ADOBE. has been granted a patent for a control system that utilizes machine learning to predict future data traffic in streaming data systems. The system optimizes server assignments for data streams to minimize costs associated with data movement and server imbalance, enhancing data management efficiency. GlobalData’s report on ADOBE gives a 360-degree view of the company including its patenting strategy. Buy the report here.

According to GlobalData’s company profile on ADOBE, User behaviour analysis was a key innovation area identified from patents. ADOBE's grant share as of June 2024 was 73%. Grant share is based on the ratio of number of grants to total number of patents.

Streaming data management using machine learning for optimization

Source: United States Patent and Trademark Office (USPTO). Credit: ADOBE Inc

The patent US12047273B2 outlines a method and system for optimizing data traffic management across multiple servers using machine learning. The core of the invention involves generating predicted data traffic for various data streams based on historical data. This prediction informs the creation of a matching plan that aligns these data streams with servers in a way that minimizes overall costs, which include switching costs associated with moving data and server imbalance costs. The method also incorporates uncertainty values related to the predictions, allowing for the selection of a planning window that ensures the total uncertainty remains below a specified threshold, thereby enhancing the reliability of the matching plan.

Additionally, the patent details operations for executing the matching plan, which may involve moving data streams between servers, adding new servers, or removing existing ones. The system is designed to allocate data streams efficiently, taking into account various factors such as server load, disk space, and bandwidth utilization. By leveraging machine learning and optimization techniques, the invention aims to improve data management strategies in environments with multiple data streams and servers, ultimately leading to cost-effective and balanced resource utilization.

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