Patents by Inventor Robert James Oberbreckling
Robert James Oberbreckling 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: 20230409958Abstract: Techniques for optimizing the sampling of both the highest resource source category and lower resource source categories of a given categorical source data set with an initial distribution such that a target distribution of the given categorical source data set may be reached are described. Data items of the respective categories may be indexed and sampled such that the number of data items that are sampled more than once are tracked. Sampling, according to the target distribution, may continue until stop criteria are satisfied. In some cases, the respective indexes are used to determine the moment at which the highest resource source category is fully sampled, therefore minimizing the number of duplicate data items and optimizing the use of the respective categories of data items. The target distribution may then be used to train a machine learning model.Type: ApplicationFiled: June 17, 2022Publication date: December 21, 2023Inventor: Robert James Oberbreckling
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Patent number: 11704321Abstract: The present disclosure related to techniques for analyzing data from multiple different data sources to determine a relationship between the data (also referred to herein a “data relationship discovery”). The relationships between any two compared datasets may be used to determine one or more recommendations for merging (e.g., joining), or “blending,” the data sets together. Relationship discovery may include determining a relationship between a subset of data, such as a relationship between a pair of columns, or column pair, each column in a different dataset of the datasets that are compared. Given two datasets to process for relationship discovery, relationship discovery may identify and recommends a ranked subset of column pairs between two compared datasets. The ranked column pairs identified as a relationship may be useful for blending the datasets with respect to those column pairs.Type: GrantFiled: March 23, 2020Date of Patent: July 18, 2023Assignee: Oracle International CorporationInventors: Robert James Oberbreckling, Luis E. Rivas, Michael Malak, Glenn Allen Murray
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Patent number: 11163527Abstract: The present disclosure relates to techniques for analysis of data from multiple different data sources to determine similarity amongst the datasets. Determining a similarity between datasets may be useful for downstream processing of those datasets for different uses. A graphical interface may be provided to display detailed results including: a similarity prediction, data similarity prediction, column order similarity prediction, document type similarity prediction, prediction of overlapping or related columns, orphaned column prediction (e.g., a left orphaned column or a right orphaned column).Type: GrantFiled: August 27, 2019Date of Patent: November 2, 2021Assignee: Oracle International CorporationInventors: Robert James Oberbreckling, Luis E. Rivas
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Patent number: 10990763Abstract: Systems and methods are disclosed to improve a topic modeling system that tunes a topic model for a set of topics from a corpus of documents, by allowing users to pre-inform the tuning process with bias parameters for desired associations in the topic model. In embodiments, the topic model may be a Latent Dirichlet Allocation (LDA) model. In embodiments, the bias parameter may indicate a fixed association where a particular word in a particular document is associated with a particular topic. In embodiments, the bias parameter may specify a weight value that biases the inference process with regard to a particular association. Advantageously, the disclosed features allow users to specify a small number of parameters to steer the tuning process towards a set of desired topics. As a result, the topic model may be generated more quickly and with more useful topics.Type: GrantFiled: May 9, 2019Date of Patent: April 27, 2021Assignee: Oracle International CorporationInventors: Daniel Peterson, Jean-Baptiste Frederic George Tristan, Robert James Oberbreckling
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Patent number: 10885056Abstract: Techniques are disclosed for standardization of data. According to a first technique, standard representation terms are determined for to-be-standardized data using the to-be-standardized data itself and without using any external reference data. According to a second technique, a combination of the to-be-standardized data and an external reference is used to determine standard representation terms for the to-be-standardized data.Type: GrantFiled: September 25, 2018Date of Patent: January 5, 2021Assignee: ORACLE INTERNATIONAL CORPORATIONInventors: Michael Malak, Luis E. Rivas, Mark L. Kreider, Philip Ogren, Robert James Oberbreckling
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Publication number: 20200279019Abstract: Systems and methods are disclosed to improve a topic modeling system that tunes a topic model for a set of topics from a corpus of documents, by allowing users to pre-inform the tuning process with bias parameters for desired associations in the topic model. In embodiments, the topic model may be a Latent Dirichlet Allocation (LDA) model. In embodiments, the bias parameter may indicate a fixed association where a particular word in a particular document is associated with a particular topic. In embodiments, the bias parameter may specify a weight value that biases the inference process with regard to a particular association. Advantageously, the disclosed features allow users to specify a small number of parameters to steer the tuning process towards a set of desired topics. As a result, the topic model may be generated more quickly and with more useful topics.Type: ApplicationFiled: May 9, 2019Publication date: September 3, 2020Inventors: Daniel Peterson, Jean-Baptiste Frederic George Tristan, Robert James Oberbreckling
