Patents by Inventor Dhaivat P. Parikh
Dhaivat P. Parikh 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: 10414416Abstract: Geo-defect repair modeling with location uncertainty is provided. A method includes logically dividing a railroad network into segments each of a specified length. The method also includes identifying, via a computer processor, geo-defects and approximated locations of the geo-defects occurring at each inspection run for each of the segments. The method also includes calculating, via the computer processor, a rate of increase in amplitude of each of the geo-defects for each of the segments between inspection runs, determining a correlation of the geo-defects between the inspection runs as a function of the approximated locations, and predicting a deterioration rate for each of the geo-defects based on the calculating.Type: GrantFiled: August 8, 2013Date of Patent: September 17, 2019Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Arun Hampapur, Hongfei Li, Dhaivat P. Parikh
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Patent number: 9764746Abstract: Geo-defect repair modeling is provided. A method includes logically dividing a railroad network according to spatial and temporal dimensions with respect to historical data collected. The spatial dimensions include line segments of a specified length and the temporal dimensions include inspection run data for inspections performed for each of the line segments over a period of time. The method also includes creating a track deterioration model from the historical data, identifying geo-defects occurring at each inspection run from the track deterioration model, calculating a track deterioration condition from the track deterioration model by analyzing quantified changes in the geo-defects measured at each inspection run, and calculating a derailment risk based on track conditions determined from the inspection run data and the track deterioration condition.Type: GrantFiled: August 8, 2013Date of Patent: September 19, 2017Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Debarun Bhattacharjya, Arun Hampapur, Qing He, Hongfei Li, Dhaivat P. Parikh
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Patent number: 9744978Abstract: Geo-defect repair modeling is provided. A method includes logically dividing a railroad network according to spatial and temporal dimensions with respect to historical data collected. The spatial dimensions include line segments of a specified length and the temporal dimensions include inspection run data for inspections performed for each of the line segments over a period of time. The method also includes creating a track deterioration model from the historical data, identifying geo-defects occurring at each inspection run from the track deterioration model, calculating a track deterioration condition from the track deterioration model by analyzing quantified changes in the geo-defects measured at each inspection run, and calculating a derailment risk based on track conditions determined from the inspection run data and the track deterioration condition.Type: GrantFiled: May 31, 2013Date of Patent: August 29, 2017Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Debarun Bhattacharjya, Arun Hampapur, Qing He, Hongfei Li, Dhaivat P. Parikh
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Patent number: 9561810Abstract: Predicting operational changes in a multi-detector environment includes generating, via a computer processing device, a factor matrix for each univariate time series data in a set of sparse time series data collected from data sources, identifying a subset of the time series data as a feature selection based on application of a loss function, and generating a predictive model from the subset of the time series data.Type: GrantFiled: April 30, 2013Date of Patent: February 7, 2017Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Arun Hampapur, Qing He, Hongfei Li, Zhiguo Li, Dhaivat P. Parikh
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Patent number: 9463815Abstract: Predicting operational changes in a multi-detector environment includes generating, via a computer processing device, a factor matrix for each univariate time series data in a set of sparse time series data collected from data sources, identifying a subset of the time series data as a feature selection based on application of a loss function, and generating a predictive model from the subset of the time series data.Type: GrantFiled: August 8, 2013Date of Patent: October 11, 2016Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Arun Hampapur, Qing He, Hongfei Li, Zhiguo Li, Dhaivat P. Parikh
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Patent number: 9187104Abstract: An aspect of an online learning system includes collecting data, via a computer processing device, from a plurality of data sources including multiple disparate detectors, the data including at least one of historical alarm data, failures, maintenance records, and environment observations. The data is stored in tables each corresponding to a subject of measurement. The online learning system also includes identifying common fields shared across the tables, merging at least a portion of the data across the tables having the common fields, and creating an integrated data model based on results of the merging.Type: GrantFiled: April 30, 2013Date of Patent: November 17, 2015Assignee: International Buslness Machines CorporationInventors: Dongping Fang, Arun Hampapur, Qing He, Hongfei Li, Zhiguo Li, Dhaivat P. Parikh, Buyue Qian
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Publication number: 20140200952Abstract: An aspect of scalable rule logicalization for asset health management includes aggregating data, via a computer processing device, from data sources, extracting a set of features from the data, projecting the features to a lower dimensional space, generating a prediction based on the projecting, logicalizing a decision boundary for the prediction, and estimating a confidence level of the prediction based on the decision boundary.Type: ApplicationFiled: August 8, 2013Publication date: July 17, 2014Applicant: International Business Machines CorporationInventors: Arun Hampapur, Hongfei Li, Dhaivat P. Parikh, Buyue Qian
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Publication number: 20140200829Abstract: Geo-defect repair modeling with location uncertainty is provided. A method includes logically dividing a railroad network into segments each of a specified length. The method also includes identifying, via a computer processor, geo-defects and approximated locations of the geo-defects occurring at each inspection run for each of the segments. The method also includes calculating, via the computer processor, a rate of increase in amplitude of each of the geo-defects for each of the segments between inspection runs, determining a correlation of the geo-defects between the inspection runs as a function of the approximated locations, and predicting a deterioration rate for each of the geo-defects based on the calculating.Type: ApplicationFiled: August 8, 2013Publication date: July 17, 2014Applicant: International Business Machines CorporationInventors: Arun Hampapur, Hongfei Li, Dhaivat P. Parikh
