Skip to main content

Evolven is Granted US Patent for Change Grouping Analytics Technology in IT Environments

Evolven Software was granted a new patent from the United States Patent and Trademark Office. The patent covers its unique "change grouping analytics" technology that features a major part of the company's Changes Analytics solutions (which analyze and prioritize changes to configuration parameters of applications in information technology systems).

Specifically, the granted patent covers these capabilities:

- Multi-level clustering of changes based on similar characteristics (e.g. environment, action, artifact, impact, insight)

- The assignment of each cluster with a descriptor (based on context-free grammar)

- The identification of potential IT issues before they occur, or the ad-hoc investigation capabilities, based on the grouping technology

'Change Grouping' capabilities are essential today, as an average business system includes tens or hundreds of thousands of configuration parameters. If any of these parameters are misconfigured or omitted, the change may negatively affect proper operations of the IT system.

Evolven Change Analytics technology helps IT professionals keep track of configuration changes carried out in IT and cloud environments.

Evolven detects and analyzes granulars changes carried out in the hybrid cloud environment. The collected changes are grouped into clusters, represent IT actions that caused change to multiple configuration items.

Contextual-based grouping may include item location (e.g. similar root file path), environment (e.g. which operating system and what hardware is used), version and other details. The clustering may be performed in multiple levels wherein a different distance measure is used in each level, to further improve the clustering process of the configuration items.

This method provides extremely valuable analytics and insights to IT and business users, allowing them to prevent and better manage problems that can occur in IT systems, thereby significantly reducing mean time to resolution (MTTR).

"Changes are the root cause for over 90% of our customers' performance and stability issues," said Sasha Gilenson, Evolven CEO. "This patent strengthens the unique value of our Change Analytics technology that allows enterprises to track issues back to the changes that caused them originally for fast troubleshooting and future prevention."

Evolven's technology has been granted U.S. Patent No. US20170195178A1

Evolven's patented technology helps companies:

- Accelerate root-cause analysis and troubleshooting time

- Prevent performance and stability incidents

- Avoid compliance and security issues by automatically detecting unauthorized and undesired changes

"By leveraging Evolven's exclusive machine learning capabilities, enterprises can make important correlations across silos, recognize patterns, automatically identify anomalies, detect unauthorized changes and apply efficient troubleshooting by smart root cause analysis," said Boštjan Kaluža, Chief Data Scientist at Evolven. "With Evolven's AIOPs capabilities, IT professionals can generate actionable insights behind the huge amount of operations data, minimizing the time to find and diagnose stability issues, preventing outages and downtimes, and enhancing IT support around critical business services."

The Latest

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

Evolven is Granted US Patent for Change Grouping Analytics Technology in IT Environments

Evolven Software was granted a new patent from the United States Patent and Trademark Office. The patent covers its unique "change grouping analytics" technology that features a major part of the company's Changes Analytics solutions (which analyze and prioritize changes to configuration parameters of applications in information technology systems).

Specifically, the granted patent covers these capabilities:

- Multi-level clustering of changes based on similar characteristics (e.g. environment, action, artifact, impact, insight)

- The assignment of each cluster with a descriptor (based on context-free grammar)

- The identification of potential IT issues before they occur, or the ad-hoc investigation capabilities, based on the grouping technology

'Change Grouping' capabilities are essential today, as an average business system includes tens or hundreds of thousands of configuration parameters. If any of these parameters are misconfigured or omitted, the change may negatively affect proper operations of the IT system.

Evolven Change Analytics technology helps IT professionals keep track of configuration changes carried out in IT and cloud environments.

Evolven detects and analyzes granulars changes carried out in the hybrid cloud environment. The collected changes are grouped into clusters, represent IT actions that caused change to multiple configuration items.

Contextual-based grouping may include item location (e.g. similar root file path), environment (e.g. which operating system and what hardware is used), version and other details. The clustering may be performed in multiple levels wherein a different distance measure is used in each level, to further improve the clustering process of the configuration items.

This method provides extremely valuable analytics and insights to IT and business users, allowing them to prevent and better manage problems that can occur in IT systems, thereby significantly reducing mean time to resolution (MTTR).

"Changes are the root cause for over 90% of our customers' performance and stability issues," said Sasha Gilenson, Evolven CEO. "This patent strengthens the unique value of our Change Analytics technology that allows enterprises to track issues back to the changes that caused them originally for fast troubleshooting and future prevention."

Evolven's technology has been granted U.S. Patent No. US20170195178A1

Evolven's patented technology helps companies:

- Accelerate root-cause analysis and troubleshooting time

- Prevent performance and stability incidents

- Avoid compliance and security issues by automatically detecting unauthorized and undesired changes

"By leveraging Evolven's exclusive machine learning capabilities, enterprises can make important correlations across silos, recognize patterns, automatically identify anomalies, detect unauthorized changes and apply efficient troubleshooting by smart root cause analysis," said Boštjan Kaluža, Chief Data Scientist at Evolven. "With Evolven's AIOPs capabilities, IT professionals can generate actionable insights behind the huge amount of operations data, minimizing the time to find and diagnose stability issues, preventing outages and downtimes, and enhancing IT support around critical business services."

The Latest

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...