Patents by Inventor Larry Peranich
Larry Peranich 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: 20200151628Abstract: A computer-implemented method for technologically improving a computer-implemented machine-learning model, the method comprising receiving, by a model, at least a first data record; generating a first score representing a first likelihood that the first data record is associated with a first classification, in response to feedback received from one or more data sources communicating with at least one computing system on which the model is implemented; generating a second score to represent a second likelihood that the first data record is associated with the first classification, in response to the first score being higher than a threshold value.Type: ApplicationFiled: November 12, 2019Publication date: May 14, 2020Applicant: FICOInventors: Scott M. Zoldi, Larry Peranich, Jehangir Athwal, Uwe Mayer, Sajama
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Patent number: 10510025Abstract: A computer-implemented method includes receiving a new data record associated with a transaction, and generating, using an adaptive model executed by the computer, a score to represent a likelihood that the transaction is associated with fraud. The adaptive model employs feedback from one or more external data sources, the feedback containing information about one or more previous data records associated with fraud and non-fraud by at least one of the one or more external data sources. Further, the adaptive model uses the information about the one or more previous data records as input variables to update scoring parameters used to generate the score for the new data record.Type: GrantFiled: February 29, 2008Date of Patent: December 17, 2019Assignee: FAIR ISAAC CORPORATIONInventors: Scott M. Zoldi, Larry Peranich, Jehangir Athwal, Uwe Mayer, Sajama
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Publication number: 20140180974Abstract: The current subject matter describes scoring of transactions associated with a profiling entity so as to determine risk associated with the transactions. Data characterizing at least one new transaction can be received. A latent dirichlet allocation (LDA) model trained on historical data can be obtained. Based on new words in the received data, the LDA model can update a topic probability mixture vector. Based on the updated topic probability mixture vector, numerical values of one or more predictive features can be calculated. Based on the numerical values of the one or more predicted features, the at least one transaction in the received data can be scored. Related apparatus, systems, techniques and articles are also described.Type: ApplicationFiled: December 21, 2012Publication date: June 26, 2014Applicant: FAIR ISAAC CORPORATIONInventors: Matthew Bochner Kennel, Hua Li, Larry Peranich
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Patent number: 8676726Abstract: A system and method for automated variable creation for adaptive fraud analytics are disclosed. A data structure for creation of rules is generated. The data structure represents nodes and associations between nodes from inputs for fraud/non-fraud conditions, and is generated from fraud and non-fraud data collected in an adaptive modeling process from past transactions. All unique paths between nodes of the data structure are determined to define a rule for each path. Each rule is then converted to a binary indicator variable to generate a set of binary indicator variables, and one or more complex variables is derived from the set of binary indicator variables. The one or more binary indicator variables and one or more complex variables can be provided to an adaptive scoring engine to score new transactions or to predict future behaviors.Type: GrantFiled: December 30, 2010Date of Patent: March 18, 2014Assignee: Fair Isaac CorporationInventors: Prodip Hore, Scott M. Zoldi, Larry Peranich
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Patent number: 8645301Abstract: A computer-implemented method and system for automated entity identification for efficient profiling in an event probability prediction system. A first subset of entities belonging to one or more entity classes is defined. At least one historical profile is constructed for each entity in the subset of entities based on a set of possible outcomes of transaction behavior of each entity in the first subset of entities. Based on the historical profiles, a second subset of entities having transaction behavior associated with a transaction is selected, the transaction behavior being predictive of at least one targeted outcome from the set of possible outcomes. The first subset of entities is redefined with the second subset of entities.Type: GrantFiled: December 10, 2012Date of Patent: February 4, 2014Assignee: Fair Isaac CorporationInventors: Anthony Vaiciulis, Larry Peranich, Uwe Mayer, Scott M. Zoldi, Shane De Zilwa
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Publication number: 20130103629Abstract: A computer-implemented method and system for automated entity identification for efficient profiling in an event probability prediction system. A first subset of entities belonging to one or more entity classes is defined. At least one historical profile is constructed for each entity in the subset of entities based on a set of possible outcomes of transaction behavior of each entity in the first subset of entities. Based on the historical profiles, a second subset of entities having transaction behavior associated with a transaction is selected, the transaction behavior being predictive of at least one targeted outcome from the set of possible outcomes. The first subset of entities is redefined with the second subset of entities.Type: ApplicationFiled: December 10, 2012Publication date: April 25, 2013Inventors: Anthony Vaiciulis, Larry Peranich, Uwe Mayer, Scott Zoldi, Shane De Zilwa
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Patent number: 8332338Abstract: A computer-implemented method and system for automated entity identification for efficient profiling in an event probability prediction system. A first subset of entities belonging to one or more entity classes is defined. At least one historical profile is constructed for each entity in the subset of entities based on a set of possible outcomes of transaction behavior of each entity in the first subset of entities. Based on the historical profiles, a second subset of entities having transaction behavior associated with a transaction is selected, the transaction behavior being predictive of at least one targeted outcome from the set of possible outcomes. The first subset of entities is redefined with the second subset of entities.Type: GrantFiled: February 17, 2012Date of Patent: December 11, 2012Assignee: Fair Isaac CorporationInventors: Anthony Vaiciulis, Larry Peranich, Uwe Mayer, Scott Zoldi, Shane De Zilwa
