Patents by Inventor Matthew Elsner
Matthew Elsner 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: 11895094Abstract: The example embodiments are directed to a system and method for managing blockchain transaction processing. In an example, the method includes one or more of receiving a message transmitted from a client device, the message including a predefined structural format for processing by a service providing computing system, determining a type of the message and detecting one or more sensitive fields within the message based on the determined type of the message, anonymizing values of the one or more sensitive fields within the message while leaving the predefined structural format intact, and transmitting the anonymized message including the one or more anonymized values with the predefined structural format remaining intact to the service providing computing system. The system can anonymize data from a private network before it is transmitted to a public service.Type: GrantFiled: November 18, 2019Date of Patent: February 6, 2024Assignee: International Business Machines CorporationInventors: David G. Druker, Matthew Elsner, Ariel Farkash, Igor Gokhman, Brian R. Matthiesen, Patrick R. Wardrop, Ilgen B. Yuceer
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Patent number: 11362910Abstract: A tiered machine learning-based infrastructure comprises a first machine learning (ML) tier configured to execute within an enterprise network environment and that learns statistics for a set of use cases locally, and to alert deviations from the learned distributions. Use cases typically are independent from one another. A second machine learning tier executes external to the enterprise network environment and provides further learning support, e.g., by determining a correlation among multiple independent use cases that are running locally in the first tier. Preferably, the second tier executes in a cloud compute environment for scalability and performance.Type: GrantFiled: July 17, 2018Date of Patent: June 14, 2022Assignee: International Business Machines CorporationInventors: Jian Lin, Matthew Elsner, Ronald Williams, Michael Josiah Bolding, Yun Pan, Paul Sherwood Taylor, Cheng-Ta Lee
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Patent number: 11238366Abstract: A machine learning (ML)-based technique for user behavior analysis that detects when users deviate from expected behavior. A ML model is trained using training data derived from activity data from a first set of users. The model is refined in a computationally-efficient manner by identifying a second set of users that constitute a “watch list.” At a given time, a differential data ingestion operation is then performed to incorporate data for the second set of users into the training data, while also pruning at least a portion of the data set corresponding to data associated with any user included in the first set but not in the second set. These operations update the training data used for the machine learning. The machine learning model is then refined based on the updated training data that incorporates the activity data ingested from the users identified in the watch list.Type: GrantFiled: May 10, 2018Date of Patent: February 1, 2022Assignee: International Business Machines CorporationInventors: Michael Josiah Bolding, Matthew Elsner, Jian Lin, Matthew Paul Ouellette, Yun Pan
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Patent number: 10938845Abstract: A machine learning-based technique for user behavior analysis that detects when users deviate from expected behavior. In this approach, a set of user groups are provided, preferably based on information provided from a user registry. A set of training data for each of the set of user groups is then obtained, preferably by collecting security events generated for a collection of the users over a given time period (e.g., a last thirty (30) days). A machine learning system is then trained using the set of training data to produce a model that includes a set of clusters in user behavior model, wherein a cluster is a learned user group that corresponds to a defined user group. Once the model is built, it is used to identify users that deviate from their expected group behavior. In particular, the system compares a current behavior of a user against the model and flags anomalous behavior. The user behavior analysis may be implemented in a security platform, such as a SIEM.Type: GrantFiled: May 10, 2018Date of Patent: March 2, 2021Assignee: International Business Machines CorporationInventors: Matthew Elsner, Jian Lin, Ronald Williams, Ilgen Banu Yuceer
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Publication number: 20200084184Abstract: The example embodiments are directed to a system and method for managing blockchain transaction processing. In an example, the method includes one or more of receiving a message transmitted from a client device, the message including a predefined structural format for processing by a service providing computing system, determining a type of the message and detecting one or more sensitive fields within the message based on the determined type of the message, anonymizing values of the one or more sensitive fields within the message while leaving the predefined structural format intact, and transmitting the anonymized message including the one or more anonymized values with the predefined structural format remaining intact to the service providing computing system. The system can anonymize data from a private network before it is transmitted to a public service.Type: ApplicationFiled: November 18, 2019Publication date: March 12, 2020Inventors: David G. Druker, Matthew Elsner, Ariel Farkash, Igor Gokhman, Brian R. Matthiesen, Patrick R. Wardrop, Ilgen B. Yuceer
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Publication number: 20200028862Abstract: A tiered machine learning-based infrastructure comprises a first machine learning (ML) tier configured to execute within an enterprise network environment and that learns statistics for a set of use cases locally, and to alert deviations from the learned distributions. Use cases typically are independent from one another. A second machine learning tier executes external to the enterprise network environment and provides further learning support, e.g., by determining a correlation among multiple independent use cases that are running locally in the first tier. Preferably, the second tier executes in a cloud compute environment for scalability and performance.Type: ApplicationFiled: July 17, 2018Publication date: January 23, 2020Applicant: International Business Machines CorporationInventors: Jian Lin, Matthew Elsner, Ronald Williams, Michael Josiah Bolding, Yun Pan, Paul Sherwood Taylor, Cheng-Ta Lee
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Patent number: 10523638Abstract: The example embodiments are directed to a system and method for managing blockchain transaction processing. In an example, the method includes one or more of receiving a message transmitted from a client device, the message including a predefined structural format for processing by a service providing computing system, determining a type of the message and detecting one or more sensitive fields within the message based on the determined type of the message, anonymizing values of the one or more sensitive fields within the message while leaving the predefined structural format intact, and transmitting the anonymized message including the one or more anonymized values with the predefined structural format remaining intact to the service providing computing system. The system can anonymize data from a private network before it is transmitted to a public service.Type: GrantFiled: March 13, 2019Date of Patent: December 31, 2019Assignee: International Business Machines CorporationInventors: David G. Druker, Matthew Elsner, Ariel Farkash, Igor Gokhman, Brian R. Matthiesen, Patrick R. Wardrop, Ilgen B. Yuceer
