Kathryn L Ferguson

US Patent:

20230069498, Mar 2, 2023

Filed:

Aug 31, 2021

Appl. No.:

17/462642

Inventors:

- Spring TX, US
Kathryn Ferguson - Boise ID, US
Manjunath Bhuyar - Boise ID, US
Yeswanth Siva Tej Gowd Kuruba - Bangalore, IN
Purnendu Ghosh - Bangalore, IN
Vijay Raghava Reddy Pulkam - Boise ID, US

Assignee:

Hewlett-Packard Development Company, L.P. - Spring TX

International Classification:

G06F 11/07
G06F 11/34
G06N 20/00

Abstract:

An example non-transitory computer-readable storage medium comprises instructions executable by a processor to receive data including event log data and repair event data indicative of failures of the plurality of information technology (IT) devices, generate a plurality of images corresponding to the plurality of event codes of the event log data, and train a predictive data model using the plurality of images as inputs to the predictive data model and the repair event data as known outputs. For example, the predictive data model is trained to identify a plurality of failure windows and operational windows associated with the plurality of IT devices based on the repair event data and the event log data, and classify a plurality of sliding windows associated with the plurality of images based on the plurality of failure windows and operational windows.

US Patent:

20230012285, Jan 12, 2023

Filed:

Jan 10, 2020

Appl. No.:

17/783305

Inventors:

- Spring TX, US
Kathryn Janet Ferguson - Boise ID, US
Mark Q. Shaw - Boise ID, US
Jan Allebach - Boise ID, US

Assignee:

Hewlett-Packard Development Company, L.P. - Spring TX

International Classification:

G10L 25/51

Abstract:

Example implementations relate to audio samples to detect device anomalies. For example, computing device, comprising: a processing resource and a non-transitory computer readable medium storing instructions executable by the processing resource to: generate a matrix of audio information for a plurality of audio samples of a device, select audio information from one of the plurality of audio samples, generate a plurality of principal components for the selected audio information utilizing a principal component expansion, select a principal component from the plurality of principal components based on a quantity of variance, and detect an anomaly of the device based on a comparison between a real time audio sample of the device and the selected principal component.