Xin Wang

US Patent:

7603717, Oct 13, 2009

Filed:

Nov 18, 2002

Appl. No.:

10/298872

Inventors:

Bob Atkinson - Woodinville WA, US
Brian A. LaMacchia - Seattle WA, US
John DeTreville - Seattle WA, US
Muthukrishnan Paramasivam - Seattle WA, US
Xin Wang - Torrance CA, US
Thomas DeMartini - Culver City CA, US

Assignee:

Microsoft Corporation - Redmond WA

International Classification:

G06F 17/30
G06F 15/16

US Classification:

726 26, 709229

Abstract:

A computer-implemented mechanism for granting rights to a resource is described. A license identifies one or more principals, resources, rights and conditions in fields of the license. The license fields include one or more instances of one or more variables. The variables are universally quantified so that each variable may be any one of a set of values. All instances of any given variable are bound to the same value.

US Patent:

7792966, Sep 7, 2010

Filed:

Jun 26, 2007

Appl. No.:

11/768311

Inventors:

Sunil Bharadwaj - Portland OR, US
Michael Fiedler - Mainz, DE
Xin Wang - San Jose CA, US

Assignee:

International Business Machines Corporation - Armonk NY

International Classification:

G06F 15/173
G06F 15/177

US Classification:

709226, 709220

Abstract:

A method and apparatus are provided for performing zone entity modifications to entities in a network wherein a set of generic zone entity modification commands with an estimated lower processing cost is selected for each modification. Prior zone entity modifications in the network and the associated quantity of entities and processing time associated therewith are stored in a data structure. In response to a zone entity modification request, data associated with the current request is evaluated in view of processing time for prior zone entity modifications. An estimate of processing time for the zone entity modification request is calculated for at least two sets of generic zone entity modification commands. The set of generic commands with an estimated lower processing cost is selected and applied to the current zone entity modification request.

US Patent:

7945097, May 17, 2011

Filed:

May 21, 2007

Appl. No.:

11/804772

Inventors:

Manoj Biswas - Bellevue WA, US
Sashi Raghupathy - Redmond WA, US
Xin Wang - Bellevue WA, US
Ming Ye - Bellevue WA, US

Assignee:

Microsoft Corporation - Redmond WA

International Classification:

G06K 9/34
G06K 9/62

US Classification:

382186, 382113, 382179, 382224

Abstract:

A method for classifying digital ink receives digital ink comprising ink strokes. A plurality of the ink strokes can be classified. A temporal line grouping is performed on a plurality of the classified ink strokes that are grouped to form a temporal line group. The temporal line group is segmented into a cluster. The cluster can be classified.

US Patent:

8620084, Dec 31, 2013

Filed:

Jun 26, 2008

Appl. No.:

12/147175

Inventors:

Alexander Kolmykov-Zotov - Sammamish WA, US
Sashi Raghupathy - Redmond WA, US
Xin Wang - Bellevue WA, US

Assignee:

Microsoft Corporation - Redmond WA

International Classification:

G06K 9/00

US Classification:

382187, 382170, 382156

Abstract:

Shape recognition is performed based on determining whether one or more ink strokes is not part of a shape or a partial shape. Ink strokes are divided into segments and the segments analyzed employing a relative angular distance histogram. The histogram analysis yields stable, incremental, and discriminating featurization results. Neural networks may also be employed along with the histogram analysis to determine complete shapes from partial shape entries and autocomplete suggestions provided to users for conversion of the shape into a known object.

US Patent:

20080195931, Aug 14, 2008

Filed:

Oct 27, 2006

Appl. No.:

11/589028

Inventors:

Sashi Raghupathy - Redmond WA, US
Paul A. Viola - Seattle WA, US
Michael Shilman - Seattle WA, US
Xin Wang - Bellevue WA, US

Assignee:

Microsoft Corporation - Redmond WA

International Classification:

G06F 17/00

US Classification:

715230

Abstract:

Annotation recognition and parsing is accomplished by first recognizing and grouping shapes such that relationships between the annotations and the underlying text and/or images can be determined. The recognition and grouping is followed by categorization of recognized annotations according to predefined types. The classification may be according to functionality, relation to content, and the like. In a third phase, the annotations are anchored to the underlying text or images they are found to be related to.

