Glasses Device Object Tracking via Segmented Processing
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Solution Overview
Problem
Current devices lack efficient methods for tracking user interactions and product costs in real-time without requiring user input, limiting their ability to provide enhanced productivity and shopping experiences.
Innovation Solution
A glasses device equipped with sensors, a recognition module, and connectivity features that detect user interactions and recognize products, generating engagement and cost data for automatic tracking and comparison, enabling alerts for lower prices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automatic object recognition and tracking is implemented via glasses device, then user interaction tracking and cost monitoring capability is improved, but device complexity increases
Solution Approach 1:
The system divides functionality between the glasses device (capturing image data and basic object recognition) and a remote server or mobile device (performing complex cost comparison and data processing). This segmentation allows the glasses to remain relatively simple while still achieving sophisticated automatic tracking and monitoring capabilities through distributed processing.
Solution Approach 2:
An intermediary computing system (server or mobile device) mediates between the glasses device and the user, handling complex operations like cost database queries, price comparisons, and detailed analysis. This intermediary approach enables advanced functionality without burdening the wearable device with excessive computational complexity.
2Measurement precision
If real-time object recognition and cost tracking is performed, then measurement precision of user interactions and product costs is improved, but use of energy by glasses device increases
Solution Approach 1:
The system segments computational tasks between the energy-constrained glasses device and a power充足的 remote server. The glasses capture and transmit image data, while the server performs energy-intensive operations like detailed object recognition, cost database searching, and price comparison analysis, thereby preserving battery life while maintaining high tracking accuracy.
Solution Approach 2:
The glasses device performs only the minimum necessary processing locally (capturing image data and basic transmission), while deferring more computationally expensive operations to the remote server. This partial action approach at the wearable device level reduces energy consumption while the server performs the excessive/comprehensive analysis needed for high precision tracking.
3Ease of operation
If automatic object recognition without user input is implemented, then ease of operation is improved, but reliability of object identification may worsen due to false recognitions
Solution Approach 1:
The system implements feedback mechanisms where the server validates object recognition results against cost databases and product information. Automatic recognition is performed, but the system cross-references results with known product data, cost information, and image analysis to verify accuracy. This feedback loop maintains ease of operation by keeping the process automatic while improving reliability through validation.
Solution Approach 2:
The system performs preliminary actions by pre-loading cost databases, product information, and reference data onto the server before actual tracking begins. This preliminary preparation allows the automatic recognition system to quickly verify and validate objects against pre-existing data, improving reliability without requiring user input during the actual tracking process.
Data Source
AI summary
Techniques for object tracking via glasses device are described and are implementable to enable different attributes of objects to be tracked via a glasses device. In implementations, user engagement with an object of interest is detected and tracked. Based on the user engagement with the object of interest, engagement data is generated that includes attributes of the user engagement. Further, cost tracking for objects can be implemented based on images of objects captured via a glasses device. Object records can be created for detected objects and can include object attributes such as object identifiers, object costs, object locations, etc.