Parham D Mohadjer

8448056, May 21, 2013

Dec 17, 2010

12/972341

Jon D. Pulsipher - North Bend WA, US
Parham Mohadjer - Redmond WA, US
Nazeeh Amin ElDirghami - Redmond WA, US
Shao Liu - Bellevue WA, US
Patrick Orville Cook - Monroe WA, US
James Chadon Foster - Redmond WA, US
Szymon P. Stachniak - Kirkland WA, US
Tommer Leyvand - Seattle WA, US
Joseph Bertolami - Seattle WA, US
Michael Taylor Janney - Sammamish WA, US
Kien Toan Huynh - Redmond WA, US
Charles Claudius Marais - Duvall WA, US
Spencer Dean Perreault - Bellevue WA, US
Robert John Fitzgerald - Kirkland WA, US
Wayne Richard Bisson - Seattle WA, US
Craig Carroll Peeper - Kirkland WA, US

Microsoft Corporation - Redmond WA

G06F 11/00

714819, 382103, 348169

Technology for testing a target recognition, analysis, and tracking system is provided. A searchable repository of recorded and synthesized depth clips and associated ground truth tracking data is provided. Data in the repository is used by one or more processing devices each including at least one instance of a target recognition, analysis, and tracking pipeline to analyze performance of the tracking pipeline. An analysis engine provides at least a subset of the searchable set responsive to a request to test the pipeline and receives tracking data output from the pipeline on the at least subset of the searchable set. A report generator outputs an analysis of the tracking data relative to the ground truth in the at least subset to provide an output of the error relative to the ground truth.

20080320458, Dec 25, 2008

Jun 21, 2007

11/766428

David Floyd Aronson - Woodinville WA, US
Parham Mohadjer - Redmond WA, US
Matthew Russell Kimball - Seattle WA, US
Bruce Michael Dawson - Woodinville WA, US

Microsoft Corporation - Redmond WA

G06F 9/45

717154

Systems and methods for using memory usage to pinpoint sub-optimal code for gaming systems are provided herein. Memory usage characteristics, such as latency, cache misses, load-hit-store, memory address misuse, and wasted cache bandwidth are presented, preferably in a graphical format, to provide the developer with information for optimizing source code. A trace analysis is performed on source code undergoing optimization. Relevant data is extracted from the trace analysis, sorted as necessary, and presented to the user. The user may be presented with multiple results sorting mechanisms as well as ways to change the presentation of the results.

20090113251, Apr 30, 2009

Oct 26, 2007

11/925226

Andrew Goossen - Issaquah WA, US
Parham Mohadjer - Redmond WA, US
Matthew Kimball - Seattle WA, US
John Howard Palevich - Medina WA, US
Juan Carlos Arevalo Baeza - Bellevue WA, US
Jason Matthew Gould - Woodinville WA, US
Matthew Lee - Redmond WA, US
Michael Burrows - Sammamish WA, US
Karen Elaine Stevens - Woodinville WA, US
David Aronson - Austin TX, US

Microsoft Corporation - Redmond WA

G06F 11/34
G06F 15/00

714 46, 702183, 714E11192

A method for analyzing the performance of a video game uses a diagnostic tool that is associated with application code of the video game. The diagnostic tool is activated when the video game is in operation, and real-time performance data is captured and displayed. A warning is generated when a performance metric violates a pre-set condition. The warning may be displayed on a display screen that is used to provide information for rectifying the violation.

20090113303, Apr 30, 2009

Oct 31, 2007

11/931432

Andrew Goossen - Issaquah WA, US
Parham Mohadjer - Redmond WA, US
Matthew Kimball - Seattle WA, US
Randal N. Linden - Mercer Island WA, US
David Aronson - Woodinville WA, US

Microsoft Corporation - Redmond WA

G06F 3/00

715719, 463 43, 709213

The performance of a video game is analyzed using non-intrusive capture and storage of game data. A non-linear capture format is used for capturing run-time game data. The run-time game data includes run-time parameters associated with execution of an application code as well as run-time parameters associated with hardware of a game platform upon which the application code is being executed. The captured data is stored in a storage medium using a non-contiguous storage format.

20130251204, Sep 26, 2013

May 17, 2013

13/896598

Parham Mohadjer - Redmond WA, US
Nazeeh Amin ElDirghami - Redmond WA, US
Shao Liu - Bellevue WA, US
Patrick Orville Cook - Monroe WA, US
James Chadon Foster - Redmond WA, US
Szymon P. Stachniak - Kirkland WA, US
Tommer Leyvand - Seattle WA, US
Joseph Bertolami - Seattle WA, US
Michael Taylor Janney - Sammamish WA, US
Kien Toan Huynh - Redmond WA, US
Charles Claudius Marais - Duvall WA, US
Spencer Dean Perreault - Bellevue WA, US
Robert John Fitzgerald - Kirkland WA, US
Wayne Richard Bisson - Seattle WA, US
Craig Carroll Peeper - Kirkland WA, US
Michael Johnson - Redmond WA, US

MICROSOFT CORPORATION - Redmond WA

G06T 7/00

382103

Technology for testing a target recognition, analysis, and tracking system is provided. A searchable repository of recorded and synthesized depth clips and associated ground truth tracking data is provided. Data in the repository is used by one or more processing devices each including at least one instance of a target recognition, analysis, and tracking pipeline to analyze performance of the tracking pipeline. An analysis engine provides at least a subset of the searchable set responsive to a request to test the pipeline and receives tracking data output from the pipeline on the at least subset of the searchable set. A report generator outputs an analysis of the tracking data relative to the ground truth in the at least subset to provide an output of the error relative to the ground truth.

20200349466, Nov 5, 2020

May 3, 2019

16/403459

- Redmond WA, US
Jacquelyn Marie KRONES - Seattle WA, US
Benjamin NOAH - Seattle WA, US
Parham MOHADJER - Redmond WA, US
Russell Mark EAMES - Redmond WA, US
Richard Kenneth BARRAZA - Redmond WA, US
Joshua Jay HINDS - Duvall WA, US
Semiha Ece KAMAR EDEN - Redmond WA, US
Besmira NUSHI - Redmond WA, US
Eric Joel HORVITZ - Kirkland WA, US

G06N 20/00

The present disclosure relates to systems, methods and computer readable media for evaluating performance of a machine learning system and providing one or more performance views representative of the determined performance. For example, systems disclosed herein may receive or identify performance information including outputs, accuracy data, and feature data associated with a plurality of test instances. In addition, systems disclosed herein may provide one or more performance views via a graphical user interface including graphical elements (e.g., interactive elements) and indications of accuracy data and other performance data with respect to feature clusters associated with select groupings of test instances from the plurality of test instances. The performance views may include interactive features to enable a user to view and intuitively understand performance of the machine learning system with respect to clustered groupings of test instances that share common characteristics.