LLM Home Assistant Hierarchical Multi-Agent Structure
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing smart home solutions struggle to design user-specific and context-aware reasoning algorithms that fit individual user preferences and living patterns, requiring large efforts for model training or rule creation.
Innovation Solution
A Large Language Model (LLM)-based home assistant system that uses a hierarchical multi-LLM-agent structure to generate user-specific suggestions or actions based on personal activity data and user queries, without the need for re-training or fine-tuning the LLM model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional neural networks or manually created rules are used for reasoning algorithms, then the system can operate with existing models, but it requires large efforts for model training or rule creation to fit individual user preferences and living patterns
Solution Approach 1:
The patent uses off-the-shelf LLM models as pre-trained copies that already contain general knowledge and reasoning capabilities. Instead of training new models from scratch or creating detailed rules, the system copies existing LLM capabilities and adapts them to smart home contexts through prompting and hierarchical agent structures, significantly reducing training effort while maintaining adaptability
Solution Approach 2:
The hierarchical multi-LLM-agent structure enables a single LLM-based system to perform multiple functions including understanding user queries, analyzing personal activity data, generating suggestions, and executing actions. This universal architecture handles diverse smart home tasks without requiring separate specialized models for each function
2Loss of time
If off-the-shelf LLM models are used without re-training or fine-tuning, then the effort for knowledge model training is reduced, but the system must still achieve user-specific and context-aware suggestions
Solution Approach 1:
The system performs preliminary actions by collecting and storing personal activity data from sensing devices before user interactions. This historical data is used to create user profiles and context information that are fed into the LLM during inference, enabling personalized suggestions without requiring time-consuming model re-training
Solution Approach 2:
The hierarchical agent structure incorporates feedback loops where the system monitors user responses and activity patterns, uses this feedback to refine user profiles, and adjusts subsequent suggestions accordingly. This continuous feedback mechanism improves suggestion accuracy over time without modifying the underlying LLM model
Data Source
AI summary
A method includes obtaining personal activity data of a user generated by one or more sensing devices in proximity to the user. The method also includes receiving a user query from the user via a user interface. The method further includes using a large language model (LLM) based digital assistant to generate one or more user-specific suggestions or actions based on the personal activity data and the user query, the digital assistant comprising a hierarchical multi-LLM-agent structure that includes one or more pre-trained LLMs.