Michael Anthony Bender

US Patent:

20220374220, Nov 24, 2022

Filed:

May 18, 2021

Appl. No.:

17/323216

Inventors:

- NEW YORK NY, US
Martin G. Keen - Cary NC, US
Jeremy R. Fox - Georgetown TX, US
Michael Bender - Rye Brook NY, US

International Classification:

G06F 8/65
H04L 9/06

Abstract:

A processor may load one or more potential updates to a trusted source from an identified trusted source. The processor may determine acceptable update windows. The processor may apply at least one of the one or more potential updates to the computing device. The processor may identify if a steady state has been reached. The processor may determine whether the at least one update has succeeded.

US Patent:

20220309102, Sep 29, 2022

Filed:

Mar 23, 2021

Appl. No.:

17/209364

Inventors:

- Armonk NY, US
Aaron K. Baughman - Cary NC, US
Michael Bender - Rye Brook NY, US
Martin G. Keen - Cary NC, US

International Classification:

G06F 16/907

Abstract:

In an approach for derivation of progressively variant dark data utility for legacy system candidate microservices, a processor analyzes a variability of data stored in a legacy system for a plurality of data metrics. A processor measures, for each data metric, a utility of the data using an intra-analysis and a meta-analysis. A processor correlates the plurality of data metrics to candidate microservices. A processor generates insights for the candidate microservices based on the utility of the data.

US Patent:

20210209144, Jul 8, 2021

Filed:

Jan 3, 2020

Appl. No.:

16/734041

Inventors:

- Armonk NY, US
Martin G. Keen - Cary NC, US
Michael Bender - Rye Brook NY, US
Victor Povar - Vancouver, CA

Assignee:

International Business Machines Corporation - Armonk NY

International Classification:

G06F 16/36
G06F 16/35
H04L 29/08

Abstract:

First and second sets of sensor data from first and second IoT device sensors are collected. A first set of significant sensor data representing a first event is extracted from the first set of sensor data. A set of terms comprising portions of the first set of significant sensor data is weighted. By analyzing the weighted set of terms, a first set of critical variables describing the first event is identified and added to an ontology. Similarly, a second set of significant sensor data is extracted and a second set of critical variables describing the second event is identified and added to the ontology. Using the ontology, it is determined that the first event is of an event type of the second event. The first sensor and the second sensor are classified to be different variants of a class of sensors that is configurable to sense the event type.

US Patent:

20210166162, Jun 3, 2021

Filed:

Dec 2, 2019

Appl. No.:

16/700340

Inventors:

- Armonk NY, US
Martin G. Keen - Cary NC, US
Michael Bender - Rye Brook NY, US

Assignee:

International Business Machines Corporation - Armonk NY

International Classification:

G06Q 10/04
G06N 3/04
G06F 17/18

Abstract:

In an embodiment, a correlated forecast is computer generated by a processor that receives input data for historic values of a first input variable, creates forecast data for future values of the first input variable using the historic values of the first input variable, generates diagnostic data based on a diagnostic analysis of the forecast data, creates a first diagnostic variable that includes a first diagnostic value from a first cognitive process, generates a feature vector based on a second cognitive process that determines the feature vector by identifying a correlation between the first diagnostic variable and a second diagnostic variable, and generates a final forecast using the feature vector as an input for a cognitive forecasting process, where the first cognitive process determines the first diagnostic value based on the diagnostic data.

US Patent:

20210064709, Mar 4, 2021

Filed:

Aug 30, 2019

Appl. No.:

16/556291

Inventors:

- Armonk NY, US
Martin G. Keen - Cary NC, US
Michael Bender - Rye Brook NY, US
Jeremy R. Fox - Georgetown TX, US

International Classification:

G06F 17/50
G06T 7/00
B33Y 50/00
B29C 64/386

Abstract:

In an approach to determining the optimal size and shape of additive manufactured packaging, a delivery location corpus, a storage location corpus, and a transportation mechanism corpus are received. A notification to design a package for an item is received. The optimal design attributes are determined for the delivery location. The optimal design attributes are determined for the storage location. The optimal design attributes are determined for the transportation mechanism. The additive manufactured packaging for the item is designed based on the optimal design attributes for the delivery location, the storage location, and the transportation mechanism.

US Patent:

20200394273, Dec 17, 2020

Filed:

Jun 12, 2019

Appl. No.:

16/438715

Inventors:

- ARMONK NY, US
Martin G. Keen - Cary NC, US
Michael Bender - Rye Brook NY, US
Aaron K. Baughman - Silver Spring MD, US

International Classification:

G06F 17/28
G06N 20/00
G06F 17/27

Abstract:

A self-learning natural-language generation (NLG) system receives raw data from Internet-of-Things sensors or other data sources and a set of natural-language reports previously generated from the raw data by a legacy report-generation mechanism. The system divides the reports into two groups that are distinguished by differences in temporal characteristics of the reports or of the raw data from which each report is generated. The system performs a diachronic linguistic analysis that correlates values of the temporal characteristics with differences between linguistic features of each report group's natural-language text. The system creates translation rules that instruct the NLG system how to reproduce these differences and uses the rules to translate the raw data into its own natural-language reports. The system then compares the new and legacy reports and, if the new reports do not accurately reproduce the linguistic differences, analyzes more reports to improve its ability to accurately generate natural-language text.

US Patent:

20200380352, Dec 3, 2020

Filed:

May 29, 2019

Appl. No.:

16/424654

Inventors:

- Armonk NY, US
Martin G. Keen - Cary NC, US
Michael Bender - Rye Brook NY, US
Aaron K. Baughman - Cary NC, US

International Classification:

G06N 3/08
G06N 3/04

Abstract:

A neural network system for generating a quality assurance alert is provided. A computing device analyzes a quality assurance profile. A computing device arranges data in neurons of, at least, a first layer of a neural network. A computing device generates a threshold level of prediction of quality assurance based, at least in part, on output data from a neural network. A computing device applies output data from a neural network to a regression profile to determine a probability that a quality assurance issue will occur. A computing device generates a message that includes a quality assurance evaluation based, at least, on the determined probability that the quality assurance issue will occur.