Ming Jin Phones & Addresses
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14037 La Barca Dr, La Mirada, CA 90638
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Fullerton, CA
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5782 Kingman Ave, Buena Park, CA 90621 (714) 522-8964
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Los Angeles, CA
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Anaheim, CA
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Rowland Heights, CA
Business Records
Name / Title
Company / Classification
Phones & Addresses
President
EMING SOFTWARE INC
Prepackaged Software Services
Prepackaged Software Services
18 Red Rock, Irvine, CA 92604
2616 Pkwy Dr, El Monte, CA 91732
2616 Pkwy Dr, El Monte, CA 91732
Managing
Convegen Company LLC
Import/Export
Import/Export
780 Nogales St, Whittier, CA 91748
ORIENTAL HEALTH CENTER LLC
AJ AND MS CONSTRUCTION INC
President
Sae Young, Inc
1725 Venice Blvd, Los Angeles, CA 90006
President
AMRICH FINANCIAL GROUP, INC
1680 S Garfield #101, Alhambra, CA 91801
President
CHINA CONTINENTAL INVESTMENT GROUP, INC
Financial Services
Financial Services
2 N Lk Ave #645, Pasadena, CA 91101
Pasadena, CA 91101
(626) 564-8495
Pasadena, CA 91101
(626) 564-8495
President
INTERNATIONAL COMMERCIAL SUPPLIES INC
8151 Blewett St, Rosemead, CA 91770
Publications
Us Patents
Modulation Bit Added To Worst Case Codeword
View pageUS Patent:
7397398, Jul 8, 2008
Filed:
May 9, 2006
Appl. No.:
11/431122
Inventors:
Ming Jin - Lake Forest CA, US
Hiauchoon Kee - Singapore, SG
Zhenyu Sun - Singapore, SG
Liu Li - Singapore, SG
Myint Ngwe - Singapore, SG
Hiauchoon Kee - Singapore, SG
Zhenyu Sun - Singapore, SG
Liu Li - Singapore, SG
Myint Ngwe - Singapore, SG
Assignee:
Seagate Technology LLC - Scotts Valley CA
International Classification:
H03M 7/00
US Classification:
341 59, 341 58
Abstract:
A data word is error correction encoded to provide a worst case codeword without bit transitions between worst case codeword bits. A modulation bit is calculated as a function of the worst case codeword. The modulation bit has a bit polarity opposite a bit polarity of the worst case codeword bits. The worst case codeword bits are added with the modulation bit to form a modulated code word.
Calibrating A Defect Scan Parameter For A Disk Drive
View pageUS Patent:
7656763, Feb 2, 2010
Filed:
Jun 4, 2007
Appl. No.:
11/757824
Inventors:
Ming Jin - Lake Forest CA, US
Teik Ee Yeo - Trabuco Canyon CA, US
Teik Ee Yeo - Trabuco Canyon CA, US
Assignee:
Western Digital Technologies, Inc. - Lake Forest CA
International Classification:
G11B 5/09
US Classification:
369 5315, 369 5317, 369 4714, 369 4432
Abstract:
A method is disclosed for performing a defect scan for a disk drive. Data is recorded on a first data area of a disk substantially free from defects and on a second data area of the disk substantially affected by at least one defect. A defect scan parameter is initialized with an initial setting. The first data area is read to determine a first defect threshold, and the second data area is read to determine a second defect threshold. A margin is saved representing a difference between the first and second defect thresholds. The setting for the defect scan parameter is adjusted, and the elements of reading the first and second data areas and saving a corresponding margin are repeated at least once. A setting is then selected for the defect scan parameter in response to the saved margins.
