18446375. PREDICTING DOWNSTREAM SCHEDULE EFFECTS OF USER TASK ASSIGNMENTS simplified abstract (Oracle International Corporation)
Contents
- 1 PREDICTING DOWNSTREAM SCHEDULE EFFECTS OF USER TASK ASSIGNMENTS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 PREDICTING DOWNSTREAM SCHEDULE EFFECTS OF USER TASK ASSIGNMENTS - 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
PREDICTING DOWNSTREAM SCHEDULE EFFECTS OF USER TASK ASSIGNMENTS
Organization Name
Oracle International Corporation
Inventor(s)
Robert St. Pierre of Campbell CA (US)
Mark Pearson of Redwood Shores CA (US)
PREDICTING DOWNSTREAM SCHEDULE EFFECTS OF USER TASK ASSIGNMENTS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18446375 titled 'PREDICTING DOWNSTREAM SCHEDULE EFFECTS OF USER TASK ASSIGNMENTS
Simplified Explanation
The patent application describes a system that uses machine learning to match workers with tasks in a work environment based on their qualifications and past performance data. The system predicts downstream effects of task assignments, such as delays and performance improvements on subsequent tasks.
- The system identifies workers with qualifications matching recommended qualifications for a task.
- A trained machine learning model is applied to task performance data to generate predictions of downstream effects of task assignments.
Potential Applications
This technology could be applied in various industries where task assignments to workers need to be optimized for efficiency and performance.
Problems Solved
This technology helps in efficiently assigning tasks to workers based on their qualifications and past performance data, leading to improved overall performance in a work environment.
Benefits
The system helps in reducing delays, improving task performance, and optimizing task assignments in a work environment.
Potential Commercial Applications
"Optimizing Task Assignments in Work Environments" - This technology could be used in industries such as manufacturing, logistics, and customer service to improve task assignment processes.
Possible Prior Art
There may be prior art related to task assignment optimization systems in various industries, but specific examples are not provided in the patent application.
Unanswered Questions
How does the system handle real-time changes in worker qualifications or task requirements?
The patent application does not specify how the system adapts to changes in worker qualifications or task requirements over time.
What measures are in place to ensure data privacy and security of the task performance data used by the system?
The patent application does not detail the security measures implemented to protect the task performance data used in the machine learning model.
Original Abstract Submitted
Techniques for managing task assignments to workers in a work environment are disclosed. A system identifies one or more workers with qualifications that match recommended qualifications to perform a task in a work environment. The system applies a trained machine learning model to task performance data associated with the worker, such as a past history of tasks performed and statistics associated with the performance of the task. The machine learning model generates a prediction of downstream effects associated with assigning the task to the user. The downstream effects include delays and performance improvements on subsequent tasks performed by the worker, as well as effects on tasks performed by other workers, at work centers in the work environment.
