Dynamic NLP Interface for Accurate Voice App Actions
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Solution Overview
Problem
Challenges exist in interfacing with third-party applications using limited interfaces, leading to increased computing resource consumption, latency, and inaccurate execution of actions due to insufficient input parameters.
Innovation Solution
A system and method that dynamically updates natural language processing techniques in real-time to accurately process voice inputs, leveraging a protocol to receive declarations from applications and modify NLP to improve parameter detection, enabling seamless voice-based interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If additional interfaces are used to communicate with applications, then the ability to execute actions is improved, but computing resource consumption increases
Solution Approach 1:
The digital assistant's NLP component serves multiple functions: it processes voice inputs, receives application declarations, updates its processing techniques, and executes actions. This multi-functionality eliminates the need for separate specialized interfaces for each function, thereby improving adaptability while controlling resource consumption.
Solution Approach 2:
The NLP component dynamically updates its own processing techniques based on application declarations without requiring external intervention. The system serves itself by automatically adapting to new applications and functions, reducing the need for additional manual configuration interfaces and associated computational overhead.
2Adaptability or versatility
If additional interfaces are used to communicate with applications, then the ability to execute actions is improved, but device complexity increases
Solution Approach 1:
The patent merges multiple functions into a single integrated NLP component within the digital assistant. This component handles voice input processing, application communication, dynamic technique updates, and action execution all through one unified interface, thereby reducing overall device complexity while maintaining high adaptability.
Solution Approach 2:
The NLP component is designed as a universal interface that can communicate with various applications through dynamic updates. This single multi-functional component replaces what would traditionally require multiple specialized interfaces, simplifying the overall system architecture.
3Adaptability or versatility
If additional interfaces are used to communicate with applications, then the ability to execute actions is improved, but latency increases
Solution Approach 1:
The NLP component receives and processes application declarations in advance before voice input is required. By pre-updating its techniques based on application capabilities, the system eliminates delays that would occur during action execution, thereby reducing latency while maintaining the ability to execute diverse actions.
Solution Approach 2:
The digital assistant continuously maintains updated NLP techniques through ongoing communication with applications. This continuous update process ensures that the system is always ready to execute actions without interruption or delay, eliminating the latency that would result from on-demand interface setup.
4Productivity
If standard NLP techniques are used without dynamic updates, then processing speed is maintained, but parameter detection accuracy decreases
Solution Approach 1:
The NLP component dynamically adjusts its processing techniques in real-time based on application-specific declarations. This dynamic adaptation allows the system to maintain high processing speed while improving parameter detection accuracy for each specific application context, resolving the trade-off between speed and precision.
Solution Approach 2:
The system changes its NLP processing parameters and techniques based on application declarations. By adjusting these parameters dynamically, the system achieves both fast processing and high accuracy for parameter detection, as the techniques are optimized for each specific application rather than using a fixed generic approach.
Data Source
AI summary
Dynamic interfacing with applications is provided. For example, a system receives a first input audio signal. The system processes, via a natural language processing technique, the first input audio signal to identify an application. The system activates the application for execution on the client computing device. The application declares a function the application is configured to perform. The system modifies the natural language processing technique responsive to the function declared by the application. The system receives a second input audio signal. The system processes, via the modified natural language processing technique, the second input audio signal to detect one or more parameters. The system determines that the one or more parameters are compatible for input into an input field of the application. The system generates an action data structure for the application. The system inputs the action data structure into the application, which executes the action data structure.