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公开(公告)号：US20200175559A1
公开(公告)日：2020-06-04
申请号：US16208681
申请日：2018-12-04
Applicant: SAP SE
Inventor： Sean Saito , Truc Viet Le , Chaitanya Joshi , Rajalingappaa Shanmugamani
Abstract: Methods, systems, and computer-readable storage media for providing a set of column pairs, each column pair including a column of a bank statement table, and a column of a super invoice table, each column pair corresponding to a modality, the super invoice table including at least one row including data associated with multiple invoices, for each column pair, determining a feature descriptor based on an operator, a feature vector being provided based on feature descriptors of the set of column pairs, inputting the feature vector to a ML model that processes the feature vector to determine a probability of a match between the bank statement, and a super invoice represented by the super invoice table, and outputting a binary output representing one of a match and no match between the bank statement, and the super invoice based on the probability.
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Patent Agency Ranking
公开(公告)号：US11645686B2
公开(公告)日：2023-05-09
申请号：US16210070
申请日：2018-12-05
Applicant: SAP SE
Inventor： Truc Viet Le , Sean Saito , Chaitanya Krishna Joshi , Rajalingappaa Shanmugamani
IPC: G06Q30/04 , G06N20/00 , G06F18/2411 , G06F18/22 , G06V30/418
CPC classification number: G06Q30/04 , G06F18/22 , G06F18/2411 , G06N20/00 , G06V30/418
Abstract: Methods, systems, and computer-readable storage media for providing, by a machine learning (ML) platform, a first binary classifier, processing, by the first binary classifier a super-set of invoices to provide a plurality of sets of invoices based on matching pairs of invoices in the super-set of invoices, providing, by the ML platform, a second binary classifier, processing, by the second binary classifier, a bank statement and the plurality of sets of invoices to define two or more super-invoices based on aggregate features of invoices in the plurality of sets of invoices, and match the bank statement to a super-invoice of the two or more super-invoices, and outputting a match of the bank statement to the super-invoice.
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公开(公告)号：US20230059579A1
公开(公告)日：2023-02-23
申请号：US18045030
申请日：2022-10-07
Applicant: SAP SE
Inventor： Rajalingappaa Shanmugamani , Jiaxuan Zhang
Abstract: Implementations of the present disclosure include methods, systems, and computer-readable storage mediums for receiving first and second data sets, both the first and second data sets including structured data in a plurality of columns, for each of the first data set and the second data set, inputting each column into an encoder specific to a column type of a respective column, the encoder providing encoded data for the first data set, and the second data set, respectively, providing a first multi-dimensional vector based on encoded data of the first data set, providing a second multi-dimensional vector based on encoded data of the second data set, and outputting the first multi-dimensional vector and the second multi-dimensional vector to a loss-function, the loss-function processing the first multi-dimensional vector and the second multi-dimensional vector to provide an output, the output representing matched data points between the first and second data sets.
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公开(公告)号：US11468024B2
公开(公告)日：2022-10-11
申请号：US15937216
申请日：2018-03-27
Applicant: SAP SE
Inventor： Rajalingappaa Shanmugamani , Jiaxuan Zhang
Abstract: Implementations of the present disclosure include methods, systems, and computer-readable storage mediums for receiving first and second data sets, both the first and second data sets including structured data in a plurality of columns, for each of the first data set and the second data set, inputting each column into an encoder specific to a column type of a respective column, the encoder providing encoded data for the first data set, and the second data set, respectively, providing a first multi-dimensional vector based on encoded data of the first data set, providing a second multi-dimensional vector based on encoded data of the second data set, and outputting the first multi-dimensional vector and the second multi-dimensional vector to a loss-function, the loss-function processing the first multi-dimensional vector and the second multi-dimensional vector to provide an output, the output representing matched data points between the first and second data sets.
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公开(公告)号：US20200184281A1
公开(公告)日：2020-06-11
申请号：US16210070
申请日：2018-12-05
Applicant: SAP SE
Inventor： Truc Viet LE , Sean Saito , Chaitanya Krishna Joshi , Rajalingappaa Shanmugamani
Abstract: Methods, systems, and computer-readable storage media for providing, by a machine learning (ML) platform, a first binary classifier, processing, by the first binary classifier a super-set of invoices to provide a plurality of sets of invoices based on matching pairs of invoices in the super-set of invoices, providing, by the ML platform, a second binary classifier, processing, by the second binary classifier, a bank statement and the plurality of sets of invoices to define two or more super-invoices based on aggregate features of invoices in the plurality of sets of invoices, and match the bank statement to a super-invoice of the two or more super-invoices, and outputting a match of the bank statement to the super-invoice.
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公开(公告)号：US20190303465A1
公开(公告)日：2019-10-03
申请号：US15937216
申请日：2018-03-27
Applicant: SAP SE
Inventor： Rajalingappaa Shanmugamani , Jiaxuan Zhang
Abstract: Implementations of the present disclosure include methods, systems, and computer-readable storage mediums for receiving first and second data sets, both the first and second data sets including structured data in a plurality of columns, for each of the first data set and the second data set, inputting each column into an encoder specific to a column type of a respective column, the encoder providing encoded data for the first data set, and the second data set, respectively, providing a first multi-dimensional vector based on encoded data of the first data set, providing a second multi-dimensional vector based on encoded data of the second data set, and outputting the first multi-dimensional vector and the second multi-dimensional vector to a loss-function, the loss-function processing the first multi-dimensional vector and the second multi-dimensional vector to provide an output, the output representing matched data points between the first and second data sets.
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