Amazon technologies, inc. (20240221730). MULTI-DEVICE SPEECH PROCESSING simplified abstract
Contents
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 20240221730 titled 'MULTI-DEVICE SPEECH PROCESSING
Simplified Explanation: The patent application discusses 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 to process the input, and based on the output of an intermediary node of the model, decides whether to continue processing on the device or send the output to the remote system for further processing.
- The device processes an input using an on-device machine learning model.
- It determines whether to continue processing based on the output of an intermediary node.
- If the output of the intermediary node meets a condition, the device generates an output.
- If the output does not meet the condition, it sends the output to the remote system for further processing.
Potential Applications: This technology could be applied in various fields such as healthcare, finance, and manufacturing where real-time processing of data is required.
Problems Solved: This technology addresses the challenge of efficiently processing large amounts of data by offloading processing to a remote system when necessary.
Benefits: The benefits of this technology include improved processing efficiency, reduced device workload, and the ability to leverage remote systems for complex processing tasks.
Commercial Applications: Title: "Enhanced Data Processing Technology for Remote Systems" This technology could be commercially used in industries such as telecommunication, autonomous vehicles, and IoT devices to enhance data processing capabilities and optimize resource utilization.
Prior Art: Researchers can explore prior art related to distributed computing, edge computing, and machine learning models to understand the evolution of similar technologies.
Frequently Updated Research: Researchers are continuously exploring ways to optimize distributed processing techniques and improve the efficiency of machine learning models for real-time applications.
Questions about the Technology: 1. How does this technology improve the overall processing efficiency of devices? 2. What are the potential security implications of offloading processing tasks to remote systems?
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.
