Knowledge Currency Search System for Reliable Consumer Results
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Solution Overview
Problem
Current digital search systems, including those using AI digital assistants, often return biased, unreliable, and irrelevant results due to their focus on brand advertisers and content producers' marketing, failing to prioritize consumer needs and provide personalized, reliable knowledge effectively.
Innovation Solution
The implementation of a multi-stage classification process and Knowledge Currency Score (KCS) system that integrates user feedback, community input, and expert opinions to refine and personalize search results, utilizing a semantic knowledge graph database and AI-driven Natural Language Processing to enhance relevance and trustworthiness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If digital search systems use AI digital assistants to search for information, then the search capability is enhanced, but the results are biased and unreliable due to focus on brand advertisers and content producers' marketing
Solution Approach 1:
The patent segments the search system into multiple independent components: a knowledge graph database storing structured facts and relationships, a multi-stage classification process with separate expert and community review streams, and a result generation module. This segmentation allows each component to be optimized independently, with the knowledge graph providing reliable structured data separate from the marketing-oriented content delivery web.
Solution Approach 2:
The patent introduces a knowledge graph as an intermediary layer between users and information sources. This knowledge graph acts as a mediator that stores verified facts and relationships, filtering out biased marketing content while preserving reliable knowledge. The system queries this intermediary knowledge structure rather than directly scraping content from advertisers and content producers.
2Quantity of substance
If digital search systems return more comprehensive results, then the quantity of information is increased, but the relevance and trustworthiness for consumer needs decreases
Solution Approach 1:
The patent performs preliminary classification and verification of information before it reaches the user. The multi-stage classification process pre-sorts content through expert review and community feedback mechanisms, tagging and organizing information by reliability and relevance criteria before query time. This preliminary action ensures that when results are returned, they are already filtered for consumer needs rather than requiring post-processing by the user.
Solution Approach 2:
The patent changes the parameters by which information is organized and retrieved. Instead of organizing by popularity or marketing spend, the system uses parameters such as expert-verified accuracy, community feedback scores, and direct relevance to consumer needs. These parameter changes transform the search results from marketing-oriented to consumer-oriented while maintaining comprehensive coverage.
3Ease of manufacture
If digital search systems prioritize brand advertisers and content producers, then the revenue model is sustained, but the consumer needs and personalized knowledge are not prioritized
Solution Approach 1:
The patent creates a universal knowledge graph that serves multiple functions simultaneously: it provides reliable information to consumers, offers verified data to advertisers for targeted marketing, and maintains a sustainable revenue model. This multi-functional knowledge structure allows the system to prioritize consumer needs while still generating revenue through various legitimate channels including advertising, subscriptions, and enterprise solutions.
Data Source
AI summary
An optimized, human-centered personalized search service where content is fully classified by community interests, experts by just in time learning, problem solving and digital assistants. This Knowledge Currency method has the capacity to acquire, organize, store, rank, and filter knowledge about facts and relationships. The knowledge refinery process is broken down into stages that can be parallel workflow leveraging a multi-step content topic extraction and refinery process for personalized searches with domain knowledge experts to connect collaboratively as well as knowledge reliability ranking score for element nodes on a knowledge graph.