Dynamic Redfish URI Binding for Voice Command Context
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Solution Overview
Problem
Voice command systems are unable to effectively implement complex commands due to a lack of information about expected parameters, leading to incomplete processing and mapping of Redfish URI bindings, which are statically derived and not dynamically adaptable to session context.
Innovation Solution
A system that processes audio data to identify speech commands, maps context data to missing parameters, and selects replacement data to dynamically generate Redfish query parameters, allowing for dynamic URI binding based on session-oriented interactions and user intent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static Redfish URI binding is used, then system simplicity is maintained, but adaptability to session context and voice command complexity is reduced
Solution Approach 1:
The patent implements dynamic Redfish URI binding by transitioning from static pre-defined mappings to a dynamic query generation system. The system analyzes session context and voice commands in real-time, constructing appropriate Redfish query URIs on-demand. This allows the system to adapt to varying session states and complex voice commands while maintaining manageable complexity through automated query generation.
2Ease of operation
If voice commands without parameter information are used, then ease of operation is improved, but command processing accuracy deteriorates
Solution Approach 1:
The system incorporates feedback mechanisms where the NLP module analyzes the session context and provides information about expected parameters back to the command processing system. This feedback loop enables the system to understand implicit parameter requirements from voice commands and accurately map them to appropriate Redfish query parameters, maintaining both ease of voice operation and processing accuracy.
Solution Approach 2:
The patent introduces an intermediary NLP (Natural Language Processing) module that acts as a mediator between the simple voice command input and the structured Redfish query output. This intermediary layer translates informal voice commands with implicit parameters into accurate structured queries by leveraging session context information, thereby maintaining ease of operation while ensuring command processing accuracy.
3Manufacturing precision
If context data mapping is performed, then completeness of parameter mapping is improved, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-establishing session context and pre-processing voice commands before final URI binding. The NLP module continuously analyzes session context and prepares parameter mappings in advance, so when a voice command is received, the mapping process can proceed quickly using pre-computed context information, reducing overall processing time while maintaining completeness.
Data Source
AI summary
A system for data processing, comprising a first processor operating under algorithmic control and configured to receive audio data during a first session and to convert the audio data into encoded electrical data. A second processor operating under algorithmic control and configured to identify speech data in the encoded electrical data and to convert the speech data to text data. The second processor further configured to process the text data to identify one or more commands and one or more missing parameters of the commands. The second processor further configured to map context data to one or more commands and the one or more missing parameters and to select replacement parameter data corresponding to the missing parameter data from the mapped context data.