Needs-Matching Navigator for Neutral Wellbeing Search
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Solution Overview
Problem
Existing search engines and social networking systems fail to provide unbiased, life-beneficial answers and solutions that match individual needs and circumstances, often prioritizing commercial interests or user profiles over user wellbeing, and lack scalability and effective needs-matching capabilities.
Innovation Solution
The Needs-Matching Navigator System (MNS) facilitates a social network that integrates ICT to help users define and match their needs with the best available solutions, optimizing for user wellbeing through trust-based, unbiased information provision and scalable, life-beneficial solutions, using algorithms and protocols to navigate knowledge networks and social networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If search engines prioritize commercial interests and user profiles, then advertising revenue and user targeting are improved, but answer objectivity and user wellbeing are worsened
Solution Approach 1:
The system segments the information retrieval process into distinct components: a needs assessment module that evaluates user wellbeing requirements, a search module that retrieves information, and a filtering module that applies objective criteria. This segmentation allows the system to maintain answer objectivity while still delivering relevant results, resolving the contradiction between commercial interests and answer reliability.
Solution Approach 2:
The patent introduces an intermediary needs-matching mechanism that stands between user queries and search results. This intermediary assesses whether information serves user wellbeing rather than merely advancing commercial interests, thereby maintaining answer objectivity while still enabling effective information delivery.
2Adaptability or versatility
If search engines use predetermined preference orientations and profile presumptions, then query result personalization is improved, but answer neutrality and user benefit are worsened
Solution Approach 1:
The system dynamically adjusts between personalization and neutrality based on the specific query and user needs assessment. Rather than applying fixed profile presumptions, the system adapts its personalization level to match the actual wellbeing requirements identified in each query, maintaining answer neutrality while delivering relevant results.
Solution Approach 2:
The patent changes the parameters used for result ranking from commercial and profile-based metrics to wellbeing-oriented parameters. By altering the evaluation criteria from advertising-relevant factors to user benefit factors, the system achieves both personalization and neutrality simultaneously.
3Device complexity
If systems focus on single-variable optimization, then mathematical simplicity is improved, but life-beneficial outcome accuracy is worsened
Solution Approach 1:
The system segments complex multi-variable wellbeing problems into manageable sub-problems that can be addressed individually. Each sub-problem is optimized using appropriate mathematical methods, then the results are integrated to achieve accurate life-beneficial outcomes without requiring overly complex overall mathematical models.
Solution Approach 2:
Rather than attempting to optimize all variables simultaneously, the system applies partial optimization to the most critical wellbeing factors first, then progressively addresses additional variables. This approach achieves sufficient accuracy for life-beneficial outcomes without the mathematical complexity of full multi-variable optimization.
Data Source
AI summary
A needs-matching navigator system and social network facilitator appurtenances including, for a large user plurality, software driven modules residing on electronic communications enabled platforms and devices. Beyond altruistically enhancing flourishing life horizons and life quality metrics, the modules facilitate (A) knowing respective user bias, profile, perspective, wellbeing orientation, and privacy preference; (B) understanding user needs description and wellbeing criteria; (C) finding answer and solutions to the needs by user biased projecting the description onto electronically stored knowledge-bases; (D) matching the user to the answers and solutions; and preferably (E) creating an instant electronic communications interactive community for the respective user, by inverse projecting large subsets of the answers and solutions back onto the large plurality of users; according to said users' profiles and needs descriptions. This navigable community may be classified into spontaneous castes; having various degrees of relevant understanding, expertise, experience, and/or curiosity about these answer and/or solution projections.