Neural Query Aggregator for Dynamic Deep Web Retrieval
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Solution Overview
Problem
Conventional internet search tools are ineffective in providing current information from dynamic 'deep web' sources, such as proprietary databases, due to their static nature and inability to handle rapid data changes, and are not optimized for diverse networking devices like smartphones and televisions, which limits their functionality and user experience.
Innovation Solution
A method utilizing neural networks to process informal user queries into formal queries, store interface context securely on client devices, and search databases, allowing for context-sensitive and conversational querying across multiple devices, thereby enhancing information retrieval from dynamic sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional internet search tools are used to search dynamic deep web sources, then the search tool structure remains simple and static, but the information currentness and reliability deteriorate due to inability to handle rapid data changes
Solution Approach 1:
The patent implements dynamic information gathering agents that continuously monitor and update deep web sources in real-time, transforming the static search tool into a dynamic system that adapts to rapid data changes. The agents can be deployed and configured dynamically to track specific data sources, ensuring information currentness without requiring complete system restructuring.
Solution Approach 2:
The search system is segmented into independent information gathering agents that can operate autonomously on different deep web sources. Each agent handles specific data sources separately, allowing the system to manage complexity through modular decomposition while maintaining reliable, up-to-date information from multiple dynamic sources simultaneously.
2Adaptability or versatility
If conventional search tools are designed for static external web sources, then the device structure remains simple, but the adaptability to diverse networking devices like smartphones and televisions deteriorates
Solution Approach 1:
The patent creates a universal search platform that can operate across multiple device types (smartphones, tablets, televisions, computers) through a common agent-based architecture. The information gathering agents and search functionality remain device-agnostic, while presentation layers adapt to specific device characteristics, achieving broad compatibility without duplicating core functionality for each device.
Solution Approach 2:
The system introduces presentation layers as intermediaries between the core search functionality and diverse user devices. These presentation layers translate the universal search results into device-specific formats and interfaces, allowing the complex adaptability requirements to be isolated from the core search engine while maintaining simplicity in the fundamental search mechanism.
3Loss of information
If statistical models are used to process user queries, then the processing method remains simple and fast, but the context awareness and query understanding capability deteriorate
Solution Approach 1:
The patent implements context-aware processing where the system automatically maintains and updates user context profiles based on query history and behavior patterns. The context information is self-updated and self-managed without requiring explicit user input or complex real-time analysis, reducing processing complexity while improving context sensitivity through automated context accumulation and utilization.
Data Source
AI summary
A method of providing information to a user is provided. The method includes; establishing an user system interface between a client device and an information system; processing informal queries input from the client device with at least one neural network that converts the informal queries from the client device into formal queries; storing interface context in a browser of the client device, the interface context created in forming formal queries from informal queries, wherein the client device contains unique interface context in the client device's browser that is secure to the client device, the interface context aiding in the determination of future formal queries from future informal queries; searching at least one database in response to the formal queries; and providing responses to the informal queries processed by the neural network to a user through the client device.