Patents by Inventor Nigel Hinds
Nigel Hinds 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: 11455522Abstract: A mobile electronic device such as a smartphone is used in conjunction with a deep learning system to detect and respond to personal danger. The deep learning system monitors current information (such as location, audio, biometrics, etc.) from the smartphone and generates a risk score by comparing the information to a routine profile for the user. If the risk score exceeds a predetermined threshold, an alert is sent to the smartphone which presents an alert screen to the user. The alert screen allows the user to cancel the alert (and notify the deep learning system) or confirm the alert (and immediately transmit an emergency message). Multiple emergency contacts can be designated, e.g., one for a low-level risk, another for an intermediate-level risk, and another for a high-level risk, and the emergency message can be sent to a selected contact depending upon the severity of the risk score.Type: GrantFiled: November 17, 2017Date of Patent: September 27, 2022Assignee: International Business Machines CorporationInventors: Steven A. Cordes, Michael S. Gordon, Nigel Hinds, Maja Vukovic
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Patent number: 11424911Abstract: An example operation may include one or more of receiving, via a network, tag data that is read from a tag associated with a physical object and signed with a key assigned to the tag, determining, via a blockchain peer, that the signed tag data is validly signed based on a corresponding key pair of the tag which is accessible to the blockchain peer, determining, via the blockchain peer, whether the tag data satisfies of one or more predefined conditions of the physical object, and storing the determination via a blockchain database.Type: GrantFiled: March 3, 2020Date of Patent: August 23, 2022Assignee: International Business Machines CorporationInventors: Chandrasekhar Narayanaswami, Daniel Joseph Friedman, Nigel Hinds, Abhilash Narendra, Arun Paidimarri, James Thomas Rayfield, Roman Vaculin, Zhiyuan Li
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Patent number: 11327171Abstract: A computer-implemented method executed by one or more satellites for assessing crop development by using synthetic aperture radar (SAR) is presented. The method includes generating SAR images from scanning fields including crops, monitoring grown of the crops within the fields during a predetermined time period, and estimating a height of the crops during the predetermined time period by using interferometric information from one or more of the SAR images and tracking change in height and growth rates. The method further includes differentiating between crops in different fields by monitoring changes in the height of the crops during an entire growing season.Type: GrantFiled: April 30, 2020Date of Patent: May 10, 2022Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Levente Klein, Siyuan Lu, Fernando Jimenez Marianno, Nigel Hinds
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Patent number: 11267482Abstract: In an approach to predicting physiological and behavioral states utilizing models representing relationships between driver health states and vehicle dynamics data, one or more computer processors capture one or more vehicle motion parameters. The one or more computer processors to capture one or more physiological parameters; identify contextual data associated with the one or more captured vehicle motion parameters and the one or more captured physiological parameters; predict one or more driving behavior parameters by utilizing one or more physical models fed with the one or more vehicle motion parameters and the identified contextual data; predict one or more driver health parameters by utilizing a model trained with the one or more captured physiological parameters and the identified contextual data; generate a risk assessment based on the one or more predicted driving behavior parameters and the one or more predicted driver health parameters.Type: GrantFiled: October 11, 2019Date of Patent: March 8, 2022Assignee: International Business Machines CorporationInventors: Julien Monteil, Yassine Lassoued, Sergio Cabrero Barros, Rodrigo Hernan Ordonez-Hurtado, Martin Mevissen, Sergiy Zhuk, Nigel Hinds, Bo Wen, Jeffrey Rogers
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Publication number: 20210281395Abstract: An example operation may include one or more of receiving, via a network, tag data that is read from a tag associated with a physical object and signed with a key assigned to the tag, determining, via a blockchain peer, that the signed tag data is validly signed based on a corresponding key pair of the tag which is accessible to the blockchain peer, determining, via the blockchain peer, whether the tag data satisfies of one or more predefined conditions of the physical object, and storing the determination via a blockchain database.Type: ApplicationFiled: March 3, 2020Publication date: September 9, 2021Inventors: Chandrasekhar Narayanaswami, Daniel Joseph Friedman, Nigel Hinds, Abhilash Narendra, Arun Paidimarri, James Thomas Rayfield, Roman Vaculin, Zhiyuan Li
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Publication number: 20210107501Abstract: In an approach to predicting physiological and behavioral states utilizing models representing relationships between driver health states and vehicle dynamics data, one or more computer processors capture one or more vehicle motion parameters. The one or more computer processors to capture one or more physiological parameters; identify contextual data associated with the one or more captured vehicle motion parameters and the one or more captured physiological parameters; predict one or more driving behavior parameters by utilizing one or more physical models fed with the one or more vehicle motion parameters and the identified contextual data; predict one or more driver health parameters by utilizing a model trained with the one or more captured physiological parameters and the identified contextual data; generate a risk assessment based on the one or more predicted driving behavior parameters and the one or more predicted driver health parameters.Type: ApplicationFiled: October 11, 2019Publication date: April 15, 2021Inventors: Julien Monteil, Yassine Lassoued, Sergio Cabrero Barros, Rodrigo Hernan Ordonez-Hurtado, Martin Mevissen, Sergiy Zhuk, Nigel Hinds, Bo Wen, Jeffrey Rogers
