Man N Chan

7089425, Aug 8, 2006

Mar 18, 2003

10/392591

Man Chan - San Jose CA, US

CI4 Technologies, Inc. - Mountain View CA

H04L 9/32

713189, 713156, 713165, 726 26, 726 27, 380201

Network users are authorized individual access during a log-on session to encrypted content on content media at the user without the necessity of individualizing the content media for the particular user. The content may comprise multimedia data. The content media, which may be mass produced and distributed, includes a computer program which generates a unique configuration identifier upon instantiation of the program to begin a user access session. The program creates a virtual directory structure for the content that is uniquely determined for that session by the configuration identifier. The configuration identifier is uploaded to a remote server which uses the configuration identifier with other information identifying the content media and the user authorize user access. The remote server creates and downloads to a browser of the user an encrypted message containing URLs for accessing the content in the virtual directory structure and containing transformations of a decryption algorithm and decryption keys for the encrypted content. Encrypted multimedia content selected by the user is stored in a temporary file, decrypted in a moving time window, and rendered After rendering, the decrypted portion of the temporary file is scrambled to preclude further access.

20210304071, Sep 30, 2021

Sep 29, 2020

17/037292

- Rancho Santa Margarita CA, US
Man Chan - San Jose CA, US

G06N 20/00
G06F 8/60
G06F 8/36

Systems and methods for generating prebuilt machine learning framework objects comprising sets of prebuilt machine learning components and one or more data mapping requirements. The components are associated with a respective machine learning service. One or more datasets are obtained. A user-specified context for creating a particular machine learning application is obtained. A particular prebuilt object is selected based on the datasets and the context. One more candidate data mappings are identified based on the data mapping requirements and the datasets. A particular data mapping is selected. A particular set of prebuilt components is selected from the plurality of prebuilt components. The particular machine learning application is generated from the particular prebuilt object based on the particular data mapping and the particular set of prebuilt components, the particular machine learning application comprising an executable application. The machine learning application is deployed.

20200364233, Nov 19, 2020

Aug 5, 2019

16/532404

- Rancho Santa Margarita CA, US
Man Chan - San Jose CA, US

G06F 16/2457
G06F 16/22
G06F 16/93
G06N 20/00

An example method comprises receiving documents to generate a corpus, generating an index of the documents, searching the corpus using the index and a search criteria to generate search results, ordering the search results, providing the search results to a user device, receiving a selection of one or more documents considered to be relevant, receiving a selection of one or more documents considered to be irrelevant, updating a machine learning model based on the selection of the one or more documents considered to be relevant and the one or more documents considered to be irrelevant, the machine learning model configured to generate a probability of likelihood of relevancy for at least a subset of the documents, re-ordering the search results based on the probability of likelihood of relevancy for each of the at least a subset of the documents, and providing the ordered search results based on the probability.

20200065354, Feb 27, 2020

Oct 31, 2019

16/670058

- San Mateo CA, US
Neehar Giri - Saratoga CA, US
Man Chan - San Jose CA, US
Isabelle Chai - San Mateo CA, US
Rahul Choudhry - Sunnyvale CA, US
Kitae Kim - London, GB
Stanley Poon - Los Altos CA, US
Brian Li - Fremont CA, US
Geeta Deodhar - Foster City CA, US
Elliott Yama - Danville CA, US

G06F 17/18
G06F 17/17
G06Q 30/02
G06F 16/435

The present disclosure describes a system, method, and computer program for real-time and computationally efficient calculation of a recommended value range for a quote variable, such as price, discount, volume, or closing time. The system uses the highest-density interval (HDI) of probability density function (PDF) as a recommended or suggested value range for a quote variable. PDFs for the quote variable are precomputed for groups of related inputs, and each PDF is summarized as an array of discrete points. A dimension reduction technique is applied to the PDF inputs in both the training and real-time (non-training) phases to reduce the number of possible combinations of PDFs. During a quote-creation process, a PDF look-up table enables the system to efficiently identify an applicable PDF from the group of precomputed PDFs based on reduced input values.

20190228261, Jul 25, 2019

Dec 12, 2018

16/218425

- Colo de Caza CA, US
Man Chan - San Jose CA, US

WeR.AI, Inc. - Coto de Caza CA

G06K 9/62
G06N 20/00
G06F 16/22

Systems and methods for generating prebuilt machine learning framework objects comprising sets of prebuilt machine learning components and one or more data mapping requirements. The components are associated with a respective machine learning service. One or more datasets are obtained. A user-specified context for creating a particular machine learning application is obtained. A particular prebuilt object is selected based on the datasets and the context. One more candidate data mappings are identified based on the data mapping requirements and the datasets. A particular data mapping is selected. A particular set of prebuilt components is selected from the plurality of prebuilt components. The particular machine learning application is generated from the particular prebuilt object based on the particular data mapping and the particular set of prebuilt components, the particular machine learning application comprising an executable application. The machine learning application is deployed.

20180349324, Dec 6, 2018

Jun 6, 2017

15/615091

- San Mateo CA, US
Neehar Giri - Saratoga CA, US
Man Chan - San Jose CA, US
Isabelle Chai - San Mateo CA, US
Rahul Choudhry - Sunnyvale CA, US
Kitae Kim - London, GB
Stanley Poon - Los Altos CA, US
Brian Li - Fremont CA, US
Geeta Deodhar - Foster City CA, US
Elliott Yama - Danville CA, US

G06F 17/18
G06F 17/17
G06Q 30/02
G06F 17/30

The present disclosure describes a system, method, and computer program for real-time and computationally efficient calculation of a recommended value range for a quote variable, such as price, discount, volume, or closing time. The system uses the highest-density interval (HDI) of probability density function (PDF) as a recommended or suggested value range for a quote variable. PDFs for the quote variable are precomputed for groups of related inputs, and each PDF is summarized as an array of discrete points. A dimension reduction technique is applied to the PDF inputs in both the training and real-time (non-training) phases to reduce the number of possible combinations of PDFs. During a quote-creation process, a PDF look-up table enables the system to efficiently identify an applicable PDF from the group of precomputed PDFs based on reduced input values.