Mohammad Sabah

US Patent:

20220321672, Oct 6, 2022

Filed:

Mar 14, 2022

Appl. No.:

17/654666

Inventors:

- Los Angeles CA, US
Mo LIN - Los Angeles CA, US
Mohammad SABAH - San Pedro CA, US

International Classification:

H04L 67/306
G06N 20/00

Abstract:

Embodiments of the present disclosure provide for improved management, selection, and provision of network asset data objects. For example, embodiments described throughout are configured to score various network assets corresponding to real-world assets, items, products, and/or the like, for selection and provision associated with one or more particular user profile identifiers. Embodiments are configured to train network asset scoring model(s) based on specific data, such as prioritized network asset data set(s), such that the trained network asset scoring model(s) efficiently generate more accurate, improved scores for network asset data objects. Additional or alternative embodiments are configured to provide data tagged network asset set(s) utilizing specially trained model(s), such as one or more multi-armed bandit models and/or one or more network asset scoring model(s).

US Patent:

20220207557, Jun 30, 2022

Filed:

Dec 31, 2020

Appl. No.:

17/139904

Inventors:

- Los Angeles CA, US
Mohammad SABAH - San Pedro CA, US
Qiqi AI - Gardena CA, US
Mo LIN - Los Angeles CA, US
Aakash PATHAK - Marina Del Rey CA, US

International Classification:

G06Q 30/02

Abstract:

Embodiments of the present disclosure provide mechanisms for selection of a user survey data object from a set of user data objects, and processing of survey engagement data associated with a selected user survey data object. The user survey data object selected is appropriate for providing associated with a particular user data object, and the survey engagement data received associated therewith enables programmatic selection and use of particular ranking model(s) for use in generating and providing an output ranked item data object set. Example embodiments utilize selected ranking model(s) of a set of ranking models to programmatically generate and output an output ranked item data object set for a particular user profile.

US Patent:

20170031895, Feb 2, 2017

Filed:

Oct 14, 2016

Appl. No.:

15/294527

Inventors:

- Pleasanton CA, US
Shuangshuang Jiang - San Francisco CA, US
Mohammad Sabah - San Jose CA, US

International Classification:

G06F 17/27
G06N 99/00
G06N 7/00
G06F 17/30

Abstract:

A system for identifying address components includes an interface and a processor. The interface is to receive an address for parsing. The processor is to determine a matching model of a set of models based at least in part on a matching probability for each model for a tokenized address, which is based on the address for parsing, and associate each component of the tokenized address with an identifier based at least in part on the matching model, wherein each component of the set of components is associated with an identifier, and wherein probabilities of each component of the set of components are determined using training addresses.

US Patent:

20160180264, Jun 23, 2016

Filed:

Dec 22, 2014

Appl. No.:

14/578954

Inventors:

- Pleasanton CA, US
Mohammad Sabah - San Jose CA, US
Adeyemi Ajao - San Francisco CA, US
James Fan - San Mateo CA, US
Parag Avinash Namjoshi - Redwood City CA, US
Kevin Mun Joun Tham - Alameda CA, US
Vladimir Giverts - San Francisco CA, US

International Classification:

G06Q 10/06

Abstract:

A system for determining retention risk comprises a grouper, a filter, a normalizer, a feature vector extractor, a model builder, and a predictor. The grouper is for determining a set of time series of transactions where each is associated with one employee. The filter is for filtering the set of time series of transactions based on an employee transition characteristic to determine a subset of time series. The normalizer is for determining a model set of time series by normalizing the subset of time series. The feature vector extractor is for determining a set of feature vectors determined from a time series of the model set of time series. The model builder is for determining one or more models based at least in part on the set of feature vectors. The predictor is for predicting retention risk for a given employee using the one or more models.

US Patent:

20160180291, Jun 23, 2016

Filed:

Dec 22, 2014

Appl. No.:

14/578978

Inventors:

- Pleasanton CA, US
Mohammad Sabah - San Jose CA, US
Adeyemi Ajao - San Francisco CA, US
James Fan - San Mateo CA, US
Parag Avinash Namjoshi - Redwood City CA, US
Vivek Tawde - San Francisco CA, US

International Classification:

G06Q 10/10
G06Q 10/06

Abstract:

A system for rating job transitions includes a probability determiner for determining a set of probabilities, a grouper for determining a group of job transition histories, a filter for determining a subset of job transition histories from the group of job transition histories by filtering based at least in part on a transition characteristic, a normalizer for determining a model set of job transition histories by normalizing the subset of job transition histories, a feature vector extractor for determining a set of feature vectors using the model set of job transition histories, a model builder for determining a model based at least in part on the set of feature vectors, and a rater for rating potential job transitions of a selected employee based on the model using a set of test feature vectors.

US Patent:

20140101142, Apr 10, 2014

Filed:

Oct 4, 2012

Appl. No.:

13/644318

Inventors:

- Los Gatos CA, US
Mohammad SABAH - San Jose CA, US
Vijay BHARADWAJ - Belmont CA, US
Sasi PARTHASARATHY - Santa Clara CA, US
Siddharth ANGRISH - San Jose CA, US

Assignee:

NETFLIX INC. - Los Gatos CA

International Classification:

G06F 17/30

US Classification:

707723, 707748, 707E17014

Abstract:

Techniques are described for determining relationships between user activities and determining search results and content recommendations based on the relationships. A plays-related-to-searches application may determine a relationship score between plays of a media title and searches of a query by determining a distance between a projection of the search onto the space of the users and a projection of plays of the media title onto the space of the users. A plays-after-searches application may determine a score for plays of the streaming media title given the search by multiplying a number of times plays of the media title occur after the query is entered by the number of times any play occurs, and dividing by a product of the number of times plays of the media title occur after any query is entered and the number of times plays of any media title occur after the query is entered.