International business machines corporation (20240119343). MULTI-OBJECTIVE WORK PRIORITIZATION FOR COMMON ASSETS simplified abstract
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 20240119343 titled 'MULTI-OBJECTIVE WORK PRIORITIZATION FOR COMMON ASSETS
Simplified Explanation
The patent application describes a method for prioritizing work requests related to a physical asset by encoding them as multidimensional representations, reducing them to one-dimensional structures, and inputting them into a machine learning model for prioritization.
- 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
- Prioritizing work requests based on the machine learning model
- Outputting the priorities of each work request
Potential Applications
This technology can be applied in various industries such as manufacturing, maintenance, and facility management to prioritize work requests efficiently.
Problems Solved
1. Efficient prioritization of work requests related to physical assets 2. Automation of the prioritization process using machine learning
Benefits
1. Improved efficiency in handling work requests 2. Better allocation of resources based on priority levels 3. Reduction in manual effort for prioritization tasks
Potential Commercial Applications
Optimizing maintenance schedules, streamlining facility management processes, enhancing asset management systems
Possible Prior Art
There may be prior art related to machine learning models for prioritizing work requests, data structures for encoding multidimensional representations, and methods for automating task prioritization.
What is the impact of this technology on workforce productivity?
This technology can significantly improve workforce productivity by automating the prioritization of work requests, allowing employees to focus on executing tasks rather than determining their priority.
How does this technology compare to traditional methods of work request prioritization?
This technology offers a more efficient and accurate way of prioritizing work requests compared to traditional manual methods. By utilizing machine learning models and data structures, it can handle a large volume of requests and provide objective prioritization based on predefined criteria.
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.