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Publication number: 20140200951Abstract: An aspect of scalable rule logicalization for asset health management includes aggregating data, via a computer processing device, from data sources, extracting a set of features from the data, projecting the features to a lower dimensional space, generating a prediction based on the projecting, logicalizing a decision boundary for the prediction, and estimating a confidence level of the prediction based on the decision boundary.Type: ApplicationFiled: April 30, 2013Publication date: July 17, 2014Applicant: International Business Machines CorporationInventors: Arun Hampapur, Hongfei Li, Dhaivat P. Parikh, Buyue Qian
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Publication number: 20140200873Abstract: An aspect of an online learning system includes collecting data, via a computer processing device, from a plurality of data sources including multiple disparate detectors, the data including at least one of historical alarm data, failures, maintenance records, and environment observations. The data is stored in tables each corresponding to a subject of measurement. The online learning system also includes identifying common fields shared across the tables, merging at least a portion of the data across the tables having the common fields, and creating an integrated data model based on results of the merging.Type: ApplicationFiled: August 8, 2013Publication date: July 17, 2014Applicant: International Business Machines CorporationInventors: Dongping Fang, Arun Hampapur, Qing He, Hongfei Li, Zhiguo Li, Dhaivat P. Parikh, Buyue Qian
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Publication number: 20140200872Abstract: An aspect of an online learning system includes collecting data, via a computer processing device, from a plurality of data sources including multiple disparate detectors, the data including at least one of historical alarm data, failures, maintenance records, and environment observations. The data is stored in tables each corresponding to a subject of measurement. The online learning system also includes identifying common fields shared across the tables, merging at least a portion of the data across the tables having the common fields, and creating an integrated data model based on results of the merging.Type: ApplicationFiled: April 30, 2013Publication date: July 17, 2014Applicant: International Business Machines CorporationInventors: Dongping Fang, Arun Hampapur, Qing He, Hongfei Li, Zhiguo Li, Dhaivat P. Parikh, Buyue Qian
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Publication number: 20140200830Abstract: Geo-defect repair modeling is provided. A method includes logically dividing a railroad network according to spatial and temporal dimensions with respect to historical data collected. The spatial dimensions include line segments of a specified length and the temporal dimensions include inspection run data for inspections performed for each of the line segments over a period of time. The method also includes creating a track deterioration model from the historical data, identifying geo-defects occurring at each inspection run from the track deterioration model, calculating a track deterioration condition from the track deterioration model by analyzing quantified changes in the geo-defects measured at each inspection run, and calculating a derailment risk based on track conditions determined from the inspection run data and the track deterioration condition.Type: ApplicationFiled: August 8, 2013Publication date: July 17, 2014Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Debarun Bhattacharjya, Arun Hampapur, Qing He, Hongfei Li, Dhaivat P. Parikh
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Publication number: 20140200870Abstract: Predicting operational changes in a multi-detector environment includes generating, via a computer processing device, a factor matrix for each univariate time series data in a set of sparse time series data collected from data sources, identifying a subset of the time series data as a feature selection based on application of a loss function, and generating a predictive model from the subset of the time series data.Type: ApplicationFiled: August 8, 2013Publication date: July 17, 2014Applicant: International Business Machines CorporationInventors: Arun Hampapur, Qing He, Hongfei Li, Zhiguo Li, Dhaivat P. Parikh
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Publication number: 20140200869Abstract: Predicting operational changes in a multi-detector environment includes generating, via a computer processing device, a factor matrix for each univariate time series data in a set of sparse time series data collected from data sources, identifying a subset of the time series data as a feature selection based on application of a loss function, and generating a predictive model from the subset of the time series data.Type: ApplicationFiled: April 30, 2013Publication date: July 17, 2014Applicant: International Business Machines CorporationInventors: Arun Hampapur, Qing He, Hongfei Li, Zhiguo Li, Dhaivat P. Parikh
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Publication number: 20140200827Abstract: Geo-defect repair modeling is provided. A method includes logically dividing a railroad network according to spatial and temporal dimensions with respect to historical data collected. The spatial dimensions include line segments of a specified length and the temporal dimensions include inspection run data for inspections performed for each of the line segments over a period of time. The method also includes creating a track deterioration model from the historical data, identifying geo-defects occurring at each inspection run from the track deterioration model, calculating a track deterioration condition from the track deterioration model by analyzing quantified changes in the geo-defects measured at each inspection run, and calculating a derailment risk based on track conditions determined from the inspection run data and the track deterioration condition.Type: ApplicationFiled: May 31, 2013Publication date: July 17, 2014Inventors: Debarun Bhattacharjya, Arun Hampapur, Qing He, Hongfei Li, Dhaivat P. Parikh
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Publication number: 20140200828Abstract: Geo-defect repair modeling with location uncertainty is provided. A method includes logically dividing a railroad network into segments each of a specified length. The method also includes identifying, via a computer processor, geo-defects and approximated locations of the geo-defects occurring at each inspection run for each of the segments. The method also includes calculating, via the computer processor, a rate of increase in amplitude of each of the geo-defects for each of the segments between inspection runs, determining a correlation of the geo-defects between the inspection runs as a function of the approximated locations, and predicting a deterioration rate for each of the geo-defects based on the calculating.Type: ApplicationFiled: May 31, 2013Publication date: July 17, 2014Inventors: Arun Hampapur, Hongfei Li, Dhaivat P. Parikh