Patents by Inventor Brad A. Stronger
Brad A. Stronger 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: 10802687Abstract: Methods for analyzing and rendering business intelligence data allow for efficient scalability as datasets grow in size. Human intervention is minimized by augmented decision making ability in selecting what aspects of large datasets should be focused on to drive key business outcomes. Variable value combinations that are predominant drivers of key observations are automatically determined from several competing variable value combinations. The identified variable value combinations can then be then used to predict future trends underlying the business intelligence data. In another embodiment, an observed outcome is decomposed into multiple contributing drivers and the impact of each of the contributing drivers can be analyzed and numerically quantified—as a static snapshot or as a time-varying evolution.Type: GrantFiled: March 31, 2018Date of Patent: October 13, 2020Assignee: salesforce.com, inc.Inventors: Richard Martin Cooke, Arijit Sengupta, Brad A. Stronger, Griffin Chronis
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Patent number: 10796232Abstract: Methods for analyzing and rendering business intelligence data allow for efficient scalability as datasets grow in size. Human intervention is minimized by augmented decision making ability in selecting what aspects of large datasets should be focused on to drive key business outcomes. Variable value combinations that are predominant drivers of key observations are automatically determined from several competing variable value combinations. The identified variable value combinations can then be then used to predict future trends underlying the business intelligence data. In another embodiment, an observed outcome is decomposed into multiple contributing drivers and the impact of each of the contributing drivers can be analyzed and numerically quantified—as a static snapshot or as a time-varying evolution.Type: GrantFiled: February 27, 2018Date of Patent: October 6, 2020Assignee: salesforce.com, inc.Inventors: Arijit Sengupta, Brad A. Stronger, Griffin Chronis
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Patent number: 10795934Abstract: Business process provider(s) process client data. The clients use certain formats (client formats, defined by client format fields). The client format fields instantiated in documents are analyzed. Based on this analysis, the client processes are automatically grouped into different process platforms for processing. For example, similar client processes preferably are grouped together into the same process platform, in order to increase efficiency of processing. In another aspect, the user interfaces used by the business process provider(s) may be constructed from different blocks, where the blocks are automatically defined based on the analysis of client format fields.Type: GrantFiled: March 2, 2018Date of Patent: October 6, 2020Assignee: salesforce.com, inc.Inventors: Arijit Sengupta, Brad A. Stronger
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Patent number: 10176338Abstract: A method, system and computer program product for processing documents containing restricted information. One aspect concerns storing documents in a distributed but secure manner, for example using keysets.Type: GrantFiled: July 25, 2011Date of Patent: January 8, 2019Assignee: salesforce.comInventors: Brad A. Stronger, Arijit Sengupta
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Patent number: 10127130Abstract: Methods for analyzing and rendering business intelligence data allow for efficient scalability as datasets grow in size. Human intervention is minimized by augmented decision making ability in selecting what aspects of large datasets should be focused on to drive key business outcomes. Variable value combinations that are predominant drivers of key observations are automatically determined from several competing variable value combinations. The identified variable value combinations can then be then used to predict future trends underlying the business intelligence data. In another embodiment, an observed outcome is decomposed into multiple contributing drivers and the impact of each of the contributing drivers can be analyzed and numerically quantified—as a static snapshot or as a time-varying evolution.Type: GrantFiled: March 27, 2015Date of Patent: November 13, 2018Assignee: salesforce.comInventors: Arijit Sengupta, Brad A. Stronger, Griffin Chronis
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Publication number: 20180293502Abstract: Methods for analyzing and rendering business intelligence data allow for efficient scalability as datasets grow in size. Human intervention is minimized by augmented decision making ability in selecting what aspects of large datasets should be focused on to drive key business outcomes. Variable value combinations that are predominant drivers of key observations are automatically determined from several competing variable value combinations. The identified variable value combinations can then be then used to predict future trends underlying the business intelligence data. In another embodiment, an observed outcome is decomposed into multiple contributing drivers and the impact of each of the contributing drivers can be analyzed and numerically quantified—as a static snapshot or as a time-varying evolution.Type: ApplicationFiled: February 27, 2018Publication date: October 11, 2018Inventors: Arijit Sengupta, Brad A. Stronger, Griffin Chronis
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Publication number: 20180239835Abstract: Business process provider(s) process client data. The clients use certain formats (client formats, defined by client format fields). The client format fields instantiated in documents are analyzed. Based on this analysis, the client processes are automatically grouped into different process platforms for processing. For example, similar client processes preferably are grouped together into the same process platform, in order to increase efficiency of processing. In another aspect, the user interfaces used by the business process provider(s) may be constructed from different blocks, where the blocks are automatically defined based on the analysis of client format fields.Type: ApplicationFiled: March 2, 2018Publication date: August 23, 2018Inventors: Arijit Sengupta, Brad A. Stronger
