Patents by Inventor J. Justin DONALDSON
J. Justin DONALDSON 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: 20200379951Abstract: A decision tree model is generated from sample data. A visualization system may automatically prune the decision tree model based on characteristics of nodes or branches in the decision tree or based on artifacts associated with model generation. For example, only nodes or questions in the decision tree receiving a largest amount of the sample data may be displayed in the decision tree. The nodes also may be displayed in a manner to more readily identify associated fields or metrics. For example, the nodes may be displayed in different colors and the colors may be associated with different node questions or answers.Type: ApplicationFiled: December 23, 2019Publication date: December 3, 2020Inventors: J. Justin DONALDSON, Adam Ashenfelter, Francisco Martin, Jos Verwoerd, Jose Antonio Ortega, Charles Parker
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Patent number: 10565265Abstract: A document retrieval system tracks user selections of documents from query search results and uses the selections as proxies for manual user labeling of document relevance. The system trains a model representing the significance of different document features when calculating true document relevance for users. To factor in positional biases inherent in user selections in search results, the system learns positional bias values for different search result positions, such that the positional bias values are accounted for when computing document feature features that are used to compute true document relevance.Type: GrantFiled: October 12, 2016Date of Patent: February 18, 2020Assignee: salesforce.com, inc.Inventors: Zachary Alexander, Scott Thurston Rickard, Jr., Clifford Z. Huang, J. Justin Donaldson
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Patent number: 10474562Abstract: An online system ranks test cases run in connection with check-in of sets of software files in a software repository. The online system ranks the test cases higher if they are more likely to fail as a result of defects in the set of files being checked in. Accordingly, the online system informs software developers of potential defects in the files being checked in early without having to run the complete suite of test cases. The online system determines a vector representation of the files and test cases based on a neural network. The online system determines an aggregate vector representation of the set of files. The online system determines a measure of similarity between the test cases and the aggregate vector representation of the set of files. The online system ranks the test cases based on the measures of similarity of the test cases.Type: GrantFiled: September 20, 2017Date of Patent: November 12, 2019Assignee: salesforce.comInventors: J. Justin Donaldson, Benjamin Busjaeger, Siddharth Rajaram, Berk Coker, Hormoz Tarevern
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Patent number: 10409667Abstract: An online system identifies an assignment for a computer program error indicated in an error message by applying an assignment model to token sequences identified in the error message. The error message includes a sequence of execution paths of the computer program. Each execution path indicates a function call active in computer memory when the error was generated. In other words, the error message allows tracking of the sequence of nested paths up to the point where the error was generated. In one example, the error message is a stack trace message that reports active stack frames in computer memory during the execution of the program.Type: GrantFiled: June 15, 2017Date of Patent: September 10, 2019Assignee: salesforce.com, inc.Inventors: J. Justin Donaldson, Hormoz Tarevern, Sadiya Hameed, Siddharth Srivastava, Feifei Jiang
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Publication number: 20190087311Abstract: An online system ranks test cases run in connection with check-in of sets of software files in a software repository. The online system ranks the test cases higher if they are more likely to fail as a result of defects in the set of files being checked in. Accordingly, the online system informs software developers of potential defects in the files being checked in early without having to run the complete suite of test cases. The online system determines a vector representation of the files and test cases based on a neural network. The online system determines an aggregate vector representation of the set of files. The online system determines a measure of similarity between the test cases and the aggregate vector representation of the set of files. The online system ranks the test cases based on the measures of similarity of the test cases.Type: ApplicationFiled: September 20, 2017Publication date: March 21, 2019Inventors: J. Justin Donaldson, Benjamin Busjaeger, JR., Siddharth Rajaram, Berk Coker, JR., Hormoz Tarevern
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Publication number: 20180365091Abstract: An online system identifies an assignment for a computer program error indicated in an error message by applying an assignment model to token sequences identified in the error message. The error message includes a sequence of execution paths of the computer program. Each execution path indicates a function call active in computer memory when the error was generated. In other words, the error message allows tracking of the sequence of nested paths up to the point where the error was generated. In one example, the error message is a stack trace message that reports active stack frames in computer memory during the execution of the program.Type: ApplicationFiled: June 15, 2017Publication date: December 20, 2018Inventors: J. Justin Donaldson, Hormoz Tarevern, Sadiya Hameed, Siddharth Srivastava, Feifei Jiang
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Publication number: 20180101534Abstract: A document retrieval system tracks user selections of documents from query search results and uses the selections as proxies for manual user labeling of document relevance. The system trains a model representing the significance of different document features when calculating true document relevance for users. To factor in positional biases inherent in user selections in search results, the system learns positional bias values for different search result positions, such that the positional bias values are accounted for when computing document feature features that are used to compute true document relevance.Type: ApplicationFiled: October 12, 2016Publication date: April 12, 2018Inventors: Zachary Alexander, JR., Scott Thurston Rickard, JR., Clifford Z. Huang, J. Justin Donaldson
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Publication number: 20180052853Abstract: A system stores objects of different types and allows search over the objects. The system receives search requests and processes them to determine search results matching the search criteria. The system ranks the search results based on weighted aggregates of features describing objects represented by each search result. The system monitors search results that were accessed by user for further information and marks them as accessed results. The system adjusts the feature weights used for ranking search results to optimize the ranking of the search results. The system analyzes the result of using the adjusted feature weights on past searches that are stored in the system. The system determines an aggregate accessed results rank for each adjusted set of weights. The system selects a set of feature weights that optimizes the aggregate accessed results rank for past searches.Type: ApplicationFiled: August 22, 2016Publication date: February 22, 2018Inventors: Scott Thurston Rickard, JR., Clifford Z. Huang, J. Justin Donaldson
