Meng Chen

US Patent:

20130151689, Jun 13, 2013

Filed:

Dec 8, 2011

Appl. No.:

13/314235

Inventors:

Pradeep GanapathyRaj - Bellevue WA, US
Michael McLean - Snoqualmie WA, US
Radu Serbanescu - Kirkland WA, US
Meng Hua Chen - Issaquah WA, US

Assignee:

Microsoft Corporation - Redmond WA

International Classification:

G06F 15/173

US Classification:

709224

Abstract:

Provisioning capacity measuring may be provided. First, a provisioning monitoring job may run on a grid manager that spawns a plurality of farm monitoring jobs onto a respective plurality of farms. Next, a user count for each of the respective plurality of farms may be respectively provided. The user count may comprise a number of users on each of the respective plurality of farms. An event alert may be provided when the user count for any of the respective plurality of farms is greater than a threshold.

US Patent:

20220270611, Aug 25, 2022

Filed:

Feb 23, 2021

Appl. No.:

17/183006

Inventors:

- Mountain View CA, US
Divya BEERAM - Newwark CA, US
Meng CHEN - Sunnyvale CA, US
Neo YUCHEN - Arcadia CA, US
Wan Yu ZHANG - Milpitas CA, US
Nivethitha KUMAR - Cupertino CA, US
Kavita SUNDAR - Redwood City CA, US
Tomer TAL - Cupertino CA, US

International Classification:

G10L 17/04
G06F 21/32
G10L 17/26
G10L 17/18
G10L 21/0208
G06N 20/20

Abstract:

Certain embodiments of the present disclosure provide techniques training a user detection model to identify a user of a software application based on voice recognition. The method generally includes receiving a data set including a plurality of voice interactions with users of a software application. For each respective recording in the data set, a spectrogram representation is generated based on the respective recording. A plurality of voice recognition models are trained. Each of the plurality of voice recognition models is trained based on the spectrogram representation for each of the plurality of voice recordings in the data set. The plurality of voice recognition models are deployed to an interactive voice response system.

US Patent:

20220215023, Jul 7, 2022

Filed:

Mar 28, 2022

Appl. No.:

17/656703

Inventors:

- Mountain View CA, US
Meng CHEN - Sunnyvale CA, US
Jing ZHANG - Poughkeepsie NY, US
Wenting CAI - Mountain View CA, US
Crystal MENG - Mountain View CA, US

International Classification:

G06F 16/2452
G06F 16/2457
G06F 16/242

Abstract:

Systems of the present disclosure generate database queries for financial information requested in a natural-language form. A natural-language processing (NLP) financial aggregator receives a request for financial information and extracts NLP features of the request, including keywords. The NLP financial aggregator determines a type of the request based on the features and creates a query in a database-query language for the financial information based on the type and on the features. The NLP financial aggregator submits the query to a database where the financial information is stored. The software then receives the financial information from the database and sends the information to the user.

US Patent:

20210342951, Nov 4, 2021

Filed:

Apr 30, 2020

Appl. No.:

16/863535

Inventors:

- Mountain View CA, US
Meng CHEN - Mountain View CA, US

Assignee:

INTUIT INC. - Mountain View CA

International Classification:

G06Q 40/00

Abstract:

Systems and methods that may be used to provide guidance and or tag suggestions to a user of an electronic accounting system and or service that overcome the shortcomings associated with user-defined tags.

US Patent:

20210149671, May 20, 2021

Filed:

Nov 19, 2019

Appl. No.:

16/688697

Inventors:

- Mountain View CA, US
Meng Chen - Sunnyvale CA, US
Linxia Liao - Fremont CA, US
Yehezkel Shraga Resheff - Jerusalem, IL

Assignee:

Intuit Inc. - Mountain View CA

International Classification:

G06F 9/30
G06N 5/02
G06N 3/08

Abstract:

A machine learning method. A source domain data structure and a target domain data structure are combined into a unified data structure. First data in the source domain data structure are latent with respect to second data in the target domain data structure. The unified data structure includes user vectors that combine the first data and the second data. The user vectors are transformed into a transformed data structure by applying a mapping function to the user vectors. The mapping function relates, using at least one parameter, first relationships in the source domain data structure to second relationships in the target domain data structure. The at least one parameter is based on a combination of affinity scores relating items with which the user interacted and did not interact. The transformed data structure is input into a machine learning model, from which is obtained a recommendation relating to the target domain.

US Patent:

20210103935, Apr 8, 2021

Filed:

Dec 17, 2020

Appl. No.:

17/125131

Inventors:

- Mountain View CA, US
Ngoc Nhung Ho - Sunnyvale CA, US
Bei Huang - Menlo Park CA, US
Meng Chen - San Jose CA, US

Assignee:

Intuit Inc. - Mountain View CA

International Classification:

G06Q 30/00
G06N 20/00

Abstract:

A method and system identify assistance offerings that are likely to be relevant to users of a data management system. The method and system utilize a multivariate random forest regression machine learning process to train an assistance offerings recommendation model to recommend relevant assistance offerings to users of the data management system. The multivariate random forest regression machine learning process replaces zero values in the training set data with negative numbers to increase the accuracy of the machine learning process.

US Patent:

20180350007, Dec 6, 2018

Filed:

May 31, 2017

Appl. No.:

15/610596

Inventors:

Meng Chen - Mountain View CA, US
Lei Pei - Mountain View CA, US
Zachary Grove Jennings - Mountain View CA, US
Ngoc Nhung Thi Ho - Mountain View CA, US

Assignee:

Intuit Inc. - Mountain View CA

International Classification:

G06Q 40/00
G06F 17/30
G06N 7/00

Abstract:

A method includes obtaining data related to a plurality of historical transactions, where each historical transaction is associated with a label based on a click stream created by the first user, generating a vector of features from the data related to each historical transaction, training, using the vectors and labels, a multinomial classifier to generate a probability that a specific transaction belongs to a specific classification with respect to income, obtaining data related to a new transaction from a financial stream for a second financial account of a second user of the financial service, generating a new vector of features from the data related to the new transaction, determining a classification with respect to income for the new transaction, and presenting the classification to the second user for review in a view of a graphical user interface.

US Patent:

20180285773, Oct 4, 2018

Filed:

Mar 31, 2017

Appl. No.:

15/476647

Inventors:

- Mountain View CA, US
Lei PEI - Mountain View CA, US
Meng CHEN - Mountain View CA, US
Nhung HO - Mountain View CA, US

International Classification:

G06N 99/00
G06N 5/04

Abstract:

The present disclosure provides a composite machine-learning system for a transaction labeling service. A transaction labeling service receives at least one descriptive string describing a transaction associated with a user. The service identifies a preliminary grouping from a generalized scheme. The service extracts a set of N-grams from the descriptive string and converts the N-grams and the preliminary grouping into a set of features. A machine-learning model determines a label from a labeling scheme for the transaction based on the features. User input related to the label includes an accuracy indicator and a reliability indicator. If the reliability indicator satisfies a reliability condition, a set of training data for the machine-learning model is updated based on the descriptive string and the label. The machine-learning model is then trained against the updated set of training data.