Conversation Summary Generation Using Intent Embeddings
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Solution Overview
Problem
Current techniques for identifying meaningful information and/or phrases in transcript data from call centers consume significant computing resources and fail to generate effective conversation summaries, leading to inefficiencies in understanding customer journeys and needs.
Innovation Solution
A transformation system using a language transformation model preprocesses text data, converts it into embeddings, and processes these with an encoder and summary generation model to generate conversation summaries, conserving resources and improving understanding of customer interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manual identification of meaningful information in transcript data is performed, then understanding of customer journeys and needs is improved, but loss of time and productivity deteriorate due to the sheer size of transcript data
Solution Approach 1:
The patent replaces manual mechanical identification of meaningful information with an automated natural language processing system. The system uses machine learning models to automatically identify key information, entities, and relationships in transcript data, eliminating the need for manual review while maintaining or improving information extraction quality.
Solution Approach 2:
The patent introduces an intermediary automated processing system between the raw transcript data and the final understanding of customer journeys. This intermediary system processes large volumes of data through structured pipelines including entity recognition, relationship extraction, and summarization modules, enabling efficient information retrieval without direct manual intervention.
2Loss of information
If current techniques are used to identify meaningful information in transcript data, then information extraction is performed, but computing resources are consumed significantly
Solution Approach 1:
The patent segments the transcript data processing into distinct modular components: preprocessing stage, entity recognition stage, relationship extraction stage, and summarization stage. Each segment processes specific aspects of the data independently, allowing for optimized resource allocation and avoiding redundant computations across the entire dataset.
Solution Approach 2:
The patent applies partial action by focusing computational resources on extracting only the most relevant meaningful information rather than processing every detail of the transcript data. The system identifies and extracts key entities and relationships while filtering out less important information, reducing overall computing resource requirements.
3Loss of information
If manual navigation of large volumes of transcript data is performed, then comprehensive understanding is achieved, but productivity deteriorates
Solution Approach 1:
The patent replaces manual navigation and analysis of transcript data with an automated systematic processing system. The system comprehensively analyzes all transcript data through structured pipelines including entity recognition, relationship mapping, and pattern identification, achieving comprehensive understanding at machine speed rather than human speed.
Solution Approach 2:
The patent performs preliminary processing actions on transcript data before detailed analysis, including preprocessing steps like tokenization, normalization, and initial filtering. This preliminary action prepares the data for more efficient subsequent processing and enables faster comprehensive analysis by reducing the complexity of the raw data early in the pipeline.
Data Source
AI summary
A device may receive text data associated with a chatbot, a live chat, or an interactive voice response system, and may preprocess the text data with one or more preprocessing techniques to generate preprocessed data and key intents. The device may convert the preprocessed data and the key intents into embeddings, and may combine the embeddings into an input vector. The device may process the input vector, with a language model, to identify relationships between words and phrases of the text data, and may process the input vector and the relationships, with a summary generation model, to generate a summary of the text data. The device may perform one or more actions based on the summary of the text data.