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Publication number: 20190349391Abstract: A machine learning-based technique for user behavior analysis that detects when users deviate from expected behavior. In this approach, a set of user groups are provided, preferably based on information provided from a user registry. A set of training data for each of the set of user groups is then obtained, preferably by collecting security events generated for a collection of the users over a given time period (e.g., a last thirty (30) days). A machine learning system is then trained using the set of training data to produce a model that includes a set of clusters in user behavior model, wherein a cluster is a learned user group that corresponds to a defined user group. Once the model is built, it is used to identify users that deviate from their expected group behavior. In particular, the system compares a current behavior of a user against the model and flags anomalous behavior. The user behavior analysis may be implemented in a security platform, such as a SIEM.Type: ApplicationFiled: May 10, 2018Publication date: November 14, 2019Applicant: International Business Machines CorporationInventors: Matthew Elsner, Jian Lin, Ronald Williams, Ilgen Banu Yuceer
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Publication number: 20190347578Abstract: A machine learning (ML)-based technique for user behavior analysis that detects when users deviate from expected behavior. A ML model is trained using training data derived from activity data from a first set of users. The model is refined in a computationally-efficient manner by identifying a second set of users that constitute a “watch list.” At a given time, a differential data ingestion operation is then performed to incorporate data for the second set of users into the training data, while also pruning at least a portion of the data set corresponding to data associated with any user included in the first set but not in the second set. These operations update the training data used for the machine learning. The machine learning model is then refined based on the updated training data that incorporates the activity data ingested from the users identified in the watch list.Type: ApplicationFiled: May 10, 2018Publication date: November 14, 2019Applicant: International Business Machines CorporationInventors: Michael Josiah Bolding, Matthew Elsner, Jian Lin, Matthew Paul Ouellette, Yun Pan
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Publication number: 20190215309Abstract: The example embodiments are directed to a system and method for managing blockchain transaction processing. In an example, the method includes one or more of receiving a message transmitted from a client device, the message including a predefined structural format for processing by a service providing computing system, determining a type of the message and detecting one or more sensitive fields within the message based on the determined type of the message, anonymizing values of the one or more sensitive fields within the message while leaving the predefined structural format intact, and transmitting the anonymized message including the one or more anonymized values with the predefined structural format remaining intact to the service providing computing system. The system can anonymize data from a private network before it is transmitted to a public service.Type: ApplicationFiled: March 13, 2019Publication date: July 11, 2019Inventors: David G. Druker, Matthew Elsner, Ariel Farkash, Igor Gokhman, Brian R. Matthiesen, Patrick R. Wardrop, Ilgen B. Yuceer
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Patent number: 10333902Abstract: The example embodiments are directed to a system and method for managing blockchain transaction processing. In an example, the method includes one or more of receiving a message transmitted from a client device, the message including a predefined structural format for processing by a service providing computing system, determining a type of the message and detecting one or more sensitive fields within the message based on the determined type of the message, anonymizing values of the one or more sensitive fields within the message while leaving the predefined structural format intact, and transmitting the anonymized message including the one or more anonymized values with the predefined structural format remaining intact to the service providing computing system. The system can anonymize data from a private network before it is transmitted to a public service.Type: GrantFiled: December 19, 2017Date of Patent: June 25, 2019Assignee: International Business Machines CorporationInventors: David G. Druker, Matthew Elsner, Ariel Farkash, Igor Gokhman, Brian R. Matthiesen, Patrick R. Wardrop, Ilgen B. Yuceer
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Publication number: 20190190890Abstract: The example embodiments are directed to a system and method for managing blockchain transaction processing. In an example, the method includes one or more of receiving a message transmitted from a client device, the message including a predefined structural format for processing by a service providing computing system, determining a type of the message and detecting one or more sensitive fields within the message based on the determined type of the message, anonymizing values of the one or more sensitive fields within the message while leaving the predefined structural format intact, and transmitting the anonymized message including the one or more anonymized values with the predefined structural format remaining intact to the service providing computing system. The system can anonymize data from a private network before it is transmitted to a public service.Type: ApplicationFiled: December 19, 2017Publication date: June 20, 2019Inventors: David G. Druker, Matthew Elsner, Ariel Farkash, Igor Gokhman, Brian R. Matthiesen, Patrick R. Wardrop, Ilgen B. Yuceer
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Patent number: 4839828Abstract: A color graphic display having a read/write control system for a buffer memory therein. The invention provides Line-on-Line and Underpaint by way of a method which invleves reading the contents of a frame buffer storage location for which new pixel data is being provided, comparing those contents with data representing a display background characteristic or color, and if the result of the comparison is positive, storing the new pixel data in the frame buffer storage location. If the result of the comparison is negative, a selected data value different from the new pixel data is stored in the frame buffer storage location.Type: GrantFiled: January 21, 1986Date of Patent: June 13, 1989Assignee: International Business Machines CorporationInventors: Matthew Elsner, Yoshio Iida, Edward Y. Kwong, Omar M. Rahim