17793107. TRANSPORT SLICE IDENTIFIER ENCODING IN DATA PLANE FOR AI/ML-BASED CLASSIFICATION MODEL simplified abstract (RAKUTEN MOBILE, INC.)
Contents
- 1 TRANSPORT SLICE IDENTIFIER ENCODING IN DATA PLANE FOR AI/ML-BASED CLASSIFICATION MODEL
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 TRANSPORT SLICE IDENTIFIER ENCODING IN DATA PLANE FOR AI/ML-BASED CLASSIFICATION MODEL - 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
TRANSPORT SLICE IDENTIFIER ENCODING IN DATA PLANE FOR AI/ML-BASED CLASSIFICATION MODEL
Organization Name
Inventor(s)
Amit Dhamija of Bengaluru (IN)
TRANSPORT SLICE IDENTIFIER ENCODING IN DATA PLANE FOR AI/ML-BASED CLASSIFICATION MODEL - A simplified explanation of the abstract
This abstract first appeared for US patent application 17793107 titled 'TRANSPORT SLICE IDENTIFIER ENCODING IN DATA PLANE FOR AI/ML-BASED CLASSIFICATION MODEL
Simplified Explanation
The abstract describes a method for identifying a transport network slice in a data plane of a transport network using a network device. The method involves generating a transport slice identifier, transmitting a configuration message to request a transport network path, obtaining predictions using AI/ML models, and applying the required configurations to the transport network slice.
- Identifying transport network slices in a data plane using network devices
- Generating transport slice identifiers and requesting network paths
- Obtaining predictions using AI/ML models based on historical information
- Applying required configurations to the transport network slice
Potential Applications
This technology could be applied in telecommunications, networking, and cloud computing industries to efficiently manage and optimize transport network slices.
Problems Solved
This technology solves the problem of efficiently identifying and configuring transport network slices in a data plane, improving network performance and reliability.
Benefits
The benefits of this technology include enhanced network management, optimized resource allocation, improved network efficiency, and enhanced user experience.
Potential Commercial Applications
Potential commercial applications of this technology include network infrastructure management software, network optimization tools, and cloud service providers offering enhanced networking services.
Possible Prior Art
One possible prior art could be traditional methods of manually configuring transport network slices without the use of AI/ML models for prediction and optimization.
What are the specific AI/ML models used for prediction in this technology?
The abstract mentions the use of AI/ML models for obtaining predictions of required configurations. It would be helpful to know the specific algorithms or models used for this purpose.
How does this technology compare to existing methods of configuring transport network slices?
It would be interesting to explore how this technology improves upon traditional methods of configuring transport network slices and the specific advantages it offers in terms of efficiency and performance.
Original Abstract Submitted
Provided are methods and apparatuses for identifying a transport network slice in a data plane of a transport network by a network device. In an embodiment, the method includes generating a transport slice identifier corresponding to the transport network slice. The method further includes transmitting a configuration message requesting rendering of a transport network path assigned to the transport network slice. The method further includes obtaining a prediction, using an artificial intelligence/machine learning (AI/ML) model, of at least one required configuration of the transport network slice, based at least on historical information related to the transport network slice. The method further includes applying the at least one required configuration to the transport network slice.
