公开(公告)号：US20190188611A1

公开(公告)日：2019-06-20

申请号：US15841662

申请日：2017-12-14

Applicant: Business Objects Software Limited

Inventor： Ying Wu ,  Paul Pallath ,  Paul O'Hara

IPC: G06Q10/04 ,  G06Q30/02 ,  G06K9/62 ,  G06N5/02 ,  G06N5/04 ,  G06F17/18

CPC classification number: G06Q10/04 ,  G06F17/18 ,  G06K9/6256 ,  G06N5/02 ,  G06N5/04 ,  G06N20/20 ,  G06Q10/00 ,  G06Q30/0202

Abstract: A method includes receiving training data including sequential data, determining a plurality of future time points, generating a first prediction by applying a first forecasting algorithm to the training data, generating a second prediction by applying a second forecasting algorithm to the training data, extracting predicted values from the first prediction and the second prediction that corresponds to a future time point of the plurality of future time points, applying a regression model in sequence on each of the plurality of future time points to generate a final predicted value of each of the plurality of future time points, and outputting the final predicted values of the plurality of future time points.

公开(公告)号：US11037096B2

公开(公告)日：2021-06-15

申请号：US15856814

申请日：2017-12-28

Applicant: Business Objects Software Limited

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

IPC: G06Q10/08 ,  G06N7/00

Abstract: A method includes receiving a plurality of items, grouping the plurality of items into a plurality of clusters, where each of the plurality of clusters comprises items having similar features to one another, applying a classification model to each cluster to predict whether each item of a cluster will be delivered on time or delivered late, applying a regression model that determines an expected measure of tardiness of each item predicted to be delivered late, and outputting a delivery date prediction for each item predicted to be delivered late based on the expected measure of tardiness of the item.

公开(公告)号：US20190205828A1

公开(公告)日：2019-07-04

申请号：US15856814

申请日：2017-12-28

Applicant: Business Objects Software Limited

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

IPC: G06Q10/08 ,  G06N7/00

CPC classification number: G06Q10/0833 ,  G06N7/005

Abstract: A method includes receiving a plurality of items, grouping the plurality of items into a plurality of clusters, where each of the plurality of clusters comprises items having similar features to one another, applying a classification model to each cluster to predict whether each item of a cluster will be delivered on time or delivered late, applying a regression model that determines an expected measure of tardiness of each item predicted to be delivered late, and outputting a delivery date prediction for each item predicted to be delivered late based on the expected measure of tardiness of the item.

公开(公告)号：US11928562B2

公开(公告)日：2024-03-12

申请号：US17023051

申请日：2020-09-16

Applicant: Business Objects Software Limited

Inventor： Paul O'Hara ,  Ying Wu

IPC: G06N20/00 ,  G06F16/23 ,  G06N3/08

CPC classification number: G06N20/00 ,  G06F16/2379 ,  G06N3/08

Abstract: A system and method include input of data records to a first trained predictive model to obtain a predicted value associated with each input data record. A model region is then associated with each of the input data records based on the first trained predictive model, the input data records and the predicted values. Enhanced input data records are generated by, for each model region, adding derived values of engineered features associated with the model region to input data records associated with the model region and default values of the engineered features associated with the model region to input training records not associated with the model region. The enhanced input data records are input to a second trained predictive model to obtain an enhanced predicted value associated with each input data record.

公开(公告)号：US20220083905A1

公开(公告)日：2022-03-17

申请号：US17023051

申请日：2020-09-16

Applicant: Business Objects Software Limited

Inventor： Paul O'Hara ,  Ying Wu

IPC: G06N20/00 ,  G06F16/23

Abstract: A system and method include input of data records to a first trained predictive model to obtain a predicted value associated with each input data record. A model region is then associated with each of the input data records based on the first trained predictive model, the input data records and the predicted values. Enhanced input data records are generated by, for each model region, adding derived values of engineered features associated with the model region to input data records associated with the model region and default values of the engineered features associated with the model region to input training records not associated with the model region. The enhanced input data records are input to a second trained predictive model to obtain an enhanced predicted value associated with each input data record.