Linked Hypergraphs for Big-Data Accuracy and Anonymous Engagement
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Solution Overview
Problem
Existing technologies for data processing and user engagement with news content and other large data sets face challenges in efficiently managing and enhancing data accuracy, credibility, and user trust, particularly in the context of big data applications such as news, social media, traffic control, and automated driving, where issues like data volume, speed, and user anonymity are critical.
Innovation Solution
The use of hypergraphs and linked hypergraphs for data processing and enhancement, enabling contextual navigation and user engagement through systems that generate and manage multi-dimensional hypergraphs, ensuring user anonymity and trustworthiness by minimizing bias and maintaining user identity protection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional data processing methods are used for news content and big data, then data volume can be managed, but data accuracy and credibility deteriorate
Solution Approach 1:
The patent segments data processing into multiple stages: initial data collection, hypergraph construction, contextual relationship extraction, and incremental refinement. Each stage processes data in manageable portions while maintaining overall accuracy through the structured hypergraph framework that organizes entities and their relationships hierarchically.
Solution Approach 2:
The patent introduces hypergraphs as an intermediary data structure between raw data and final processed output. The hypergraph serves as a mediator that captures complex contextual relationships among entities, enabling accurate data processing while managing large volumes of information through structured representation of multi-way relationships.
2Ease of operation
If user engagement with news content is enabled, then user interaction increases, but user trust and credibility deteriorate
Solution Approach 1:
The patent implements feedback mechanisms where user interactions with news content are tracked and fed back into the hypergraph system. This allows the system to learn from user behavior patterns, refine contextual relationships, and improve content delivery while maintaining credibility through transparent and accountable processing of user engagement data.
Solution Approach 2:
The patent replaces traditional mechanical user authentication and tracking systems with context-based engagement models. Instead of relying on user identities and personal data, the system uses contextual relationships within hypergraphs to enable anonymous yet meaningful user engagement, thereby maintaining trust while facilitating interaction.
3Speed
If data processing speed is increased for real-time news, then news timeliness improves, but data accuracy deteriorates
Solution Approach 1:
The patent performs preliminary actions by pre-processing and organizing data intohypergraph structures before real-time news events occur. This includes establishing entity relationships, contextual frameworks, and processing pipelines in advance, enabling rapid real-time processing without sacrificing accuracy when news events are processed.
Solution Approach 2:
The patent implements dynamic data processing where the system adapts its processing speed and depth based on the nature of the news content and contextual importance. Critical time-sensitive information is processed rapidly through optimized hypergraph queries, while less time-critical data undergoes more thorough verification, balancing speed and accuracy dynamically.
4Reliability
If user anonymity is protected, then user privacy is maintained, but user engagement capability deteriorates
Solution Approach 1:
The patent creates anonymized copies of user interaction data that preserve engagement patterns without containing personally identifiable information. These synthetic user profiles enable meaningful user engagement and preference tracking while maintaining privacy, as the system operates on copied behavioral data rather than actual user identities.
Data Source
AI summary
Disclosed herein are technologies (including computing systems) for data processing and enhancement as well as for user engagement with the enhanced and processed data. Specifically, in some examples, a computing system is included for development and control of a contextual hypergraph system. The contextual hypergraph system is a product of the combination of two or more hypergraphs of structured data linked together via one or more contexts. And, various operations for user engagement with the contextual hypergraph system are available to users and disclosed herein. Also, data on the users can be structured in a hypergraph and that hypergraph can be a basis for a contextual hypergraph of the contextual hypergraph system. Also, in some embodiments, the users of the contextual hypergraph system are anonymous. And, processes for maintaining anonymity are also disclosed herein.