Determining audience segments of users that contributed to a metric anomaly

    公开(公告)号:US11646947B2

    公开(公告)日:2023-05-09

    申请号:US17200329

    申请日:2021-03-12

    Applicant: Adobe Inc.

    CPC classification number: H04L41/142 G06N5/045 G06N5/01

    Abstract: The present disclosure is directed toward systems and methods for identifying contributing audience segments associated with a metric anomaly. One or more embodiments described herein identify contributing factors based on statistical analysis and machine learning. Additionally, one or more embodiments identify audience segments associated with each contributing factor. In one or more embodiments, the systems and methods provide an interactive display that enables a user to select a particular anomaly for further analysis. The interactive display also provides additional interfaces through which the user can view informational displays that illustrate the factors and segments that caused the particular anomaly and how those factors correlate with each other.

    DETERMINING AUDIENCE SEGMENTS OF USERS THAT CONTRIBUTED TO A METRIC ANOMALY

    公开(公告)号:US20210203563A1

    公开(公告)日:2021-07-01

    申请号:US17200329

    申请日:2021-03-12

    Applicant: Adobe Inc.

    Abstract: The present disclosure is directed toward systems and methods for identifying contributing audience segments associated with a metric anomaly. One or more embodiments described herein identify contributing factors based on statistical analysis and machine learning. Additionally, one or more embodiments identify audience segments associated with each contributing factor. In one or more embodiments, the systems and methods provide an interactive display that enables a user to select a particular anomaly for further analysis. The interactive display also provides additional interfaces through which the user can view informational displays that illustrate the factors and segments that caused the particular anomaly and how those factors correlate with each other.

    Identifying factors that contribute to a metric anomaly

    公开(公告)号:US10972332B2

    公开(公告)日:2021-04-06

    申请号:US14841293

    申请日:2015-08-31

    Applicant: Adobe Inc.

    Abstract: The present disclosure is directed toward systems and methods for identifying contributing factors associated with a metric anomaly. One or more embodiments described herein identify contributing factors based on statistical analysis and machine learning. Additionally, one or more embodiments identify sub-factors associated with each contributing factor. In one or more embodiments, the systems and methods provide an interactive display that enables a user to select a particular anomaly for further analysis. The interactive display also provides additional interfaces through which the user can view informational displays that illustrate the factors that caused the particular anomaly and how those factors correlate with each other.

    Communication notification trigger modeling preview

    公开(公告)号:US10855783B2

    公开(公告)日:2020-12-01

    申请号:US15412580

    申请日:2017-01-23

    Applicant: Adobe Inc.

    Abstract: In some embodiments, a real-time and interactive preview of alerts is provided in a user interface. A computer system parses a set of rules that specifies an alert definition. Each rule identifies a set of observations and an alert trigger criterion based on user input in the user interface. For a rule, the computer system accesses historical data corresponding to the set of observations identified by the rule and determines, based on an analysis of the historical data, time points that trigger alerts over a time period according to the rule. The analysis is based on the alert trigger criterion identified by the rule. The computer system aggregates, based on the alert definition, the time points determined for the rule with time points determined for another rule from the set of rules. Further, the computer system generates an alert preview over the time period for presentation at the user interface.

    Identifying contributing factors to a metric anomaly

    公开(公告)号:US12184475B2

    公开(公告)日:2024-12-31

    申请号:US17192687

    申请日:2021-03-04

    Applicant: Adobe Inc.

    Abstract: The present disclosure is directed toward systems and methods for identifying contributing factors associated with a metric anomaly. One or more embodiments described herein identify contributing factors based on statistical analysis and machine learning. Additionally, one or more embodiments identify sub-factors associated with each contributing factor. In one or more embodiments, the systems and methods provide an interactive display that enables a user to select a particular anomaly for further analysis. The interactive display also provides additional interfaces through which the user can view informational displays that illustrate the factors that caused the particular anomaly and how those factors correlate with each other.

    IDENTIFYING CONTRIBUTING FACTORS TO A METRIC ANOMALY

    公开(公告)号:US20210194751A1

    公开(公告)日:2021-06-24

    申请号:US17192687

    申请日:2021-03-04

    Applicant: Adobe Inc.

    Abstract: The present disclosure is directed toward systems and methods for identifying contributing factors associated with a metric anomaly. One or more embodiments described herein identify contributing factors based on statistical analysis and machine learning. Additionally, one or more embodiments identify sub-factors associated with each contributing factor. In one or more embodiments, the systems and methods provide an interactive display that enables a user to select a particular anomaly for further analysis. The interactive display also provides additional interfaces through which the user can view informational displays that illustrate the factors that caused the particular anomaly and how those factors correlate with each other.

    Identifying audiences that contribute to metric anomalies

    公开(公告)号:US10985993B2

    公开(公告)日:2021-04-20

    申请号:US14855655

    申请日:2015-09-16

    Applicant: Adobe Inc.

    Abstract: The present disclosure is directed toward systems and methods for identifying contributing audience segments associated with a metric anomaly. One or more embodiments described herein identify contributing factors based on statistical analysis and machine learning. Additionally, one or more embodiments identify audience segments associated with each contributing factor. In one or more embodiments, the systems and methods provide an interactive display that enables a user to select a particular anomaly for further analysis. The interactive display also provides additional interfaces through which the user can view informational displays that illustrate the factors and segments that caused the particular anomaly and how those factors correlate with each other.

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