Su Feng Chen

US Patent:

6742161, May 25, 2004

Filed:

Mar 7, 2000

Appl. No.:

09/520892

Inventors:

Barnaby James - Los Gatos CA
Su Chen - San Jose CA

Assignee:

ScanSoft, Inc. - Peabody MA

International Classification:

G06F 1721

US Classification:

715500, 715530

Abstract:

The present invention is a system and method for performing document recognition and processing in a distributed computing environment. The invention uses applications which are remotely located from one or more users and may be accessed via a network. One or more users utilize terminals including computers, facsimile machines, and/or scanners to transmit documents to be processed to a network or a network server which in turn transmits the documents to various computer software applications which process the documents at a network processing location. Once the documents have been processed, the processed documents are transmitted to the users according to one or more preferences associated with a user identification and/or authentication which may be determined by either a network server or an application server. Users utilizing a computer terminal make use of various data transfer programs capable of transferring document data over a network to an application server at a remote location and receiving processed document data via a network.

US Patent:

7558290, Jul 7, 2009

Filed:

Dec 16, 2005

Appl. No.:

11/303651

Inventors:

Antonio Nucci - Burlingame CA, US
Su Chen - Somerset NJ, US

Assignee:

Narus, Inc. - Sunnyvale CA

International Classification:

H04J 3/18

US Classification:

370477

Abstract:

An important component of network monitoring is to collect traffic data which is a bottleneck due to large data size. We introduce a new table compression method called “Group Compression” to address this problem. This method uses a small training set to learn the relationship among columns and group them; the result is a “compression plan”. Based on this plan, each group is compressed separately. This method can reduce the compressed size to 60%-70% of the IP flow logs compressed by GZIP.

US Patent:

8516157, Aug 20, 2013

Filed:

Apr 20, 2011

Appl. No.:

13/091090

Inventors:

Antonio Nucci - San Jose CA, US
Su Chen - Somerset NJ, US

Assignee:

Narus, Inc. - Sunnyvale CA

International Classification:

G06F 15/16
H04J 3/18

US Classification:

709247, 709246, 370477

Abstract:

The present invention relates to a method of compressing data in a network, the data comprising a plurality of packets each having a header and a payload, the header comprising a plurality of header fields, the method comprising generating a classification tree based on at least a portion of the plurality of header fields, determining a inter-packet compression plan based on the classification tree, and performing inter-packet compression in real time for each payload of at least a first portion of the plurality of packets, the inter-packet compression being performed according to at least a portion of the inter-packet compression plan.

US Patent:

8046496, Oct 25, 2011

Filed:

Dec 12, 2007

Appl. No.:

11/955259

Inventors:

Antonio Nucci - San Jose CA, US
Su Chen - Sunnyvale CA, US

Assignee:

Narus, Inc. - Sunnyvale CA

International Classification:

G06F 15/16
H04J 3/18

US Classification:

709247, 709246, 370477

Abstract:

The present invention relates to a method of compressing data in a network, the data comprising a plurality of packets each having a header and a payload, the header comprising a plurality of header fields, the method comprising generating a classification tree based on at least a portion of the plurality of header fields, determining a inter-packet compression plan based on the classification tree, and performing inter-packet compression in real time for each payload of at least a first portion of the plurality of packets, the inter-packet compression being performed according to at least a portion of the inter-packet compression plan.

US Patent:

20220292654, Sep 15, 2022

Filed:

Mar 12, 2021

Appl. No.:

17/200338

Inventors:

- San Jose CA, US
Yifan Jiang - Austin TX, US
Yilin Wang - San Jose CA, US
Jianming Zhang - Campbell CA, US
Kalyan Sunkavalli - San Jose CA, US
Sarah Kong - Cupertino CA, US
Su Chen - San Jose CA, US
Sohrab Amirghodsi - Seattle WA, US
Zhe Lin - Fremont CA, US

International Classification:

G06T 5/50
G06T 7/194
G06T 11/60
G06T 11/00
G06N 3/04
G06N 3/08

Abstract:

The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately, efficiently, and flexibly generating harmonized digital images utilizing a self-supervised image harmonization neural network. In particular, the disclosed systems can implement, and learn parameters for, a self-supervised image harmonization neural network to extract content from one digital image (disentangled from its appearance) and appearance from another from another digital image (disentangled from its content). For example, the disclosed systems can utilize a dual data augmentation method to generate diverse triplets for parameter learning (including input digital images, reference digital images, and pseudo ground truth digital images), via cropping a digital image with perturbations using three-dimensional color lookup tables (“LUTs”). Additionally, the disclosed systems can utilize the self-supervised image harmonization neural network to generate harmonized digital images that depict content from one digital image having the appearance of another digital image.

US Patent:

20220284613, Sep 8, 2022

Filed:

Feb 26, 2021

Appl. No.:

17/186436

Inventors:

- San Jose CA, US
Jianming Zhang - Campbell CA, US
Oliver Wang - Seattle WA, US
Simon Niklaus - San Jose CA, US
Mai Long - Portland OR, US
Su Chen - San Jose CA, US

International Classification:

G06T 7/593
G06T 7/30
G06T 7/13
G06T 7/143
G01S 17/894
G01S 17/42

Abstract:

This disclosure describes one or more implementations of a depth prediction system that generates accurate depth images from single input digital images. In one or more implementations, the depth prediction system enforces different sets of loss functions across mix-data sources to generate a multi-branch architecture depth prediction model. For instance, in one or more implementations, the depth prediction model utilizes different data sources having different granularities of ground truth depth data to robustly train a depth prediction model. Further, given the different ground truth depth data granularities from the different data sources, the depth prediction model enforces different combinations of loss functions including an image-level normalized regression loss function and/or a pair-wise normal loss among other loss functions.

US Patent:

20220277514, Sep 1, 2022

Filed:

Feb 26, 2021

Appl. No.:

17/186522

Inventors:

- San Jose CA, US
Jianming Zhang - Campbell CA, US
Oliver Wang - Seattle WA, US
Simon Niklaus - San Jose CA, US
Mai Long - Portland OR, US
Su Chen - San Jose CA, US

International Classification:

G06T 17/00
G06K 9/00
G06N 3/04
G06T 7/80

Abstract:

This disclosure describes implementations of a three-dimensional (3D) scene recovery system that reconstructs a 3D scene representation of a scene portrayed in a single digital image. For instance, the 3D scene recovery system trains and utilizes a 3D point cloud model to recover accurate intrinsic camera parameters from a depth map of the digital image. Additionally, the 3D point cloud model may include multiple neural networks that target specific intrinsic camera parameters. For example, the 3D point cloud model may include a depth 3D point cloud neural network that recovers the depth shift as well as include a focal length 3D point cloud neural network that recovers the camera focal length. Further, the 3D scene recovery system may utilize the recovered intrinsic camera parameters to transform the single digital image into an accurate and realistic 3D scene representation, such as a 3D point cloud.

US Patent:

20220198671, Jun 23, 2022

Filed:

Dec 18, 2020

Appl. No.:

17/126986

Inventors:

- San Jose CA, US
Su Chen - San Jose CA, US
Shuo Yang - Bloomington IN, US

International Classification:

G06T 7/11
G06T 7/143
G06T 7/136
G06T 7/162
G06T 7/90
G06N 3/08
G06N 3/04

Abstract:

The present disclosure relates to systems, non-transitory computer-readable media, and methods that utilize a deep neural network to process object user indicators and an initial object segmentation from a digital image to efficiently and flexibly generate accurate object segmentations. In particular, the disclosed systems can determine an initial object segmentation for the digital image (e.g., utilizing an object segmentation model or interactive selection processes). In addition, the disclosed systems can identify an object user indicator for correcting the initial object segmentation and generate a distance map reflecting distances between pixels of the digital image and the object user indicator. The disclosed systems can generate an image-interaction-segmentation triplet by combining the digital image, the initial object segmentation, and the distance map. By processing the image-interaction-segmentation triplet utilizing the segmentation neural network, the disclosed systems can provide an updated object segmentation for display to a client device.