AUTOMATIC RULE GENERATION FOR NEXT-ACTION RECOMMENDATION ENGINE

    公开(公告)号:US20210027338A1

    公开(公告)日:2021-01-28

    申请号:US16520556

    申请日:2019-07-24

    Abstract: A system can recommend a next action for a user. A memory can store user data corresponding to the user and can include historic interaction points. A behavior pattern can be identified based on two or more interaction points stored in the user data. An intent of the user based on the behavior pattern can be identified. The intent can be based on a previous behavior pattern of another user. Several probabilities that the user will meet one or more objectives can be determined based on the intent. The probabilities can be scored using and used to assign a policy to the first user. A next action can be recommended based on the policy and executed with respect to the user. The outcome of the recommended next action can be stored to the user data.

    ATTRIBUTE DIVERSITY FOR FREQUENT PATTERN ANALYSIS

    公开(公告)号:US20200301966A1

    公开(公告)日:2020-09-24

    申请号:US16355996

    申请日:2019-03-18

    Abstract: A data processing server may receive a set of data objects for frequent pattern (FP) analysis. The set of data objects may be analyzed using an attribute diversity technique. For the set of data attributes of the set of data objects, the server may arrange the attributes in one or more dimensions. The server may initialize a set of centroids on data points and identify mean values of nearby data points. Based on an iteration of the mean value calculation, the server may identify a set of attributes corresponding to final mean values as being groups of similarly frequent attributes. These groups of similarly frequent attributes may be analyzed using an FP analysis procedure to identify frequent patterns of data attributes.

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