Patents by Inventor Gal Braun
Gal Braun 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: 11967020Abstract: A distributed, cross reality system efficiently and accurately compares location information that includes image frames. Each of the frames may be represented as a numeric descriptor that enables identification of frames with similar content. The resolution of the descriptors may vary for different computing devices in the distributed system based on degree of ambiguity in image comparisons and/or computing resources for the device. A descriptor computed for a cloud-based component operating on maps of large areas that can result in ambiguous identification of multiple image frames may use high resolution descriptors. High resolution descriptors reduce computationally intensive disambiguation processing. A portable device, which is more likely to operate on smaller maps and less likely to have the computational resources to compute a high resolution descriptor, may use a lower resolution descriptor.Type: GrantFiled: December 20, 2022Date of Patent: April 23, 2024Assignee: Magic Leap, Inc.Inventors: Elad Joseph, Gal Braun, Ali Shahrokni
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Patent number: 11790079Abstract: Disclosed herein are systems and methods for enabling the automatic detection of executable code from a stream of bytes. In some embodiments, the stream of bytes can be sourced from the hidden areas of files that traditional malware detection solutions ignore. In some embodiments, a machine learning model is trained to detect whether a particular stream of bytes is executable code. Other embodiments described herein disclose systems and methods for automatic feature extraction using a neural network. Given a new file, the systems and methods may preprocess the code to be inputted into a trained neural network. The neural network may be used as a “feature generator” for a malware detection model. Other embodiments herein are directed to systems and methods for identifying, flagging, and/or detecting threat actors which attempt to obtain access to library functions independently.Type: GrantFiled: December 27, 2022Date of Patent: October 17, 2023Inventors: Shlomi Salem, Roy Ronen, Assaf Nativ, Amit Zohar, Gal Braun, Pavel Ferencz, Eitan Shterenbaum, Tal Maimon
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Publication number: 20230222731Abstract: A distributed, cross reality system efficiently and accurately compares location information that includes image frames. Each of the frames may be represented as a numeric descriptor that enables identification of frames with similar content. The resolution of the descriptors may vary for different computing devices in the distributed system based on degree of ambiguity in image comparisons and/or computing resources for the device. A descriptor computed for a cloud-based component operating on maps of large areas that can result in ambiguous identification of multiple image frames may use high resolution descriptors. High resolution descriptors reduce computationally intensive disambiguation processing. A portable device, which is more likely to operate on smaller maps and less likely to have the computational resources to compute a high resolution descriptor, may use a lower resolution descriptor.Type: ApplicationFiled: December 20, 2022Publication date: July 13, 2023Applicant: Magic Leap, Inc.Inventors: Elad Joseph, Gal Braun, Ali Shahrokni
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Publication number: 20230146847Abstract: Disclosed herein are systems and methods for enabling the automatic detection of executable code from a stream of bytes. In some embodiments, the stream of bytes can be sourced from the hidden areas of files that traditional malware detection solutions ignore. In some embodiments, a machine learning model is trained to detect whether a particular stream of bytes is executable code. Other embodiments described herein disclose systems and methods for automatic feature extraction using a neural network. Given a new file, the systems and methods may preprocess the code to be inputted into a trained neural network. The neural network may be used as a “feature generator” for a malware detection model. Other embodiments herein are directed to systems and methods for identifying, flagging, and/or detecting threat actors which attempt to obtain access to library functions independently.Type: ApplicationFiled: December 27, 2022Publication date: May 11, 2023Inventors: Shlomi Salem, Roy Ronen, Assaf Nativ, Amit Zohar, Gal Braun, Pavel Ferencz, Eitan Shterenbaum, Tal Maimon
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Patent number: 11580218Abstract: Disclosed herein are systems and methods for enabling the automatic detection of executable code from a stream of bytes. In some embodiments, the stream of bytes can be sourced from the hidden areas of files that traditional malware detection solutions ignore. In some embodiments, a machine learning model is trained to detect whether a particular stream of bytes is executable code. Other embodiments described herein disclose systems and methods for automatic feature extraction using a neural network. Given a new file, the systems and methods may preprocess the code to be inputted into a trained neural network. The neural network may be used as a “feature generator” for a malware detection model. Other embodiments herein are directed to systems and methods for identifying, flagging, and/or detecting threat actors which attempt to obtain access to library functions independently.Type: GrantFiled: September 21, 2021Date of Patent: February 14, 2023Assignee: Sentinel Labs Israel Ltd.Inventors: Shlomi Salem, Roy Ronen, Assaf Nativ, Amit Zohar, Gal Braun, Pavel Ferencz, Eitan Shterenbaum, Tal Maimon
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Patent number: 11562525Abstract: A distributed, cross reality system efficiently and accurately compares location information that includes image frames. Each of the frames may be represented as a numeric descriptor that enables identification of frames with similar content. The resolution of the descriptors may vary for different computing devices in the distributed system based on degree of ambiguity in image comparisons and/or computing resources for the device. A descriptor computed for a cloud-based component operating on maps of large areas that can result in ambiguous identification of multiple image frames may use high resolution descriptors. High resolution descriptors reduce computationally intensive disambiguation processing. A portable device, which is more likely to operate on smaller maps and less likely to have the computational resources to compute a high resolution descriptor, may use a lower resolution descriptor.Type: GrantFiled: February 11, 2021Date of Patent: January 24, 2023Assignee: Magic Leap, Inc.Inventors: Elad Joseph, Gal Braun, Ali Shahrokni
