Voice Command Corpus Matching for UI Trace Invocation
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Solution Overview
Problem
Existing natural language processing systems, such as IBM Watson, face inaccuracies in processing voice-activated commands due to language peculiarities and human reasoning, leading to incorrect outcomes in interpreting verbal content and translating it into physical actions.
Innovation Solution
A system that converts verbal content from an audio stream into text, identifies relevant segments, and searches a corpus to find matching user interface traces, generating an instruction to invoke a specific application action based on the received data, utilizing a knowledge engine and detection engine for pattern recognition and reasoning algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If natural language processing systems process voice-activated commands based on acquired knowledge, then the system can understand and respond to verbal content, but the processing accuracy deteriorates due to language peculiarities and human reasoning complexities
Solution Approach 1:
The patent segments the voice command processing into distinct phases: audio capture, text conversion, keyword identification, corpus search, and user interface trace invocation. By dividing the complex natural language processing into manageable segments, each phase can be optimized independently, improving overall accuracy while maintaining versatility in handling various voice commands.
Solution Approach 2:
The patent introduces an intermediary corpus containing pre-stored user interface traces and a detection engine that acts as a mediator between the converted text and the application execution. This intermediary layer translates natural language intent into precise technical actions, bridging the gap between ambiguous human speech and deterministic system responses, thereby improving accuracy without sacrificing adaptability.
2Measurement precision
If a corpus search is performed to identify user interface traces, then the accuracy of translating verbal content to physical actions improves, but the processing time increases
Solution Approach 1:
The patent performs preliminary actions by pre-capturing and storing user interface traces in the corpus before they are needed for command execution. User interactions, application states, and interface elements are captured and organized in advance, creating a ready-to-query knowledge base. This preliminary preparation eliminates the need for real-time analysis during command processing, reducing latency while maintaining high accuracy through precise corpus matching.
Solution Approach 2:
The patent extracts only the essential and relevant portions of the corpus that match the identified keywords from the voice command. Rather than searching or analyzing the entire corpus, the system extracts specific user interface traces that directly correspond to the command intent, significantly reducing processing time while preserving translation accuracy by focusing on highly relevant matches.
3Reliability
If voice commands are translated into physical actions through pattern recognition, then the reliability of action execution improves, but the system complexity increases due to multiple processing components
Solution Approach 1:
The patent implements a universal detection engine that handles multiple functions: audio processing, text conversion, keyword identification, corpus search coordination, and user interface trace invocation. This multi-functional component consolidates what could be separate complex modules into a single coordinated system, maintaining high reliability through comprehensive processing while reducing overall system complexity by eliminating redundant components and interfaces.
Data Source
AI summary
A system, computer program product, and method are provided for use with an intelligent computer platform to convert audio data intents to one or more physical actions. The aspect of converting audio data intent includes receiving audio, converting the audio to text, parsing the text into segments, identifying a physical action and associated application that are proximal in time to the received audio. A corpus is searched for evidence of the text to identify evidence of a physical user interface trace with a select application. An outcome is generated from the evidence. The outcome includes an instruction to invoke a user interface trace with the select application as a representation of the received audio.