Signiant Awarded Patent for its Intelligent Transport Architecture

SaaS, Cloud, Machine learning, Data, Media, MAM, Content

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Signiant Inc has announced, that it has been issued patent number 10,735,516 by the U.S. Patent and Trademark Office for its invention titled “Cloud-Based Authority To Enhance Point-To-Point Data Transfer With Machine Learning.” The patent describes methods and systems that incorporate machine learning to evaluate anonymized historical transfer information collected by its SaaS products.  This history is used as training data to build a model that predicts the fastest possible way to move or access media. With this patent, Signiant expands its leadership in SaaS-based solutions for the global exchange of high value media assets.

Signiant’s intelligent transport chooses the optimal number of parallel transport streams and selects either standard TCP or Signiant’s proprietary UDP-based acceleration protocol based on a variety of inputs including current and historical network conditions, available compute, storage type and the characteristics of the data set. The new architecture is capable of multiple Gbps transfer speeds and is already deployed in Signiant’s SDCX (Software-Defined Content Exchange) SaaS platform for transfers between public and/or private cloud services and/or storage. The platform is capable of working with any size file, data sets with an unlimited number of files as well as growing files and streams.

“When early transfer tests showed speeds greater than 10 Gbps using settings derived through machine learning that weren’t intuitive or obvious, we knew we were onto something disruptive,” said Signiant CTO, Ian Hamilton. “As networks evolve and increase in bandwidth, Signiant’s transport becomes even more valuable and is now capable of speeds of 10s of Gbps. Standard transfer tools simply aren’t designed to take advantage of available bandwidth and so with today’s fatter pipes, this new architecture becomes even more critical as datasets get bigger and workflows are increasingly globally distributed.”

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