Patents by Inventor Ashwin Srinivasan
Ashwin Srinivasan 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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Publication number: 20170109653Abstract: This disclosure relates generally to multi-sensor data, and more particularly to summarizing multi-sensor data. In one embodiment, the method includes computing plurality of histograms from sensor data associated with a plurality of sensors. The respective histograms of each sensor are clustered into a first plurality of sensor-clusters, and a first set of rules is extracted therefrom. First set of rules defines patterns of histograms of a set of sensors occurring frequently over a time-period. Two or more sensor-clusters from amongst the first plurality of sensor-clusters are merged selectively to obtain a second plurality of sensor-clusters. Second set of rules are extracted from the second plurality of sensor-clusters, and a set of correlated sensors are identified therefrom based on the second set of rules. Third set of rules are extracted from the set of correlated sensors, the third set of rules summarizes the multi-sensor data to represent prominent co-occurring sensor behaviors.Type: ApplicationFiled: March 2, 2016Publication date: April 20, 2017Applicant: Tata Consultancy Services LimitedInventors: Puneet AGARWAL, Gautam Shroff, Sarmimala Saikia, Ashwin Srinivasan
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Publication number: 20160371376Abstract: Methods and systems for searching logical patterns in voluminous multi sensor data from the industrial internet is provided. The method retrieves instances of patterns in time-series data where patterns are specified logically, using a sequence of symbols. The logical symbols used are a subset of the qualitative abstractions specifically, the concepts of steady, increasing, decreasing. Patterns can include symbol-sequences for multiple sensors, approximate duration as well as slope values for each symbol. To facilitate efficient querying, each sensor time-series is pre-processed into a sequence of logical symbols. Each position in the resulting compressed sequence is registered across a TRIE-based index structure corresponding to the multiple logical patterns it may belong to. Logical multi-sensor patterns are efficiently retrieved and ranked using such a structure. This method of indexing and searching provides an efficient mechanism for exploratory analysis of voluminous multi-sensor data.Type: ApplicationFiled: June 17, 2016Publication date: December 22, 2016Applicant: Tata Consultancy Services LimitedInventors: Ehtesham HASSAN, Mohit Yadav, Puneet Agarwal, Gautam Shroff, Ashwin Srinivasan
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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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Publication number: 20160180229Abstract: A method and a system for interpreting a dataset comprising a plurality of items is described herein. The method may include computing a rule set pertaining to the dataset, generating a rule cover, calculating a plurality of distances between the plurality of rule pairs in the rule cover and generating a distance matrix based on the calculated plurality of distances between the plurality of rule pairs, storing the calculated plurality of distances between the plurality of rule pairs, clustering the overlapping rules within the rule cover using the distance matrix; selecting a representative rule from each cluster, determining at least one exception for each representative rule in the rule cover selected from each cluster and interpreting the dataset using the representative rules and the at least one exception determined for each representative rule in the rule set.Type: ApplicationFiled: December 16, 2015Publication date: June 23, 2016Applicant: Tata Consultancy Services LimitedInventors: Puneet AGARWAL, Gautam SHROFF, Sarmimala SAIKIA, Ashwin SRINIVASAN
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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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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
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Patent number: 8589396Abstract: 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: GrantFiled: January 6, 2010Date of Patent: November 19, 2013Assignee: 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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Publication number: 20120197892Abstract: 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: ApplicationFiled: March 29, 2012Publication date: August 2, 2012Applicant: 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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Publication number: 20120191713Abstract: 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: ApplicationFiled: April 2, 2012Publication date: July 26, 2012Applicant: 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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Publication number: 20120191712Abstract: 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: ApplicationFiled: March 28, 2012Publication date: July 26, 2012Applicant: 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: 8229929Abstract: A system and associated method 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: January 6, 2010Date of Patent: July 24, 2012Assignee: 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: 8209273Abstract: The invention relates to ranking Service Level Agreement violations. A method for ranking said Service Level Agreements comprising determining a set of attributes for Service Level Agreements subject to violation, and predicting importance of Service Level Agreement violations using a model which performs ordinal regression based on said attributes of Service Level Agreements.Type: GrantFiled: August 12, 2008Date of Patent: June 26, 2012Assignee: International Business Machines CorporationInventors: Rob Goris, Laurent S. Mignet, Ashwin Srinivasan
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Publication number: 20110166850Abstract: 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: ApplicationFiled: January 6, 2010Publication date: July 7, 2011Applicant: 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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Publication number: 20110167064Abstract: A system and associated method 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: ApplicationFiled: January 6, 2010Publication date: July 7, 2011Applicant: 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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Publication number: 20090076992Abstract: The invention relates to ranking Service Level Agreement violations. A method for ranking said Service Level Agreements comprising determining a set of attributes for Service Level Agreements subject to violation, and predicting importance of Service Level Agreement violations using a model which performs ordinal regression based on said attributes of Service Level Agreements.Type: ApplicationFiled: August 12, 2008Publication date: March 19, 2009Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Rob Goris, Laurent S. Mignet, Ashwin Srinivasan