International business machines corporation (20240135228). Forecasting Information Technology and Environmental Impact on Key Performance Indicators simplified abstract
Contents
- 1 Forecasting Information Technology and Environmental Impact on Key Performance Indicators
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Forecasting Information Technology and Environmental Impact on Key Performance Indicators - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
Forecasting Information Technology and Environmental Impact on Key Performance Indicators
Organization Name
international business machines corporation
Inventor(s)
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 20240135228 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 computer 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 events to computing resources or impacts to organizational processes. The output includes forecasted IT events or KPI impacts, which are correlated with computing resources or processes to generate remedial action recommendations.
- Machine learning computer models trained on historical data
- Forecasting IT events and KPI impacts
- Generating correlation graph data structures
- Correlating forecasted output with computing resources or processes
- Generating remedial action recommendations based on forecasts and correlations
Potential Applications
This technology could be applied in various industries such as finance, healthcare, and manufacturing to predict IT events and their impacts on key performance indicators, allowing organizations to proactively address potential issues.
Problems Solved
This technology helps organizations anticipate IT events and their effects on operational processes, enabling them to take preventive measures and optimize performance based on forecasted information.
Benefits
The benefits of this technology include improved decision-making, enhanced operational efficiency, reduced downtime, and cost savings through proactive management of IT events and their impacts on key performance indicators.
Potential Commercial Applications
Potential commercial applications of this technology include IT service management, predictive maintenance, risk management, and performance optimization in various industries.
Possible Prior Art
One possible prior art for this technology could be existing predictive analytics tools used in IT operations management and business intelligence software.
Unanswered Questions
How does this technology handle real-time data processing for forecasting IT events and KPI impacts?
The patent application does not specify the real-time data processing capabilities of the machine learning models for forecasting IT events and KPI impacts.
What types of remedial action recommendations are generated based on the forecasted output and correlation results?
The patent application does not detail the specific types of remedial action recommendations that are generated for addressing forecasted IT events and their impacts on key performance indicators.
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.