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Publication number: 20180225027Abstract: Methods for analyzing and rendering business intelligence data allow for efficient scalability as datasets grow in size. Human intervention is minimized by augmented decision making ability in selecting what aspects of large datasets should be focused on to drive key business outcomes. Variable value combinations that are predominant drivers of key observations are automatically determined from several competing variable value combinations. The identified variable value combinations can then be then used to predict future trends underlying the business intelligence data. In another embodiment, an observed outcome is decomposed into multiple contributing drivers and the impact of each of the contributing drivers can be analyzed and numerically quantified—as a static snapshot or as a time-varying evolution.Type: ApplicationFiled: March 31, 2018Publication date: August 9, 2018Inventors: Richard Martin Cooke, Arijit Sengupta, Brad A. Stronger, Griffin Chronis
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Patent number: 9940405Abstract: Business process provider(s) process client data. The clients use certain formats (client formats, defined by client format fields). The client format fields instantiated in documents are analyzed. Based on this analysis, the client processes are automatically grouped into different process platforms for processing. For example, similar client processes preferably are grouped together into the same process platform, in order to increase efficiency of processing. In another aspect, the user interfaces used by the business process provider(s) may be constructed from different blocks, where the blocks are automatically defined based on the analysis of client format fields.Type: GrantFiled: April 5, 2011Date of Patent: April 10, 2018Assignee: BEYONDCORE HOLDINGS, LLCInventors: Arijit Sengupta, Brad A. Stronger
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Publication number: 20160292214Abstract: Operations, such as data processing operations, can be improved by applying clustering and statistical techniques to observed behaviors in the data processing operations.Type: ApplicationFiled: June 16, 2016Publication date: October 6, 2016Inventors: Arijit Sengupta, Brad A. Stronger, Daniel Kane
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Patent number: 9390121Abstract: Operations, such as data processing operations, can be improved by applying clustering and statistical techniques to observed behaviors in the data processing operations.Type: GrantFiled: July 11, 2014Date of Patent: July 12, 2016Assignee: BeyondCore, Inc.Inventors: Arijit Sengupta, Brad A. Stronger, Daniel Kane
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Patent number: 9141655Abstract: Methods for analyzing and rendering business intelligence data allow for efficient scalability as datasets grow in size. Human intervention is minimized by augmented decision making ability in selecting what aspects of large datasets should be focused on to drive key business outcomes. Variable value combinations that are predominant drivers of key observations are automatically determined from several competing variable value combinations. The identified variable value combinations can then be then used to predict future trends underlying the business intelligence data. In another embodiment, an observed outcome is decomposed into multiple contributing drivers and the impact of each of the contributing drivers can be analyzed and numerically quantified—as a static snapshot or as a time-varying evolution.Type: GrantFiled: March 27, 2015Date of Patent: September 22, 2015Assignee: BeyondCore, Inc.Inventors: Arijit Sengupta, Brad A. Stronger, Griffin Chronis
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Patent number: 9135286Abstract: Methods for analyzing and rendering business intelligence data allow for efficient scalability as datasets grow in size. Human intervention is minimized by augmented decision making ability in selecting what aspects of large datasets should be focused on to drive key business outcomes. Variable value combinations that are predominant drivers of key observations are automatically determined from several competing variable value combinations. The identified variable value combinations can then be then used to predict future trends underlying the business intelligence data. In another embodiment, an observed outcome is decomposed into multiple contributing drivers and the impact of each of the contributing drivers can be analyzed and numerically quantified—as a static snapshot or as a time-varying evolution.Type: GrantFiled: March 27, 2015Date of Patent: September 15, 2015Assignee: BeyondCore, Inc.Inventors: Arijit Sengupta, Brad A. Stronger, Griffin Chronis
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Patent number: 9135290Abstract: Methods for analyzing and rendering business intelligence data allow for efficient scalability as datasets grow in size. Human intervention is minimized by augmented decision making ability in selecting what aspects of large datasets should be focused on to drive key business outcomes. Variable value combinations that are predominant drivers of key observations are automatically determined from several competing variable value combinations. The identified variable value combinations can then be then used to predict future trends underlying the business intelligence data. In another embodiment, an observed outcome is decomposed into multiple contributing drivers and the impact of each of the contributing drivers can be analyzed and numerically quantified—as a static snapshot or as a time-varying evolution.Type: GrantFiled: March 27, 2015Date of Patent: September 15, 2015Assignee: BeyondCore, Inc.Inventors: Arijit Sengupta, Brad A. Stronger, Griffin Chronis
