公开(公告)号：US20230342628A1

公开(公告)日：2023-10-26

申请号：US17728238

申请日：2022-04-25

Applicant: BUSINESS OBJECTS SOFTWARE LTD.

Inventor： Jacques DOAN HUU

IPC: G06N5/00 ,  G06N20/00

CPC classification number: G06N5/003 ,  G06N20/00

Abstract: Systems and methods include identification first members of a child level of a dimension hierarchy which are associated with boundaries between second members of a parent level of the dimension hierarchy, training of a decision tree model based on data associated with the child level, extraction of predicates on the child level from the trained decision tree model, determination of a value based on the identified first members of the child level and on the extracted predicates on the child level, and determination, based on the value, whether to include the parent level and the child level within training data or to include the parent level and not include the child level within the training data.

公开(公告)号：US20220335347A1

公开(公告)日：2022-10-20

申请号：US17231057

申请日：2021-04-15

Applicant: BUSINESS OBJECTS SOFTWARE LTD

Inventor： Jacques DOAN HUU

IPC: G06Q10/06 ,  G06N20/00 ,  G06F17/15 ,  G06K9/62

Abstract: Provided is a system and method which can identify a causal relationship for anomalies in a time-series signal based on co-occurring and preceding anomalies in another time-series signal. In one example, the method may include identifying a recurring anomaly within a time-series signal of a first data value, determining a time-series signal of a second data value that is a cause of the recurring anomaly in the time-series signal of the first data value based on a preceding and co-occurring anomaly in the time-series signal of the second data value, and storing a correlation between the preceding and co-occurring anomaly in the time-series signal of the second data value and the recurring anomaly in the time-series signal of the first data value.

公开(公告)号：US20230342659A1

公开(公告)日：2023-10-26

申请号：US17728259

申请日：2022-04-25

Applicant: BUSINESS OBJECTS SOFTWARE LTD.

Inventor： Jacques DOAN HUU

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: Systems and methods include reception of a plurality of records, each of the plurality of records associating each of a plurality of features with a respective value, a second feature with a value, and a target feature with a value, a first machine learning model trained based on the plurality of records to output a value of the target feature based on values of each of the plurality of features, a second machine learning model trained based on the plurality of records to output a value of the second feature based on the values of each of the plurality of features, determination, based on the trained second machine learning model, of a first one or more of the plurality of features which are correlated to the second feature, determination of an influence of each of the first one or more features on the trained first machine learning model, and determination of a first value associated with the second feature based on the determined influences and on the trained second machine learning model.

公开(公告)号：US20230342632A1

公开(公告)日：2023-10-26

申请号：US17726712

申请日：2022-04-22

Applicant: Business Objects Software Ltd.

Inventor： Jacques DOAN HUU

IPC: G06N5/02 ,  G06N5/04

CPC classification number: G06N5/022 ,  G06N5/047

Abstract: Provided is a system and method which generates a composite machine learning model that can filter downtime data from a time-series data signal and perform a prediction on remaining time-series data. In one example, the method may include detecting a pattern of downtime data within a time-series data signal, removing a subset of data from the time-series data based on the detected pattern of downtime and building a machine learning model to make predictions based on remaining data in the time-series data, generating segregation instructions configured to remove downtime data from a time-series data signal of a same type and to predict zero on future dates matching the downtime segregation codes, and building a composite machine learning model that includes the trained machine learning model and the segregation instructions for filtering data that is input to the trained machine learning models.

公开(公告)号：US20220335314A1

公开(公告)日：2022-10-20

申请号：US17233600

申请日：2021-04-19

Applicant: BUSINESS OBJECTS SOFTWARE LTD.

Inventor： Jacques DOAN HUU ,  Elouan ARGOUARCH

IPC: G06N5/04 ,  G06N20/00

Abstract: Provided is a system and method which decomposes a predicted output signal of a time-series forecasting model into a plurality of sub signals that correspond to a plurality of components, and determines and displays a global contribution of each component. In one example, the method may include iteratively predicting an output signal of a time-series data value via execution of a time-series model, decomposing the predicted output signal into a plurality of component signals corresponding to a plurality of components of the time-series machine learning algorithm, respectively, and displaying the plurality of global values via a user interface.