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Publication number: 20220391496Abstract: Disclosed herein are systems and methods for enabling the automatic detection of executable code from a stream of bytes. In some embodiments, the stream of bytes can be sourced from the hidden areas of files that traditional malware detection solutions ignore. In some embodiments, a machine learning model is trained to detect whether a particular stream of bytes is executable code. Other embodiments described herein disclose systems and methods for automatic feature extraction using a neural network. Given a new file, the systems and methods may preprocess the code to be inputted into a trained neural network. The neural network may be used as a “feature generator” for a malware detection model. Other embodiments herein are directed to systems and methods for identifying, flagging, and/or detecting threat actors which attempt to obtain access to library functions independently.Type: ApplicationFiled: September 21, 2021Publication date: December 8, 2022Inventors: Shlomi Salem, Roy Ronen, Assaf Nativ, Amit Zohar, Gal Braun, Pavel Ferencz, Eitan Shterenbaum, Tal Maimon
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Publication number: 20220019659Abstract: Disclosed herein are systems and methods for enabling the automatic detection of executable code from a stream of bytes. In some embodiments, the stream of bytes can be sourced from the hidden areas of files that traditional malware detection solutions ignore. In some embodiments, a machine learning model is trained to detect whether a particular stream of bytes is executable code. Other embodiments described herein disclose systems and methods for automatic feature extraction using a neural network. Given a new file, the systems and methods may preprocess the code to be inputted into a trained neural network. The neural network may be used as a “feature generator” for a malware detection model. Other embodiments herein are directed to systems and methods for identifying, flagging, and/or detecting threat actors which attempt to obtain access to library functions independently.Type: ApplicationFiled: September 21, 2021Publication date: January 20, 2022Inventors: Shlomi Salem, Roy Ronen, Assaf Nativ, Amit Zohar, Gal Braun, Pavel Ferencz, Eitan Shterenbaum, Tal Maimon
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Patent number: 11210392Abstract: Disclosed herein are systems and methods for enabling the automatic detection of executable code from a stream of bytes. In some embodiments, the stream of bytes can be sourced from the hidden areas of files that traditional malware detection solutions ignore. In some embodiments, a machine learning model is trained to detect whether a particular stream of bytes is executable code. Other embodiments described herein disclose systems and methods for automatic feature extraction using a neural network. Given a new file, the systems and methods may preprocess the code to be inputted into a trained neural network. The neural network may be used as a “feature generator” for a malware detection model. Other embodiments herein are directed to systems and methods for identifying, flagging, and/or detecting threat actors which attempt to obtain access to library functions independently.Type: GrantFiled: July 3, 2020Date of Patent: December 28, 2021Assignee: Sentinel Labs Israel Ltd.Inventors: Shlomi Salem, Roy Ronen, Assaf Nativ, Amit Zohar, Gal Braun, Pavel Ferencz, Eitan Shterenbaum, Tai Maimon
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Publication number: 20210256755Abstract: A distributed, cross reality system efficiently and accurately compares location information that includes image frames. Each of the frames may be represented as a numeric descriptor that enables identification of frames with similar content. The resolution of the descriptors may vary for different computing devices in the distributed system based on degree of ambiguity in image comparisons and/or computing resources for the device. A descriptor computed for a cloud-based component operating on maps of large areas that can result in ambiguous identification of multiple image frames may use high resolution descriptors. High resolution descriptors reduce computationally intensive disambiguation processing. A portable device, which is more likely to operate on smaller maps and less likely to have the computational resources to compute a high resolution descriptor, may use a lower resolution descriptor.Type: ApplicationFiled: February 11, 2021Publication date: August 19, 2021Applicant: Magic Leap, Inc.Inventors: Elad Joseph, Gal Braun, All Shahrokni
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Publication number: 20200372150Abstract: Disclosed herein are systems and methods for enabling the automatic detection of executable code from a stream of bytes. In some embodiments, the stream of bytes can be sourced from the hidden areas of files that traditional malware detection solutions ignore. In some embodiments, a machine learning model is trained to detect whether a particular stream of bytes is executable code. Other embodiments described herein disclose systems and methods for automatic feature extraction using a neural network. Given a new file, the systems and methods may preprocess the code to be inputted into a trained neural network. The neural network may be used as a “feature generator” for a malware detection model. Other embodiments herein are directed to systems and methods for identifying, flagging, and/or detecting threat actors which attempt to obtain access to library functions independently.Type: ApplicationFiled: July 3, 2020Publication date: November 26, 2020Inventors: Shlomi Salem, Roy Ronen, Assaf Nativ, Amit Zohar, Gal Braun, Pavel Ferencz, Eitan Shterenbaum, Tal Maimon
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Patent number: 10762200Abstract: Disclosed herein are systems and methods for enabling the automatic detection of executable code from a stream of bytes. In some embodiments, the stream of bytes can be sourced from the hidden areas of files that traditional malware detection solutions ignore. In some embodiments, a machine learning model is trained to detect whether a particular stream of bytes is executable code. Other embodiments described herein disclose systems and methods for automatic feature extraction using a neural network. Given a new file, the systems and methods may preprocess the code to be inputted into a trained neural network. The neural network may be used as a “feature generator” for a malware detection model. Other embodiments herein are directed to systems and methods for identifying, flagging, and/or detecting threat actors which attempt to obtain access to library functions independently.Type: GrantFiled: May 20, 2020Date of Patent: September 1, 2020Assignee: Sentinel Labs Israel Ltd.Inventors: Shlomi Salem, Roy Ronen, Assaf Nativ, Amit Zohar, Gal Braun, Pavel Ferencz, Eitan Shterenbaum, Tal Maimon