Message Clustering by Contact List Threshold
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Users face challenges in efficiently locating important or relevant email messages due to the lack of automated clustering by topic, leading to wasted time and resources in manually organizing messages.
Innovation Solution
A computer-implemented method for clustering messages based on contact lists, where messages associated with a threshold number of contacts are grouped together, allowing for automatic detection and indication of subsets of messages related to specific contact lists.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually create labels or folders to organize messages, then message organization is improved, but time and resources are wasted
Solution Approach 1:
The system automatically clusters messages by detecting contact lists and analyzing communication patterns without requiring user intervention. The algorithm autonomously identifies groups of contacts who communicate together and organizes messages into clusters based on these detected contact lists, eliminating the need for manual label assignment while maintaining effective organization
Solution Approach 2:
The system changes the organizational parameter from manual user-defined labels to algorithmically detected contact list associations. By analyzing communication patterns and contact relationships, the system dynamically determines message clustering based on actual usage patterns rather than static manual categorization
2Productivity
If messages are not clustered or arranged by topic, then storage simplicity is maintained, but time to locate important messages increases
Solution Approach 1:
The system performs preliminary clustering of messages by detecting contact lists and organizing messages into topic-based groups before the user needs to search for them. This advance organization based on communication patterns enables rapid message retrieval without requiring users to manually categorize messages as they arrive
3Loss of time
If automated message clustering is implemented, then time to locate messages is reduced, but system complexity increases
Solution Approach 1:
The system introduces an intermediary algorithm that acts as a mediator between raw message data and user needs. This algorithm detects contact lists by analyzing communication patterns and automatically clusters messages without requiring complex user interfaces or manual configuration, simplifying the overall system while achieving automated organization
Data Source
AI summary
Systems and methods for clustering messages are provided. In some aspects, a method includes receiving a set of communications. The set of communications includes a set of messages. Each communication in at least a portion of communications in the set of communications is associated with a set of contacts. The method also includes determining a first contact list based on the set of communications. The method also includes determining a first subset of the set of messages, each message in the first subset of the set of messages being associated with a set of contacts including at least a first threshold number of contacts in the first contact list. The method also includes providing an indication of the first subset of the set of messages.