Object Classification Feedback for Low-Bandwidth Image Recognition
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Solution Overview
Problem
Existing technologies fail to effectively highlight objects and text blocks of interest in images on user devices, limiting user access to relevant information and requiring excessive processing resources.
Innovation Solution
An object classifier system that processes image data to identify and highlight objects or text blocks of interest based on user input, utilizing a specification executor and presentation component to provide visual or audible feedback, and leveraging a network effect for improved processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If object classification processing is performed on user devices, then user access to relevant information is enhanced, but processing resources and bandwidth requirements increase
Solution Approach 1:
The patent introduces a server as an intermediary between multiple user devices and the object classification processing. The server receives image data from user devices, performs the computationally intensive object classification, and returns results to users. This mediator approach allows user devices to access relevant information without bearing the full processing resource burden locally.
Solution Approach 2:
The server provides a universal object classification service that can serve multiple user devices simultaneously. Instead of each device having dedicated processing capabilities, a single multi-functional server handles classification requests from numerous users, efficiently utilizing processing resources to benefit the entire user base.
2Loss of information
If object classification processing is performed on user devices, then user access to relevant information is enhanced, but bandwidth requirements increase
Solution Approach 1:
The server acts as a centralized intermediary that receives image data from user devices, performs object classification, and returns only the essential results (identified objects and their locations). This approach minimizes the bandwidth required compared to transferring full high-resolution images or extensive metadata between devices and users.
Solution Approach 2:
The system extracts only the essential information needed for object classification from the original image data, processes it centrally, and returns only the extracted results to user devices. This extraction approach reduces bandwidth requirements by transmitting minimal necessary data rather than complete image files.
3Measurement precision
If specialized object recognition capabilities are developed for each user need, then recognition precision is improved, but device complexity increases
Solution Approach 1:
The server implements a universal object classification system that can handle multiple types of objects and classification criteria through a single platform. Developers can deploy different classification algorithms and criteria on the same server infrastructure, providing specialized recognition capabilities without requiring separate systems for each use case.
Solution Approach 2:
The system allows dynamic deployment and updating of classification algorithms and criteria on the server. New object recognition capabilities can be added, modified, or removed without changing the underlying system architecture or user device software, enabling flexible adaptation to new requirements while maintaining system simplicity.
Data Source
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AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving user input for an object classification of interest for image data (e.g. single frame image, continuous video, etc.) from a user device, and for each object specified by identified object data to belong to the object classification of interest, displaying data that presents each object and/or text block of interest on a user device.