Read Error Recovery Using Soft Information
View pageUS Patent:
7738201, Jun 15, 2010
Filed:
Aug 18, 2006
Appl. No.:
11/507063
Inventors:
Ming Jin - Lake Forest CA, US
Hiau Choon Kee - Singapore, SG
Myint Ngwe - Singapore, SG
Liu Li - Singapore, SG
Hiau Choon Kee - Singapore, SG
Myint Ngwe - Singapore, SG
Liu Li - Singapore, SG
Assignee:
Seagate Technology LLC - Scotts Valley CA
International Classification:
G11B 5/09
G11B 20/10
G11B 20/10
US Classification:
360 39
Abstract:
In general, this disclosure describes read recovery techniques for data storage devices that use soft information associated with multiple read operations to detect data. Specifically, the read recovery techniques comprise computing soft information for each bit detected during a first read operation of a data storage medium, computing soft information for each bit detected during a second read operation of the data storage medium and averaging the soft information computed during the first and second read operations to determine the value of each of the bits.
Disk Drive Expediting Defect Scan When Quality Metric Exceeds A More Stringent Threshold
View pageUS Patent:
8014094, Sep 6, 2011
Filed:
Aug 31, 2009
Appl. No.:
12/551207
Inventors:
Ming Jin - Lake Forest CA, US
Assignee:
Western Digital Technologies, Inc. - Irvine CA
International Classification:
G11B 27/36
US Classification:
360 31
Abstract:
A disk drive is disclosed comprising a head actuated over a disk comprising a plurality of data tracks. Data is read from one of the data tracks to generate a read signal, and a quality metric is generated in response to the read signal. When the quality metric exceeds a first threshold, a defect is detected in at least part of the data track. When the quality metric exceeds a second threshold different than the first threshold, the data track is reread to regenerate the quality metric, and when the quality metric exceeds the second threshold at least twice, the defect is detected.
Dynamic Early Termination Of Iterative Decoding For Turbo Equalization
View pageUS Patent:
20080115038, May 15, 2008
Filed:
Oct 26, 2006
Appl. No.:
11/589264
Inventors:
Hiau Choon Kee - Singapore, SG
Ming Jin - Lake Forest CA, US
Zhen Yu Sun - Singapore, SG
Myint Ngwe - Singapore, SG
Ming Jin - Lake Forest CA, US
Zhen Yu Sun - Singapore, SG
Myint Ngwe - Singapore, SG
Assignee:
Seagate Technology LLC - Scotts Valley CA
International Classification:
H03M 13/00
US Classification:
714760
Abstract:
A method and apparatus for selectively terminating turbo equalization is disclosed. At least two iterations of turbo equalization are performed. The number of errors corrected between the first iteration and the second iteration are calculated. In one embodiment, if the sign of corresponding bits in the data block is different between the two iterations, an error was corrected. If the number of errors corrected is greater than a stopping value, a subsequent iteration of turbo equalization is performed. If the number of errors corrected is less than or equal to the stopping value, then associated values for the data are output and the turbo equalization is terminated.
Machine Learning Model For Analysis Of Instruction Sequences
View pageUS Patent:
20210256350, Aug 19, 2021
Filed:
Dec 18, 2020
Appl. No.:
17/127908
Inventors:
- Irvine CA, US
Matthew Wolff - Laguna Niguel CA, US
John Brock - Irvine CA, US
Brian Wallace - Irvine CA, US
Andy Wortman - Irvine CA, US
Jian Luan - Irvine CA, US
Mahdi Azarafrooz - Irvine CA, US
Andrew Davis - Portland OR, US
Michael Wojnowicz - Irvine CA, US
Derek Soeder - Irvine CA, US
David Beveridge - Portland OR, US
Eric Petersen - Beaverton OR, US
Ming Jin - Irvine CA, US
Ryan Permeh - Irvine CA, US
Matthew Wolff - Laguna Niguel CA, US
John Brock - Irvine CA, US
Brian Wallace - Irvine CA, US
Andy Wortman - Irvine CA, US
Jian Luan - Irvine CA, US
Mahdi Azarafrooz - Irvine CA, US
Andrew Davis - Portland OR, US
Michael Wojnowicz - Irvine CA, US
Derek Soeder - Irvine CA, US
David Beveridge - Portland OR, US
Eric Petersen - Beaverton OR, US
Ming Jin - Irvine CA, US
Ryan Permeh - Irvine CA, US
International Classification:
G06N 3/04
G06F 21/56
G06F 21/56
Abstract:
A system is provided for classifying an instruction sequence with a machine learning model. The system may include at least one processor and at least one memory. The memory may include program code that provides operations when executed by the at least one processor. The operations may include: processing an instruction sequence with a trained machine learning model configured to detect one or more interdependencies amongst a plurality of tokens in the instruction sequence and determine a classification for the instruction sequence based on the one or more interdependencies amongst the plurality of tokens; and providing, as an output, the classification of the instruction sequence. Related methods and articles of manufacture, including computer program products, are also provided.
