Dynamic Command Library via Spoken Term Correlation
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Solution Overview
Problem
Existing systems for executing spoken commands are limited to pre-defined commands, restricting the number of executable commands and requiring manual intervention for unsupported commands, which hinders flexibility and scalability.
Innovation Solution
A system that captures spoken terms, presents an error message when unsupported, records and correlates user interface selections with these terms, allowing the system to expand its command set dynamically based on frequency of usage, eliminating the need for manual command definition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system uses a pre-defined library of spoken commands, then the system can execute recognized commands reliably, but the number of executable commands is limited and requires manual definition
Solution Approach 1:
The system automatically learns and adds new commands by observing user interactions with the UI. When a user speaks an unrecognized command and manually performs UI selections to achieve the desired result, the system records this selection sequence and correlates it with the spoken terms. This self-learning mechanism eliminates the need for administrators to manually define commands, allowing the system to autonomously expand its command repertoire while maintaining reliable execution of learned commands
Solution Approach 2:
The command library transitions from a static, pre-defined set to a dynamic, self-evolving collection. The system continuously adapts by incorporating new commands based on user behavior patterns. The command recognition capability grows over time as the system learns from frequent spoken terms and their associated UI selection sequences, enabling the system to support an expanding number of commands without manual intervention
2Reliability
If administrators manually define supported commands, then command execution is controlled and reliable, but it requires significant manual intervention and administration time
Solution Approach 1:
The system performs automatic command definition by monitoring user interactions. When users speak unrecognized commands and complete tasks through UI selections, the system automatically records these sequences, correlates spoken terms with actions, and adds them to the command library. This self-service approach eliminates the time-consuming manual command definition process while maintaining execution control through the same reliable correlation and playback mechanism
Solution Approach 2:
The system performs preliminary learning by observing and recording user interactions before formal command recognition is needed. By capturing selection sequences and correlating them with spoken terms during initial user exploration, the system prepares the command library in advance, eliminating the need for subsequent manual command definition and reduction
3Device complexity
If the system supports only pre-contemplated commands, then system complexity remains manageable, but the system lacks flexibility for new commands
Solution Approach 1:
The system maintains structural simplicity while gaining flexibility through automatic learning. Instead of requiring complex manual command definition processes, the system self-configures by observing user behavior and automatically building the command library. This approach preserves system simplicity by eliminating administrative overhead while enabling flexible adaptation to new commands through automated pattern recognition and correlation
Solution Approach 2:
The system dynamically changes the parameter of command recognition capability by learning from usage frequency. As spoken terms are repeated and correlated with UI selection sequences, the system adjusts its recognition parameters to include new commands. This allows the system to maintain simple structure while adapting to diverse commands through parameter evolution based on observed usage patterns
Data Source
AI summary
Systems and methods to implement commands based on selection sequences to a user interface are disclosed. Exemplary implementations may: store, electronic storage, a library of terms utterable by users that facilitate implementation of intended results; obtain audio information representing sounds captured by a client computing platform; detect the spoken terms uttered by the user present within the audio information; determine whether the spoken terms detected are included in the library of terms; responsive to determination that the spoken terms are not included in the library of terms, effectuate presentation of an error message via the user interface; record a selection sequence that the user performs subsequent to the presentation of the error message that causes a result; correlate the selection sequence with the spoken terms based on the selection sequence recorded subsequent to error message to generate correlation; and store the correlation to the electronic storage.