Patents by Inventor Khaled Salem
Khaled Salem 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: 11776150Abstract: An x-ray image laterality detection system is provided. The x-ray image laterality detection system includes a detection computing device. The processor of the computing device is programmed to execute a neural network model for analyzing x-ray images, wherein the neural network model is trained with training x-ray images as inputs and observed laterality classes associated with the training x-ray images as outputs. The process is also programmed to receive an unclassified x-ray image, analyze the unclassified x-ray image using the neural network model, and assign a laterality class to the unclassified x-ray image. If the assigned laterality class is not target laterality, the processor is programmed to adjust the unclassified x-ray image to derive a corrected x-ray image having the target laterality and output the corrected x-ray image. If the assigned laterality class is the target laterality, the processor is programmed to output the unclassified x-ray image.Type: GrantFiled: July 26, 2021Date of Patent: October 3, 2023Assignee: GE Precision Healthcare LLCInventors: Khaled Salem Younis, Ravi Soni, Katelyn Rose Nye, Gireesha Chinthamani Rao, John Michael Sabol, Yash N. Shah
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Patent number: 11682135Abstract: An x-ray image orientation detection and correction system including a detection and correction computing device is provided. The processor of the computing device is programmed to execute a neural network model that is trained with training x-ray images as inputs and observed x-ray images as outputs. The observed x-ray images are the training x-ray images adjusted to have a reference orientation. The processor is further programmed to receive an unclassified x-ray image, analyze the unclassified x-ray image using the neural network model, and assign an orientation class to the unclassified x-ray image. If the assigned orientation class is not the reference orientation, the processor is programmed to adjust an orientation of the unclassified x-ray image using the neural network model, and output a corrected x-ray image. If the assigned orientation class is the reference orientation, the processor is programmed to output the unclassified x-ray image.Type: GrantFiled: November 29, 2019Date of Patent: June 20, 2023Assignee: GE PRECISION HEALTHCARE LLCInventors: Khaled Salem Younis, Katelyn Rose Nye, Gireesha Chinthamani Rao, German Guillermo Vera Gonzalez, Gopal B. Avinash, Ravi Soni, Teri Lynn Fischer, John Michael Sabol
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Publication number: 20210350186Abstract: An x-ray image laterality detection system is provided. The x-ray image laterality detection system includes a detection computing device. The processor of the computing device is programmed to execute a neural network model for analyzing x-ray images, wherein the neural network model is trained with training x-ray images as inputs and observed laterality classes associated with the training x-ray images as outputs. The process is also programmed to receive an unclassified x-ray image, analyze the unclassified x-ray image using the neural network model, and assign a laterality class to the unclassified x-ray image. If the assigned laterality class is not target laterality, the processor is programmed to adjust the unclassified x-ray image to derive a corrected x-ray image having the target laterality and output the corrected x-ray image. If the assigned laterality class is the target laterality, the processor is programmed to output the unclassified x-ray image.Type: ApplicationFiled: July 26, 2021Publication date: November 11, 2021Inventors: Khaled Salem Younis, Ravi Soni, Katelyn Rose Nye, Gireesha Chinthamani Rao, John Michael Sabol, Yash N. Shah
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Patent number: 11113577Abstract: An x-ray image laterality detection system is provided. The x-ray image laterality detection system includes a detection computing device. The processor of the computing device is programmed to execute a neural network model for analyzing x-ray images, wherein the neural network model is trained with training x-ray images as inputs and observed laterality classes associated with the training x-ray images as outputs. The process is also programmed to receive an unclassified x-ray image, analyze the unclassified x-ray image using the neural network model, and assign a laterality class to the unclassified x-ray image. If the assigned laterality class is not target laterality, the processor is programmed to adjust the unclassified x-ray image to derive a corrected x-ray image having the target laterality and output the corrected x-ray image. If the assigned laterality class is the target laterality, the processor is programmed to output the unclassified x-ray image.Type: GrantFiled: February 27, 2020Date of Patent: September 7, 2021Assignee: GE PRECISION HEALTHCARE LLCInventors: Khaled Salem Younis, Ravi Soni, Katelyn Rose Nye, Gireesha Chinthamani Rao, John Michael Sabol, Yash N. Shah