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Patent number: 9129226Abstract: A combined computer/human approach is used to detect actionable insights in large data sets. Automated computer analysis used to identify patterns (e.g., possibly meaningful patterns or subsets within the data). These are presented to humans for feedback, where the humans may have little to no training in the statistical methods used to detect actionable insights. Feedback from the humans is used to improve the pattern detection and facilitate the detection of actionable insights.Type: GrantFiled: December 4, 2011Date of Patent: September 8, 2015Assignee: BeyondCore, Inc.Inventors: Arijit Sengupta, Brad A. Stronger
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Publication number: 20150220577Abstract: Methods for analyzing and rendering business intelligence data allow for efficient scalability as datasets grow in size. Human intervention is minimized by augmented decision making ability in selecting what aspects of large datasets should be focused on to drive key business outcomes. Variable value combinations that are predominant drivers of key observations are automatically determined from several competing variable value combinations. The identified variable value combinations can then be then used to predict future trends underlying the business intelligence data. In another embodiment, an observed outcome is decomposed into multiple contributing drivers and the impact of each of the contributing drivers can be analyzed and numerically quantified—as a static snapshot or as a time-varying evolution.Type: ApplicationFiled: March 27, 2015Publication date: August 6, 2015Inventors: Arijit Sengupta, Brad A. Stronger, Griffin Chronis
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Patent number: 9098810Abstract: Methods for analyzing and rendering business intelligence data allow for efficient scalability as datasets grow in size. Human intervention is minimized by augmented decision making ability in selecting what aspects of large datasets should be focused on to drive key business outcomes. Variable value combinations that are predominant drivers of key observations are automatically determined from several competing variable value combinations. The identified variable value combinations can then be then used to predict future trends underlying the business intelligence data. In another embodiment, an observed outcome is decomposed into multiple contributing drivers and the impact of each of the contributing drivers can be analyzed and numerically quantified—as a static snapshot or as a time-varying evolution.Type: GrantFiled: March 27, 2015Date of Patent: August 4, 2015Assignee: BeyondCore, Inc.Inventors: Arijit Sengupta, Brad A. Stronger, Griffin Chronis
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Publication number: 20150205695Abstract: Methods for analyzing and rendering business intelligence data allow for efficient scalability as datasets grow in size. Human intervention is minimized by augmented decision making ability in selecting what aspects of large datasets should be focused on to drive key business outcomes. Variable value combinations that are predominant drivers of key observations are automatically determined from several competing variable value combinations. The identified variable value combinations can then be then used to predict future trends underlying the business intelligence data. In another embodiment, an observed outcome is decomposed into multiple contributing drivers and the impact of each of the contributing drivers can be analyzed and numerically quantified—as a static snapshot or as a time-varying evolution.Type: ApplicationFiled: March 27, 2015Publication date: July 23, 2015Inventors: Arijit Sengupta, Brad A. Stronger, Griffin Chronis
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Publication number: 20150205827Abstract: Methods for analyzing and rendering business intelligence data allow for efficient scalability as datasets grow in size. Human intervention is minimized by augmented decision making ability in selecting what aspects of large datasets should be focused on to drive key business outcomes. Variable value combinations that are predominant drivers of key observations are automatically determined from several competing variable value combinations. The identified variable value combinations can then be then used to predict future trends underlying the business intelligence data. In another embodiment, an observed outcome is decomposed into multiple contributing drivers and the impact of each of the contributing drivers can be analyzed and numerically quantified—as a static snapshot or as a time-varying evolution.Type: ApplicationFiled: March 27, 2015Publication date: July 23, 2015Inventors: Arijit Sengupta, Brad A. Stronger, Griffin Chronis
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Publication number: 20150206055Abstract: Methods for analyzing and rendering business intelligence data allow for efficient scalability as datasets grow in size. Human intervention is minimized by augmented decision making ability in selecting what aspects of large datasets should be focused on to drive key business outcomes. Variable value combinations that are predominant drivers of key observations are automatically determined from several competing variable value combinations. The identified variable value combinations can then be then used to predict future trends underlying the business intelligence data. In another embodiment, an observed outcome is decomposed into multiple contributing drivers and the impact of each of the contributing drivers can be analyzed and numerically quantified—as a static snapshot or as a time-varying evolution.Type: ApplicationFiled: March 27, 2015Publication date: July 23, 2015Inventors: Arijit Sengupta, Brad A. Stronger, Griffin Chronis