Patents by Inventor Jeffrey M. Achtermann
Jeffrey M. Achtermann 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: 11816131Abstract: A method and system. Target clusterability is calculated as an average of a respective clusterability of at least one target data item comprised by a target domain. Target-side matchability is calculated as an average of a respective matchability of each target centroid of the target domain to source centroids of a source domain, wherein the source domain comprises at least one source data item. Source-side matchability is calculated as an average of a respective matchability of each source centroid of said source centroids to the target centroids. Source-target pair matchability is calculated as an average of the target-side matchability and the source-side matchability. Cross-domain clusterability between the target domain and the source domain is calculated as a linear combination of the calculated target clusterability and the calculated source-target pair matchability. The cross-domain clusterability is transferred to a device.Type: GrantFiled: March 25, 2019Date of Patent: November 14, 2023Assignee: KYNDRYL, INC.Inventors: Jeffrey M. Achtermann, Indrajit Bhattacharya, Kevin W. English, Shantanu R. Godbole, Sachindra Joshi, Ashwin Srinivasan, Ashish Verma
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Patent number: 10972474Abstract: Methods and apparatus, including computer program products, implementing and using techniques for logically grouping Internet of Things (IoT) devices. One or more logical zones are defined. Each logical zone includes one or more physical zones, one or more virtual zones, or a combination of physical and virtual zones. Each IoT device is associated with at least one logical zone. Communication between the IoT devices is restricted based on the zones with which the IoT devices are associated.Type: GrantFiled: April 18, 2017Date of Patent: April 6, 2021Assignee: International Business Machines CorporationInventors: Jeffrey M. Achtermann, Harrison Kurtz, Maharaj Mukherjee, Joanna W. Ng
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Patent number: 10965684Abstract: Methods for logically grouping Internet of Things (IoT) devices are described. One or more logical zones are defined. Each logical zone includes one or more physical zones, one or more virtual zones, or a combination of physical and virtual zones. Each IoT device is associated with at least one logical zone. Communication between the IoT devices is restricted based on the zones with which the IoT devices are associated.Type: GrantFiled: November 16, 2018Date of Patent: March 30, 2021Assignee: International Business Machines CorporationInventors: Jeffrey M. Achtermann, Harrison Kurtz, Maharaj Mukherjee, Joanna W. Ng
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Patent number: 10949938Abstract: Methods and apparatus, including computer program products, implementing and using techniques for chain of custody tracking for an object. Several sets of Internet of Things (IoT) sensors are organized in a network. Each set of sensors is configured to record one or more events relating to the object. Each event includes an event time, an event location, and an entity that is a custodian for the object at the time of the event. When the object changes custodians, proper custodianship is verified based on input from at least one set of IoT sensors.Type: GrantFiled: April 18, 2017Date of Patent: March 16, 2021Assignee: International Business Machines CorporationInventors: Jeffrey M. Achtermann, Rahul Gupta, Arnaud A. Mathieu, Maharaj Mukherjee
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Patent number: 10949939Abstract: Methods for chain of custody tracking for an object are described. Several sets of Internet of Things (IoT) sensors are organized in a network. Each set of sensors is configured to record one or more events relating to the object. Each event includes an event time, an event location, and an entity that is a custodian for the object at the time of the event. When the object changes custodians, proper custodianship is verified based on input from at least one set of IoT sensors.Type: GrantFiled: November 16, 2018Date of Patent: March 16, 2021Assignee: International Business Machines CorporationInventors: Jeffrey M. Achtermann, Rahul Gupta, Arnaud A. Mathieu, Maharaj Mukherjee
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Patent number: 10389578Abstract: In a method for providing an automatic learned response in a network, a collection system observes user responses to the incoming system indicators and to parameter types and associated parameter values used in the user responses. The collection system creates alert event entries to includes the incoming system indicators, confidence thresholds, the user responses, and the parameter types and associated parameter values used in the user responses. When the collection system receives new system indicators, the collection system determines whether the new system indicators match the system indicators in one or more alert event entries. When the new system indicators match the system indicators in one or more alert event entries and the confidence level exceeds the confidence threshold, the collection system automatically creates a new response based on the user response, the parameter types, and the associated parameter values in the matching alert event entries.Type: GrantFiled: March 6, 2017Date of Patent: August 20, 2019Assignee: International Business Machines CorporationInventors: Jeffrey M. Achtermann, Michael Bender, Timothy J. Hahn, Hari H. Madduri, Leucir Marin, Jr.
