Virtual Assistant Command Recognition via Speech Vector Comparison
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Solution Overview
Problem
Virtual assistants face difficulties in recognizing slightly different versions of spoken commands, requiring expert knowledge and significant data for ontology design, leading to a confusing and time-consuming training process.
Innovation Solution
A virtual assistant device generates a speech vector for modified user commands and compares it with stored vectors, initiating actions if differences are within a threshold, and provides feedback to users on how to teach the device to recognize unrecognized commands, eliminating the need for extensive ontology design.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional ontology design methods are used to enable recognition of slightly different command versions, then command recognition accuracy is improved, but device complexity and training time increase significantly
Solution Approach 1:
The patent replaces the mechanical/manual ontology design process with an automated machine learning system. The virtual assistant uses automatic speech recognition and natural language processing algorithms to learn command variations from user interactions, eliminating the need for expert-designed ontologies. This substitution reduces device complexity while maintaining or improving recognition accuracy.
Solution Approach 2:
The virtual assistant performs self-training by automatically learning from user commands without requiring external expert intervention. The system continuously improves its command recognition capabilities through automated feedback loops and machine learning, allowing it to adapt to new command variations autonomously rather than requiring manual ontology updates.
2Adaptability or versatility
If extensive ontology design and large data sets are used to train the virtual assistant, then adaptability to different command versions is improved, but training time and user burden increase
Solution Approach 1:
The system performs preliminary automated preparation of training data and model configuration before user interaction begins. By pre-configuring the machine learning framework and establishing automated feedback mechanisms in advance, the system eliminates the need for time-consuming manual training processes, allowing rapid adaptation to new commands.
Solution Approach 2:
The virtual assistant implements continuous feedback loops where user corrections and interactions automatically refine the command recognition model. This real-time feedback mechanism allows the system to adapt to command variations dynamically during normal operation, eliminating the need for separate, time-consuming training sessions.
3Measurement precision
If the virtual assistant requires manual training for each command variation, then recognition precision is improved, but ease of operation deteriorates
Solution Approach 1:
The virtual assistant automatically performs the training function that would otherwise require user effort. By implementing self-learning capabilities through machine learning algorithms, the system maintains high recognition precision while completely eliminating the need for users to manually train or configure command variations.
Solution Approach 2:
The patent replaces the manual user action of training the assistant with an automated computational system. The machine learning algorithms automatically process user interactions and adjust recognition parameters, substituting the mechanical process of manual training with an automated intelligent system that maintains precision without user burden.
Data Source
AI summary
A device includes a screen and one or more processors configured to provide, at the screen, a graphical user interface (GUI) configured to display data associated with multiple devices on the screen. The GUI is also configured to illustrate a label and at least one control input for each device of the multiple devices. The GUI is also configured to provide feedback to a user. The feedback indicates that a verbal command is not recognized with an action to be performed. The GUI is also configured to provide instructions for the user on how to teach the one or more processors which action is to be performed in response to receiving the verbal command.