Anh P Trinh

US Patent:

20190228002, Jul 25, 2019

Filed:

Feb 6, 2019

Appl. No.:

16/268503

Inventors:

Christopher T. Nguyen - Los Altos CA, US
Anh H. Trinh - Milpitas CA, US
Bach D. Bui - Milpitas CA, US

International Classification:

G06F 16/23
H04L 29/08
G06Q 10/10
G06F 8/30
G06F 16/30
H04L 12/18
G06F 16/955
G06F 16/28
G06N 20/00
G06F 16/27
G06F 16/22

Abstract:

A data analysis system stores in-memory representation of a distributed data structure across a plurality of processors of a parallel or distributed system. Client applications interact with the in-memory distributed data structure to process queries using the in-memory distributed data structure and to modify the in-memory distributed data structure. The data analysis system creates uniform resource identifier (URI) to identify each in-memory distributed data structure. The URI can be communicated from one client application to another application using communication mechanisms outside the data analysis system, for example, by email, thereby allowing other client devices to interact with a particular in-memory distributed data structure. The in-memory distributed data structure can be a machine learning model that is trained by one client device and executed by another client device. A client application can interact with the in-memory distributed data structure using different programming languages.

US Patent:

20180322394, Nov 8, 2018

Filed:

May 4, 2018

Appl. No.:

15/971267

Inventors:

- Mountain View CA, US
Nhan Vu Lam Chi - Ho Chi Minh City, VN
Binh Han - Mountain View CA, US
Anh H. Trinh - Mountain View CA, US
Mohammad Saffar - Mountain View CA, US

International Classification:

G06N 3/08
G06N 3/04
G06F 15/18
G06K 9/62

Abstract:

Sequence data, such as time series data is analyzed using neural networks, for example, recurrent neural networks. The sequence data is obtained from a source. For example, a sequence data may represent time series data obtained from a sensor. As another example, the sequence of data may represent a sequence of user interactions performed by a user with an online system. The sequences of data are provided as input to a neural network. A feature vector representation of each input sequence data is extracted from the neural network. The feature vector representation is used for clustering the sequence data. Salient features of clusters of sequence data are determined. The salient features of clusters of sequence data are provided for display via a user interface.