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Publication number: 20190220470Abstract: A method and system. Target clusterability is calculated as an average of a respective clusterability of at least one target data item comprised by a target domain. Target-side matchability is calculated as an average of a respective matchability of each target centroid of the target domain to source centroids of a source domain, wherein the source domain comprises at least one source data item. Source-side matchability is calculated as an average of a respective matchability of each source centroid of said source centroids to the target centroids. Source-target pair matchability is calculated as an average of the target-side matchability and the source-side matchability. Cross-domain clusterability between the target domain and the source domain is calculated as a linear combination of the calculated target clusterability and the calculated source-target pair matchability. The cross-domain clusterability is transferred to a device.Type: ApplicationFiled: March 25, 2019Publication date: July 18, 2019Inventors: JEFFREY M. ACHTERMANN, INDRAJIT BHATTACHARYA, KEVIN W. ENGLISH, SHANTANU R. GODBOLE, SACHINDRA JOSHI, ASHWIN SRINIVASAN, ASHISH VERMA
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Patent number: 10311086Abstract: A method and system. Target clusterability is calculated as an average of a respective clusterability of at least one target data item comprised by a target domain. Target-side matchability is calculated as an average of a respective matchability of each target centroid of the target domain to source centroids of a source domain, wherein the source domain comprises at least one source data item. Source-side matchability is calculated as an average of a respective matchability of each source centroid of said source centroids to the target centroids. Source-target pair matchability is calculated as an average of the target-side matchability and the source-side matchability. Cross-domain clusterability between the target domain and the source domain is calculated as a linear combination of the calculated target clusterability and the calculated source-target pair matchability. The cross-domain clusterability is transferred to a device.Type: GrantFiled: March 15, 2016Date of Patent: June 4, 2019Assignee: International Business Machines CorporationInventors: Jeffrey M. Achtermann, Indrajit Bhattacharya, Kevin W. English, Shantanu R. Godbole, Sachindra Joshi, Ashwin Srinivasan, Ashish Verma
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Publication number: 20190109856Abstract: Methods for logically grouping Internet of Things (IoT) devices are described. One or more logical zones are defined. Each logical zone includes one or more physical zones, one or more virtual zones, or a combination of physical and virtual zones. Each IoT device is associated with at least one logical zone. Communication between the IoT devices is restricted based on the zones with which the IoT devices are associated.Type: ApplicationFiled: November 16, 2018Publication date: April 11, 2019Inventors: Jeffrey M. Achtermann, Harrison Kurtz, Maharaj Mukherjee, Joanna W. Ng
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Publication number: 20190102855Abstract: Methods for chain of custody tracking for an object are described. Several sets of Internet of Things (IoT) sensors are organized in a network. Each set of sensors is configured to record one or more events relating to the object. Each event includes an event time, an event location, and an entity that is a custodian for the object at the time of the event. When the object changes custodians, proper custodianship is verified based on input from at least one set of IoT sensors.Type: ApplicationFiled: November 16, 2018Publication date: April 4, 2019Inventors: Jeffrey M. Achtermann, Rahul Gupta, Arnaud A. Mathieu, Maharaj Mukherjee
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Publication number: 20180302412Abstract: Methods and apparatus, including computer program products, implementing and using techniques for logically grouping Internet of Things (IoT) devices. One or more logical zones are defined. Each logical zone includes one or more physical zones, one or more virtual zones, or a combination of physical and virtual zones. Each IoT device is associated with at least one logical zone. Communication between the IoT devices is restricted based on the zones with which the IoT devices are associated.Type: ApplicationFiled: April 18, 2017Publication date: October 18, 2018Inventors: Jeffrey M. Achtermann, Harrison Kurtz, Maharaj Mukherjee, Joanna W. Ng
