Intelligent Response Routing for Faster, Up-to-Date Chatbot Answers
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Solution Overview
Problem
Existing chatbots struggle with providing up-to-date and precise responses due to outdated data sources, leading to increased complexity and response time, and often fail to dynamically adapt to user queries.
Innovation Solution
A system utilizing an intelligent manager that processes user queries through a scoring model to dynamically update a database with the latest information from multiple knowledge sources, combining prestored and predicted responses to provide timely and accurate answers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple data sources are added to improve response accuracy, then the quality of responses is improved, but the response time increases
Solution Approach 1:
The system performs preliminary actions by pre-processing message requests to generate intents before the actual query is executed. The intent generation occurs in advance, allowing the system to prepare response candidates ahead of time, thus reducing the actual response time when multiple data sources are queried.
Solution Approach 2:
The system dynamically adjusts its operation by concurrently transmitting queries to multiple data sources based on the generated intent. Instead of sequential processing, the system dynamically parallelizes the data source queries, adapting the processing approach to minimize response time while maintaining accuracy across multiple sources.
2Reliability
If multiple data sources are added to improve information freshness, then the up-to-date nature of responses is improved, but the system complexity increases
Solution Approach 1:
The system segments the complex task of querying multiple data sources by first generating a specific intent from the message request. This intent acts as a segmentation key that directs queries to relevant data sources, breaking down the complexity into manageable steps: intent generation, targeted querying, and response aggregation.
Solution Approach 2:
The system introduces an intent as an intermediary between the user's message request and the multiple data sources. This intermediary structures and refines the query, making it easier to manage complexity by translating natural language requests into targeted data source queries, thus simplifying the interaction with multiple sources.
3Device complexity
If conventional chatbot processing is used, then the system simplicity is maintained, but the response time increases
Solution Approach 1:
The system performs preliminary intent generation from the message request before executing the actual query processing. This pre-processing step prepares the query in advance, allowing for faster execution when multiple data sources need to be accessed, thus reducing response time without significantly increasing overall system complexity.
Solution Approach 2:
The system dynamically parallelizes the querying process by concurrently transmitting queries to multiple data sources based on the generated intent. This dynamic approach allows the system to maintain relative simplicity while achieving faster response times through parallel processing of data source queries.
Data Source
AI summary
Systems and methods for providing dynamic responses to a user query are disclosed herein. A system receives a message request from a computing device, and generates an intent associated with the message request based on a pre-processing of the message request. Further, the system concurrently transmits a query to a database for a prestored response to the message request based on the generated intent, and the generated intent to a knowledge source searcher. The knowledge source searcher asynchronously queries one or more knowledge sources for a predicted response to the message request. Furthermore, the system receives the prestored response from the database and the predicted response from the knowledge source searcher, determines a response to the message request, from at least one of the prestored response and the predicted response, based on a scoring model, and transmits the determined response to the computing device.