Patents by Inventor Jeff Graham
Jeff Graham has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 11919831Abstract: Phase-stabilized ammonium nitrate (PSAN) prill including ammonium nitrate and a potassium salt are provided. The PSAN prill can be explosive grade and low density. The PSAN prill may include a porosity enhancing agent such as an interfacial surface modifier or a pore former. Methods of preparing the PSAN prill and related emulsions are also provided.Type: GrantFiled: February 5, 2020Date of Patent: March 5, 2024Assignee: Dyno Nobel Asia Pacific Pty LimitedInventors: Brian Graham, Jeff Gore
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Publication number: 20230005238Abstract: Techniques are disclosed for extracting micro-features at a pixel-level based on characteristics of one or more images. Importantly, the extraction is unsupervised, i.e., performed independent of any training data that defines particular objects, allowing a behavior-recognition system to forgo a training phase and for object classification to proceed without being constrained by specific object definitions. A micro-feature extractor that does not require training data is adaptive and self-trains while performing the extraction. The extracted micro-features are represented as a micro-feature vector that may be input to a micro-classifier which groups objects into object type clusters based on the micro-feature vectors.Type: ApplicationFiled: September 13, 2022Publication date: January 5, 2023Applicant: Intellective Ai, Inc.Inventors: Wesley Kenneth COBB, Rajkiran K. GOTTUMUKKAL, Kishor Adinath SAITWAL, Ming-Jung SEOW, Gang XU, Lon W. RISINGER, Jeff GRAHAM
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Patent number: 11468660Abstract: Techniques are disclosed for extracting micro-features at a pixel-level based on characteristics of one or more images. Importantly, the extraction is unsupervised, i.e., performed independent of any training data that defines particularly objects, allowing a behavior-recognition system to forgo a training phase and for object classification to proceed without being constrained by specify object definitions. A micro-feature extractor that does not require training data is adaptive and self-trains while performing the extraction. The extracted micro-features are represented as a micro-feature vector that may be input to a micro-classifier which groups object into object type clusters based on the micro-feature vectors.Type: GrantFiled: July 17, 2020Date of Patent: October 11, 2022Assignee: Intellective Ai, Inc.Inventors: Wesley Kenneth Cobb, Rajkiran K. Gottumukkal, Kishor Adinath Saitwal, Ming-Jung Seow, Gang Xu, Lon W. Risinger, Jeff Graham
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Publication number: 20210042556Abstract: Techniques are disclosed for extracting micro-features at a pixel-level based on characteristics of one or more images. Importantly, the extraction is unsupervised, i.e., performed independent of any training data that defines particularly objects, allowing a behavior-recognition system to forgo a training phase and for object classification to proceed without being constrained by specify object definitions. A micro-feature extractor that does not require training data is adaptive and self-trains while performing the extraction. The extracted micro-features are represented as a micro-feature vector that may be input to a micro-classifier which groups object into object type clusters based on the micro-feature vectors.Type: ApplicationFiled: July 17, 2020Publication date: February 11, 2021Applicant: Intellective Ai, Inc.Inventors: Wesley Kenneth COBB, Rajkiran K. GOTTUMUKKAL, Kishor Adinath SAITWAL, Ming-Jung SEOW, Gang XU, Lon W. RISINGER, Jeff GRAHAM
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Patent number: 10755131Abstract: Techniques are disclosed for extracting micro-features at a pixel-level based on characteristics of one or more images. Importantly, the extraction is unsupervised, i.e., performed independent of any training data that defines particular objects, allowing a behavior-recognition system to forgo a training phase and for object classification to proceed without being constrained by specific object definitions. A micro-feature extractor that does not require training data is adaptive and self-trains while performing the extraction. The extracted micro-features are represented as a micro-feature vector that may be input to a micro-classifier which groups objects into object type clusters based on the micro-feature vectors.Type: GrantFiled: July 12, 2018Date of Patent: August 25, 2020Assignee: Intellective Ai, Inc.Inventors: Wesley Kenneth Cobb, Rajkiran K. Gottumukkal, Kishor Adinath Saitwal, Ming-Jung Seow, Gang Xu, Lon W. Risinger, Jeff Graham
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Publication number: 20190180135Abstract: Techniques are disclosed for extracting micro-features at a pixel-level based on characteristics of one or more images. Importantly, the extraction is unsupervised, i.e., performed independent of any training data that defines particular objects, allowing a behavior-recognition system to forgo a training phase and for object classification to proceed without being constrained by specific object definitions. A micro-feature extractor that does not require training data is adaptive and self-trains while performing the extraction. The extracted micro-features are represented as a micro-feature vector that may be input to a micro-classifier which groups objects into object type clusters based on the micro-feature vectors.Type: ApplicationFiled: July 12, 2018Publication date: June 13, 2019Applicant: Omni AI, Inc.Inventors: Wesley Kenneth COBB, Rajkiran K. GOTTUMUKKAL, Kishor Adinath SAITWAL, Ming-Jung SEOW, Gang XU, Lon W. RISINGER, Jeff GRAHAM
