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公开(公告)号：US20180293502A1
公开(公告)日：2018-10-11
申请号：US15907230
申请日：2018-02-27
Applicant: salesforce.com, inc.
Inventor： Arijit Sengupta , Brad A. Stronger , Griffin Chronis
Abstract: 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. Similarly, differences in observations between two groups can be decomposed into multiple contributing sub-groups for each of the groups and pairwise differences among sub-groups can be quantified and analyzed.
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公开(公告)号：US10802687B2
公开(公告)日：2020-10-13
申请号：US15942518
申请日：2018-03-31
Applicant: salesforce.com, inc.
Inventor： Richard Martin Cooke , Arijit Sengupta , Brad A. Stronger , Griffin Chronis
IPC: G06F17/00 , G06F3/0484 , G06F17/18 , G06Q30/02 , G06F16/248 , G06K9/62 , G06K9/00 , G06K9/46
Abstract: 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. Similarly, differences in observations between two groups can be decomposed into multiple contributing sub-groups for each of the groups and pairwise differences among sub-groups can be quantified and analyzed.
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公开(公告)号：US10796232B2
公开(公告)日：2020-10-06
申请号：US15907230
申请日：2018-02-27
Applicant: salesforce.com, inc.
Inventor： Arijit Sengupta , Brad A. Stronger , Griffin Chronis
Abstract: 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. Similarly, differences in observations between two groups can be decomposed into multiple contributing sub-groups for each of the groups and pairwise differences among sub-groups can be quantified and analyzed.
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公开(公告)号：US20180225027A1
公开(公告)日：2018-08-09
申请号：US15942518
申请日：2018-03-31
Applicant: salesforce.com, inc.
Inventor： Richard Martin Cooke , Arijit Sengupta , Brad A. Stronger , Griffin Chronis
IPC: G06F3/0484 , G06F17/30 , G06K9/62 , G06Q30/02 , G06F17/18
CPC classification number: G06F3/04842 , G06F16/248 , G06F17/18 , G06K9/00523 , G06K9/469 , G06K9/6201 , G06K9/6254 , G06Q30/0201
Abstract: 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. Similarly, differences in observations between two groups can be decomposed into multiple contributing sub-groups for each of the groups and pairwise differences among sub-groups can be quantified and analyzed.
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