Message Thread Clustering via Affinity Groups
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Solution Overview
Problem
Existing message processing systems lack effective methods to group and cluster messages across different messaging techniques, leading to limited organization and retrieval options for users with large collections of messages.
Innovation Solution
A method and apparatus that compute thread signatures using affinity groups of message addresses to relate and cluster messages and threads, allowing for the creation of groups of related messages and threads based on probability analysis of message address occurrences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If messages are grouped using traditional methods (email threads or manual folders), then organization is achieved, but the system cannot effectively cluster messages across different messaging techniques
Solution Approach 1:
The affinity group module creates universal groups of message addresses that can cluster messages across different messaging techniques (email, instant messaging, social network messaging, cellular messages) using a common probability-based approach. This single system handles multiple message types without requiring separate clustering mechanisms for each type.
Solution Approach 2:
The system changes the parameter of message organization from traditional folder-based or thread-based grouping to probability-based affinity grouping. By computing probability values that indicate how likely two message addresses appear together, the system transforms the organization approach and enables cross-messaging-technique clustering.
2Measurement precision
If affinity groups are computed for all message addresses, then effective clustering is achieved, but processing time and computational resources increase
Solution Approach 1:
The system computes affinity groups selectively rather than for all possible message address pairs. The affinity group module processes messages and identifies meaningful groups based on probability thresholds, performing partial computation only where needed to achieve effective clustering without exhaustive processing of all message combinations.
Data Source
AI summary
A method and apparatus of a device that clusters threads of messages is described. In an exemplary method, the device receives multiple message threads, where each message thread includes one or more messages that are related to each message in that thread. For each of the message threads, the device computes a thread signature using affinity groups, where each affinity group is a group of messages that are related to each other. Furthermore, the device creates a group of related messages using the thread signatures.