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Patent number: 10049293Abstract: Techniques are disclosed for extracting micro-features at a pixel-level based on characteristics of one or more images. Importantly, the extraction is unsupervised, i.e., performed independent of any training data that defines particular objects, allowing a behavior-recognition system to forgo a training phase and for object classification to proceed without being constrained by specific object definitions. A micro-feature extractor that does not require training data is adaptive and self-trains while performing the extraction. The extracted micro-features are represented as a micro-feature vector that may be input to a micro-classifier which groups objects into object type clusters based on the micro-feature vectors.Type: GrantFiled: March 16, 2017Date of Patent: August 14, 2018Assignee: Omni Al, Inc.Inventors: Wesley Kenneth Cobb, Rajkiran K. Gottumukkal, Kishor Adinath Saitwal, Ming-Jung Seow, Gang Xu, Lon W. Risinger, Jeff Graham
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Publication number: 20180032834Abstract: Techniques are disclosed for extracting micro-features at a pixel-level based on characteristics of one or more images. Importantly, the extraction is unsupervised, i.e., performed independent of any training data that defines particular objects, allowing a behavior-recognition system to forgo a training phase and for object classification to proceed without being constrained by specific object definitions. A micro-feature extractor that does not require training data is adaptive and self-trains while performing the extraction. The extracted micro-features are represented as a micro-feature vector that may be input to a micro-classifier which groups objects into object type clusters based on the micro-feature vectors.Type: ApplicationFiled: March 16, 2017Publication date: February 1, 2018Inventors: Wesley Kenneth COBB, Rajkiran K. GOTTUMUKKAL, Kishor Adinath SAITWAL, Ming-Jung SEOW, Gang XU, Lon W. RISINGER, Jeff GRAHAM
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Patent number: 9633275Abstract: Techniques are disclosed for extracting micro-features at a pixel-level based on characteristics of one or more images. Importantly, the extraction is unsupervised, i.e., performed independent of any training data that defines particular objects, allowing a behavior-recognition system to forgo a training phase and for object classification to proceed without being constrained by specific object definitions. A micro-feature extractor that does not require training data is adaptive and self-trains while performing the extraction. The extracted micro-features are represented as a micro-feature vector that may be input to a micro-classifier which groups objects into object type clusters based on the micro-feature vectors.Type: GrantFiled: August 18, 2009Date of Patent: April 25, 2017Inventors: Wesley Kenneth Cobb, Rajkiran K. Gottumukkal, Kishor Adinath Saitwal, Ming-Jung Seow, Gang Xu, Lon W. Risinger, Jeff Graham
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Patent number: 8224796Abstract: A computer-implemented method for data loss prevention may include: 1) indentifying an external device, 2) intercepting a write attempt to a file on the external device, 3) creating a sandbox version of the file, 4) performing the write attempt on the sandbox version of the file, and then 5) analyzing the sandbox version of the file for potential data-loss violations. Various other methods, systems, and computer-readable media are also disclosed.Type: GrantFiled: September 11, 2009Date of Patent: July 17, 2012Assignee: Symantec CorporationInventors: Amit Shinde, Jeff Graham, Rajesh Upadhyay
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Publication number: 20110044536Abstract: Techniques are disclosed for extracting micro-features at a pixel-level based on characteristics of one or more images. Importantly, the extraction is unsupervised, i.e., performed independent of any training data that defines particular objects, allowing a behavior-recognition system to forgo a training phase and for object classification to proceed without being constrained by specific object definitions. A micro-feature extractor that does not require training data is adaptive and self-trains while performing the extraction. The extracted micro-features are represented as a micro-feature vector that may be input to a micro-classifier which groups objects into object type clusters based on the micro-feature vectors.Type: ApplicationFiled: August 18, 2009Publication date: February 24, 2011Inventors: Wesley Kenneth Cobb, Rajkiran Kumar Gottumukkal, Kishor Adinath Saitwal, Min-Jung Seow, Gang Xu, Lon William Risinger, Jeff Graham
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Patent number: 7766526Abstract: Disclosed is an illumination device. The device includes a light engine in an acoustic tile that introduces light into a plurality of optical fibers using an LED. In between the LED and the fiber ends, an LCD is used to modify the light introduced. To do this, the LCD is programmed to display petal-shaped sections which individually change in contrast to become more occluded and then more transparent. This creates an aesthetically pleasing effect resembling stars.Type: GrantFiled: September 13, 2007Date of Patent: August 3, 2010Assignee: iSky Panel Systems, Inc.Inventors: Daniel Dwyer, J. Boyd Hildebrant, Jeff Graham
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Publication number: 20080062713Abstract: Disclosed is an illumination device. The device includes a light engine in an acoustic tile that introduces light into a plurality of optical fibers using an LED. In between the LED and the fiber ends, an LCD is used to modify the light introduced. To do this, the LCD is programmed to display petal-shaped sections which individually change in contrast to become more occluded and then more transparent. This creates an aesthetically pleasing effect resembling stars.Type: ApplicationFiled: September 13, 2007Publication date: March 13, 2008Inventors: Daniel Dwyer, J. Boyd Hildebrant, Jeff Graham