Oracle international corporation (20240127141). PREDICTING DOWNSTREAM SCHEDULE EFFECTS OF USER TASK ASSIGNMENTS simplified abstract
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 20240127141 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 the downstream effects of assigning tasks to workers, such as delays and performance improvements on subsequent tasks.
- The system identifies workers with qualifications matching recommended qualifications for tasks.
- A trained machine learning model is applied to task performance data to generate predictions of downstream effects.
- Downstream effects include delays, performance improvements on subsequent tasks, and effects on tasks performed by other workers in the work environment.
Potential Applications
This technology could be applied in various industries where task assignments to workers play a crucial role in optimizing productivity and efficiency. Some potential applications include:
- Manufacturing plants
- Call centers
- Healthcare facilities
Problems Solved
This technology addresses the following problems in task assignment management:
- Matching workers with tasks based on qualifications and past performance data
- Predicting downstream effects of task assignments on worker performance and overall productivity
Benefits
The use of machine learning in task assignment management offers several benefits, including:
- Improved worker productivity and efficiency
- Optimal task allocation based on qualifications and performance data
- Enhanced overall performance and output in the work environment
Potential Commercial Applications
The technology described in the patent application has potential commercial applications in industries where efficient task assignment management is critical for success. Some potential commercial applications include:
- Task management software companies
- Human resources consulting firms
- Productivity optimization services
Possible Prior Art
One possible prior art for this technology could be traditional task assignment methods based on manual evaluation of worker qualifications and performance data. However, the use of machine learning to predict downstream effects of task assignments is a novel approach that sets this technology apart from conventional methods.
Unanswered Questions
How does the system handle real-time changes in worker qualifications and performance data?
The patent application does not provide details on how the system adapts to real-time changes in worker qualifications and performance data. This aspect is crucial for ensuring the accuracy and effectiveness of task assignments in dynamic work environments.
What measures are in place to protect worker privacy and data security in the system?
The patent application does not address the issue of worker privacy and data security in the system. It is essential to consider how sensitive worker information is handled and protected to maintain trust and compliance with data privacy regulations.
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.
