17968937. Forecasting Information Technology and Environmental Impact on Key Performance Indicators simplified abstract (INTERNATIONAL BUSINESS MACHINES CORPORATION)

Forecasting Information Technology and Environmental Impact on Key Performance Indicators

Organization Name

INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor(s)

Avirup Saha of Kolkata (IN)

Neelamadhav Gantayat of Bangalore (IN)

Renuka Sindhgatta Rajan of Bangalore (IN)

SAMPATH Dechu of Acton MA (US)

Ravi Shankar Arunachalam of Bangalore (IN)

Kushal Mukherjee of New Delhi (IN)

Forecasting Information Technology and Environmental Impact on Key Performance Indicators - A simplified explanation of the abstract

This abstract first appeared for US patent application 17968937 titled 'Forecasting Information Technology and Environmental Impact on Key Performance Indicators

Simplified Explanation

The patent application describes mechanisms for forecasting IT and environmental impacts on key performance indicators (KPIs) using machine learning models trained on historical data. The models can forecast IT events given KPIs or KPI impacts given IT events, and generate correlation graph data structures to map these events to computing resources or organizational processes. Remedial action recommendations are then generated based on the forecasted output and correlation output.

• Machine learning models trained on historical data
• Forecasting IT events and KPI impacts
• Generating correlation graph data structures
• Generating remedial action recommendations based on forecasted output

Potential Applications

This technology can be applied in various industries such as finance, healthcare, and manufacturing to predict and mitigate potential IT disruptions or environmental impacts on key performance indicators.

Problems Solved

This technology helps organizations proactively address IT and environmental issues that could impact their key performance indicators, leading to improved operational efficiency and reduced downtime.

Benefits

The use of machine learning models for forecasting IT events and KPI impacts can help organizations make informed decisions, optimize resource allocation, and enhance overall performance.

Potential Commercial Applications

Potential commercial applications of this technology include IT service management, environmental monitoring systems, and predictive maintenance solutions for various industries.

Possible Prior Art

One possible prior art could be existing predictive analytics tools that forecast IT events or environmental impacts, but may not incorporate machine learning models or correlation graph data structures for generating remedial action recommendations.

How does this technology handle real-time data for forecasting IT events and KPI impacts?

The patent application does not specify how real-time data is processed and utilized for forecasting purposes.

What types of remedial action recommendations are typically generated based on the forecasted output and correlation output?

The patent application does not provide examples or details on the specific types of remedial action recommendations that are generated.

Original Abstract Submitted

Mechanisms are provided for forecasting information technology (IT) and environmental impacts on key performance indicators (KPIs). Machine learning (ML) computer model(s) are trained on historical data representing IT events and KPIs of organizational processes (OPs). The ML computer model(s) forecast IT events given KPIs, or KPI impact given IT events. Correlation graph data structure(s) are generated that map at least one of IT events to IT computing resources, or KPI impacts to OPs. The trained ML computer model(s) process input data to generate a forecast output that specifies at least one of a forecasted IT event or a KPI impact. The forecasted output is correlated with at least one of IT computing resource(s) or OP(s), at least by applying the correlation graph data structure(s) to the forecast output to generate a correlation output. A remedial action recommendation is generated based on the forecast output and correlation output.