International business machines corporation (20240291724). Allocation of Resources to Process Execution in View of Anomalies simplified abstract
Contents
Allocation of Resources to Process Execution in View of Anomalies
Organization Name
international business machines corporation
Inventor(s)
Neelamadhav Gantayat of Bangalore (IN)
Renuka Sindhgatta Rajan of Bengaluru (IN)
Ravi Shankar Arunachalam of Bangalore (IN)
SAMPATH Dechu of Acton MA (US)
Allocation of Resources to Process Execution in View of Anomalies - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240291724 titled 'Allocation of Resources to Process Execution in View of Anomalies
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 of organizational processes.
- Machine learning computer models are trained on historical data of events and KPIs in organizational processes.
- The models forecast KPI impact based on events.
- Correlation graph data structures are generated to map events to IT computing resources or KPI impacts to organizational processes.
- A unified model is trained to model both organizational processes and IT resources.
- The trained models process input data to generate a forecast output of IT events or KPI impacts.
- The forecasted output is correlated with IT computing resources or organizational processes using correlation graph data structures.
- Remedial action recommendations, including resource allocations, are generated based on the forecast output and correlation output.
Potential Applications: - Forecasting IT and environmental impacts on KPIs in various industries. - Optimizing resource allocation based on forecasted outputs. - Improving decision-making processes in organizations.
Problems Solved: - Lack of accurate forecasting tools for IT and environmental impacts on KPIs. - Inefficient resource allocation based on historical data. - Difficulty in correlating events with IT resources and KPI impacts.
Benefits: - Enhanced predictive capabilities for organizations. - Improved resource management and decision-making. - Increased efficiency and performance in organizational processes.
Commercial Applications: Title: "Predictive Analytics for Organizational Performance Optimization" This technology can be applied in industries such as finance, healthcare, and manufacturing to optimize operations, improve efficiency, and drive better business outcomes.
Questions about the technology: 1. How does this technology improve decision-making processes in organizations? 2. What are the key advantages of using machine learning models for forecasting IT and environmental impacts on KPIs?
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 events and kpis of organizational processes (ops). the ml computer model(s) forecast kpi impact given events. correlation graph data structure(s) are generated that map at least one of events to it computing resources, or kpi impacts to ops. a unified model is trained to model ops and it resources. the trained ml computer model(s) and unified model 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 that comprises a resource allocation is generated based on the forecast output and correlation output.
