LLM Response Generation Using Vectorized Conversation Context
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Solution Overview
Problem
Existing communication systems lack the ability to effectively utilize previous interaction content and contextual attributes to generate tailored and accurate responses during live communication sessions.
Innovation Solution
A host platform utilizing large language models (LLMs) to analyze interaction content, identify contextual attributes, and generate vectorized representations of conversations, which are stored in a vector database for retrieval and use in generating customized responses during live sessions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional communication systems are used without LLMs and vector databases, then system complexity is low, but response accuracy and customization capability are insufficient
Solution Approach 1:
The system performs preliminary actions by storing and vectorizing previous interaction content before live sessions occur. Historical conversations are pre-processed, converted to vector representations, and stored in a vector database, enabling the LLM to quickly retrieve and utilize relevant context during live communications without real-time processing delays
Solution Approach 2:
The patent introduces an intermediary layer consisting of the vector database and LLM framework that bridges traditional communication systems and intelligent response generation. This intermediary captures, stores, and retrieves contextual information, allowing the system to maintain low complexity at the communication interface while achieving high response accuracy through the intermediary's processing capabilities
2Loss of information
If previous interaction content is not utilized, then processing speed is fast, but response relevance and customer understanding are poor
Solution Approach 1:
The system performs preliminary vectorization and storage of interaction content as it occurs during initial sessions. By pre-processing and storing conversations in vector format with contextual attributes during the interaction itself, the system eliminates the need for time-consuming post-processing while ensuring complete context retention is available for future sessions
Solution Approach 2:
The patent creates vectorized copies of interaction content that preserve the essential contextual information in a compact, efficiently queryable format. These vector representations serve as lightweight copies that can be rapidly searched and retrieved without requiring access to the full original conversation data, reducing processing time while maintaining context retention
3Ease of operation
If generic responses are used without contextual analysis, then system operation is simple, but response quality and customer satisfaction are low
Solution Approach 1:
The system implements self-service by enabling the LLM to autonomously analyze contextual attributes, retrieve relevant historical interactions, and generate customized responses without requiring manual configuration or intervention. The automated contextual analysis and response generation maintain operational simplicity while dramatically improving response quality through AI-driven personalization
Solution Approach 2:
The patent dynamically changes response parameters based on analyzed contextual attributes such as customer mood, conversation topic, and historical interaction patterns. By adjusting response tone, content, and style according to these parameters, the system maintains simple operation through automated parameter adjustment while achieving high response quality tailored to each specific interaction context
Data Source
AI summary
An example operation may include one or more of receiving interaction content from a communication session between a source device and a service provider device of a service provider, identifying a search criteria from the interaction content, retrieving a subset of vectors from a plurality of vectors stored in a vector database based on the search criteria of the interaction content, wherein the subset of vectors includes previous interaction content with the service provider, generating a response for the communication session based on execution of a large language model (LLM) on the subset of vectors, and outputting the response to at least one of the source device and the service provider device during the communication session. The example operation may further include an AI agent that performs an action based on the response.