Clustered Search Processing Using Contextual Relevance Scoring
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Solution Overview
Problem
Current search systems fail to provide relevant search results due to their reliance on textual similarity, which does not account for user-specific information, leading to irrelevant results and inefficient search processes, especially on mobile devices.
Innovation Solution
The system groups search results into clusters based on conceptual relevance, using context-specific data sources and user-specific data such as location and behavior to rank and modify cluster scores, providing more intuitive and relevant search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If search results are grouped based on textual similarity, then grouping is achieved, but user relevance is not improved because user-specific information is not considered
Solution Approach 1:
The patent segments search results into multiple clusters based on different dimensions (textual similarity, user preferences, behavior patterns, location). Each cluster represents a different aspect of relevance, allowing the system to present organized results that adapt to user needs without overwhelming complexity in any single dimension.
Solution Approach 2:
The patent adds new dimensions to the traditional textual similarity grouping by incorporating user-specific dimensions such as location, behavior patterns, and preferences. This transforms the search result organization from a single-dimensional textual approach to a multi-dimensional framework that better captures user relevance.
2Loss of information
If multiple search queries are performed to obtain relevant results, then search completeness is improved, but search time and user effort increase
Solution Approach 1:
The patent performs preliminary clustering and organization of search results using multiple dimensions (textual similarity, user location, behavior patterns) in a single processing step. This preliminary multi-dimensional organization eliminates the need for users to perform multiple sequential search queries, as relevant results are pre-organized and presented together in the initial search results page.
3Ease of operation
If predefined groups of data types are used for search results, then organization is achieved, but relevance to user context is lost
Solution Approach 1:
The patent makes the search result clustering dynamic by incorporating real-time user context information such as current location, recent behavior patterns, and preference data. Instead of static predefined groups, the clusters are dynamically generated and adjusted based on the specific user's context, making the organization both easy to navigate and highly relevant to the user's situation.
Data Source
AI summary
Methods and apparatus for searching data and grouping search results into clusters that are ordered according to search relevance. Each cluster comprises one or more data type, such as images, web pages, local information, news, advertisements, and the like. In one embodiment, a search term is evaluated for related concepts indicating categories of data sources to search. Data sources may also be identified by context information such as a location of a client device, a currently running application, and the like. Search results in each cluster are ordered by relevance and each cluster is given a score based on an aggregate of the relevance within the cluster. Each cluster score may be modified based on one or more corresponding concepts and/or context information. The clusters are ordered based on the modified scores. Content, including advertisements, may also be added to the ordered list to appear as another cluster.