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Publication number: 20210271931Abstract: An x-ray image laterality detection system is provided. The x-ray image laterality detection system includes a detection computing device. The processor of the computing device is programmed to execute a neural network model for analyzing x-ray images, wherein the neural network model is trained with training x-ray images as inputs and observed laterality classes associated with the training x-ray images as outputs. The process is also programmed to receive an unclassified x-ray image, analyze the unclassified x-ray image using the neural network model, and assign a laterality class to the unclassified x-ray image. If the assigned laterality class is not target laterality, the processor is programmed to adjust the unclassified x-ray image to derive a corrected x-ray image having the target laterality and output the corrected x-ray image. If the assigned laterality class is the target laterality, the processor is programmed to output the unclassified x-ray image.Type: ApplicationFiled: February 27, 2020Publication date: September 2, 2021Inventors: KHALED SALEM YOUNIS, Ravi Soni, Katelyn Rose Nye, Gireesha Chinthamani Rao, John Michael Sabol, Yash N. Shah
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Patent number: 11054495Abstract: Some embodiments disclosed herein include systems and method for verifying meter accuracy. The system may include an electric vehicle charging station that includes a submeter that measures an amount of energy discharged from the electric vehicle charging station and to a connected electric vehicle. A meter test device may also be connected to the electric vehicle charging station to determine the accuracy of the submeter in local time.Type: GrantFiled: July 17, 2019Date of Patent: July 6, 2021Assignee: SAN DIEGO GAS & ELECTRIC COMPANYInventors: Khaled Salem, Faridaddin Katiraei, Amin Zamani, Bahman Koosha
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Publication number: 20210166351Abstract: An x-ray image orientation detection and correction system including a detection and correction computing device is provided. The processor of the computing device is programmed to execute a neural network model that is trained with training x-ray images as inputs and observed x-ray images as outputs. The observed x-ray images are the training x-ray images adjusted to have a reference orientation. The processor is further programmed to receive an unclassified x-ray image, analyze the unclassified x-ray image using the neural network model, and assign an orientation class to the unclassified x-ray image. If the assigned orientation class is not the reference orientation, the processor is programmed to adjust an orientation of the unclassified x-ray image using the neural network model, and output a corrected x-ray image. If the assigned orientation class is the reference orientation, the processor is programmed to output the unclassified x-ray image.Type: ApplicationFiled: November 29, 2019Publication date: June 3, 2021Inventors: Khaled Salem Younis, Katelyn Rose Nye, Gireesha Chinthamani Rao, German Guillermo Vera Gonzalez, Gopal B. Avinash, Ravi Soni, Teri Lynn Fischer, John Michael Sabol
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Publication number: 20190339347Abstract: Some embodiments disclosed herein include systems and method for verifying meter accuracy. The system may include an electric vehicle charging station that includes a submeter that measures an amount of energy discharged from the electric vehicle charging station and to a connected electric vehicle. A meter test device may also be connected to the electric vehicle charging station to determine the accuracy of the submeter in local time.Type: ApplicationFiled: July 17, 2019Publication date: November 7, 2019Applicant: San Diego Gas & Electric CompanyInventors: Khaled Salem, Faridaddin Katiraei, Amin Zamani, Bahman Koosha
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Patent number: 10393849Abstract: Some embodiments disclosed herein include systems and method for verifying meter accuracy. The system may include an electric vehicle charging station that includes a submeter that measures an amount of energy discharged from the electric vehicle charging station and to a connected electric vehicle. A meter test device may also be connected to the electric vehicle charging station to determine the accuracy of the submeter in local time.Type: GrantFiled: January 3, 2017Date of Patent: August 27, 2019Assignee: San Diego Gas & Electric CompanyInventors: Khaled Salem, Faridaddin Katiraei, Amin Zamani, Bahman Koosha
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Publication number: 20180188346Abstract: Some embodiments disclosed herein include systems and method for verifying meter accuracy. The system may include an electric vehicle charging station that includes a submeter that measures an amount of energy discharged from the electric vehicle charging station and to a connected electric vehicle. A meter test device may also be connected to the electric vehicle charging station to determine the accuracy of the submeter in local time.Type: ApplicationFiled: January 3, 2017Publication date: July 5, 2018Applicant: San Diego Gas & Electric CompanyInventors: Khaled Salem, Faridaddin Katiraei, Amin Zamani, Bahman Koosha