Chatbot Query Routing With Specialized Machine Learning Models
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Solution Overview
Problem
Existing chatbot systems face inefficiencies due to the use of a single bot trained for widespread responses, leading to errors, long response times, and operational inefficiencies, as well as requiring extensive code updates for content changes across multiple platforms and channels.
Innovation Solution
A computing platform trains multiple machine learning models for specific chatbots, dynamically routes queries based on intent analysis, and updates conversation flows without code changes, using a centralized knowledge base and multi-layered taxonomy for accurate responses across channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single chatbot is configured to respond to all queries, then the chatbot can handle widespread response capabilities, but it becomes error-prone and unable to respond accurately to many queries
Solution Approach 1:
The patent divides a single chatbot system into multiple specialized chatbots, each trained on specific datasets for particular domains or tasks. This segmentation allows each bot to excel at its specific function while maintaining overall system versatility through the collection of specialized bots.
Solution Approach 2:
The patent creates a universal chatbot framework that can perform multiple functions by routing queries to different specialized chatbots based on the query type. The system maintains a centralized knowledge base and routing mechanism that enables one universal system to deliver specialized responses across diverse domains.
2Adaptability or versatility
If a single chatbot is trained for widespread responses, then it can cover many query types, but it takes a long period to access relevant information and provide responses
Solution Approach 1:
By segmenting the chatbot system into multiple specialized bots, each bot processes only its specific domain queries, reducing the time needed to search and process information. The routing mechanism quickly directs queries to the appropriate bot, eliminating the need for a single bot to search through all possible domains.
Solution Approach 2:
Each specialized chatbot is pre-trained on its specific dataset and domain knowledge in advance. This preliminary training allows the bots to immediately process their designated query types without needing to search or adapt during runtime, significantly reducing response time.
3Measurement precision
If chatbot responses are developed on a platform by platform basis, then each platform can be optimized, but content changes cause operational inefficiencies due to coordinating code modifications and redeployment
Solution Approach 1:
The patent implements a universal knowledge base and centralized content management system that serves multiple chatbot platforms. Content changes are made in one location and automatically distributed to all platforms, eliminating the need for separate code modifications and redeployment for each platform while maintaining platform-specific optimizations.
Solution Approach 2:
The system creates and maintains a single source of truth for chatbot content and knowledge, which is then copied or distributed to multiple platforms. This allows platforms to be optimized independently while content updates propagate automatically from the central repository.
4Adaptability or versatility
If conversation flows are deployed in multiple channels, then the chatbot can serve various communication methods, but code updates and redeployment are required for any flow changes
Solution Approach 1:
The patent creates a universal conversation flow management system that handles multiple communication channels (chat, voice, web, etc.) through a single centralized framework. Conversation flow changes are made in one location and automatically applied across all channels, eliminating the need for separate code updates for each channel while maintaining channel-specific optimizations.
Solution Approach 2:
The system introduces a centralized conversation flow management layer that acts as an intermediary between the conversation logic and various communication channels. This mediator handles the routing and adaptation of conversation flows to different channels without requiring channel-specific code modifications.
5Extent of automation
If a chatbot programmatically implements conversation flows with multiple questions, then it can collect information systematically, but it cannot respond if the question is not properly worded and requires manual programming for new responses
Solution Approach 1:
The patent implements self-service mechanisms where the chatbot system automatically adapts to new query types and conversation flows without requiring manual programming. The system uses machine learning and natural language processing to understand and respond to improperly worded questions, and new responses are automatically generated or learned from interactions rather than requiring manual code updates.
Solution Approach 2:
The system dynamically adjusts its conversation flow parameters and processing methods based on the incoming query characteristics. Rather than following rigid predetermined flows, the chatbot adapts its information collection approach and response generation based on the specific query received, allowing it to handle improperly worded questions and new response types without reprogramming.
Data Source
AI summary
Aspects of the disclosure relate to using machine learning methods for chatbot selection. A computing platform may train a plurality of machine learning models, each corresponding to a chatbot. The computing platform may train an additional machine learning model to route queries to the plurality of machine learning models based on contents of the queries. The computing platform may receive a query, and may analyze the query using the additional machine learning model. The computing platform may route, based on the query analysis, the query to the plurality of machine learning models. The computing platform may generate, using the plurality of machine learning models, a response to the query. The computing platform may send the response to the query and one or more commands directing a client device to display the response to the query, which may cause the client device to display the response to the query.