AR Object Recognition with Pre-stored Reference Data
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Solution Overview
Problem
Existing systems lack efficient and real-time methods for tracking progress toward predefined limits and generating recommendations in augmented reality applications, particularly in contexts like shopping or nutritional tracking.
Innovation Solution
A system utilizing machine learning to analyze image data from augmented reality devices, identify objects, determine their characteristics, and compare them to user-defined goals or limits, generating notifications and recommendations for the user in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If real-time analysis of image data is performed to track progress toward predefined limits, then the timeliness and responsiveness of progress tracking is improved, but the computing resources and processing time required increase
Solution Approach 1:
The system pre-processes and stores reference data for objects, characteristics, and goals before runtime. When analyzing image data, the system compares captured images against pre-stored reference information, avoiding the need to perform complex analysis from scratch in real-time. This preliminary preparation reduces the computational burden during actual progress tracking while maintaining real-time responsiveness.
2Measurement precision
If machine learning algorithms are used to identify object characteristics and generate recommendations, then the accuracy and intelligence of progress tracking is improved, but the device complexity and processing overhead increase
Solution Approach 1:
The system introduces an intermediary layer that bridges simple image capture and complex machine learning analysis. This intermediary layer uses pre-processed reference data and structured comparison protocols to filter and prepare data before it reaches the machine learning algorithms. This reduces the complexity of the overall system while maintaining high accuracy in object characteristic identification.
3Adaptability or versatility
If comprehensive recommendations and alternative suggestions are generated in real-time, then the usefulness and value of the system to users is improved, but the processing time and computational load increase
Solution Approach 1:
The system pre-generates and stores potential recommendations and alternative suggestions based on predefined goals and historical data. When a user query is received, the system quickly retrieves and adapts pre-prepared recommendations rather than generating them from scratch. This maintains comprehensive recommendation capabilities while ensuring real-time responsiveness.
Data Source
AI summary
Systems for dynamically recognizing progress and generating recommendations are provided. In some examples, a system may image data from an augmented reality device. The image data may include video images, still images, images of machine-readable code, and the like. The received image data may be analyzed in real-time to identify an object within the data. In some examples, machine learning may be used to identify one or more characteristics of the object. The identified characteristics may be compared to one or more pre-defined goals or limits and a notification may be generated based on the comparison. The notification may be transmitted to the augmented reality device and displayed on the augmented reality device. In some examples, based on the comparison, machine learning may be used to generate one or more recommendations and a notification may be generated including the recommendations and may be transmitted to the augmented reality device for display.