Method and system for contraindicating firmware and driver updates

Patent metadata

Field	Value
Publication number	US-11144302-B2
Application number	US-201916670682-A
Country	US
Kind code	B2
Filing date	Oct 31, 2019
Priority date	Oct 31, 2019
Publication date	Oct 12, 2021
Grant date	Oct 12, 2021

Abstract

A method and system for contraindicating firmware and driver updates. Specifically, the disclosed method and system entail discerning whether installation of a hardware device firmware and/or device driver update, targeting a hardware device on a host device, would succeed or fail given a set of features (or indicators) reflective of the current host device state and metadata respective to the hardware device update. Further, the determination may employ predictive machine learning techniques.

First claim

What is claimed is:

1. A method for installing hardware device updates, comprising:
receiving a first hardware device update concerning a first hardware device on a host device;
submitting a first feature set, pertinent to predicting an installation outcome for the first hardware device update, for processing;
receiving, following the processing, a first predicted update outcome for the first hardware device update; and
attempting, based on the first predicted update outcome, an installation of the first hardware device update onto the host device,
wherein the first feature set comprises a set of indicator values aggregated from the host device and metadata descriptive of the first hardware device update, the indicator values indicating a design architecture associated with the first hardware device update, the hardware device, one or more dependencies of the hardware device update, and a name associated with a manufacturer for each of the one or more dependencies, and
wherein processing of the first feature set comprises using a naïve Bayes classifier as a predictive machine learning algorithm.

2. The method of claim 1 , wherein the first hardware device update is one selected from a group consisting of a firmware update applicable to hardware device firmware stored on the first hardware device and a driver update applicable to a hardware device driver stored within a host operating system executing on the host device.

3. The method of claim 1 , wherein the first predicted update outcome anticipates a successful installation of the first hardware device update onto the host device.

4. The method of claim 1 , further comprising:
obtaining a first actual update outcome observed for the installation of the first hardware device update onto the host device; and
submitting the first actual update outcome for storage.

5. The method of claim 4 , wherein the first actual update outcome indicates one selected from a group consisting of a complete and an incomplete, installation of the first hardware device update onto the host device.

6. The method of claim 1 , further comprising:
receiving a second hardware device update concerning a second hardware device on the host device;
submitting a second feature set, pertinent to predicting an installation outcome for the second hardware device update, for processing;
receiving, following the processing, a second predicted update outcome for the second hardware device update; and
passing, based on the second predicted update outcome, on an installation of the second hardware device update onto the host device.

7. The method of claim 6 , wherein the second predicted update outcome anticipates an unsuccessful installation of the second hardware device update onto the host device.

8. A non-transitory computer readable medium (CRM) comprising computer readable program code, which when executed by a computer processor, enables the computer processor to:
receive a first hardware device update concerning a first hardware device on a host device;
submit a first feature set, pertinent to predicting an installation outcome for the first hardware device update, for processing;
receive, following the processing, a first predicted update outcome for the first hardware device update; and
attempt, based on the first predicted update outcome, an installation of the first hardware device update onto the host device, wherein the first feature set comprises a set of indicator values aggregated from the host device and metadata descriptive of the first hardware device update, the indicator values indicating a design architecture associated with the first hardware device update, the hardware device, one or more dependencies of the hardware device update, and a name associated with a manufacturer for each of the one or more dependencies, and wherein processing of the first feature set comprises using a naïve Bayes classifier as a predictive machine learning algorithm.

9. The non-transitory CRM of claim 8 , wherein the first hardware device update is one selected from a group consisting of a firmware update applicable to hardware device firmware stored on the first hardware device and a driver update applicable to a hardware device driver stored within a host operating system executing on the host device.

10. The non-transitory CRM of claim 8 , wherein the first predicted update outcome anticipates a successful installation of the first hardware device update onto the host device.

11. The non-transitory CRM of claim 8 , comprising computer readable program code, which when executed by the computer processor, further enables the computer processor to:
obtain a first actual update outcome observed for the installation of the first hardware device update onto the host device; and
submit the first actual update outcome for storage.

12. The non-transitory CRM of claim 11 , wherein the first actual update outcome indicates one selected from a group consisting of a complete and an incomplete, installation of the first hardware device update onto the host device.

13. A system, comprising:
a plurality of host devices comprising a host device comprising a hardware device; and
an update agent executing on the host device and programmed to:
receive a hardware device update concerning the hardware device;
submit a feature set, pertinent to predicting an installation outcome for the hardware device update, for processing;
receive, following the processing, a predicted update outcome for the hardware device update; and
attempt, based on the predicted update outcome, an installation of the hardware device update onto the host device,
wherein the feature set comprises a set of indicator values aggregated from the host device and metadata descriptive of the hardware device update, the indicator values indicating a design architecture associated with the hardware device update, the hardware device, one or more dependencies of the hardware device update, and a name associated with a manufacturer for each of the one or more dependencies, and
wherein processing of the first feature set comprises using a naïve Bayes classifier as a predictive machine learning algorithm.

14. The system of claim 13 , further comprising:
an update failure predictor comprising a computer processor and operatively connected to the plurality of host devices,
wherein the feature set is submitted to and the predicted update outcome is received from the update failure predictor.

Assignees

• Emc Ip Holding Co Llc

Inventors

• Arakkal Rajeev
• Shetty Sajna N
• Anthuvan Felix Stephen
• Voovaiah Jagadeesh Nerambol
• Katte Shrinidhi
• Kumar Sachin

Classifications

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• G06N3/08

  Learning methods · CPC title

• G06F8/61

  Installation · CPC title

• G06F8/65 Primary

  Updates (security arrangements therefor G06F21/57) · CPC title

• G06N20/00

  Machine learning · CPC title