Graph-Based Conversational Structure Analysis
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Solution Overview
Problem
Current methods for analyzing conversational speech face challenges in capturing conversational structure due to variations in speech patterns, mid-conversation context switches, domain-specific vocabulary, and cultural differences, making it difficult to train effective machine learning models for insights from conversations.
Innovation Solution
A system that generates graph models from speech words in real-time, without requiring supervised machine learning, by converting speech to text and creating nodes and edges representing word relationships, allowing for the identification of key phrases and topics, and determining context switches to analyze conversational structure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are trained to analyze conversational speech, then insights from conversations can be obtained, but the training becomes prohibitively expensive and impossible due to variations in speech patterns, context switches, domain-specific vocabulary, and cultural differences
Solution Approach 1:
The patent introduces an intermediary representation layer between raw speech and analysis results. Speech is first converted to text transcripts, then processed through sentence embedding models that create standardized vector representations. This intermediary embedding space acts as a mediator that transforms diverse, unstructured speech variations into a consistent format that can be analyzed without requiring complex trained models for each specific variation.
Solution Approach 2:
The patent replaces the traditional machine learning training mechanism with a combination of speech-to-text conversion and pre-trained sentence embedding models. Instead of training custom models on diverse conversational data, the system substitutes this with off-the-shelf NLP models that have already been trained on large corpora, eliminating the need for organization-specific model training while still enabling conversational structure analysis.
2Productivity
If traditional speech analysis methods are used, then conversational structure can be captured, but extensive model training is required which is expensive and time-consuming
Solution Approach 1:
The patent leverages pre-trained sentence embedding models that have already performed the computationally intensive training phase on large language corpora before deployment. By using these pre-trained models, the system eliminates the need for organizations to perform time-consuming model training themselves, while still benefiting from models that have learned robust linguistic patterns and relationships.
Solution Approach 2:
The patent uses sentence embedding models that have been trained on extensive public datasets, effectively copying the knowledge and patterns learned from those large corpora. This allows the system to leverage pre-acquired linguistic knowledge without replicating the training process, significantly reducing time and resource requirements while maintaining analysis quality.
Data Source
AI summary
Embodiments are directed to organizing conversations. Words may be provided from a conversation stream. Each word may be mapped to a graph model based on characteristics of each word. The graph model may be partitioned based on one or more attributes of a nodes and edges included in the graph model such that nodes associated with relationship strength that exceeds a threshold value may be grouped together. Sentence models may be generated based on sentences included in the conversation stream. Combined models may be generated based on the sentence models and the graph such that each sentence model may be associated with one or more partitions of the graph model. A conversation digest may be generated based on the combined model such that the conversation digest identifies one or more dominant portions of the conversation that include key subject matter.