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).

  • Patent number: 10972474
    Abstract: 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: Grant
    Filed: April 18, 2017
    Date of Patent: April 6, 2021
    Assignee: International Business Machines Corporation
    Inventors: Jeffrey M. Achtermann, Harrison Kurtz, Maharaj Mukherjee, Joanna W. Ng
  • Patent number: 10965684
    Abstract: 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: Grant
    Filed: November 16, 2018
    Date of Patent: March 30, 2021
    Assignee: International Business Machines Corporation
    Inventors: Jeffrey M. Achtermann, Harrison Kurtz, Maharaj Mukherjee, Joanna W. Ng
  • Patent number: 10949938
    Abstract: 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: Grant
    Filed: April 18, 2017
    Date of Patent: March 16, 2021
    Assignee: International Business Machines Corporation
    Inventors: Jeffrey M. Achtermann, Rahul Gupta, Arnaud A. Mathieu, Maharaj Mukherjee
  • Patent number: 10949939
    Abstract: 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: Grant
    Filed: November 16, 2018
    Date of Patent: March 16, 2021
    Assignee: International Business Machines Corporation
    Inventors: Jeffrey M. Achtermann, Rahul Gupta, Arnaud A. Mathieu, Maharaj Mukherjee
  • Patent number: 10389578
    Abstract: 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: Grant
    Filed: March 6, 2017
    Date of Patent: August 20, 2019
    Assignee: International Business Machines Corporation
    Inventors: Jeffrey M. Achtermann, Michael Bender, Timothy J. Hahn, Hari H. Madduri, Leucir Marin, Jr.
  • Publication number: 20190220470
    Abstract: 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: Application
    Filed: March 25, 2019
    Publication date: July 18, 2019
    Inventors: JEFFREY M. ACHTERMANN, INDRAJIT BHATTACHARYA, KEVIN W. ENGLISH, SHANTANU R. GODBOLE, SACHINDRA JOSHI, ASHWIN SRINIVASAN, ASHISH VERMA
  • Patent number: 10311086
    Abstract: 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: Grant
    Filed: March 15, 2016
    Date of Patent: June 4, 2019
    Assignee: International Business Machines Corporation
    Inventors: Jeffrey M. Achtermann, Indrajit Bhattacharya, Kevin W. English, Shantanu R. Godbole, Sachindra Joshi, Ashwin Srinivasan, Ashish Verma
  • Publication number: 20190109856
    Abstract: 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: Application
    Filed: November 16, 2018
    Publication date: April 11, 2019
    Inventors: Jeffrey M. Achtermann, Harrison Kurtz, Maharaj Mukherjee, Joanna W. Ng
  • Publication number: 20190102855
    Abstract: 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: Application
    Filed: November 16, 2018
    Publication date: April 4, 2019
    Inventors: Jeffrey M. Achtermann, Rahul Gupta, Arnaud A. Mathieu, Maharaj Mukherjee
  • Publication number: 20180300831
    Abstract: 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: Application
    Filed: April 18, 2017
    Publication date: October 18, 2018
    Inventors: Jeffrey M. Achtermann, Rahul Gupta, Arnaud A. Mathieu, Maharaj Mukherjee
  • Publication number: 20180302412
    Abstract: 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: Application
    Filed: April 18, 2017
    Publication date: October 18, 2018
    Inventors: Jeffrey M. Achtermann, Harrison Kurtz, Maharaj Mukherjee, Joanna W. Ng
  • Publication number: 20180254961
    Abstract: 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: Application
    Filed: March 6, 2017
    Publication date: September 6, 2018
    Inventors: Jeffrey M. ACHTERMANN, Michael BENDER, Timothy J. HAHN, Hari H. MADDURI, Leucir MARIN, JR.
  • Publication number: 20160196328
    Abstract: 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: Application
    Filed: March 15, 2016
    Publication date: July 7, 2016
    Inventors: JEFFREY M. ACHTERMANN, INDRAJIT BHATTACHARYA, KEVIN W. ENGLISH, SHANTANU R. GODBOLE, SACHINDRA JOSHI, ASHWIN SRINIVASAN, ASHISH VERMA
  • Patent number: 9336296
    Abstract: 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: Grant
    Filed: January 6, 2014
    Date of Patent: May 10, 2016
    Assignee: International Business Machines Corporation
    Inventors: Jeffrey M. Achtermann, Indrajit Bhattacharya, Kevin W. English, Jr., Shantanu R. Godbole, Sachindra Joshi, Ashwin Srinivasan, Ashish Verma
  • Patent number: 8812297
    Abstract: 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: Grant
    Filed: April 9, 2010
    Date of Patent: August 19, 2014
    Assignee: International Business Machines Corporation
    Inventors: Jeffrey M. Achtermann, Indrajit Bhattacharya, Kevin W. English, Shantanu R. Godbole, Ajay K. Gupta, Ashish Verma
  • Publication number: 20140122492
    Abstract: 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: Application
    Filed: January 6, 2014
    Publication date: May 1, 2014
    Inventors: JEFFREY M. ACHTERMANN, INDRAJIT BHATTACHARYA, KEVIN W. ENGLISH, JR., SHANTANU R. GODBOLE, SACHINDRA JOSHI, ASHWIN SRINIVASAN, ASHISH VERMA
  • Patent number: 8661039
    Abstract: 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: Grant
    Filed: April 2, 2012
    Date of Patent: February 25, 2014
    Assignee: International Business Machines Corporation
    Inventors: Jeffrey M. Achtermann, Indrajit Bhattacharya, Kevin W. English, Jr., Shantanu R. Godbole, Sachindra Joshi, Ashwin Srinivasan, Ashish Verma
  • Patent number: 8655884
    Abstract: 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: Grant
    Filed: March 29, 2012
    Date of Patent: February 18, 2014
    Assignee: International Business Machines Corporation
    Inventors: Jeffrey M. Achtermann, Indrajit Bhattacharya, Kevin W. English, Jr., Shantanu R. Godbole, Sachindra Joshi, Ashwin Srinivasan, Ashish Verma
  • Patent number: 8639696
    Abstract: 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: Grant
    Filed: March 28, 2012
    Date of Patent: January 28, 2014
    Assignee: International Business Machines Corporation
    Inventors: Jeffrey M. Achtermann, Indrajit Bhattacharya, Kevin W. English, Jr., Shantanu R. Godbole, Sachindra Joshi, Ashwin Srinivasan, Ashish Verma
  • Patent number: 8589396
    Abstract: A system and associated method for cross-guided data clustering by aligning target clusters in a target domain to source clusters in a source domain. The cross-guided clustering process takes the target domain and the source domain as inputs. A common word attribute shared by both the target domain and the source domain is a pivot vocabulary, and all other words in both domains are a non-pivot vocabulary. The non-pivot vocabulary is projected onto the pivot vocabulary to improve measurement of similarity between data items. Source centroids representing clusters in the source domain are created and projected to the pivot vocabulary. Target centroids representing clusters in the target domain are initially created by conventional clustering method and then repetitively aligned to converge with the source centroids by use of a cross-domain similarity graph that measures a respective similarity of each target centroid to each source centroid.
    Type: Grant
    Filed: January 6, 2010
    Date of Patent: November 19, 2013
    Assignee: International Business Machines Corporation
    Inventors: Jeffrey M. Achtermann, Indrajit Bhattacharya, Kevin W. English, Jr., Shantanu R. Godbole, Sachindra Joshi, Ashwin Srinivasan, Ashish Verma