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Publication number: 20180300831Abstract: Methods and apparatus, including computer program products, implementing and using techniques for chain of custody tracking for an object. Several sets of Internet of Things (IoT) sensors are organized in a network. Each set of sensors is configured to record one or more events relating to the object. Each event includes an event time, an event location, and an entity that is a custodian for the object at the time of the event. When the object changes custodians, proper custodianship is verified based on input from at least one set of IoT sensors.Type: ApplicationFiled: April 18, 2017Publication date: October 18, 2018Inventors: Jeffrey M. Achtermann, Rahul Gupta, Arnaud A. Mathieu, Maharaj Mukherjee
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Publication number: 20180254961Abstract: In a method for providing an automatic learned response in a network, a collection system observes user responses to the incoming system indicators and to parameter types and associated parameter values used in the user responses. The collection system creates alert event entries to includes the incoming system indicators, confidence thresholds, the user responses, and the parameter types and associated parameter values used in the user responses. When the collection system receives new system indicators, the collection system determines whether the new system indicators match the system indicators in one or more alert event entries. When the new system indicators match the system indicators in one or more alert event entries and the confidence level exceeds the confidence threshold, the collection system automatically creates a new response based on the user response, the parameter types, and the associated parameter values in the matching alert event entries.Type: ApplicationFiled: March 6, 2017Publication date: September 6, 2018Inventors: Jeffrey M. ACHTERMANN, Michael BENDER, Timothy J. HAHN, Hari H. MADDURI, Leucir MARIN, JR.
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Publication number: 20160196328Abstract: A method and system. Target clusterability is calculated as an average of a respective clusterability of at least one target data item comprised by a target domain. Target-side matchability is calculated as an average of a respective matchability of each target centroid of the target domain to source centroids of a source domain, wherein the source domain comprises at least one source data item. Source-side matchability is calculated as an average of a respective matchability of each source centroid of said source centroids to the target centroids. Source-target pair matchability is calculated as an average of the target-side matchability and the source-side matchability. Cross-domain clusterability between the target domain and the source domain is calculated as a linear combination of the calculated target clusterability and the calculated source-target pair matchability. The cross-domain clusterability is transferred to a device.Type: ApplicationFiled: March 15, 2016Publication date: July 7, 2016Inventors: JEFFREY M. ACHTERMANN, INDRAJIT BHATTACHARYA, KEVIN W. ENGLISH, SHANTANU R. GODBOLE, SACHINDRA JOSHI, ASHWIN SRINIVASAN, ASHISH VERMA
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Patent number: 9336296Abstract: A method and system for evaluating cross-domain clusterability upon a target domain and a source domain. Target clusterability is calculated as an average of a respective clusterability of at least one target data item comprised by the target domain. Target-side matchability is calculated as an average of a respective matchability of each target centroid of the target domain to source centroids of the source domain, wherein the source domain comprises at least one source data item. Source-side matchability is calculated as an average of a respective matchability of each source centroid of said source centroids to the target centroids. Source-target pair matchability is calculated as an average of the target-side matchability and the source-side matchability. Cross-domain clusterability between the target domain and the source domain is calculated as a linear combination of the calculated target clusterability and the calculated source-target pair matchability.Type: GrantFiled: January 6, 2014Date of Patent: May 10, 2016Assignee: International Business Machines CorporationInventors: Jeffrey M. Achtermann, Indrajit Bhattacharya, Kevin W. English, Jr., Shantanu R. Godbole, Sachindra Joshi, Ashwin Srinivasan, Ashish Verma
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Patent number: 8812297Abstract: Determining synonyms of words in a set of documents. Particularly, when provided with a word or phrase as input, in exemplary embodiments there is afforded the return of a predetermined number of “top” synonym words (or phrases) for an input word (or phrase) in a specific collection of text documents. Further, a user is able to provide ongoing and iterative positive or negative feedback on the returned synonym words, by manually accepting or rejecting such words as the process is underway.Type: GrantFiled: April 9, 2010Date of Patent: August 19, 2014Assignee: International Business Machines CorporationInventors: Jeffrey M. Achtermann, Indrajit Bhattacharya, Kevin W. English, Shantanu R. Godbole, Ajay K. Gupta, Ashish Verma
