Neural Network Intent Recognition via Zero-Shot Learning
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Solution Overview
Problem
Conversational artificial intelligence systems are inflexible and slow to adapt to new information due to their reliance on extensive training data and specific models for each task, making them cumbersome to update and limit their usability.
Innovation Solution
Implementing a zero-shot approach that uses pre-determined labels and trained neural network models to recognize user intents and entities, allowing for flexible addition of new commands without extensive retraining, enabling real-time updates and natural language interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If systems deploy variety of different models specifically trained to each task, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent employs a universal neural network model that can perform multiple intent recognition tasks through zero-shot learning, eliminating the need for separate specialized models for each task. The model is trained on pre-determined labels and can generalize to recognize user intents across different domains without task-specific training, thereby reducing model management complexity while maintaining recognition accuracy.
Solution Approach 2:
The patent segments the intent recognition process into distinct components: pre-determined label definitions, neural network inference, and post-processing. This segmentation allows the system to use a single versatile model rather than multiple specialized models, simplifying the overall system architecture while preserving the ability to accurately recognize diverse user intents.
2Adaptability or versatility
If models are retrained on newly annotated data, then adaptability is improved, but loss of time increases
Solution Approach 1:
The patent implements zero-shot learning where the neural network is pre-trained on a comprehensive set of pre-determined labels that cover potential user intents. This preliminary training enables the model to immediately recognize new intents without requiring retraining, as the model has already learned to generalize across different intent categories during the initial training phase.
Solution Approach 2:
The patent changes the approach from retraining model parameters to adjusting inference parameters. When new intents need to be recognized, the system adds new pre-determined labels and adjusts the inference process rather than retraining the entire model, thereby achieving adaptability without the time cost of retraining.
3Measurement precision
If extensive training data is used, then measurement precision is improved, but quantity of substance increases
Solution Approach 1:
The patent performs preliminary training of the neural network on a comprehensive set of pre-determined labels that encompass various entities and intents. This upfront training investment enables the model to achieve high recognition accuracy without requiring extensive additional training data for each specific task, as the model learns generalizable patterns during the initial training phase.
Solution Approach 2:
The patent uses a single neural network model trained on diverse pre-determined labels that can recognize multiple entity types and intents. This universal approach allows the model to achieve high accuracy across different recognition tasks without requiring separate extensive training datasets for each task, thereby reducing the total quantity of training data needed.
4Measurement precision
If manual preparation of training data is performed, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent performs preliminary preparation of pre-determined labels that cover a broad range of potential user commands and intents. This upfront preparation of structured label definitions enables the neural network to learn from well-organized data, achieving high command recognition accuracy without requiring extensive manual data preparation for each specific use case.
Solution Approach 2:
The patent changes the approach from extensive manual annotation of training data to the creation of pre-determined label structures. By defining comprehensive label categories in advance, the system reduces the need for manual data preparation while maintaining high recognition accuracy, as the neural network learns to map inputs to these pre-defined categories.
Data Source
AI summary
Systems and methods determine an intent of a received voice input corresponds to an intent label and determine an entity for the intent label. The entity may be responsive to a formulation associated with the intent. A value for the entity may be determined and populated to provide the entity as a command to one or more interaction environments. The interaction environment may execute commands responsive to a user input based on the value associated with the entity.