公开(公告)号：US20230126022A1

公开(公告)日：2023-04-27

申请号：US17507083

申请日：2021-10-21

Applicant: SAP SE

Inventor： Richa Namballa ,  Jing Wang

IPC: G06K9/00 ,  G06K9/46 ,  G06K9/62 ,  G06T7/70 ,  G06N20/20

Abstract: The present disclosure involves systems, software, and computer implemented methods for automatically identifying table locations and table cell types of located tables. One example method includes receiving a request to detect tables. Features are extracted from an input spreadsheet and provided to a trained table detection model trained to predict whether worksheet cells are table cells or background cells and to a cell classification model that is trained to classify worksheet cells by cell structure type. The table detection model generates binary classifications that indicate whether cells are table cells or background cells. A contour detection process is performed on the binary classifications to generate table location information that describes at least one table boundary in the spreadsheet. The trained cell classification model generates a cell structure type classification for each cell that is included in a table boundary generated by the contour detection process.

公开(公告)号：US20210117839A1

公开(公告)日：2021-04-22

申请号：US16810443

申请日：2020-03-05

Applicant: SAP SE

Inventor： Ninad Kulkarni ,  Jing Wang ,  Pankti Jayesh Kansara ,  Mario Ponce Midence ,  James Rapp

IPC: G06N7/00 ,  G06N20/00 ,  G06Q10/04 ,  G06Q30/02 ,  G06Q10/10

Abstract: The present disclosure involves systems, software, and computer implemented methods for proactively predicting demand based on sparse transaction data. One example method includes receiving a request to predict transaction dates for a plurality of transaction entities for a future time period. Historical transaction data for the transaction entities is identified for a plurality of categories of transacted items. The plurality of categories are organized using a hierarchy of levels. Multiple levels of the hierarchy are iterated over, starting at a lowest level. For each current level in the iteration, a plurality of transaction date prediction models are trained and tested. Heuristics for the plurality of trained transaction date prediction models are compared to determine a most accurate transaction date prediction model. The most accurate transaction date prediction model is used to make a prediction of transaction dates for the current level for the future time period.

公开(公告)号：US12094232B2

公开(公告)日：2024-09-17

申请号：US17507083

申请日：2021-10-21

Applicant: SAP SE

Inventor： Richa Namballa ,  Jing Wang

IPC: G06V30/413 ,  G06F18/214 ,  G06F18/24 ,  G06N20/20 ,  G06T7/70 ,  G06V10/94 ,  G06V30/182

CPC classification number: G06V30/413 ,  G06F18/214 ,  G06F18/24 ,  G06N20/20 ,  G06T7/70 ,  G06V10/95 ,  G06V30/182 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30176

Abstract: The present disclosure involves systems, software, and computer implemented methods for automatically identifying table locations and table cell types of located tables. One example method includes receiving a request to detect tables. Features are extracted from an input spreadsheet and provided to a trained table detection model trained to predict whether worksheet cells are table cells or background cells and to a cell classification model that is trained to classify worksheet cells by cell structure type. The table detection model generates binary classifications that indicate whether cells are table cells or background cells. A contour detection process is performed on the binary classifications to generate table location information that describes at least one table boundary in the spreadsheet. The trained cell classification model generates a cell structure type classification for each cell that is included in a table boundary generated by the contour detection process.

公开(公告)号：US11854022B2

公开(公告)日：2023-12-26

申请号：US16810443

申请日：2020-03-05

Applicant: SAP SE

Inventor： Ninad Kulkarni ,  Jing Wang ,  Pankti Jayesh Kansara ,  Mario Ponce Midence ,  James Rapp

IPC: G06Q30/00 ,  G06Q30/0202 ,  G06Q30/0601 ,  G06N20/00 ,  G06Q10/047 ,  G06Q10/1093 ,  G06N7/01

CPC classification number: G06Q30/0202 ,  G06N7/01 ,  G06N20/00 ,  G06Q10/047 ,  G06Q10/1093 ,  G06Q30/0605

Abstract: The present disclosure involves systems, software, and computer implemented methods for proactively predicting demand based on sparse transaction data. One example method includes receiving a request to predict transaction dates for a plurality of transaction entities for a future time period. Historical transaction data for the transaction entities is identified for a plurality of categories of transacted items. The plurality of categories are organized using a hierarchy of levels. Multiple levels of the hierarchy are iterated over, starting at a lowest level. For each current level in the iteration, a plurality of transaction date prediction models are trained and tested. Heuristics for the plurality of trained transaction date prediction models are compared to determine a most accurate transaction date prediction model. The most accurate transaction date prediction model is used to make a prediction of transaction dates for the current level for the future time period.