Ranker Model for Dialog-Intent Identification in Assistant Systems
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Solution Overview
Problem
Assistant systems face challenges in accurately identifying dialog-intents associated with user requests and interacting with users about execution results without disrupting their activities, particularly in determining the most suitable agents for task execution and delivering results in appropriate modalities.
Innovation Solution
The implementation of a ranker model for identifying dialog-intents based on semantic-intents, slots, and context information, along with training the model using historical and real-time data, and determining suitable modalities for result delivery to match user contexts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a ranker model is implemented to identify dialog-intents based on semantic-intents, slots, and context information, then the accuracy of intent identification is improved, but the computational complexity and training time increase
Solution Approach 1:
The patent segments the intent identification process into multiple components: semantic-intent identification, slot extraction, context information processing, and ranker model ranking. This segmentation allows each component to be optimized independently while working together to achieve high overall accuracy in dialog-intent identification.
Solution Approach 2:
The patent applies preliminary action by pre-processing context information, extracting slots from user requests, and identifying semantic-intents before feeding them into the ranker model. This preliminary preparation of data structures and features reduces the computational burden during the actual ranking and identification phase.
2Adaptability or versatility
If the ranker model is re-trained using historical and real-time data during runtime, then the adaptability and accuracy are improved, but the processing time and computational resources increase
Solution Approach 1:
The patent implements periodic action by re-training the ranker model at scheduled intervals using accumulated historical data and real-time data. This periodic re-training ensures the model adapts to changing user behaviors and contexts while maintaining system responsiveness between training cycles.
Solution Approach 2:
The patent maintains continuity of useful action by continuously collecting and preprocessing training data in the background during runtime, so that when re-training occurs, the model can be updated with the most current and relevant information without interrupting the assistant system's normal operations.
3Productivity
If multiple agents are called in parallel to execute tasks, then the task execution efficiency is improved, but the system complexity and resource consumption increase
Solution Approach 1:
The patent merges multiple agent execution processes into a unified parallel processing framework managed by the ranker model. This consolidation allows the system to coordinate multiple agents efficiently, reducing redundant operations and managing complexity through a centralized control mechanism.
Solution Approach 2:
The patent creates a universal agent execution framework that can handle multiple types of tasks through a common interface and ranking mechanism. This multi-functional system can dynamically select and coordinate different agents based on the specific task requirements, reducing the need for task-specific customization.
4Measurement precision
If context information is extensively processed to identify dialog-intents, then the accuracy of user request understanding is improved, but the processing time and computational load increase
Solution Approach 1:
The patent applies partial action by selectively processing only the most relevant portions of context information based on the current dialog state and user request type. The ranker model prioritizes processing high-value context features while skipping or simplifying processing of less relevant information, achieving good accuracy without excessive processing time.
Data Source
AI summary
In one embodiment, a method includes receiving a user request from a first user at a client system, wherein the user request is associated with a semantic-intent, identifying dialog-intents associated with the user request by the client system based on the semantic-intent and context information associated with the user request, wherein each dialog-intent is a sub-intent of the semantic-intent; determining agents for executing tasks associated with the dialog-intents by the client system, and presenting information returned from the agents responsive to executing the tasks at the client system.