公开(公告)号：US20250053836A1

公开(公告)日：2025-02-13

申请号：US18931779

申请日：2024-10-30

Applicant: BUSINESS OBJECTS SOFTWARE LTD.

Inventor： Jacques DOAN HUU ,  Elouan ARGOUARCH

IPC: G06N5/04 ,  G06N20/00

Abstract: Provided are a system and method which iteratively predicts an output signal of a time-series data value via execution of a time-series machine learning model on input data, decomposes the predicted output signal into a plurality of component signals corresponding to a plurality of components of the time-series machine learning model, the plurality of component signals comprising a trend signal. a cyclic signal, and a fluctuation signal, determines a plurality of global values respectively corresponding to the plurality of component signals for a first subset of the predicted output signal, where a global value is determined based on an absolute value of a respective component signal within the first subset, constructs a plurality of bars respectively corresponding to global values of the plurality of component signals, and displays the plurality of bars via a user interface.

公开(公告)号：US20230342671A1

公开(公告)日：2023-10-26

申请号：US17728207

申请日：2022-04-25

Applicant: Business Objects Software Ltd.

Inventor： Jacques DOAN HUU

IPC: G06N20/20 ,  G06K9/62 ,  G06F16/9038

CPC classification number: G06N20/20 ,  G06K9/6257 ,  G06F16/9038

Abstract: Provided is a system and method which build a composite time-series machine learning model including a core model and a debrief model that includes a combination of the core model and a surrogate model. In one example, the method may include executing the plurality of models on test data and determining accuracy values and interpretability toughness values for the plurality models, selecting a most accurate model as a core model based on the accuracy values and select a most interpretable model as a surrogate model from among other models remaining in the plurality of models based on the interpretability toughness values, building a composite model comprising the core model, the surrogate model, and instructions for generating a debrief model for debriefing the core model based on a combination of the core model and the surrogate model, and storing the composite model within the memory.

公开(公告)号：US20220366315A1

公开(公告)日：2022-11-17

申请号：US17313460

申请日：2021-05-06

Applicant: BUSINESS OBJECTS SOFTWARE LTD.

Inventor： Louis DESREUMAUX ,  Jacques DOAN HUU

IPC: G06N20/20 ,  G06N5/00

Abstract: Systems and methods include determination of a first plurality of sets of data, each including values associated with respective ones of a first plurality of features, partial training of a first machine-learning model based on the first plurality of sets of data, determination of one or more of the first plurality of features to remove based on the partially-trained first machine-learning model, removal of the one or more of the first plurality of features to generate a second plurality of sets of data, partial training of a second machine-learning model based on the second plurality of sets of data, determination that a performance of the partially-trained second machine-learning model is less than a threshold, addition, in response to the determination, of the one or more of the first plurality of features to the second plurality of sets of data, and training of the partially-trained first machine-learning model based on the first plurality of sets of data.

公开(公告)号：US20220188383A1

公开(公告)日：2022-06-16

申请号：US17120400

申请日：2020-12-14

Applicant: BUSINESS OBJECTS SOFTWARE LTD.

Inventor： Jacques DOAN HUU

IPC: G06F17/18 ,  G06N20/00 ,  G06N3/04 ,  G06N3/08

Abstract: Provided is a system and method which trains a model based on a horizon-wise cost function which accounts for error across a horizon rather than just a next point in time thereby improving the accuracy of the trained model in the long term. In one example, the method may include storing time-series data, executing a training iteration for a machine learning model based on one or more parameter values, determining error values between the predicted values output by the machine learning model and actual values of the time-series data for a plurality of intervals included in a horizon of the time-series data, generating a total error value for the horizon based on the determined error values for the intervals, and storing the generated total error value for the horizon. The method also enables a user to dynamically adjust a weight for each interval of the horizon.