Isolating Data For Analysis To Avoid Malicious Attacks
View pageUS Patent:
20180157826, Jun 7, 2018
Filed:
Feb 1, 2018
Appl. No.:
15/886610
Inventors:
- Irvine CA, US
Derek A. Soeder - Irvine CA, US
Matthew Wolff - Newport Beach CA, US
Ming Jin - Irvine CA, US
Xuan Zhao - Irvine CA, US
Derek A. Soeder - Irvine CA, US
Matthew Wolff - Newport Beach CA, US
Ming Jin - Irvine CA, US
Xuan Zhao - Irvine CA, US
International Classification:
G06F 21/51
G06F 21/53
H04L 29/06
G06N 99/00
G06F 21/53
H04L 29/06
G06N 99/00
Abstract:
Determining, by a machine learning model in an isolated operating environment, whether a file is safe for processing by a primary operating environment. The file is provided, when the determining indicates the file is safe for processing, to the primary operating environment for processing by the primary operating environment. When the determining indicates the file is unsafe for processing, the file is prevented from being processed by the primary operating environment. The isolated operating environment can be maintained on an isolated computing system remote from a primary computing system maintaining the primary operating system. The isolating computing system and the primary operating system can communicate over a cloud network.
Machine Learning Model For Analysis Of Instruction Sequences
View pageUS Patent:
20180075348, Mar 15, 2018
Filed:
Nov 7, 2016
Appl. No.:
15/345433
Inventors:
- Irvine CA, US
Matthew Wolff - Laguna Niguel CA, US
John Brock - Irvine CA, US
Brian Wallace - Irvine CA, US
Andrew Wortman - Irvine CA, US
Jian Luan - Irvine CA, US
Mahdi Azarafrooz - Irvine CA, US
Andrew Davis - Portland OR, US
Michael Wojnowicz - Irvine CA, US
Derek Soeder - Irvine CA, US
David Beveridge - Portland OR, US
Eric Petersen - Portland OR, US
Ming Jin - Irvine CA, US
Ryan Permeh - Irvine CA, US
Matthew Wolff - Laguna Niguel CA, US
John Brock - Irvine CA, US
Brian Wallace - Irvine CA, US
Andrew Wortman - Irvine CA, US
Jian Luan - Irvine CA, US
Mahdi Azarafrooz - Irvine CA, US
Andrew Davis - Portland OR, US
Michael Wojnowicz - Irvine CA, US
Derek Soeder - Irvine CA, US
David Beveridge - Portland OR, US
Eric Petersen - Portland OR, US
Ming Jin - Irvine CA, US
Ryan Permeh - Irvine CA, US
International Classification:
G06N 3/08
G06F 9/30
G06F 9/30
Abstract:
In one respect, there is provided a system for classifying an instruction sequence with a machine learning model. The system may include at least one processor and at least one memory. The memory may include program code that provides operations when executed by the at least one processor. The operations may include: processing an instruction sequence with a trained machine learning model configured to detect one or more interdependencies amongst a plurality of tokens in the instruction sequence and determine a classification for the instruction sequence based on the one or more interdependencies amongst the plurality of tokens; and providing, as an output, the classification of the instruction sequence. Related methods and articles of manufacture, including computer program products, are also provided.