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Publication number: 20140122492Abstract: A method and system for evaluating cross-domain clusterability upon a target domain and a source domain. Target clusterability is calculated as an average of a respective clusterability of at least one target data item comprised by the target domain. Target-side matchability is calculated as an average of a respective matchability of each target centroid of the target domain to source centroids of the source domain, wherein the source domain comprises at least one source data item. Source-side matchability is calculated as an average of a respective matchability of each source centroid of said source centroids to the target centroids. Source-target pair matchability is calculated as an average of the target-side matchability and the source-side matchability. Cross-domain clusterability between the target domain and the source domain is calculated as a linear combination of the calculated target clusterability and the calculated source-target pair matchability.Type: ApplicationFiled: January 6, 2014Publication date: May 1, 2014Inventors: JEFFREY M. ACHTERMANN, INDRAJIT BHATTACHARYA, KEVIN W. ENGLISH, JR., SHANTANU R. GODBOLE, SACHINDRA JOSHI, ASHWIN SRINIVASAN, ASHISH VERMA
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Patent number: 8661039Abstract: A process for evaluating cross-domain clusterability upon a target domain and a source domain. The cross-domain clusterability is calculated as a linear combination of a target clusterability and a source-target pair matchability, by use of a trade-off parameter that determines relative contribution of the target clusterability and the source-target pair matchability. The target clusterability quantifies how clusterable the target domain is. The source-target pair matchability is calculated as an average of a target-side matchability and a source-side matchability, which quantifies how well target centroids of the target domain are aligned with the source centroids and how well source centroids of the source domain are aligned with the target centroids, respectively.Type: GrantFiled: April 2, 2012Date of Patent: February 25, 2014Assignee: International Business Machines CorporationInventors: Jeffrey M. Achtermann, Indrajit Bhattacharya, Kevin W. English, Jr., Shantanu R. Godbole, Sachindra Joshi, Ashwin Srinivasan, Ashish Verma
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Patent number: 8655884Abstract: A computer system for evaluating cross-domain clusterability upon a target domain and a source domain. The cross-domain clusterability is calculated as a linear combination of a target clusterability and a source-target pair matchability, by use of a trade-off parameter that determines relative contribution of the target clusterability and the source-target pair matchability. The target clusterability quantifies how clusterable the target domain is. The source-target pair matchability is calculated as an average of a target-side matchability and a source-side matchability, which quantifies how well target centroids of the target domain are aligned with the source centroids and how well source centroids of the source domain are aligned with the target centroids, respectively.Type: GrantFiled: March 29, 2012Date of Patent: February 18, 2014Assignee: International Business Machines CorporationInventors: Jeffrey M. Achtermann, Indrajit Bhattacharya, Kevin W. English, Jr., Shantanu R. Godbole, Sachindra Joshi, Ashwin Srinivasan, Ashish Verma
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Patent number: 8639696Abstract: A computer program product evaluating cross-domain clusterability upon a target domain and a source domain. The cross-domain clusterability is calculated as a linear combination of a target clusterability and a source-target pair matchability, by use of a trade-off parameter that determines relative contribution of the target clusterability and the source-target pair matchability. The target clusterability quantifies how clusterable the target domain is. The source-target pair matchability is calculated as an average of a target-side matchability and a source-side matchability, which quantifies how well target centroids of the target domain are aligned with the source centroids and how well source centroids of the source domain are aligned with the target centroids, respectively.Type: GrantFiled: March 28, 2012Date of Patent: January 28, 2014Assignee: International Business Machines CorporationInventors: Jeffrey M. Achtermann, Indrajit Bhattacharya, Kevin W. English, Jr., Shantanu R. Godbole, Sachindra Joshi, Ashwin Srinivasan, Ashish Verma