Offer driven deep learning capability

Patent metadata

Field	Value
Publication number	US-11599834-B2
Application number	US-201916683980-A
Country	US
Kind code	B2
Filing date	Nov 14, 2019
Priority date	Nov 14, 2019
Publication date	Mar 7, 2023
Grant date	Mar 7, 2023

Abstract

A system, method, and computer-readable medium are disclosed for configuring and offering upgrade capability to information handling systems. A deep learning or machine learning model (DL/ML) model is trained to optimize a particular configuration and use cases for an information handling system and provides various levels of upgrades for the use cases. Levels are identified as base or upgrade and mapped to a licensing layer that enables or disables use of performance levels based on weights that enable base output level classes and disable upgrade output level classes. An offer to upgrade is made as to upgrade levels upon a determination of probabilities of performance output level classes.

First claim

What is claimed is:

1. A computer-implementable method for configuring a deep learning/machine learning (DL/ML) model to optimize information handling systems:
training the DL/ML model to optimize a particular configuration and use case for an information handling system and provide output level classes of an artificial neural network implemented as to performance level classification identified as output nodes of the artificial neural network;
selecting output level classes identified as base or upgrade;
mapping the output level classes to a licensing layer that enables or disables use of performance levels based on weights that enable base output level classes and disable upgrade output level classes; and
offering an upgrade to the output level classes upon a determination of probabilities of performance output level classes.

2. The method of claim 1 further comprising deploying the upgrade output level classes upon acceptance of the offering.

3. The method of claim 2 , wherein deploying is through a host application that interfaces with the licensing layer.

4. The method of claim 1 , wherein the DL/ML model includes one or more artificial neural networks that perform the training.

5. The method of claim 1 , wherein DL/ML model is included in a pre-installed host application on the information handling system.

6. The method of claim 1 , wherein the output level classes include intermediary output level classes.

7. The method of claim 1 further comprising training the DL/ML model for other use cases that are offered together as a business offering.

8. A system comprising:
a processor;
a data bus coupled to the processor; and
a non-transitory, computer-readable storage medium embodying computer program code, the non-transitory, computer-readable storage medium being coupled to the data bus, the computer program code interacting with a plurality of computer operations configuring a deep learning/machine learning (DL/ML) model to optimize information handling systems executable by the processor and configured for:
training the DL/ML model to optimize a particular configuration and use case for an information handling system and provide output level classes of an artificial neural network implemented as to performance level classification identified as output nodes of the artificial neural network;
selecting output level classes identified as base or upgrade;
mapping the output level classes to a licensing layer that enables or disables use of performance levels based on weights that enable base output level classes and disable upgrade output level classes; and
offering an upgrade to the output level classes upon a determination of probabilities of performance output level classes.

9. The system of claim 8 further comprising deploying the upgrade output level classes upon acceptance of the offering.

10. The system of claim 9 , wherein deploying is through a host application that interfaces with the licensing layer.

11. The system of claim 8 , wherein the DL/ML model includes one or more artificial neural networks that perform the training.

12. The system of claim 8 , wherein DL/ML model is included in a pre-installed host application on the information handling system.

13. The system of claim 8 , wherein the output level classes include intermediary output level classes.

14. The system of claim 8 further comprising training the DL/ML model for other use cases that are offered together as a business offering.

15. A non-transitory, computer-readable storage medium embodying computer program code, the computer program code comprising computer executable instructions configured for:
training the DL/ML model to optimize a particular configuration and use case for an information handling system provide output level classes of an artificial neural network implemented as to performance level classification identified as output nodes of the artificial neural network;
selecting output level classes identified as base or upgrade;
mapping the output level classes to a licensing layer that enables or disables use of performance levels based on weights that enable base output level classes and disable upgrade output level classes; and
offering an upgrade to the output level classes upon a determination of probabilities of performance output level classes.

16. The non-transitory, computer-readable storage medium of claim 15 further comprising instructions configured for deploying the upgrade output level classes upon acceptance of the offering.

17. The non-transitory, computer-readable storage medium of claim 15 , wherein the DL/ML model includes one or more artificial neural networks that perform the training.

18. The non-transitory, computer-readable storage medium of claim 15 , wherein DL/ML model is included in a pre-installed host application on the information handling system.

19. The non-transitory, computer-readable storage medium of claim 15 , wherein the output level classes include intermediary output level classes.

20. The non-transitory, computer-readable storage medium of claim 15 further comprising instructions for training the DL/ML model for other use cases that are offered together as a business offering.

Assignees

• Dell Products Lp

Inventors

• Vichare Nikhil
• Cox Tyler R
• Hammons Marc R
• Bull Spencer G

Classifications

• G06N3/09

  Supervised learning · CPC title

• G06N3/0499

  Feedforward networks · CPC title

• G06N3/04

  Architecture, e.g. interconnection topology · CPC title

• G06Q10/04 Primary

  Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" (market predictions or forecasting for commercial activities G06Q30/0202) · CPC title

• G06N3/08

  Learning methods · CPC title