Attribute Clustering for Social Network Skill Identification
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Solution Overview
Problem
Current social networking systems lack effective methods to identify and present data sets associated with core attributes within attribute clusters, making it difficult for users to quickly determine important attributes and provide targeted instructional content for team or individual development.
Innovation Solution
A content server system analyzes a social network to identify and present data sets associated with core attributes by generating attribute clusters, determining relative strengths of attributes, and recommending instructional content based on attribute weaknesses, using a combination of user interaction data and organizational structure analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the system presents all available attributes to users, then users have complete information, but users cannot quickly identify important attributes due to information overload
Solution Approach 1:
The patent segments attributes into hierarchical clusters (e.g., core attributes, secondary attributes) and organizes them by importance. The system divides the large set of attributes into manageable groups, allowing users to focus on core attributes first while maintaining access to complete information through the structured hierarchy.
Solution Approach 2:
The patent applies local quality by providing different levels of detail and organization for different parts of the attribute system. Core attributes receive prominent display and detailed analysis, while less important attributes are organized in secondary clusters. This allows the system to maintain information completeness while guiding user attention to important areas through differentiated presentation.
2Loss of information
If the system provides comprehensive attribute analysis, then users gain deep insights, but the system complexity increases making it harder to implement
Solution Approach 1:
The patent segments the complex analysis task into modular components: attribute extraction, clustering algorithms, strength calculation, and recommendation generation. Each module handles a specific aspect of the analysis independently, making the overall system more manageable while providing comprehensive analysis capabilities through the integration of these modular components.
Solution Approach 2:
The patent introduces intermediary structures such as attribute clusters and strength metrics that mediate between raw attribute data and final recommendations. These intermediaries organize and process information in structured ways, reducing the complexity of direct analysis while maintaining comprehensive insights through the multi-layered processing architecture.
3Measurement precision
If the system analyzes all user data to determine attribute strengths, then the analysis is comprehensive, but the processing time increases
Solution Approach 1:
The patent performs preliminary actions by pre-defining attribute clusters and organizing attributes into hierarchical structures before analysis. This pre-organization allows the system to focus computational resources on calculating strengths within predefined groups rather than analyzing all attributes simultaneously, maintaining precision while reducing overall processing time through structured preparation.
Solution Approach 2:
The patent implements partial action by calculating attribute strengths for core clusters first, then progressively analyzing secondary clusters based on user needs. The system provides comprehensive analysis capability but executes it in prioritized stages, delivering timely results for important attributes while maintaining the option to expand analysis depth when time permits.
Data Source
AI summary
Systems, devices, media, and methods are presented to identify a set of attributes, generate attribute clusters, and select data sets corresponding to relative values of the attributes within an attribute cluster. The systems and methods receive a first identifier associated with a first member profile of a first member, identifying a set of second members with second member profiles associated with the first member profile, generating a cluster for the first member profile and one or more of the second member profiles. The systems and methods generate a cluster representation with a set of cluster attributes, identify a plurality of data sets associated with a set of core attributes of the cluster attributes, and cause display of an identification of at least one data set.