US Patent:

20100028559, Feb 4, 2010

Filed:

May 28, 2009

Appl. No.:

12/455269

Inventors:

Mingdi Yan - Lake Oswego OR, US
Olof Ramström - Stockholm, SE
Li-Hong Liu - Portland OR, US
Xin Wang - Portland OR, US
Michael M. Lerner - Corvallis OR, US

International Classification:

B05D 3/06
C08B 37/00
C08F 120/18
C07K 14/00
B05D 3/02
C07K 16/00

US Classification:

427558, 536 551, 5263297, 530400, 427384, 5303911

Abstract:

Devices comprising functionalized materials, and embodiments of a method for making and using such devices, are disclosed. Exemplary devices include ophthalmic devices, nanoparticles, quartz crystal microbalances, microarrays, and nanocomposites. In particular embodiments, device surfaces are modified with monomers and/or polymers, typically carbohydrate monomers and/or polymers. Embodiments of a method for making and using such devices are disclosed. Monomers and/or polymers are covalently bonded to surfaces using functionalized perhalophenylazides. In some embodiments, device surfaces are functionalized with a perhalophenylazide. One or more monomers and/or polymers subsequently are covalently bonded to the device surface using the perhalophenylazide. In other embodiments, monomers and/or polymers are derivatized with a functionalized perhalophenylazide. The derivatized monomers and/or polymers then are covalently bonded to the device surface using the perhalophenylazide. In some embodiments, graphite is functionalized with perhalophenylazide to produce a functionalized graphene sheet. In other embodiments, graphene sheets are covalently attached to functionalized substrates.

US Patent:

20130252843, Sep 26, 2013

Filed:

Nov 22, 2011

Appl. No.:

13/988495

Inventors:

Mingdi Yan - Tewksbury MA, US
Xin Wang - Portland OR, US

Assignee:

THE STATE OF OREGON ACTING BY AND THROUGH THE STATE BOARD OF HIGHER EDUCATION ON BEHALF OF PORTL - Portland OR

International Classification:

G01N 33/58

US Classification:

506 9, 436 94, 436 86, 506 32

Abstract:

Disclosed embodiments concern differentiating and classifying one or more targets using a perhalophenylazide-derived nanoparticle probe, or multiple such probes. Particular embodiments concern using statistical analysis to produce score plots illustrating the level of differentiation and/or classification. Also disclosed are methods for making perhalophenylazide-derived nanoparticle probes, individually or by using a microarray technique. Particular embodiments concern methods for using the per halophenylazide-derived nanoparticle probes to diagnose, detect, and/or treat a disease. Kits comprising the perhalophenylazide-derived nanoparticle probes are also disclosed.

US Patent:

20220392059, Dec 8, 2022

Filed:

Dec 22, 2021

Appl. No.:

17/558756

Inventors:

- Shenzhen, CN
Xin WANG - Seattle WA, US
Youbing YIN - Kenmore WA, US
Hao-Yu YANG - Seattle WA, US
Yi LU - Seattle WA, US
Xinyu GUO - Redmond WA, US
Qi SONG - Seattle WA, US

Assignee:

SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION - Shenzhen

International Classification:

G06T 7/00
G06V 10/40
G06V 10/764
G06V 10/82
G06V 10/77
G06T 7/73
G06N 3/08
G16H 30/40

Abstract:

Embodiments of the disclosure provide methods and systems for representation learning from a biomedical image with a sparse convolution. The exemplary system may include a communication interface configured to receive the biomedical image acquired by an image acquisition device. The system may further include at least one processor, configured to extract a structure of interest from the biomedical image. The at least one processor is also configured to generate sparse data representing the structure of interest and input features corresponding to the sparse data. The at least one processor is further configured to apply a sparse-convolution-based model to the biomedical image, the sparse data, and the input features to generate a biomedical processing result for the biomedical image. The sparse-convolution-based model performs one or more neural network operations including the sparse convolution on the sparse data and the input features.