Query Ranking Using Profile and Feedback Credibility Signals
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Solution Overview
Problem
Current methods for surfacing relevant online content fail to provide credible and relevant information quickly, especially for nuanced queries, due to reliance on SEO and social networking systems that can be gamed and do not align with individual user values or lifestyle.
Innovation Solution
A software system that integrates user profile data, query text, and communication threads to index and rank responses, utilizing feedback and verification to ensure relevance and credibility, enabling efficient and personalized matching of user queries with multiple sources of information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If SEO and social networking systems are used to surface relevant content, then web pages can be indexed and matched to user queries, but the system fails to provide credible and relevant information quickly for nuanced queries and can be gamed
Solution Approach 1:
The patent implements feedback mechanisms where user interactions, ratings, and engagement metrics are continuously collected and used to refine the ranking algorithm. This creates a closed-loop system that learns from user behavior to improve both speed and credibility of information delivery over time.
Solution Approach 2:
The system dynamically adjusts ranking parameters based on query complexity, user profile, and context. Instead of static SEO rankings, the system modifies weighting factors in real-time to prioritize credible sources for nuanced queries while maintaining speed through optimized parameter selection.
2Adaptability or versatility
If social networking systems crowdsource ranking to users, then popularity contests emerge, but this does not quickly reveal the most relevant and credible answers for specific users
Solution Approach 1:
The system pre-processes and indexes user profiles, content metadata, and credibility indicators before queries are submitted. This preliminary organization allows the ranking algorithm to quickly retrieve and evaluate relevant information without time-consuming real-time analysis, enabling both personalization and speed.
Solution Approach 2:
The patent introduces an intermediary ranking algorithm that mediates between user preferences and content relevance. This intermediary layer translates user profiles into personalized ranking weights, avoiding direct user-to-user voting while still achieving personalized results quickly through automated computation.
3Reliability
If multiple data sources are integrated into one database, then information becomes standardized and actionable, but the system complexity increases
Solution Approach 1:
The patent segments the integrated database into modular data layers (user profiles, content repositories, credibility metrics, interaction histories) that can be independently managed and queried. This segmentation reduces integration complexity while maintaining standardized information flow across all layers through defined interfaces.
Data Source
AI summary
The proposed software takes input from numerous sources to provide as much credible and relevant information as possible in a short period of time for a given user making an inquiry. Inputs include profile data from the user via direct data input or API(s) (such as geographical, lifestyle and hobby, age, interests, and preferences), query text data on the inquiry, other user profile information, and stored communication threads between users. These 4 pieces of data are used together to index through, and rank query responses that match a user's profile, in addition to identifying and matching other users who qualify to respond to the inquiry for further communication stream. This form of SEO brings a plurality of data sources and information to one database (or a plurality of related databases) such that the information is standardized and actionable resulting in an efficient and desirable model.