Multi-Chatbot Routing Agent for Reduced Computational Load
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Users face challenges in efficiently accessing and navigating multiple artificial intelligence (AI) and machine learning (ML) chatbots due to the increasing number of options, leading to complexity, inefficiency, and unnecessary computational demands, as they often select the wrong chatbot, resulting in suboptimal responses and increased power consumption.
Innovation Solution
An agent or interface manages interactions with multiple AI/ML chatbots, providing a unified interface that selects appropriate chatbots based on user requests, learns from user feedback, and combines responses to enhance efficiency and accuracy, while leveraging both AI/ML and non-AI/ML systems for reliable data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users manually select from multiple AI/ML chatbots, then they can access diverse capabilities, but interface complexity and time to find appropriate chatbot increases
Solution Approach 1:
The patent introduces an agent as an intermediary between the user and multiple AI/ML chatbots. This agent automatically selects and routes user queries to the most appropriate chatbot based on the query content, eliminating the need for users to manually navigate through multiple interfaces. The agent acts as a mediator that handles the complexity of chatbot selection and routing, providing a simplified unified interface while maintaining access to diverse capabilities.
2Adaptability or versatility
If users manually select from multiple AI/ML chatbots, then they can access diverse capabilities, but time to obtain response increases
Solution Approach 1:
The agent performs preliminary action by pre-selecting and routing queries to the most appropriate AI/ML chatbots before the user needs a response. The system proactively determines which chatbots should handle specific query types based on their capabilities and the query content, eliminating the need for users to spend time manually selecting from multiple options. This preliminary routing action significantly reduces the time to obtain responses.
3Reliability
If all AI/ML chatbots are queried for every user request, then comprehensive coverage is achieved, but computational power consumption increases
Solution Approach 1:
The agent performs preliminary classification of user queries and pre-determines which AI/ML chatbots should be queried based on the query content and chatbot capabilities. This preliminary action filters out irrelevant chatbots before they are activated, ensuring that only the necessary chatbots are queried for each user request. This approach maintains comprehensive coverage for relevant query types while significantly reducing unnecessary computational power consumption.
Solution Approach 2:
The system applies local quality by tailoring the set of queried chatbots to the specific characteristics of each query. Instead of uniformly querying all chatbots for every request, the system adjusts which chatbots are activated based on the local context of the query (e.g., topic, complexity, user preferences). This localized approach ensures comprehensive coverage for relevant query types while minimizing computational resources spent on irrelevant chatbots.
4Measurement precision
If AI/ML chatbots are selected based on user feedback, then response accuracy improves, but system learning time increases
Solution Approach 1:
The agent incorporates feedback mechanisms that continuously learn from user interactions and chatbot performance. User feedback (explicit or implicit) is used to refine the agent's understanding of query patterns and chatbot capabilities over time. This feedback loop enables the system to improve response accuracy by selecting more appropriate chatbots for similar future queries, while the learning process occurs continuously in the background without requiring extended system learning time before operation.
Data Source
AI summary
Methods, systems, and apparatus, including computer-readable media, for managing interactions with multiple artificial intelligence chatbots. In some implementations, a text input from a user is received. The system identifies multiple chatbots that the user is authorized to access, and the system selects a subset of the multiple chatbots based on the text input from the user. The system provides the text input from the user to each of the chatbots in the subset to generate a response to the text input from each of the chatbots in the subset. The system provides an output response to the text input from the user for presentation at the user device, where the response is based on one or more of the responses generated the chatbots in the subset.