Adaptive Dialogue Orchestration via Deep Learning Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional multi-task dialogue systems require prior knowledge of entities and intents, are susceptible to updates, and struggle with scaling due to manual rule-based orchestration, making them inefficient in handling new or unknown user queries.
Innovation Solution
An adaptive dialogue orchestration system that uses a deep learning predictive model to select the best chatbot for user inputs, automating the orchestration process without needing to understand the inner workings of individual chatbots, and continually updates the model with new data to improve performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual rule-based orchestration is used to select chatbots, then the system can handle known intents and entities, but the system becomes difficult to scale and requires prior knowledge of all possible intents and entities
Solution Approach 1:
The patent replaces manual rule-based orchestration with a machine learning model that automatically selects chatbots based on learned patterns from training data. The model processes user inputs and predicts the most appropriate chatbot without requiring explicit human-defined rules, thereby substituting mechanical rule-based systems with an intelligent learning system.
Solution Approach 2:
The system transforms the orchestration approach by changing from static manual rules to dynamic learned parameters. The machine learning model learns optimal chatbot selection parameters from training data, allowing the system to adapt to new intents and entities by updating model weights rather than manually rewriting rules.
2Adaptability or versatility
If manual rules are updated to handle new intents and entities, then the system can respond to new queries, but the orchestration becomes more complex and requires continuous manual intervention
Solution Approach 1:
The machine learning model enables the system to automatically adapt to new intents and entities through self-service mechanisms. The model can be retrained with new data to learn patterns of new queries without requiring manual rule updates, allowing the system to serve itself by continuously improving through learning from new examples.
Solution Approach 2:
The system performs preliminary learning by training the machine learning model on extensive training data that includes various intents and entities. This preliminary action prepares the model to handle new queries by pre-learning patterns and relationships, reducing the need for continuous manual intervention when new query types emerge.
3Adaptability or versatility
If multiple chatbots are added to handle diverse queries, then the system covers more topics, but the orchestration rules become more difficult to manage and scale
Solution Approach 1:
The patent replaces the mechanical process of manually managing orchestration rules with multiple chatbots with an intelligent machine learning system. The model automatically processes user inputs and selects appropriate chatbots from the set, eliminating the need for complex manual rule management even as the number of chatbots increases, thereby enabling scalable orchestration.
Data Source
AI summary
In embodiments, a reusable and adaptive multi-task orchestration dialogue system orchestrates a set of single-task dialogue systems to provide multi-scenario dialogue processing. In embodiments, for each question propounded by a user, using a deep learning predictive model, a best single-task dialogue system is chosen out of the set. In embodiments, multi-task orchestration is done without the need to change, or even understand, the inner workings or mechanisms of the individual single-task dialogue systems in the set. Moreover, the multi-task orchestration is also unconcerned with what rules are set in each individual single-task dialogue system. In embodiments, prior to selection of the best single-task dialogue system to return the best answer, new intents and entities are discovered and used to update an existing dialogue path. In embodiments, additional data is continually collected, and used to retrain model so as to further improve performance.