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    MACHINE-TRAINED ADAPTIVE CONTENT TARGETING
    11.
    发明申请
    MACHINE-TRAINED ADAPTIVE CONTENT TARGETING 审中-公开

    公开(公告)号：US20180211270A1

    公开(公告)日：2018-07-26

    申请号：US15415534

    申请日：2017-01-25

    Applicant: Business Objects Software Ltd.

    Inventor： Ying Wu ,  Paul Pallath ,  Achim Becker

    IPC: G06Q30/02

    CPC classification number: G06Q30/0204 ,  G06Q30/0269

    Abstract: Systems and methods for machine-trained adaptive content targeting are provided. The system generates a recommendation model, which includes creating a plurality of offer clusters. Each offer cluster comprises offers having similar features. The system assigns a new offer to one of the plurality of offer clusters. The assigning of the new offer occurs without having to retrain the recommendation model. The system also generates a plurality of user clusters, whereby users within each of the plurality of user clusters share similar behavior. A classification model for predicting an offer cluster from the plurality of offer clusters is created for each of the plurality of user clusters. The system then performs a recommendation process for a new user that includes selecting one or more relevant offers from a predicted offer cluster based on the classification model.

    Multiple machine learning model anomaly detection framework
    13.
    发明授权
    Multiple machine learning model anomaly detection framework 有权

    公开(公告)号：US12050628B1

    公开(公告)日：2024-07-30

    申请号：US18348143

    申请日：2023-07-06

    Applicant: Business Objects Software Ltd.

    Inventor： Paul O'Hara ,  Ying Wu ,  Malte Christian Kaufmann

    IPC: G06F16/28 ,  G06F16/23

    CPC classification number: G06F16/285 ,  G06F16/2365

    Abstract: Anomalies may be detected using a multiple machine learning model anomaly detection framework. A clustering model is trained using an unsupervised machine learning algorithm on a historical anomaly dataset. A plurality of clusters of records are determined by applying the historical anomaly dataset to the clustering model. Then it is determined whether each cluster of the plurality of clusters is an anomaly-type cluster or a normal-type cluster. The plurality of labels for the plurality of records are updated based on the particular record's cluster classification. Non-pure clusters are determined from among the plurality of clusters based on a purity threshold. A supervised machine learning model is trained for each of the non-pure clusters using the records in the given cluster and the labels for each of those records. Then, predictions of an anomaly are made using the clustering model and the supervised machine learning models.

    Multi-step day sales outstanding forecasting
    17.
    发明授权
    Multi-step day sales outstanding forecasting 有权

    公开(公告)号：US11107166B2

    公开(公告)日：2021-08-31

    申请号：US16140760

    申请日：2018-09-25

    Applicant: BUSINESS OBJECTS SOFTWARE LTD.

    Inventor： Paul O'Hara ,  Ying Wu

    IPC: G06Q40/00 ,  G06N5/04 ,  G06Q10/06 ,  G06Q30/02

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, to predict future Day Sales Outstanding (DSO) forecasts for a number of future time periods. In one aspect, a method includes receiving open receivables financial line item data and revenue financial line item data, providing the open receivables financial line item data to a DSO predictor engine to generate a predicted open receivables that includes a multi-step time series forecasting regression generated from the open receivables financial line item data, providing the revenue financial line item data to the DSO predictor engine to generate a predicted revenue comprising the multi-step time series forecasting regression generated from the revenue financial line item data; generating a predicted DSO with the predicted open receivables and predicted revenue, and providing the predicted DSO to a client device.

    DATA ANONYMIZATION FOR CLOUD ANALYTICS
    19.
    发明申请
    DATA ANONYMIZATION FOR CLOUD ANALYTICS 有权

    公开(公告)号：US20220382906A1

    公开(公告)日：2022-12-01

    申请号：US17330997

    申请日：2021-05-26

    Applicant: BUSINESS OBJECTS SOFTWARE LTD.

    Inventor： Ying Wu ,  Malte Christian Kaufmann

    IPC: G06F21/62 ,  G06N5/04 ,  G06N20/00

    Abstract: A system and method including receiving numeric data of a first dataset including a plurality of columns having numeric values with one of the plurality of columns specified as a target column; generating a trained generative model based on numeric values in non-target columns of the plurality of columns; generating a trained predictive model based on numeric values in non-target columns of the plurality of columns being input variables and the target column being a target variable; generating, by the trained generative model, a new set of numeric data for the non-target columns; generating predicted target values for the non-target columns by the trained predictive model using the new set of numeric data as an input to the predictive model; and generating anonymized numeric data for the first dataset by combining the new set of numeric data and the target column populated with the generated predicted target values.

    TOP CONTRIBUTOR RECOMMENDATION FOR CLOUD ANALYTICS
    20.
    发明申请
    TOP CONTRIBUTOR RECOMMENDATION FOR CLOUD ANALYTICS 有权

    公开(公告)号：US20220382729A1

    公开(公告)日：2022-12-01

    申请号：US17329519

    申请日：2021-05-25

    Applicant: BUSINESS OBJECTS SOFTWARE LTD.

    Inventor： Ying Wu ,  Malte Christian Kaufmann ,  Alan McShane ,  Anirban Banerjee ,  Gareth Maguire

    IPC: G06F16/22 ,  G06K9/62

    Abstract: A system and method including determining, for a specified target measure column of a first dataset including a plurality of records, the metadata of the first dataset, including a probability distribution for the specified target column and dimension scores for the dimensions for the first dataset conditioned on the specified target measure column, where the first dataset comprises a plurality of columns including the at least one target measure column and a plurality of non-numeric, dimension columns for the records of the first dataset; determining, for a subset of data of the first dataset based on one or more specified variables, dimension scores for the dimensions of the subset of data approximately derived from the determined metadata of the first dataset; and providing recommendations of top contributors based on the approximated dimension scores of dimensions of the subset of data.

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