore. This mediator handles data flow and processing, reducing the complexity burden on the overall system architecture.
2Measurement precision
If the system tracks multiple object attributes over time, then change detection accuracy improves, but data processing requirements increase
Solution Approach 1:
The system extracts and stores only relevant object attributes in the object datastore that are necessary for change detection (specific, physical, functional, ownership, and location attributes). This selective extraction reduces data processing volume while maintaining detection accuracy for critical changes.
Solution Approach 2:
The system performs preliminary data organization and filtering by categorizing attributes before storage in the object datastore. This preliminary structuring enables more efficient data processing during change detection operations, reducing the computational burden when analyzing object changes over time.
Data Source
AI summary
An architecture is provided to generate an augmented reality environment and visualize or otherwise output information about changes to a physical object within the environment. The changes may include location, quantity, condition of the physical object, and so forth. Users may also use a rendering of a physical object to plan layout of the physical object in the environment. Prompts may be provided to guide placement of the physical object.