AR/MR Item Recognition and Tracking with Peer-Interest Valuation
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Solution Overview
Problem
Existing augmented and mixed-reality applications are not integrated into everyday life to enhance user efficiency and satisfaction, and users face difficulties in remembering the location of items and valuing them based on peer interests.
Innovation Solution
An automated system for item recognition, tracking, and peer interest-based valuation using computer graphics-aided visualization in AR or MR environments, comprising item recognition and registration, search accommodation, price recommendation, and computer graphics synthesis modules, enabling real-time location tracking and intelligent item valuation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated item recognition and tracking systems are implemented, then item location tracking efficiency is improved, but device complexity increases
Solution Approach 1:
The system integrates multiple functions into a single unified platform: item recognition via computer vision, location tracking through AR/MR visualization, valuation through machine learning, and social sharing capabilities. This multi-functionality resolves the contradiction by consolidating what would otherwise require multiple separate systems into one cohesive solution.
Solution Approach 2:
The patent introduces an intermediary processing layer that includes image segmentation modules, object detection models, and machine learning valuation systems. These intermediaries handle the complex processing tasks, allowing the core tracking function to remain simple while sophisticated analysis occurs in the intermediate processing stage.
2Measurement precision
If real-time object detection and image segmentation are performed, then item recognition accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-processing images through segmentation before object detection, and by pre-training machine learning models offline. The image segmentation separates foreground items from background in advance, and the trained models are ready for rapid inference, thus reducing real-time processing time while maintaining high accuracy.
Solution Approach 2:
The patent applies segmentation to divide the image processing task into distinct stages: image segmentation to separate items from background, object detection to identify and classify items, and tracking to monitor item locations. This segmentation of the processing pipeline allows each stage to be optimized independently, improving overall efficiency without sacrificing accuracy.
3Measurement precision
If machine learning valuation based on peer interests is implemented, then item valuation intelligence is improved, but computational resources required increase
Solution Approach 1:
The system uses peer interest data and comparable item data as proxies for direct valuation of unique items. Instead of performing complex computational analysis on every item, the system copies and adapts valuation patterns from similar items and peer behavior, significantly reducing computational requirements while maintaining valuation intelligence.
Solution Approach 2:
The machine learning models are trained offline and deployed as pre-trained systems that perform rapid inference rather than continuous heavy computation. The system serves itself by automatically updating valuation models with new peer interest data without requiring manual intervention or excessive computational resources during operation.
4Measurement precision
If comprehensive item metadata and pricing data are collected and analyzed, then item valuation accuracy is improved, but data processing complexity increases
Solution Approach 1:
The system extracts only the most relevant features and data points from comprehensive metadata and pricing data for valuation purposes. Rather than processing all available data, the machine learning models identify and extract key features such as item category, condition, peer interest metrics, and comparable pricing, simplifying the data processing pipeline while maintaining valuation accuracy.
Data Source
AI summary
A novel electronic system provides automated recognition, registration, and location tracking of physical, holographic, and/or virtual user items that can be visualized in a computer graphics-infused augmented-reality or mixed-reality environment with an electronic visualization device. The novel electronic system is also capable of executing intelligent machine valuation of user items and reflecting dynamic changes in valuation based on peer interests within the augmented-realty or mixed-reality environment. Each user item identified in the user's visual perspective through the electronic visualization device connected to the novel electronic system undergoes object extractions, class categorizations, and image segmentations by the novel electronic system to determine whether a particular item is substantive and worthy of location tracking, based on the item owner's preferences. The item is paired with related metadata and stored in a system database, and later retrieved by a search command in the augmented-reality or mixed-reality environment to display its known location.