Parallel Data Capture Encoding Upload System
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Solution Overview
Problem
Current data transfer methods, such as streaming and live streaming, often result in delays due to bandwidth issues and the sequential process of data capture, encoding, and uploading, which can lead to sub-optimal user experiences and reduced content quality.
Innovation Solution
A system that enables near simultaneous data capture, encoding, and uploading by operating in a parallel fashion, allowing for faster creation and dissemination of data files, thereby reducing the time to make content available to consumers and maintaining quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If sequential data capture, encoding, and uploading is used, then process simplicity is maintained, but time delay increases significantly
Solution Approach 1:
The patent divides the data processing workflow into distinct segments: data capture, encoding, and uploading. Each segment can be executed independently and in parallel, eliminating the sequential bottleneck. The system processes data through multiple pipelines that operate simultaneously, reducing total processing time while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The system performs preliminary encoding actions during the data capture phase rather than waiting for capture completion. Encoding operations begin in advance on captured data segments, and uploading operations start even before encoding finishes. This overlapping of preliminary actions eliminates idle time and significantly reduces overall processing delay.
2Speed
If live streaming is used, then real-time access is improved, but bandwidth-induced delays increase
Solution Approach 1:
The patent segments the data stream into discrete packets or blocks that can be processed and transmitted independently. This segmentation allows the system to optimize each segment's encoding and transmission separately, improving overall transmission speed while managing bandwidth constraints through selective processing of critical data segments.
Solution Approach 2:
The system dynamically adjusts encoding parameters and transmission priorities based on real-time bandwidth conditions. The parallel processing architecture enables flexible resource allocation where the system can prioritize critical data segments for immediate transmission while deferring less critical segments, optimizing speed-performance under varying bandwidth constraints.
3Manufacturing precision
If data encoding is performed sequentially after capture, then encoding quality is maintained, but processing time increases
Solution Approach 1:
The encoding process is divided into multiple independent encoding threads that process different data segments simultaneously. Each encoding thread maintains proper encoding quality standards while working in parallel on different portions of the data stream, thereby improving overall processing efficiency without compromising encoding quality through the use of multiple encoding instances.
Solution Approach 2:
The system creates multiple copies of the encoding process that run in parallel. Instead of a single sequential encoding operation, multiple encoding instances process different data segments simultaneously. This copying approach maintains encoding quality through replicated processing while significantly improving productivity through parallel execution.
Data Source
AI summary
A system is configured to allow for near simultaneous data capture, encoding, and uploading of the resultant data file containing the captured data. By operating in a substantially parallel fashion, the separate processes of capturing, encoding, and uploading one or more data files created from a data source can be completed much more quickly and efficiently, thereby making the resultant data file available to the content consumer in an overall shorter period of time than presently possible using conventional sequential data file creation techniques. The method also includes the reorganization of standard data files as part of the process to more quickly and efficiently create the data file for distribution and ultimate consumption by the content consumer user.