Masayo A Iida

US Patent:

20220237383, Jul 28, 2022

Filed:

Jan 19, 2022

Appl. No.:

17/579007

Inventors:

- Santa Clara CA, US
Edwin Sapugay - Foster City CA, US
Phani Bhushan Kumar Nivarthi - Fremont CA, US
Masayo Iida - Mountain View CA, US
Sathwik Tejaswi Madhusudhan - Santa Clara CA, US

International Classification:

G06F 40/30
G06F 40/279
G06N 20/00

Abstract:

A natural language understanding (NLU) framework includes an a concept system that performs concept matching of user utterances. The concept system generates a concept cluster model from sample utterances of an intent-entity model, and then trains a machine learning (ML) concept model based on the concept cluster model. Once trained, the concept model receives semantic vectors representing potential concepts extracted from utterances, and provides concept indicators to an ensemble scoring system. These concept indicators include indications of which concepts of the concept model that matched to the potential concepts, which intents of the intent-entity model are related to these concepts, and concept-relationship scores indicating a strength and/or uniqueness of the relationship between each concept-intent combination. Based on these concept-related indicators, the ensemble scoring system may determine and apply an ensemble scoring adjustment when determining an ensemble artifact score for each of the artifacts extracted from an utterance.

US Patent:

20200184272, Jun 11, 2020

Filed:

Dec 7, 2018

Appl. No.:

16/213981

Inventors:

- Menlo Park CA, US
Karan SAMEL - Pleasanton CA, US
Xu MIAO - Los Altos CA, US
Maram NAGENDRAPRASAD - Menlo Park CA, US
Ankit ARYA - San Jose CA, US
Adil MOHAMMED - Hyderabad, IN
Baiji HE - Mountain View CA, US
Masayo IIDA - Mountain View CA, US

International Classification:

G06K 9/62
G06N 20/00

Abstract:

One embodiment of the present invention sets forth a technique for managing machine learning. The technique includes organizing a set of reusable components for performing machine learning under a framework. The technique also includes representing, within the framework, a machine learning model as a graph-based structure that includes nodes representing a subset of the reusable components and edges representing input-output relationships between pairs of the nodes. The technique further includes validating the machine learning model based on inputs and outputs associated with the nodes and the input-output relationships represented by the edges in the graph-based structure. Finally, the technique includes generating the machine learning model according to the graph-based structure and configurations for the subset of the reusable components.

US Patent:

20190354810, Nov 21, 2019

Filed:

May 21, 2019

Appl. No.:

16/418848

Inventors:

- Menlo Park CA, US
Xu MIAO - Los Altos CA, US
Zhenjie Zhang - Fremont CA, US
Masayo IIDA - Mountain View CA, US
Maran NAGENDRAPRASAD - San Ramon CA, US

International Classification:

G06K 9/62
G06N 3/04
G06F 3/0482

Abstract:

One embodiment of the present invention sets forth a technique for processing training data for a machine learning model. The technique includes training the machine learning model using training data comprising a set of features and a set of original labels associated with the set of features. The technique also includes generating multiple groupings of the training data based on internal representations of the training data in the machine learning model. The technique further includes replacing, in a first subset of groupings of the training data, a first subset of the original labels with updated labels based at least on occurrences of values for the original labels in the first subset of groupings.

Author:

Masayo Iida

ISBN #:

0937073040

Author:

Masayo Iida

ISBN #:

0937073253