17955029. MULTI-OBJECTIVE WORK PRIORITIZATION FOR COMMON ASSETS simplified abstract (International Business Machines Corporation)
Contents
- 1 MULTI-OBJECTIVE WORK PRIORITIZATION FOR COMMON ASSETS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MULTI-OBJECTIVE WORK PRIORITIZATION FOR COMMON ASSETS - 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
MULTI-OBJECTIVE WORK PRIORITIZATION FOR COMMON ASSETS
Organization Name
International Business Machines Corporation
Inventor(s)
Rajesh Kumar Saxena of Thane East (IN)
Sandeep Sukhija of Rajasthan (IN)
MULTI-OBJECTIVE WORK PRIORITIZATION FOR COMMON ASSETS - A simplified explanation of the abstract
This abstract first appeared for US patent application 17955029 titled 'MULTI-OBJECTIVE WORK PRIORITIZATION FOR COMMON ASSETS
Simplified Explanation
The abstract of the patent application describes a method for prioritizing work requests related to a physical asset by encoding the requests as multidimensional representations, reducing them to one-dimensional structures, inputting them into a machine learning model, and outputting the prioritized list of work requests.
- Encoding work requests as multidimensional representations with classifications and sub-categories.
- Reducing multidimensional representations to one-dimensional structures while preserving variance factors.
- Inputting 1-D structures into a machine learning model for prioritization.
- Outputting prioritized list of work requests based on the machine learning model.
Potential Applications
This technology can be applied in various industries where there is a need to prioritize work requests related to physical assets, such as maintenance, repair, and operations.
Problems Solved
1. Efficient prioritization of work requests for physical assets. 2. Automation of the prioritization process using machine learning.
Benefits
1. Improved efficiency in managing work requests. 2. Better allocation of resources based on priority. 3. Reduction of downtime for physical assets.
Potential Commercial Applications
Optimizing maintenance schedules for manufacturing plants using predictive maintenance algorithms.
Possible Prior Art
There may be prior art related to machine learning models for prioritizing work requests, but specific details would need to be researched further.
Unanswered Questions
How does this technology handle real-time updates to work requests?
The article does not mention how the system would adapt to new work requests or changes in priorities once the initial prioritization has been done. This could be crucial in dynamic environments where priorities can shift quickly.
What are the potential limitations of using a machine learning model for prioritizing work requests?
The article does not discuss any potential drawbacks or challenges that may arise from relying on a machine learning model for prioritization. It would be important to consider factors such as data quality, model accuracy, and interpretability in real-world applications.
Original Abstract Submitted
Prioritizing a work request pertaining to a physical asset can include generating a data structure that encodes the work request as a multidimensional representation indicating at least one classification, each at least one classification including at least one sub-category. In response to identifying multiple work requests encoded as multidimensional representations with respect to the physical asset, each multidimensional representation can be reduced to a one-dimensional (1-D) representation that preserves a variance factor of each sub-category of each multidimensional representation. Each 1-D structure can be input to a machine learning model trained to prioritize each of the work requests. The work requests can be prioritized in accordance with the machine learning model based on the 1-D structures. The priorities of each of the work requests can be output.
