Saudi Arabian Oil Company (20240338639). SYSTEM AND METHOD FOR ASSESSING WORKER JOB PERFORMANCE FITNESS simplified abstract
Contents
SYSTEM AND METHOD FOR ASSESSING WORKER JOB PERFORMANCE FITNESS
Organization Name
Inventor(s)
Turki G. Al Zahrani of Udhailiyah (SA)
Bashar Y. Melhem of Abqaiq (SA)
SYSTEM AND METHOD FOR ASSESSING WORKER JOB PERFORMANCE FITNESS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240338639 titled 'SYSTEM AND METHOD FOR ASSESSING WORKER JOB PERFORMANCE FITNESS
Abstract: An integrated analytical model assesses the ability and fitness of a field worker to take on critical plant jobs or tasks. The inputs to the model include data about the worker's level of experience, competency, physical condition, workload, medical history, stress level as well as a live feed of health data obtained from devices worn by the worker. This data is fed into a machine learning model to assess the worker's ability to conduct work as well as the risk level to people or equipment or operations, serving as a tool to protect workers and assets. The model also provides root causes analysis for not assigning the task to the worker and recommends corrective actions.
- Simplified Explanation:
An analytical model evaluates a field worker's fitness for critical plant tasks based on various data inputs, including experience, competency, physical condition, and health data from wearable devices. The model uses machine learning to assess worker ability and risk levels, offering insights for task assignment and corrective actions.
- Key Features and Innovation:
- Integration of worker data and health metrics for task assessment - Machine learning model for worker ability and risk evaluation - Root cause analysis for task assignment decisions - Recommendations for corrective actions to improve worker performance and safety
- Potential Applications:
- Industrial plant operations - Agriculture and farming - Construction sites - Hazardous work environments
- Problems Solved:
- Assessing worker fitness for critical tasks - Identifying potential risks to workers and equipment - Improving overall safety and efficiency in the workplace
- Benefits:
- Enhanced worker safety - Optimal task assignment - Reduced risk of accidents and injuries - Improved productivity and performance
- Commercial Applications:
Title: "Enhancing Worker Safety and Efficiency in Industrial Operations" This technology can be applied in various industries such as manufacturing, agriculture, and construction to ensure worker safety, optimize task assignments, and improve overall operational efficiency. Companies can use this model to protect their workers, assets, and operations while enhancing productivity.
- Questions about Worker Fitness Assessment:
1. How does the integrated analytical model use machine learning to evaluate a field worker's ability for critical tasks? The model utilizes data on the worker's experience, competency, physical condition, and health metrics from wearable devices to assess their fitness for specific jobs, incorporating machine learning algorithms for accurate evaluations.
2. What are the key benefits of using this technology in industrial plant operations? By implementing this technology, companies can enhance worker safety, optimize task assignments, reduce risks to personnel and equipment, and ultimately improve overall productivity and performance in industrial settings.
Original Abstract Submitted
an integrated analytical model assesses the ability and fitness of a field worker to take on critical plant jobs or tasks. the inputs to the model include data about the worker's level of experience, competency, physical condition, workload, medical history, stress level as well as a live feed of health data obtained from devices worn by the worker. this data is fed into a machine learning model to assess the worker's ability to conduct work as well as the risk level to people or equipment or operations, serving as a tool to protect workers and assets. the model also provides root causes analysis for not assigning the task to the worker and recommends corrective actions.
