Nvidia corporation (20240161221). FRACTIONALIZED TRANSFERS OF SENSOR DATA FOR STREAMING AND LATENCY-SENSITIVE APPLICATIONS simplified abstract
Contents
- 1 FRACTIONALIZED TRANSFERS OF SENSOR DATA FOR STREAMING AND LATENCY-SENSITIVE APPLICATIONS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 FRACTIONALIZED TRANSFERS OF SENSOR DATA FOR STREAMING AND LATENCY-SENSITIVE APPLICATIONS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
FRACTIONALIZED TRANSFERS OF SENSOR DATA FOR STREAMING AND LATENCY-SENSITIVE APPLICATIONS
Organization Name
Inventor(s)
Aki Petteri Niemi of Vancouver (CA)
Sean Midthun Pieper of Waldport OR (US)
FRACTIONALIZED TRANSFERS OF SENSOR DATA FOR STREAMING AND LATENCY-SENSITIVE APPLICATIONS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240161221 titled 'FRACTIONALIZED TRANSFERS OF SENSOR DATA FOR STREAMING AND LATENCY-SENSITIVE APPLICATIONS
Simplified Explanation
The patent application describes techniques for implementing fractionalized data transfers between processing devices in real-time data generating and streaming applications. This involves processing image data to generate portions of an image, storing these portions in memory, setting completion indicators, and transferring the portions to another processing device.
- Processing image data to generate portions of an image
- Storing portions in memory
- Setting completion indicators for portions
- Transferring portions to another processing device
Potential Applications
The technology described in the patent application could be applied in various fields such as video streaming, image processing, real-time data analysis, and remote sensing applications.
Problems Solved
This technology solves the problem of efficiently transferring large amounts of data between processing devices in real-time applications without causing delays or interruptions in data processing.
Benefits
The benefits of this technology include improved data transfer efficiency, reduced latency in real-time applications, enhanced processing capabilities, and seamless integration of multiple processing devices.
Potential Commercial Applications
Potential commercial applications of this technology include video streaming services, remote sensing technologies, medical imaging systems, surveillance systems, and real-time data analysis platforms.
Possible Prior Art
One possible prior art for this technology could be existing data transfer protocols and techniques used in real-time applications, image processing systems, and distributed computing environments.
Unanswered Questions
How does this technology compare to existing data transfer methods in terms of speed and efficiency?
This article does not provide a direct comparison between this technology and existing data transfer methods in terms of speed and efficiency.
What are the potential limitations or challenges in implementing this technology in different types of processing devices?
This article does not address the potential limitations or challenges in implementing this technology in different types of processing devices.
Original Abstract Submitted
disclosed are apparatuses, systems, and techniques that implementing fractionalized data transfers between processing devices in real-time data generating and streaming applications. the techniques include but are not limited to processing, by a first processing device, an image data to generate a plurality of portions of an image, responsive to generating a first portion of the plurality of portions of the image, storing the first portion in a first memory device of the first processing device, setting a completion indicator for the first portion, and causing the first portion to be provided to a second processing device.
