Contextual Attribute Vectorization for Real-Time Personalized Responses
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing communication systems lack the ability to effectively utilize contextual attributes from interactions to generate tailored and accurate responses in real-time, leading to suboptimal customer engagement and service provision.
Innovation Solution
A host platform utilizing large language models (LLMs) to identify and vectorize contextual attributes from customer interactions, enabling retrieval and generation of customized responses based on previous conversations, mood, and items of interest, and storing these in a vector database for enhanced real-time communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional communication systems are used, then system simplicity is maintained, but the ability to generate personalized responses based on contextual attributes is lost
Solution Approach 1:
The patent introduces a vector database as an intermediary component between the communication system and the response generation mechanism. This vector database stores contextual attributes in vector format, enabling the system to retrieve and utilize historical interaction data without directly complicating the core communication flow. The vector database acts as a mediator that transforms unstructured interaction data into structured, queryable representations.
Solution Approach 2:
The patent segments the communication system into distinct functional modules: interaction content reception, contextual attribute identification, vectorization processing, vector database storage, and response generation. This segmentation allows each component to perform its specific function independently, making the overall complex system manageable and maintainable while enabling personalized response capabilities.
2Measurement precision
If contextual attributes are identified and stored for every interaction, then response accuracy is improved, but data processing time and storage requirements increase
Solution Approach 1:
The patent applies preliminary action by vectorizing and storing contextual attributes immediately when interactions occur, rather than processing them in real-time when needed. The vector database pre-stores these transformed representations, so when a response is needed, the system can quickly retrieve pre-processed vector data without performing time-consuming analysis at the moment of interaction.
Solution Approach 2:
The patent replaces traditional text-based contextual analysis with vector-based representation. Instead of storing and searching through raw text interactions, the system transforms interaction data into numerical vector formats that enable efficient mathematical operations and similarity searches, significantly reducing processing time while maintaining or improving accuracy.
3Productivity
If vector databases and LLMs are implemented, then customer engagement is improved, but system cost and complexity increase
Solution Approach 1:
The patent makes the vector database serve multiple functions within the communication system: storing historical interaction data, enabling contextual attribute retrieval, supporting response generation, and facilitating pattern recognition. This multi-functionality reduces the need for separate specialized components, thereby managing complexity while delivering enhanced customer engagement through personalized responses.
Data Source
AI summary
A method that includes at least one of receiving interaction content from a communication session between a source device and a service provider device of a service provider, identifying a plurality of contextual attributes of the communication session based on execution of at least one large language models (LLMs) on the interaction content, converting the interaction content and the plurality of contextual attributes of the communication session into vectorized data based on execution of an additional LLM, labelling the vectorized data with identifiers of the plurality of contextual attributes, and storing the vectorized data within a vector database.