Context-Aware Thread Detection Using ML Feature Vectors
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Solution Overview
Problem
Existing technologies fail to accurately and efficiently disentangle conversation threads in multi-user communication sessions, as they lack consideration of contextual information, leading to intermingled topics and manual disentanglement requirements.
Innovation Solution
A computer system generates feature vectors for messages, incorporating contextual information, and uses machine learning models to classify messages into threads, with a beam search mechanism for self-correction and resource optimization, leveraging long short-term memory models to improve classification accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual disentanglement of messages into threads is performed, then conversation threads can be separated, but user time and effort are consumed
Solution Approach 1:
The system automatically performs thread disentanglement by analyzing message content, context, and metadata without requiring user intervention. The classification algorithm self-corrects and refines thread assignments based on learned patterns, making the system self-sufficient for the task of separating conversations.
Solution Approach 2:
The patent replaces the mechanical manual process of dragging and dropping messages into threads with an automated machine learning-based classification system. The system uses natural language processing and contextual analysis to automatically assign messages to appropriate threads, substituting human cognitive effort with computational algorithms.
2Productivity
If automated thread detection is implemented without contextual information, then processing is faster, but classification accuracy decreases
Solution Approach 1:
The system performs preliminary processing of message data by pre-computing embeddings for messages, users, and threads. This preliminary action prepares the data in advance for faster and more accurate classification during actual thread detection, allowing the system to leverage pre-analyzed contextual information without slowing down real-time processing.
Solution Approach 2:
The patent transforms the classification problem from traditional text-based analysis to a multi-dimensional vector space using embeddings. By representing messages, users, and threads as vectors in a high-dimensional space, the system can efficiently compute similarities and relationships across multiple dimensions (content, sender, recipient, timing) simultaneously, achieving both speed and accuracy.
3Reliability
If all messages are classified into all possible threads, then no messages are missed, but computing resources are excessively consumed
Solution Approach 1:
The system segments the classification task by first identifying a small set of candidate threads for each message based on preliminary filtering criteria. Instead of comparing every message against all possible threads, the system divides the problem into stages: candidate identification, detailed classification, and verification. This segmentation dramatically reduces computational complexity while maintaining completeness.
Solution Approach 2:
The patent applies partial action by classifying messages into only the most relevant threads rather than forcing a choice among all possible threads. The system allows messages to be associated with multiple threads when appropriate, performing just enough classification work to achieve accurate results without exhaustive analysis of every possible thread assignment.
4Use of energy by moving object
If beam search with pruning is used, then computing resources are optimized, but some threads may be excluded
Solution Approach 1:
The system performs preliminary classification to identify high-probability thread assignments before applying beam search pruning. By pre-computing likelihood scores and identifying the most probable threads in advance, the system can confidently prune low-probability options without risking the exclusion of correct classifications. This preliminary action creates a foundation for safe optimization.
Solution Approach 2:
The patent incorporates feedback mechanisms where the beam search process continuously evaluates and refines thread assignments based on accumulated evidence from multiple messages. The system uses feedback from classification confidence scores and contextual consistency to adjust pruning thresholds dynamically, ensuring that potentially correct threads are retained while still achieving resource optimization.
Data Source
AI summary
A computer system identifies threads in a communication session. A feature vector is generated for a message in a communication session, wherein the feature vector includes elements for features and contextual information of the message. The message feature vector and feature vectors for a plurality of threads are processed using machine learning models each associated with a corresponding thread to determine a set of probability values for classifying the message into at least one thread, wherein the threads include one or more pre-existing threads and a new thread. A classification of the message into at least one of the threads is indicated based on the set of probability values. Classification of one or more prior messages is adjusted based on the message's classification. Embodiments of the present invention further include a method and program product for identifying threads in a communication session in substantially the same manner described above.