Orchestrator Contextual Language Selection Live Transcription
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Solution Overview
Problem
Existing systems struggle with live transcription of collaboration session audio in heterogeneous computing platforms, particularly in handling multiple languages and providing context-aware transcription settings.
Innovation Solution
The system employs a heterogeneous computing platform with a sensing hub or orchestrator that identifies collaboration personas and dynamically changes live transcription settings, including language selection, using AI models executed by specific devices without host OS involvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single language model is used for live transcription, then the system complexity is low, but the system cannot accommodate multiple languages in collaboration sessions
Solution Approach 1:
The patent divides the transcription system into multiple independent language models, each dedicated to a specific language. The orchestrator selects and activates only the necessary language model based on the collaboration session context, enabling multi-language support while maintaining manageable system complexity through modular organization.
Solution Approach 2:
The orchestrator component serves multiple functions: it identifies collaboration personas, determines language requirements, selects appropriate AI models, and manages model execution. This multi-functional design consolidates control logic into a single component that adapts the transcription system to various language scenarios without requiring separate specialized systems for each language.
2Adaptability or versatility
If multiple AI models are executed simultaneously for different languages, then all language transcriptions are available, but the energy consumption increases
Solution Approach 1:
The system dynamically adjusts which AI models are active based on real-time collaboration session context. The orchestrator continuously monitors session participants' languages and activates only the necessary language models, transitioning between different model configurations as session needs change, thereby reducing energy consumption compared to having all models running simultaneously.
Solution Approach 2:
The orchestrator autonomously manages the selection and execution of AI models based on collaboration session information without requiring manual intervention. It self-adjusts the system configuration by identifying language requirements and activating appropriate models, enabling the system to serve itself in optimizing energy usage while maintaining language coverage.
3Productivity
If the orchestrator communicates with firmware services through the host OS, then the system architecture is simple, but the transcription settings cannot be changed in real-time during collaboration sessions
Solution Approach 1:
The orchestrator acts as an intermediary component that directly communicates with firmware services through a defined interface, bypassing the host OS for time-critical transcription setting changes. This intermediary role enables real-time control of transcription parameters while maintaining a clear separation between the orchestrator's control functions and the firmware services' execution functions.
4Measurement precision
If context-aware transcription settings are implemented, then the transcription quality improves, but the system complexity increases
Solution Approach 1:
The orchestrator performs preliminary analysis of collaboration session context, including identifying participant personas and determining language requirements, before activating the appropriate AI models. This advance preparation enables the system to configure optimal transcription settings based on session needs, improving transcription accuracy while managing complexity through structured context processing.
Data Source
AI summary
Contextual language selection for live transcription of collaboration session audio in heterogenous computing platforms. In an illustrative, non-limiting embodiment, an Information Handling System (IHS) may include a heterogeneous computing platform comprising a plurality of devices and a memory coupled to the heterogeneous computing platform, where the memory comprises a plurality of sets of firmware instructions, where each set of firmware instructions, upon execution by a respective device, enables the respective device to provide a corresponding firmware service, and where at least one of the plurality of devices operates as an orchestrator configured to identify a collaboration persona and change a live transcription setting of a collaboration session based upon the collaboration persona.