Social Contact Grouping via Interaction Pattern Analysis

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Solution Overview

Problem

Existing social networking tools require manual definition of social circles, which is time-consuming, and often recommend limited contacts based on organizational affiliations, failing to include interested contacts outside the user's organization and lacking in accuracy.

Innovation Solution

An algorithm that analyzes user interaction patterns to identify co-occurrences of social contacts, clustering them into distinct social groups that can be used for subsequent interactions across various platforms, allowing for context-based tagging and fine-tuning.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If manual definition of social circles is used, then social contacts can be organized, but it is time-consuming and intensive

Engineering Contradiction:

Improvesocial contact organizationVSAvoidtime for defining social circles

Core Design Contradiction:

Ease of operationVSLoss of time

Solution Approach 1:

The system automatically analyzes user communication patterns and autonomously identifies social groups without requiring manual user input. The algorithm processes communication data, detects co-occurrence patterns of contacts, and generates social group assignments automatically, allowing the system to serve itself in organizing social contacts.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system performs preliminary analysis of communication patterns and pre-identifies potential social groups before the user needs to interact with them. By analyzing historical communication data in advance and preparing social group assignments, the system eliminates the need for manual organization when the user actually needs to use the contacts.

Inventive Principle:

Principle #10Preliminary action

2Adaptability or versatility

If organizational affiliation-based recommendations are used, then contacts within the organization are suggested, but contacts outside the organization are excluded

Engineering Contradiction:

Improvecontact recommendation scopeVSAvoidaccuracy of suggested contacts

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The system replaces the mechanical rule-based organizational affiliation filtering with an intelligent algorithm that analyzes actual communication patterns. Instead of mechanically suggesting only contacts from the same organization, the algorithm detects which contacts the user actually interacts with frequently, regardless of organizational boundaries, and recommends those based on empirical communication behavior.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Quantity of substance

If narrow organizational contact lists are provided, then contacts from the same organization are included, but the list is limited and may include unwanted contacts

Engineering Contradiction:

Improvenumber of suggested contactsVSAvoiduser interest in suggested contacts

Core Design Contradiction:

Quantity of substanceVSEase of operation

Solution Approach 1:

The system uses communication pattern data as feedback to continuously refine contact recommendations. By monitoring which contacts the user actually communicates with and how frequently, the algorithm adjusts its recommendations to match user preferences and behavior, ensuring that suggested contacts are both numerous and relevant to user interests.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS9430755B2System and method to enable communication group identification

Publication Date: 2016.08.30 VERIZON PATENT & LICENSING INC

AI summary

Methods, system and computer readable medium for discovering social groups include extracting activity related data associated with a user's social interactions from a source. The activity related data identifies information related to social contacts used during the social interactions at the source. The activity related data of the user is analyzed to identify co-occurrences of the social contacts. The co-occurrences determine a set of related contacts. The set of related contacts identified from the activity related data are clustered into distinct social groups. The social groups are used by a user to facilitate subsequent interactions.