Hypergraph-Based Anonymity for User Engagement
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Solution Overview
Problem
Current technologies for news topic content processing and user engagement with data face challenges in accuracy, credibility, and efficiency, particularly in the context of big data systems such as news and social media, traffic control, automated driving, and drug discovery, where data processing and enhancement are complex and require improved methods for user interaction.
Innovation Solution
The development of computing systems that leverage hypergraphs and linked hypergraphs to process and enhance data, enabling contextual navigation and user engagement through anonymous profiles, while maintaining data integrity and user anonymity, using machine learning for dynamic analytics and network topology mapping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data processing methods are used for news content and user engagement, then implementation is straightforward, but accuracy and credibility of data processing deteriorate
Solution Approach 1:
The patent segments data processing into multiple hierarchical levels using hypergraphs, where different layers represent different aspects of data (e.g., entities, relationships, contexts). This segmentation allows complex data to be processed in manageable units while maintaining overall accuracy through the structured organization of hypergraph nodes and edges.
Solution Approach 2:
The patent introduces hypergraphs as a multi-dimensional data structure that extends traditional graph theory by allowing edges to connect multiple nodes simultaneously (hyperedges). This dimensional change enables more accurate representation of complex relationships in news content and user engagement data, improving measurement precision through additional contextual dimensions.
2Ease of operation
If user profiles are created for personalized engagement, then user engagement improves, but user anonymity deteriorates
Solution Approach 1:
The patent introduces anonymous user profiles as an intermediary representation that captures user engagement patterns and preferences without storing or exposing personally identifiable information. The hypergraph structure serves as a mediator between user interactions and personalized content delivery, maintaining engagement effectiveness while preserving anonymity through the abstraction of user data into contextual relationship patterns.
3Speed
If data is processed in real-time for news content, then timeliness improves, but processing accuracy deteriorates
Solution Approach 1:
The patent applies preliminary processing actions to incoming news data by immediately organizing it into hypergraph structures as data arrives. This preliminary organization establishes accurate contextual relationships from the outset, enabling real-time processing without sacrificing accuracy. The hypergraph framework pre-defines relationship patterns that can be rapidly populated and queried, maintaining both speed and precision.
Solution Approach 2:
The patent implements continuous data processing through the hypergraph structure, where data ingestion, processing, and engagement occur as an unbroken sequence. The hypergraph maintains persistent contextual relationships that allow real-time queries and updates without interrupting the processing flow, ensuring both timeliness and accuracy through continuous operational state.
4Loss of information
If complex relationships in data are captured, then contextual understanding improves, but system complexity deteriorates
Solution Approach 1:
The patent employs hypergraphs as a universal data structure that can represent multiple types of relationships and data types within a single unified framework. This multi-functionality allows the system to capture complex contextual relationships across different data domains (news content, user profiles, interactions) without requiring separate processing systems, thereby improving contextual understanding while managing system complexity through consolidation.
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. In some embodiments, the technologies include processes for maintaining anonymity. And, in some examples, the data is structured in a hypergraph system. In some examples, a method includes generating, by a computing system, an anonymous user profile for engaging anonymously with user interfaces provided by the computing system. The generating of the anonymous user profile includes retrieving, by the computing system, detailed user profile information associated with an anonymous individual user. The method also includes associating, by the computing system, the user profile information with non-personally identifiable information.