Jesse Smith

US Patent:

20210294811, Sep 23, 2021

Filed:

Jan 13, 2021

Appl. No.:

17/148344

Inventors:

- Bellevue WA, US
Michael Buhrmann - North Bend WA, US
Ali Shokoufandeh - New Hope PA, US
Jesse Smith - Bellevue WA, US
Yakov Keselman - Bellevue WA, US
Kurtis Peter Dane - Newcastle WA, US

International Classification:

G06F 16/2457
G06F 16/248
G06F 16/28
G06F 16/2452
G06F 16/36
G06F 16/33
G06F 40/14
G06Q 10/10

Abstract:

A microprocessor executable method and system for determining the semantic relatedness and meaning between at least two natural language sources is described in a prescribed context. Portions of natural languages are vectorized and mathematically processed to express relatedness as a calculated metric. The metric is associable to the natural language sources to graphically present the level of relatedness between at least two natural language sources. The metric may be re-determined with algorithms designed to compare the natural language sources with a knowledge data bank so the calculated metric can be ascertained with a higher level of certainty.

US Patent:

20190294965, Sep 26, 2019

Filed:

Mar 22, 2019

Appl. No.:

16/361917

Inventors:

- Bellevue WA, US
Michael Buhrmann - North Bend WA, US
Ali Shokoufandeh - New Hope PA, US
Jesse Smith - Bellevue WA, US

International Classification:

G06N 3/08
G06N 20/00

Abstract:

A system and method for creating an intelligent agent model for providing an expert opinion on source data includes obtaining at least one first data source for training an intelligent agent model, receiving training input on the at least one first data source from an individual, extracting a set of relevant attributes of the at least one first data source based on the received training input, weighting the set of relevant attributes based on at least one of the received training input or prior received training input, forming at least one neural network that comprises the intelligent agent model based on the set of relevant attributes and the weighting, ranking or scoring at least one section of a second data source using the intelligent agent model, and outputting the ranking or scoring.

US Patent:

20180365229, Dec 20, 2018

Filed:

Jun 18, 2018

Appl. No.:

16/011143

Inventors:

- Redmond WA, US
Michael Buhrmann - North Bend WA, US
Ali Shokoufandeh - New Hope PA, US
Jesse Smith - Bellevue WA, US

International Classification:

G06F 17/27
G06F 17/30
G06N 99/00

Abstract:

A microprocessor executable method transforms unstructured natural language texts by way of a preprocessing pipeline into a structured data representation of the entities described in the original text. The structured data representation is conducive to further processing by machine methods. The transformation process is learned by a machine learned model trained to identify relevant text segments and disregard irrelevant text segments The resulting structured data representation is refined to more accurately represent the respective entities.

US Patent:

20160232160, Aug 11, 2016

Filed:

Nov 25, 2015

Appl. No.:

14/952495

Inventors:

- Redmond WA, US
Michael Buhrmann - North Bend WA, US
Ali Shokoufandeh - New Hope PA, US
Jesse Smith - Bellevue WA, US
Yakov Keselman - Bellevue WA, US
Kurtis Peter Dane - Newcastle WA, US

International Classification:

G06F 17/30
G06Q 10/10
G06F 17/22

Abstract:

A microprocessor executable method and system for determining the semantic relatedness and meaning between at least two natural language sources is described in a prescribed context. Portions of natural languages are vectorized and mathematically processed to express relatedness as a calculated metric. The metric is associable to the natural language sources to graphically present the level of relatedness between at least two natural language sources. The metric may be re-determined with algorithms designed to compare the natural language sources with a knowledge data bank so the calculated metric can be ascertained with a higher level of certainty.