Parallelized Attention Heads for Personalized Conversational Responses
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Solution Overview
Problem
Existing communication systems, such as contact centers and chatbots, struggle to provide personalized and contextually accurate responses due to the lack of effective methods for analyzing and utilizing previous interactions to inform real-time conversations.
Innovation Solution
A host platform utilizing large language models (LLMs) to extract contextual attributes from conversations, convert them into vectorized data, and store them in a database for retrieval and analysis, enabling generation of personalized responses based on previous interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional communication systems are used to provide customer information, then basic communication functions are maintained, but the system cannot provide personalized and contextually accurate responses due to lack of effective analysis of previous interactions
Solution Approach 1:
The system segments the attention mechanism into multiple independent attention heads, each capable of analyzing different aspects of interaction context separately. This allows the system to process various contextual attributes (emotional state, intent, topic) in parallel, improving contextual accuracy while managing complexity through modular design
Solution Approach 2:
The patent transforms the analysis from traditional sequential processing to a multi-dimensional parallel processing approach using multiple attention heads that simultaneously evaluate different contextual dimensions. This enables comprehensive contextual analysis without proportionally increasing system complexity
2Reliability
If multiple LLMs are executed to analyze interaction content and generate responses, then response personalization and accuracy are improved, but processing time and computational resources increase
Solution Approach 1:
The system divides the response generation task into multiple parallel attention heads that simultaneously process different aspects of the interaction. Each attention head operates independently but contributes to the overall response, enabling accurate multi-faceted analysis without sequential processing delays
Solution Approach 2:
The parallelized attention heads operate continuously and simultaneously throughout the processing pipeline, maintaining continuous useful action rather than switching between multiple LLMs sequentially. This ensures that all contextual aspects are analyzed concurrently, reducing total processing time while maintaining comprehensive analysis
3Adaptability or versatility
If parallelized attention head architecture is implemented to simultaneously identify multiple contextual attributes, then response personalization is enhanced, but computational complexity increases
Solution Approach 1:
The attention mechanism is segmented into multiple specialized attention heads, each focusing on specific contextual attributes. This segmentation allows the system to achieve high adaptability and personalization by assigning different expertise to each head, while the modular structure helps manage computational complexity through organized parallel processing
Solution Approach 2:
The parallelized attention heads share a common architectural foundation and can be applied universally across different interaction contexts. This multi-functionality allows the system to achieve high adaptability for various personalization scenarios without proportionally increasing overall system complexity
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, executing a large language model (LLM) on the interaction content, wherein the LLM comprises a plurality of attention heads which are configured to simultaneously identify a mood and an item of interest from the interaction content, generating a response to the interaction content based on the mood and the item of interest, and outputting the response to at least one of the source device and the service provider device during the communication session.