公开(公告)号：US12125075B2

公开(公告)日：2024-10-22

申请号：US18136394

申请日：2023-04-19

Applicant: SAP SE

Inventor： Julian Stoettinger ,  Volker Loch ,  Rolf Mahr ,  Rohit Kumar Gupta ,  Johannes Hoehne

IPC: G06N5/046 ,  G06F16/28 ,  G06F18/2431 ,  G06N20/00 ,  G06Q30/0282

CPC classification number: G06Q30/0282 ,  G06F16/285 ,  G06F18/2431 ,  G06N5/046 ,  G06N20/00

Abstract: Provided is a system and method that can identify whether an item is a dangerous good. The system can determine whether a product belongs in any of a number of different classes of dangerous goods from among a plurality of different regulations based on a machine learning algorithm which performs a text-based classification. In one example, the method may include receiving an identification of an object, retrieving a plurality of descriptive attributes of the object from a data store and converting the plurality of descriptive attributes into an input string, predicting whether the object is a dangerous object via execution of a text-based machine learning algorithm that receives the input string as an input, and outputting information about the prediction of the object for display via a user interface.

公开(公告)号：US20210209301A1

公开(公告)日：2021-07-08

申请号：US16734880

申请日：2020-01-06

Applicant: SAP SE

Inventor： Rohit Kumar Gupta ,  Johannes HOEHNE ,  Anoop Raveendra KATTI

IPC: G06F40/274 ,  G06F40/30 ,  G06K9/32 ,  G06F40/289

Abstract: System, method, and various embodiments for providing contextualized character recognition system are described herein. An embodiment operates by determining a plurality of predicted words of an image. An accuracy measure or each of the plurality of predicted words is identified and a replaceable word with an accuracy measure below a threshold is identified. A plurality of candidate words associated with the replaceable word are identified and a probability for each of the candidate words is calculated based on a contextual analysis. One of the candidate words with a highest probability is selected. The plurality of predicted words including the selected candidate word with the highest probability replacing the replaceable word is output.

公开(公告)号：US20230274324A1

公开(公告)日：2023-08-31

申请号：US18136394

申请日：2023-04-19

Applicant: SAP SE

Inventor： Julian Stoettinger ,  Volker Loch ,  Rolf Mahr ,  Rohit Kumar Gupta ,  Johannes Hoehne

IPC: G06Q30/0282 ,  G06F16/28 ,  G06N5/046 ,  G06N20/00 ,  G06F18/2431

CPC classification number: G06Q30/0282 ,  G06F16/285 ,  G06N5/046 ,  G06N20/00 ,  G06F18/2431

Abstract: Provided is a system and method that can identify whether an item is a dangerous good. The system can determine whether a product belongs in any of a number of different classes of dangerous goods from among a plurality of different regulations based on a machine learning algorithm which performs a text-based classification. In one example, the method may include receiving an identification of an object, retrieving a plurality of descriptive attributes of the object from a data store and converting the plurality of descriptive attributes into an input string, predicting whether the object is a dangerous object via execution of a text-based machine learning algorithm that receives the input string as an input, and outputting information about the prediction of the object for display via a user interface.

公开(公告)号：US11301627B2

公开(公告)日：2022-04-12

申请号：US16734880

申请日：2020-01-06

Applicant: SAP SE

Inventor： Rohit Kumar Gupta ,  Johannes Hoehne ,  Anoop Raveendra Katti

IPC: G06F40/274 ,  G06F40/289 ,  G06F40/30 ,  G06V10/20

Abstract: System, method, and various embodiments for providing contextualized character recognition system are described herein. An embodiment operates by determining a plurality of predicted words of an image. An accuracy measure or each of the plurality of predicted words is identified and a replaceable word with an accuracy measure below a threshold is identified. A plurality of candidate words associated with the replaceable word are identified and a probability for each of the candidate words is calculated based on a contextual analysis. One of the candidate words with a highest probability is selected. The plurality of predicted words including the selected candidate word with the highest probability replacing the replaceable word is output.

公开(公告)号：US20220092405A1

公开(公告)日：2022-03-24

申请号：US17025845

申请日：2020-09-18

Applicant: SAP SE

Inventor： Matthias Frank ,  Hoang-Vu Nguyen ,  Stefan Klaus Baur ,  Alexey Streltsov ,  Jasmin Mankad ,  Cordula Guder ,  Konrad Schenk ,  Philipp Lukas Jamscikov ,  Rohit Kumar Gupta

IPC: G06N3/08 ,  G06N3/04 ,  G06F16/81

Abstract: In an example embodiment, a deep neural network may be utilized to determine matches between candidate pairs of entities, as well as confidence scores that reflect how certain the deep neural network is about the corresponding match. The deep neural network is also able to find these matches without requiring domain knowledge that would be required if features for a machine-learned model were handcrafted, which is a drawback of prior art machine-learned models used to match entities in multiple tables. Thus, the deep neural network improves on the functioning of prior art machine learned models designed to perform the same tasks. Specifically, the deep neural network learns the relationships of tabular fields and the patterns that define a match from historical data alone, making this approach generic and applicable independent of the context.

公开(公告)号：US11663635B2

公开(公告)日：2023-05-30

申请号：US16413058

申请日：2019-05-15

Applicant: SAP SE

Inventor： Julian Stoettinger ,  Volker Loch ,  Rolf Mahr ,  Rohit Kumar Gupta ,  Johannes Hoehne

IPC: G06N5/046 ,  G06N20/00 ,  G06F18/2431 ,  G06Q30/0282 ,  G06F16/28

CPC classification number: G06Q30/0282 ,  G06F16/285 ,  G06F18/2431 ,  G06N5/046 ,  G06N20/00

Abstract: Provided is a system and method that can identify whether an item is a dangerous good. The system can determine whether a product belongs in any of a number of different classes of dangerous goods from among a plurality of different regulations based on a machine learning algorithm which performs a text-based classification. In one example, the method may include receiving an identification of an object, retrieving a plurality of descriptive attributes of the object from a data store and converting the plurality of descriptive attributes into an input string, predicting whether the object is a dangerous object via execution of a text-based machine learning algorithm that receives the input string as an input, and outputting information about the prediction of the object for display via a user interface.