Su Chi Chen

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:

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.

US Patent:

20230128792, Apr 27, 2023

Filed:

Jan 31, 2022

Appl. No.:

17/589114

Inventors:

- San Jose CA, US
Su Chen - San Jose CA, US
Scott Cohen - Sunnyvale CA, US
Zhe Lin - Fremont CA, US
Zijun Wei - San Jose CA, US
Jianming Zhang - Campbell CA, US

International Classification:

G06V 10/82
G06N 3/08
G06T 7/00

Abstract:

The present disclosure relates to systems, methods, and non-transitory computer-readable media that generates object masks for digital objects portrayed in digital images utilizing a detection-masking neural network pipeline. In particular, in one or more embodiments, the disclosed systems utilize detection heads of a neural network to detect digital objects portrayed within a digital image. In some cases, each detection head is associated with one or more digital object classes that are not associated with the other detection heads. Further, in some cases, the detection heads implement multi-scale synchronized batch normalization to normalize feature maps across various feature levels. The disclosed systems further utilize a masking head of the neural network to generate one or more object masks for the detected digital objects. In some cases, the disclosed systems utilize post-processing techniques to filter out low-quality masks.

US Patent:

20140330787, Nov 6, 2014

Filed:

May 1, 2013

Appl. No.:

13/875236

Inventors:

- SUNNYVALE CA, US
SU CHEN - CUPERTINO CA, US

Assignee:

NETAPP, INC. - SUNNYVALE CA

International Classification:

G06F 17/30

US Classification:

707659

Abstract:

Technology for maintaining a backup of namespace metadata of an expandable storage volume is disclosed. In various embodiments, the expandable storage volume backs up metadata of a namespace constituent volume of the expandable storage volume into a namespace mirror volume. The namespace constituent volume is responsible for storing the metadata for data objects stored in multiple data constituent volumes of the expandable storage volume. In response to a signal indicating that the namespace constituent volume is unavailable, the namespace mirror volume replaces the role of the namespace constituent volume. The new namespace constituent volume continues to provide metadata for a data object of the data objects in response to an operation request for the data object.