18420937. MULTI-DEVICE SPEECH PROCESSING simplified abstract (Amazon Technologies, Inc.)
Contents
- 1 MULTI-DEVICE SPEECH PROCESSING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MULTI-DEVICE SPEECH PROCESSING - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Commercial Applications
- 1.9 Prior Art
- 1.10 Frequently Updated Research
- 1.11 Questions about the Technology
- 1.12 Original Abstract Submitted
MULTI-DEVICE SPEECH PROCESSING
Organization Name
Inventor(s)
Rahul Gupta of Waltham MA (US)
Christophe Dupuy of Cambridge MA (US)
Jacob Ryan Stolee of Toronto (CA)
MULTI-DEVICE SPEECH PROCESSING - A simplified explanation of the abstract
This abstract first appeared for US patent application 18420937 titled 'MULTI-DEVICE SPEECH PROCESSING
Simplified Explanation
The patent application describes techniques for partially processing an input on a device and completing processing at a remote system. The device uses an on-device machine learning model and decides whether to stop processing at an intermediary node based on the node's output. If the output meets a condition, the device generates an output. If not, the output is sent to the remote system for further processing.
- The device processes input partially using an on-device machine learning model.
- It determines whether to stop processing at an intermediary node based on the node's output.
- If the output meets a condition, the device generates an output.
- If the output does not meet the condition, it is sent to the remote system for further processing.
- The remote system uses another machine learning model to complete processing with respect to the input.
Potential Applications
This technology could be applied in various fields such as healthcare, finance, and manufacturing where real-time processing and decision-making are crucial.
Problems Solved
This technology addresses the need for efficient processing of complex inputs by combining on-device and remote processing capabilities.
Benefits
The benefits of this technology include improved processing speed, enhanced decision-making capabilities, and the ability to handle large volumes of data effectively.
Commercial Applications
Title: Enhanced Real-time Data Processing Solutions This technology could be used in industries such as healthcare for real-time patient monitoring, in finance for fraud detection, and in manufacturing for quality control processes.
Prior Art
There may be prior art related to distributed processing techniques in the field of machine learning and data analytics.
Frequently Updated Research
Researchers are constantly exploring new methods to optimize distributed processing techniques for machine learning applications.
Questions about the Technology
1. How does this technology improve processing efficiency compared to traditional methods? 2. What are the potential security implications of transferring data between the device and the remote system?
Original Abstract Submitted
Techniques for partially processing an input on a device and completing processing at a remote system are provided. The device may process an input using an on-device machine learning (ML) model, and determine to cease processing at an intermediary node of the (ML) model based on the output of the intermediary node. Based on the output of the intermediary node satisfying a condition, the device may use the output of the intermediary node to generate an output responsive to the input. Conversely, if the output of the intermediary node does not satisfy a condition, the device may send the output of the intermediary node to the remote system, so the remote system can use another machine learning model to complete processing with respect to the input.