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Publication number: 20120173465Abstract: A system and method for automated variable creation for adaptive fraud analytics are disclosed. A data structure for creation of rules is generated. The data structure represents nodes and associations between nodes from inputs for fraud/non-fraud conditions, and is generated from fraud and non-fraud data collected in an adaptive modeling process from past transactions. All unique paths between nodes of the data structure are determined to define a rule for each path. Each rule is then converted to a binary indicator variable to generate a set of binary indicator variables, and one or more complex variables is derived from the set of binary indicator variables. The one or more binary indicator variables and one or more complex variables can be provided to an adaptive scoring engine to score new transactions or to predict future behaviors.Type: ApplicationFiled: December 30, 2010Publication date: July 5, 2012Applicant: FAIR ISAAC CORPORATIONInventors: Prodip Hore, Scott M. Zoldi, Larry Peranich
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Publication number: 20120150779Abstract: A computer-implemented method and system for automated entity identification for efficient profiling in an event probability prediction system. A first subset of entities belonging to one or more entity classes is defined. At least one historical profile is constructed for each entity in the subset of entities based on a set of possible outcomes of transaction behavior of each entity in the first subset of entities. Based on the historical profiles, a second subset of entities having transaction behavior associated with a transaction is selected, the transaction behavior being predictive of at least one targeted outcome from the set of possible outcomes. The first subset of entities is redefined with the second subset of entities.Type: ApplicationFiled: February 17, 2012Publication date: June 14, 2012Inventors: Anthony Vaiciulis, Larry Peranich, Uwe Mayer, Scott Zoldi, Shane De Zilwa
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Patent number: 8121962Abstract: A computer-implemented method and system for automated entity identification for efficient profiling in an event probability prediction system. A first subset of entities belonging to one or more entity classes is defined. At least one historical profile is constructed for each entity in the subset of entities based on a set of possible outcomes of transaction behavior of each entity in the first subset of entities. Based on the historical profiles, a second subset of entities having transaction behavior associated with a transaction is selected, the transaction behavior being predictive of at least one targeted outcome from the set of possible outcomes. The first subset of entities is redefined with the second subset of entities.Type: GrantFiled: April 25, 2008Date of Patent: February 21, 2012Assignee: Fair Isaac CorporationInventors: Anthony Vaiciulis, Larry Peranich, Uwe Mayer, Scott Zoldi, Shane De Zilwa
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Publication number: 20090271343Abstract: A computer-implemented method and system for automated entity identification for efficient profiling in an event probability prediction system. A first subset of entities belonging to one or more entity classes is defined. At least one historical profile is constructed for each entity in the subset of entities based on a set of possible outcomes of transaction behavior of each entity in the first subset of entities. Based on the historical profiles, a second subset of entities having transaction behavior associated with a transaction is selected, the transaction behavior being predictive of at least one targeted outcome from the set of possible outcomes. The first subset of entities is redefined with the second subset of entities.Type: ApplicationFiled: April 25, 2008Publication date: October 29, 2009Inventors: Anthony Vaiciulis, Larry Peranich, Uwe Mayer, Scott Zoldi, Shane De Zilwa
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Publication number: 20090222243Abstract: A computer-implemented method includes receiving a new data record associated with a transaction, and generating, using an adaptive model executed by the computer, a score to represent a likelihood that the transaction is associated with fraud. The adaptive model employs feedback from one or more external data sources, the feedback containing information about one or more previous data records associated with fraud and non-fraud by at least one of the one or more external data sources. Further, the adaptive model uses the information about the one or more previous data records as input variables to update scoring parameters used to generate the score for the new data record.Type: ApplicationFiled: February 29, 2008Publication date: September 3, 2009Inventors: Scott M. Zoldi, Larry Peranich, Jehangir Athwal, Uwe Mayer, Sajama
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Publication number: 20070244741Abstract: Predictive modeling of consumer financial behavior, including determination of likely responses to particular marketing efforts, is provided by application of consumer transaction data to predictive models associated with merchant segments. The merchant segments are derived from the consumer transaction data based on co-occurrences of merchants in sequences of transactions. Merchant vectors represent specific merchants, and are aligned in a vector space as a function of the degree to which the merchants co-occur more or less frequently than expected. Supervised segmentation is applied to merchant vectors to form the merchant segments. Merchant segment predictive models provide predictions of spending in each merchant segment for any particular consumer, based on previous spending by the consumer. Consumer profiles describe summary statistics of each consumer's spending in the merchant segments, and across merchant segments.Type: ApplicationFiled: January 15, 2007Publication date: October 18, 2007Inventors: Matthias Blume, Michael Lazarus, Larry Peranich, Frederique Vernhes, Kenneth Brown, William Caid, Ted Dunning, Gerald Russell, Kevin Sitze
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Publication number: 20050159996Abstract: Predictive modeling of consumer financial behavior, including determination of likely responses to particular marketing efforts, is provided by application of consumer transaction data to predictive models associated with merchant segments. The merchant segments are derived from the consumer transaction data based on co-occurrences of merchants in sequences of transactions. Merchant vectors represent specific merchants, and are aligned in a vector space as a function of the degree to which the merchants co-occur more or less frequently than expected. Supervised segmentation is applied to merchant vectors to form the merchant segments. Merchant segment predictive models provide predictions of spending in each merchant segment for any particular consumer, based on previous spending by the consumer. Consumer profiles describe summary statistics of each consumer's spending in the merchant segments, and across merchant segments.Type: ApplicationFiled: December 14, 2004Publication date: July 21, 2005Inventors: Michael Lazarus, Larry Peranich, Frederique Vernhes, Matthias Blume, Kenneth Brown, William Caid, Ted Dunning, Gerald Russell, Kevin Sitze