LLM Virtual Assistant Task Segmentation and Sequencing
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Solution Overview
Problem
Existing virtual assistants lack the capability to efficiently break down complex user tasks into manageable sub-tasks and execute them using available service functionalities, leading to limitations in handling intricate user requests.
Innovation Solution
The implementation of a Large Language Model (LLM)-enhanced virtual assistant system that uses a task coordinator function to identify sub-tasks, determine their order, and interface with appropriate service functionalities to complete user tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing virtual assistants handle tasks without breaking them down into sub-tasks, then the system structure remains simple, but the capability to handle complex user requests is limited
Solution Approach 1:
The patent applies segmentation by dividing complex user tasks into smaller, manageable sub-tasks. The task coordinator module receives a complex user request and segments it into multiple sub-tasks that can be handled by different service functionalities. This allows the system to handle complex requests while maintaining a modular and manageable structure.
Solution Approach 2:
The task coordinator performs preliminary action by analyzing the user request before execution and determining the sequence of sub-tasks in advance. It identifies which sub-tasks need to be performed and in what order, preparing the execution plan before actually executing the tasks. This preliminary planning enables the system to handle complex requests systematically.
2Productivity
If virtual assistants execute tasks without systematic decomposition, then the execution process is simple, but the accuracy and efficiency of completing complex requests decreases
Solution Approach 1:
The task coordinator segments the complex task into multiple sub-tasks, each of which can be executed independently or in parallel. This segmentation increases productivity by allowing more efficient task execution while the modular nature keeps the process manageable.
Solution Approach 2:
The system incorporates feedback mechanisms where the task coordinator monitors the execution of sub-tasks and adjusts the execution sequence or parameters based on the results. This feedback loop improves the accuracy and efficiency of completing complex requests by allowing dynamic adaptation during execution.
3Reliability
If the virtual assistant uses a task coordinator to identify and sequence sub-tasks, then the handling of complex requests improves, but the processing time increases
Solution Approach 1:
The task coordinator performs preliminary analysis and sequencing of sub-tasks before execution begins. By determining the optimal sequence and preparing the execution plan in advance, the system reduces unnecessary delays during actual task execution, thereby minimizing overall processing time while maintaining high reliability.
Solution Approach 2:
The system dynamically adjusts the task execution sequence and allocation based on real-time conditions. The task coordinator can reprioritize sub-tasks or change execution parameters during runtime, which optimizes the balance between accuracy and processing time by adapting to changing conditions without fixed rigid scheduling.
Data Source
AI summary
One example method includes receiving, by a virtual assistant, a task from a remote client device; transmitting a first prompt to a large language model (“LLM”), the first prompt comprising the task and a request to process the task; receiving, from the LLM in response to the first prompt, a plurality of sub-tasks and an ordering of the plurality of sub-tasks; obtaining descriptions of a plurality of available services; for each sub-task: transmitting a second prompt to the LLM, the second prompt comprising a description of the respective sub-task and the descriptions of the available services; receiving, from the LLM in response to the respective second prompt, an identification of one or more available services; and initiating, for the respective sub-task, the one or more identified available services; and after completion of the plurality of sub-tasks, generating and providing a response to the task to the remote client device.