Assistant System Real-World Text Detection
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Solution Overview
Problem
Existing systems face challenges in determining appropriate tasks based on real-world text, understanding the context beyond textual content, and efficiently executing tasks in a user-centric manner.
Innovation Solution
The proposed solution involves linking entities associated with real-world text to a knowledge graph, identifying objects containing textual content, and classifying real-world text to generate a hierarchical understanding, thereby determining suitable tasks and enhancing contextual awareness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the assistant system processes real-world text to determine tasks, then contextual awareness and task accuracy improve, but system complexity and processing time increase
Solution Approach 1:
The system segments text processing into distinct modules: optical character recognition (OCR) extracts text from images, natural language processing (NLP) analyzes the extracted text, and a task determination module executes actions. This segmentation allows each component to specialize in specific functions, improving overall accuracy while managing complexity through modular design.
Solution Approach 2:
The patent introduces an intermediary text extraction layer between image input and task execution. The OCR component acts as a mediator that converts visual text into processable data, while NLP serves as another intermediary that bridges raw text and task determination. These intermediary layers enable accurate contextual understanding without requiring the entire system to handle all processing complexities simultaneously.
2Loss of information
If the assistant system uses hierarchical text classification, then contextual understanding improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary text extraction and classification before full task determination. The hierarchical classification structure pre-organizes text into categories (e.g., identifying text type, then specific context, then actionable intent), allowing the system to quickly route processed text to appropriate task handlers without re-analyzing the entire context each time.
Solution Approach 2:
The hierarchical classification system dynamically adapts processing depth based on text complexity. Simple texts undergo minimal classification layers, while complex texts trigger deeper hierarchical analysis. This dynamic approach ensures comprehensive contextual understanding for difficult cases while maintaining fast processing for straightforward texts.
3Adaptability or versatility
If the assistant system integrates multiple processing modules, then task execution capability improves, but system reliability and ease of operation worsen
Solution Approach 1:
The patent implements a universal task determination module that handles multiple types of tasks through a single integrated interface. This module can process various text formats (images, documents, messages) and execute diverse tasks (information retrieval, reservations, communications) using common processing logic, reducing the risk of failures associated with multiple specialized components.
Solution Approach 2:
The system merges text extraction, classification, and task determination into an integrated processing pipeline. By combining these functions into a unified flow with centralized error handling and consistent data structures, the system reduces interoperability failures between separate modules while maintaining versatile task execution capability.
Data Source
AI summary
In one embodiment, a method includes accessing visual signals comprising images portraying textual content in a real-world environment associated with a first user from a client system associated with the first user, recognizing the textual content based on machine-learning models and the visual signals, determining a context associated with the first user with respect to the real-world environment based on the visual signals, executing tasks determined based on the textual content and the determined context for the first user, and sending instructions for presenting execution results of the tasks to the first user to the client system.