Voice Input Action Customization via Contextual Data Confidence
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Solution Overview
Problem
Current systems fail to effectively customize actions in response to user inputs, particularly voice commands, due to insufficient contextual data and ambiguity in queries, leading to inefficient processing and potential misinterpretation.
Innovation Solution
A computer-implemented method that receives voice inputs, determines context, identifies potential contextual data from stored user-specific data, and assesses the confidence level of this data to customize actions, supplement queries with missing parameters, and present refining prompts when confidence is low.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If contextual data is used to customize actions, then accuracy and personalization improve, but system complexity increases
Solution Approach 1:
The system segments contextual data into multiple sources including calendar data, social network data, email data, text message data, telephone conversation data, and internet activity data. Each data source is processed and integrated separately to customize actions, allowing the system to manage complexity through modular data handling while improving accuracy through comprehensive contextual analysis.
2Adaptability or versatility
If multiple data sources are integrated for contextual analysis, then personalization improves, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing contextual data from multiple sources in an organized manner. Calendar events, social network interactions, email communications, and other data sources are预先 processed and made readily available for quick retrieval during action customization, reducing processing time while maintaining high personalization capability.
Solution Approach 2:
The system implements self-service mechanisms where contextual data is automatically collected, processed, and integrated without requiring manual intervention. The system autonomously manages multiple data sources, performs confidence level assessments, and customizes actions based on integrated contextual information, reducing both processing time and operational complexity.
3Reliability
If confidence level assessment is performed, then reliability of action customization improves, but additional processing steps are required
Solution Approach 1:
The system implements feedback mechanisms by performing confidence level assessments on contextual data associations. The confidence level information feeds back into the action customization process, allowing the system to adjust its behavior based on the reliability of contextual data. This feedback loop improves reliability by ensuring that only sufficiently confident contextual associations are used for customization.
Solution Approach 2:
The system changes parameters by introducing confidence level thresholds that determine whether contextual data should be used for action customization. By adjusting these threshold parameters, the system can balance between reliability and processing complexity, using confidence level assessment only when necessary to meet specified reliability requirements.
Data Source
AI summary
Methods and systems are provided for customizing an action. In some implementations, voice input is received from a user and a context is determined from the voice input. Potential contextual data is identified based on the context and the voice input. A level of confidence is determined for an association of the potential contextual data and the context. An action is performed based on the voice input, the potential contextual data, and the level of confidence. The potential contextual data is used to customize the action.