Contextual Information Aggregation System
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Solution Overview
Problem
Users often miss events and experiences they would have liked to participate in despite having access to vast amounts of information through various sources, as existing information aggregation systems fail to provide relevant and timely information based on the user's context.
Innovation Solution
A system that continuously gathers and filters information based on a user's interests and context, using a network of servers, processors, and software components like magnets and recommendation engines to identify and report relevant content ubiquitously across various devices, including context-aware data aggregation and push notifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If users access multiple information sources (social networking sites, search engines, RSS feeds, etc.), then the quantity of available information increases, but the ability to find relevant and timely information about events and experiences decreases
Solution Approach 1:
The system segments information processing by creating specialized components: magnets for gathering content about specific topics, context analysis modules for understanding user situation, and filtering mechanisms for relevance assessment. This segmentation allows each component to focus on specific tasks, improving overall information relevance while maintaining comprehensive coverage
Solution Approach 2:
The patent introduces an intermediary system between users and information sources that actively monitors, aggregates, and filters information based on user context. This intermediary layer processes information before delivery, ensuring relevance without requiring users to manually manage multiple sources, thus solving the contradiction between information quantity and relevance
2Measurement precision
If the system continuously gathers and filters information based on user context, then the relevance of information improves, but the system complexity increases
Solution Approach 1:
The system employs universal components that perform multiple functions: magnets both gather content and initially filter it, context analysis modules serve both understanding user needs and prioritizing information, and the architecture supports multiple users and topics simultaneously. This multi-functionality reduces overall system complexity while maintaining high information relevance
Solution Approach 2:
The system performs preliminary actions by pre-configuring magnets for specific topics, pre-analyzing user context, and pre-filtering information before it reaches the user. This preliminary processing reduces the complexity of real-time decision-making while ensuring high relevance of delivered information
Data Source
AI summary
A system finds and aggregates the most relevant and current information about the people and things that a user cares about. The information gathering is based on current context (e.g., where the user is, what the user is doing, what the user is saying/typing, etc.). The result of the context based information gathering is presented ubiquitously on user interfaces of any of the various physical devices operated by the user.