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SIOS Awarded Two New Patents for Machine Learning Analytics for IT Operations

SIOS Technology has been granted two new patents from the United States Patent and Trademark Office, relating to technology advancements in developing machine learning-based IT analytics for finding and resolving application performance issues in computing infrastructures such as VMware.

U.S. Patent No. 9,772,871, titled “Apparatus and method for leveraging semi-supervised machine learning for self-adjusting policies in management of a computer infrastructure,” is the first of eight core innovations developed and implemented in SIOS iQ and covered by this patent. It validates the ability of SIOS iQ to perform unsupervised learning of an environment and its behaviors for use in IT operations for purposes such as automating performance root cause analysis, while permitting human input to be used to adjust its models.

U.S. Patent No. 9,910,707, titled “Interface for orchestration and analysis of a computer environment,” covers the Graphical User Interface (GUI) design in SIOS iQ. Specifically, the GUI is configured to provide a user with a single point of view into the computer infrastructure by converging application, compute, storage, and network behavioral attributes into higher order descriptive dimensions of IT operational quality covering performance, efficiency, reliability and capacity. With this configuration, the end user to can readily review the state of the computing environment in a single glance and efficiently explore and interact with the GUI to identify issues threatening operations and obtain recommended solutions.

Jerry Melnick, President and CEO, SIOS Technology, noted, “Virtual environments supporting applications have become enormously complex over the last 10 years. The skills and resources required to manage these infrastructures simply have not kept up, compromising IT’s ability to meet business needs. Companies will need to turn to analytics solutions driven by AI and machine learning out of necessity. These next generation tools help companies understand and prevent problems that threaten service delivery, and vastly simplify and optimize their operations.”

According to Sergey Razin, Ph.D, CTO, SIOS Technology, “We’re very proud of our industry-first innovations that combine the power of machine learning with a highly intuitive interface. For the first time, using SIOS’ patented data visualization and graphical interface, users can instantaneously identify and visually see the interactions of resources across compute, storage, network and applications that are impacting their operations.”

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SIOS Awarded Two New Patents for Machine Learning Analytics for IT Operations

SIOS Technology has been granted two new patents from the United States Patent and Trademark Office, relating to technology advancements in developing machine learning-based IT analytics for finding and resolving application performance issues in computing infrastructures such as VMware.

U.S. Patent No. 9,772,871, titled “Apparatus and method for leveraging semi-supervised machine learning for self-adjusting policies in management of a computer infrastructure,” is the first of eight core innovations developed and implemented in SIOS iQ and covered by this patent. It validates the ability of SIOS iQ to perform unsupervised learning of an environment and its behaviors for use in IT operations for purposes such as automating performance root cause analysis, while permitting human input to be used to adjust its models.

U.S. Patent No. 9,910,707, titled “Interface for orchestration and analysis of a computer environment,” covers the Graphical User Interface (GUI) design in SIOS iQ. Specifically, the GUI is configured to provide a user with a single point of view into the computer infrastructure by converging application, compute, storage, and network behavioral attributes into higher order descriptive dimensions of IT operational quality covering performance, efficiency, reliability and capacity. With this configuration, the end user to can readily review the state of the computing environment in a single glance and efficiently explore and interact with the GUI to identify issues threatening operations and obtain recommended solutions.

Jerry Melnick, President and CEO, SIOS Technology, noted, “Virtual environments supporting applications have become enormously complex over the last 10 years. The skills and resources required to manage these infrastructures simply have not kept up, compromising IT’s ability to meet business needs. Companies will need to turn to analytics solutions driven by AI and machine learning out of necessity. These next generation tools help companies understand and prevent problems that threaten service delivery, and vastly simplify and optimize their operations.”

According to Sergey Razin, Ph.D, CTO, SIOS Technology, “We’re very proud of our industry-first innovations that combine the power of machine learning with a highly intuitive interface. For the first time, using SIOS’ patented data visualization and graphical interface, users can instantaneously identify and visually see the interactions of resources across compute, storage, network and applications that are impacting their operations.”

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Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...