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Publication number: 20170140302Abstract: A system and method enables users to selectively expose and optionally monetize their data resources, for example on a web site. Data assets such as datasets and models can be exposed by the proprietor on a public gallery for use by others. Fees may be charged, for example, per new model, or per prediction using a model. Users may selectively expose public datasets or public models while keeping their raw data private.Type: ApplicationFiled: January 26, 2017Publication date: May 18, 2017Applicant: BigML, Inc.Inventors: Francisco J. MARTIN, Oscar ROVIRA, Jos VERWOERD, Poul PETERSEN, Charles PARKER, Jose Antonio ORTEGA, Beatriz GARCIA, J. Justin DONALDSON, Antonio BLASCO, Adam ASHENFELTER
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Publication number: 20170090980Abstract: We describe a high-level computational framework especially well suited to parallel operations on large datasets. In a system in accordance with this framework, there is at least one, and generally several, instances of an architecture deployment as further described. We use the term “architecture deployment” herein to mean a cooperating group of processes together with the hardware on which the processes are executed. This is not to imply a one-to-one association of any process to particular hardware. To the contrary, as detailed below, an architecture deployment may dynamically spawn another deployment as appropriate, including provisioning needed hardware. The active architecture deployments together form a system that dynamically processes jobs requested by a user-customer, in accordance with customer's monetary budget and other criteria, in a robust and automatically scalable environment.Type: ApplicationFiled: December 7, 2016Publication date: March 30, 2017Applicant: BigML, Inc.Inventors: Francisco J. Martin, Adam Ashenfelter, J. Justin Donaldson, Jos Verwoerd, Jose Antonio Ortega, Charles Parker
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Patent number: 9576246Abstract: A system and method enables users to selectively expose and optionally monetize their data resources, for example on a web site. Data assets such as datasets and models can be exposed by the proprietor on a public gallery for use by others. Fees may be charged, for example, per new model, or per prediction using a model. Users may selectively expose public datasets or public models while keeping their raw data private.Type: GrantFiled: September 12, 2013Date of Patent: February 21, 2017Assignee: BIGML, INC.Inventors: Francisco J. Martin, Oscar Rovira, Jos Verwoerd, Poul Petersen, Charles Parker, Jose Antonio Ortega, Beatriz Garcia, J. Justin Donaldson, Antonio Blasco, Adam Ashenfelter
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Patent number: 9558036Abstract: We describe a high-level computational framework especially well suited to parallel operations on large datasets. In a system in accordance with this framework, there is at least one, and generally several, instances of an architecture deployment as further described. We use the term “architecture deployment” herein to mean a cooperating group of processes together with the hardware on which the processes are executed. This is not to imply a one-to-one association of any process to particular hardware. To the contrary, as detailed below, an architecture deployment may dynamically spawn another deployment as appropriate, including provisioning needed hardware. The active architecture deployments together form a system that dynamically processes jobs requested by a user-customer, in accordance with customer's monetary budget and other criteria, in a robust and automatically scalable environment.Type: GrantFiled: May 29, 2015Date of Patent: January 31, 2017Assignee: BigML, Inc.Inventors: Francisco J. Martin, Adam Ashenfelter, J. Justin Donaldson, Jos Verwoerd, Jose Antonio Ortega, Charles Parker
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Patent number: 9269054Abstract: Systems and methods are disclosed for building and using decision trees, preferably in a scalable and distributed manner. Our system can be used to create and use classification trees, regression trees, or a combination of regression trees called a gradient boosted regression tree (GBRT). Our system leverages approximate histograms in new ways to process large datasets, or data streams, while limiting inter-process communication bandwidth requirements. Further, in some embodiments, a scalable network of computers or processors is utilized for fast computation of decision trees. Preferably, the network comprises a tree structure of processors, comprising a master node and a plurality of worker nodes or “workers,” again arranged to limit necessary communications.Type: GrantFiled: November 9, 2012Date of Patent: February 23, 2016Assignee: BigML, Inc.Inventors: Francisco J. Martin, Adam Ashenfelter, J. Justin Donaldson, Jos Verwoerd, Jose Antonio Ortega, Charles Parker
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Patent number: 9098326Abstract: We describe a high-level computational framework especially well suited to parallel operations on large datasets. In a system in accordance with this framework, there is at least one, and generally several, instances of an architecture deployment as further described. We use the term “architecture deployment” herein to mean a cooperating group of processes together with the hardware on which the processes are executed. This is not to imply a one-to-one association of any process to particular hardware. To the contrary, as detailed below, an architecture deployment may dynamically spawn another deployment as appropriate, including provisioning needed hardware. The active architecture deployments together form a system that dynamically processes jobs requested by a user-customer, in accordance with customer's monetary budget and other criteria, in a robust and automatically scalable environment.Type: GrantFiled: November 9, 2012Date of Patent: August 4, 2015Assignee: BigML, Inc.Inventors: Francisco J. Martin, Adam Ashenfelter, J. Justin Donaldson, Jos Verwoerd, Jose Antonio Ortega, Charles Parker
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Publication number: 20140101076Abstract: A system and method enables users to selectively expose and optionally monetize their data resources, for example on a web site. Data assets such as datasets and models can be exposed by the proprietor on a public gallery for use by others. Fees may be charged, for example, per new model, or per prediction using a model. Users may selectively expose public datasets or public models while keeping their raw data private.Type: ApplicationFiled: September 12, 2013Publication date: April 10, 2014Inventors: Francisco J. MARTIN, Oscar ROVIRA, Jos VERWOERD, Poul PETERSEN, Charles PARKER, Jose Antonio ORTEGA, Beatriz GARCIA, J. Justin DONALDSON, Antonio BLASCO, Adam ASHENFELTER