Amazon technologies, inc. (20240331686). RELEVANT CONTEXT DETERMINATION simplified abstract
Contents
- 1 RELEVANT CONTEXT DETERMINATION
RELEVANT CONTEXT DETERMINATION
Organization Name
Inventor(s)
Thanh Dac Tran of Logan UT (US)
Grant Strimel of Presto PA (US)
RELEVANT CONTEXT DETERMINATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240331686 titled 'RELEVANT CONTEXT DETERMINATION
Simplified Explanation
The patent application describes techniques for determining and storing relevant context information for a user input, such as a spoken input. Context information is determined on an audio frame basis, with context scores calculated for different types of data. The most relevant context is stored in a local context cache and updated as more audio frames are processed.
- Context information is determined on an audio frame basis.
- Context scores are calculated for different types of context data.
- The most relevant context is stored in a local context cache.
- The local context cache is updated as more audio frames are processed.
- The stored context data is provided to downstream components for tasks such as ASR, NLU, and SLU.
Potential Applications
The technology can be applied in speech recognition systems, virtual assistants, and other voice-controlled devices where understanding user context is crucial for accurate responses.
Problems Solved
This technology addresses the challenge of efficiently determining and storing relevant context information for user inputs, particularly in real-time applications like speech recognition.
Benefits
- Improved accuracy in understanding user inputs - Enhanced performance of speech recognition systems - Efficient storage and retrieval of context information
Commercial Applications
The technology can be utilized in virtual assistants, smart speakers, customer service chatbots, and any other applications where understanding user context is essential for providing accurate responses.
Questions about the Technology
How does this technology improve the performance of speech recognition systems?
This technology improves performance by efficiently determining and storing relevant context information for user inputs, leading to more accurate recognition and interpretation of spoken language.
What are the potential applications of this technology beyond speech recognition systems?
This technology can be applied in various voice-controlled devices, virtual assistants, and customer service chatbots to enhance user interactions and provide more personalized responses.
Original Abstract Submitted
techniques for determining and storing relevant context information for a user input, such as a spoken input, are described. in some embodiments, context information is determined to be relevant on an audio frame basis. context scores for different types of context data (e.g., prior dialog turn data, user profile data, device information, etc.) are determined for individual audio frames corresponding to a spoken input. based on the corresponding context scores, the most relevant context is stored in a local context cache. the local context cache is updated as subsequent audio frames, of the user input, are processed. the data stored in the context cache is provided to downstream components to perform tasks such as asr, nlu and slu.
