Modular Hierarchical Vision System for Autonomous Companion
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Solution Overview
Problem
Current robotic companions lack advanced intelligence and adaptability to provide personalized assistance and interaction with users, limited by their processing power and form factors, and struggle to effectively integrate with diverse digital assets and environments.
Innovation Solution
An autonomous personal companion implemented through artificial intelligence using a deep learning engine, capable of interacting with various digital assets and environments, featuring a modular hierarchical vision system for object identification and context-aware behavior, allowing it to provide personalized assistance and services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a hierarchical classifier system is used for object identification, then object recognition accuracy is improved, but processing time increases
Solution Approach 1:
The patent divides the object recognition process into multiple hierarchical levels (root classifiers, intermediate classifiers, and leaf classifiers). Each level handles specific aspects of object identification, breaking down the complex task of recognizing all possible objects into manageable segments that can be processed efficiently at each tier.
Solution Approach 2:
The system performs preliminary classification at the root level using generic classifiers that quickly eliminate large portions of the object space. This preliminary action narrows down the candidate objects before more detailed classification occurs at lower hierarchical levels, reducing overall processing time while maintaining accuracy.
2Adaptability or versatility
If deep learning models are trained on extensive data for personalized assistance, then intelligence and adaptability are improved, but computational resource requirements increase
Solution Approach 1:
The patent implements local quality by training deep learning models specifically on data relevant to each user's context, behaviors, and preferences rather than using generic models. This allows the system to achieve high personalization and adaptability for each user while optimizing computational resources by focusing processing power on user-specific patterns rather than all possible scenarios.
Data Source
AI summary
An autonomous personal companion utilizing a method of object identification that relies on a hierarchy of object classifiers for categorizing one or more objects in a scene. The classifier hierarchy is composed of a set of root classifiers trained to recognize objects based on separate generic classes. Each root acts as the parent of a tree of child nodes, where each child node contains a more specific variant of its parent object classifier. The method covers walking the tree in order to classify an object based on more and more specific object features. The system is further comprised of an algorithm designed to minimize the number of object comparisons while allowing the system to concurrently categorize multiple objects in a scene.