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Publication number: 20200256978Abstract: A computer-implemented method executed by one or more satellites for assessing crop development by using synthetic aperture radar (SAR) is presented. The method includes generating SAR images from scanning fields including crops, monitoring grown of the crops within the fields during a predetermined time period, and estimating a height of the crops during the predetermined time period by using interferometric information from one or more of the SAR images and tracking change in height and growth rates. The method further includes differentiating between crops in different fields by monitoring changes in the height of the crops during an entire growing season.Type: ApplicationFiled: April 30, 2020Publication date: August 13, 2020Inventors: Levente Klein, Siyuan Lu, Fernando Jimenez Marianno, Nigel Hinds
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Patent number: 10705204Abstract: A computer-implemented method executed by one or more satellites for assessing crop development by using synthetic aperture radar (SAR) is presented. The method includes generating SAR images from scanning fields including crops, monitoring grown of the crops within the fields during a predetermined time period, and estimating a height of the crops during the predetermined time period by using interferometric information from one or more of the SAR images and tracking change in height and growth rates. The method further includes differentiating between crops in different fields by monitoring changes in the height of the crops during an entire growing season.Type: GrantFiled: December 8, 2017Date of Patent: July 7, 2020Assignee: International Business Machines CorporationInventors: Levente Klein, Siyuan Lu, Fernando Jimenez Marianno, Nigel Hinds
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Patent number: 10424185Abstract: A mobile electronic device such as a smartphone is used in conjunction with a deep learning system to detect and respond to personal danger. The deep learning system monitors current information (such as location, audio, biometrics, etc.) from the smartphone and generates a risk score by comparing the information to a routine profile for the user. If the risk score exceeds a predetermined threshold, an alert is sent to the smartphone which presents an alert screen to the user. The alert screen allows the user to cancel the alert (and notify the deep learning system) or confirm the alert (and immediately transmit an emergency message). Multiple emergency contacts can be designated, e.g., one for a low-level risk, another for an intermediate-level risk, and another for a high-level risk, and the emergency message can be sent to a selected contact depending upon the severity of the risk score.Type: GrantFiled: November 17, 2017Date of Patent: September 24, 2019Assignee: International Business Machines CorporationInventors: Steven A. Cordes, Michael S. Gordon, Nigel Hinds, Maja Vukovic
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Publication number: 20190179009Abstract: A computer-implemented method executed by one or more satellites for assessing crop development by using synthetic aperture radar (SAR) is presented. The method includes generating SAR images from scanning fields including crops, monitoring grown of the crops within the fields during a predetermined time period, and estimating a height of the crops during the predetermined time period by using interferometric information from one or more of the SAR images and tracking change in height and growth rates. The method further includes differentiating between crops in different fields by monitoring changes in the height of the crops during an entire growing season.Type: ApplicationFiled: December 8, 2017Publication date: June 13, 2019Inventors: Levente Klein, Siyuan Lu, Fernando Jimenez Marianno, Nigel Hinds
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Publication number: 20190156191Abstract: A mobile electronic device such as a smartphone is used in conjunction with a deep learning system to detect and respond to personal danger. The deep learning system monitors current information (such as location, audio, biometrics, etc.) from the smartphone and generates a risk score by comparing the information to a routine profile for the user. If the risk score exceeds a predetermined threshold, an alert is sent to the smartphone which presents an alert screen to the user. The alert screen allows the user to cancel the alert (and notify the deep learning system) or confirm the alert (and immediately transmit an emergency message). Multiple emergency contacts can be designated, e.g., one for a low-level risk, another for an intermediate-level risk, and another for a high-level risk, and the emergency message can be sent to a selected contact depending upon the severity of the risk score.Type: ApplicationFiled: November 17, 2017Publication date: May 23, 2019Inventors: Steven A. Cordes, Michael S. Gordon, Nigel Hinds, Maja Vukovic
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Publication number: 20190156655Abstract: A mobile electronic device such as a smartphone is used in conjunction with a deep learning system to detect and respond to personal danger. The deep learning system monitors current information (such as location, audio, biometrics, etc.) from the smartphone and generates a risk score by comparing the information to a routine profile for the user. If the risk score exceeds a predetermined threshold, an alert is sent to the smartphone which presents an alert screen to the user. The alert screen allows the user to cancel the alert (and notify the deep learning system) or confirm the alert (and immediately transmit an emergency message). Multiple emergency contacts can be designated, e.g., one for a low-level risk, another for an intermediate-level risk, and another for a high-level risk, and the emergency message can be sent to a selected contact depending upon the severity of the risk score.Type: ApplicationFiled: November 17, 2017Publication date: May 23, 2019Inventors: Steven A. Cordes, Michael S. Gordon, Nigel Hinds, Maja Vukovic