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Publication number: 20200242111Abstract: The present disclosure related to techniques for analyzing data from multiple different data sources to determine a relationship between the data (also referred to herein a “data relationship discovery”). The relationships between any two compared datasets may be used to determine one or more recommendations for merging (e.g., joining), or “blending,” the data sets together. Relationship discovery may include determining a relationship between a subset of data, such as a relationship between a pair of columns, or column pair, each column in a different dataset of the datasets that are compared. Given two datasets to process for relationship discovery, relationship discovery may identify and recommends a ranked subset of column pairs between two compared datasets. The ranked column pairs identified as a relationship may be useful for blending the datasets with respect to those column pairs.Type: ApplicationFiled: March 23, 2020Publication date: July 30, 2020Applicant: Oracle International CorporationInventors: Robert James Oberbreckling, Luis E. Rivas, Michael Malak, Glenn Allen Murray
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Patent number: 10650000Abstract: The present disclosure related to techniques for analyzing data from multiple different data sources to determine a relationship between the data (also referred to herein a “data relationship discovery”). The relationships between any two compared datasets may be used to determine one or more recommendations for merging (e.g., joining), or “blending,” the data sets together. Relationship discovery may include determining a relationship between a subset of data, such as a relationship between a pair of columns, or column pair, each column in a different dataset of the datasets that are compared. Given two datasets to process for relationship discovery, relationship discovery may identify and recommends a ranked subset of column pairs between two compared datasets. The ranked column pairs identified as a relationship may be useful for blending the datasets with respect to those column pairs.Type: GrantFiled: September 14, 2017Date of Patent: May 12, 2020Assignee: Oracle International CorporationInventors: Robert James Oberbreckling, Luis E. Rivas, Michael Malak, Glenn Allen Murray
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Publication number: 20190384571Abstract: The present disclosure relates to techniques for analysis of data from multiple different data sources to determine similarity amongst the datasets. Determining a similarity between datasets may be useful for downstream processing of those datasets for different uses. A graphical interface may be provided to display detailed results including: a similarity prediction, data similarity prediction, column order similarity prediction, document type similarity prediction, prediction of overlapping or related columns, orphaned column prediction (e.g., a left orphaned column or a right orphaned column).Type: ApplicationFiled: August 27, 2019Publication date: December 19, 2019Applicant: Oracle International CorporationInventors: Robert James Oberbreckling, Luis E. Rivas
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Patent number: 10445062Abstract: The present disclosure relates to techniques for analysis of data from multiple different data sources to determine similarity amongst the datasets. Determining a similarity between datasets may be useful for downstream processing of those datasets for different uses. A graphical interface may be provided to display detailed results including: a similarity prediction, data similarity prediction, column order similarity prediction, document type similarity prediction, prediction of overlapping or related columns, orphaned column prediction (e.g., a left orphaned column or a right orphaned column). Detecting similarities may be useful for leveraging prior data transformations generated for the datasets that are analyzed.Type: GrantFiled: September 15, 2017Date of Patent: October 15, 2019Assignee: Oracle International CorporationInventors: Robert James Oberbreckling, Luis E. Rivas
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Publication number: 20190102441Abstract: Techniques are disclosed for standardization of data. According to a first technique, standard representation terms are determined for to-be-standardized data using the to-be-standardized data itself and without using any external reference data. According to a second technique, a combination of the to-be-standardized data and an external reference is used to determine standard representation terms for the to-be-standardized data.Type: ApplicationFiled: September 25, 2018Publication date: April 4, 2019Applicant: Oracle International CorporationInventors: Michael Malak, Luis E. Rivas, Mark L. Kreider, Philip Ogren, Robert James Oberbreckling
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Publication number: 20180074786Abstract: The present disclosure relates to techniques for analysis of data from multiple different data sources to determine similarity amongst the datasets. Determining a similarity between datasets may be useful for downstream processing of those datasets for different uses. A graphical interface may be provided to display detailed results including: a similarity prediction, data similarity prediction, column order similarity prediction, document type similarity prediction, prediction of overlapping or related columns, orphaned column prediction (e.g., a left orphaned column or a right orphaned column). Detecting similarities may be useful for leveraging prior data transformations generated for the datasets that are analyzed.Type: ApplicationFiled: September 15, 2017Publication date: March 15, 2018Applicant: Oracle International CorporationInventors: Robert James Oberbreckling, Luis E. Rivas
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Publication number: 20180075104Abstract: The present disclosure related to techniques for analyzing data from multiple different data sources to determine a relationship between the data (also referred to herein a “data relationship discovery”). The relationships between any two compared datasets may be used to determine one or more recommendations for merging (e.g., joining), or “blending,” the data sets together. Relationship discovery may include determining a relationship between a subset of data, such as a relationship between a pair of columns, or column pair, each column in a different dataset of the datasets that are compared. Given two datasets to process for relationship discovery, relationship discovery may identify and recommends a ranked subset of column pairs between two compared datasets. The ranked column pairs identified as a relationship may be useful for blending the datasets with respect to those column pairs.Type: ApplicationFiled: September 14, 2017Publication date: March 15, 2018Applicant: Oracle International CorporationInventors: Robert James Oberbreckling, Luis E. Rivas, Michael Malak, Glenn Allen Murray