公开(公告)号：US11954440B1

公开(公告)日：2024-04-09

申请号：US17478583

申请日：2021-09-17

申请人： AppZen, Inc.

发明人： Edris Naderan ,  Parivesh Priye ,  Amrit Singhal ,  Arghyadeep Giri ,  Debashish Panigrahi ,  Hyram Du ,  Kunal Verma

IPC分类号： G06F40/295 ,  G06F16/11 ,  G06F18/21 ,  G06N20/00 ,  G06V30/412

CPC分类号： G06F40/295 ,  G06F16/116 ,  G06F18/2178 ,  G06N20/00 ,  G06V30/412

摘要： A non-transitory computer readable storage medium has instructions executed by a processor to invoke an image processing module to ingest a digital invoice. An evaluation module derives metrics from the digital invoice. A semantic document processing module forms entity extracts from the digital invoice, where each entity extract from the digital invoice has a potential mapping to a trained machine learning model element. An entity extraction correction module overrides the potential mapping to the trained machine learning model element when user feedback from a similar entity extract from a previously processed digital invoice exists to produce a processed digital invoice with a user feedback element inconsistent with the potential mapping to the trained machine learning model element. The processed digital invoice is delivered to an accounting module for final disposition of the digital invoice.

公开(公告)号：US11954739B2

公开(公告)日：2024-04-09

申请号：US17555312

申请日：2021-12-17

申请人： AppZen, Inc.

发明人： Kunal Verma ,  Anant D. Kale

IPC分类号： G06Q40/00 ,  G06F16/2453 ,  G06F16/2455 ,  G06F40/30 ,  G06N5/047 ,  G06N7/01 ,  G06N20/00 ,  G06N20/10 ,  G06Q40/12

CPC分类号： G06Q40/12 ,  G06F16/24542 ,  G06F16/24564 ,  G06F40/30 ,  G06N5/047 ,  G06N7/01 ,  G06N20/00 ,  G06N20/10

摘要： In one aspect a computerized method for detecting anomalies in expense reports of an enterprise includes the step of implementing a semantic analysis algorithm on an expense report data submitted by an employee, wherein the expense report data is provided in a computer-readable format. The method includes the step of, with one or more machine learning algorithms, detecting an anomaly in expense report data. The method includes the step of obtaining an augmentation of the expense report data with a set of web scale data. The method includes the step of verifying receipts associated with an expense report. The method includes the step of determining that the employee or any employee has previously claimed an expense in the expense report data. The method includes the step of identifying an inappropriate expense in the expense report data.

公开(公告)号：US20210248365A1

公开(公告)日：2021-08-12

申请号：US16785427

申请日：2020-02-07

申请人： AppZen, Inc.

发明人： Edris NADERAN ,  Thomas James WHITE ,  Deepti CHAFEKAR ,  Debashish PANIGRAHI ,  Kunal VERMA ,  Snigdha PUROHIT

IPC分类号： G06K9/00 ,  G06K9/62

摘要： A computer implemented method includes receiving a document with line item textual entries and an attachment containing images of different objects characterizing different transactions. The images of the different objects are split into individual image objects. Attributes from the individual image objects are extracted. The line item textual entries are matched with the individual image objects to form matched image objects. The matched image objects include ambiguous matches with multiple individual image objects assigned to a single line item textual entry or a single individual image object assigned to multiple line item textual entries. An assignment model is applied to resolve the ambiguous matches. The assignment model defines priority constraints, assigns pairs of line item textual entries and individual image objects that meet highest priority constraints, removes highest priority constraints when ambiguous matches remain, and repeats these operations until no ambiguous matches remain. One-to-one matches of line item textual entries and individual image objects are returned.

公开(公告)号：US20220114676A1

公开(公告)日：2022-04-14

申请号：US17555312

申请日：2021-12-17

申请人： AppZen, Inc.

发明人： Kunal VERMA ,  Anant D. KALE

IPC分类号： G06Q40/00 ,  G06N7/00 ,  G06N5/04 ,  G06N20/00 ,  G06F16/2455 ,  G06F16/2453 ,  G06F40/30 ,  G06N20/10

摘要： In one aspect a computerized method for detecting anomalies in expense reports of an enterprise includes the step of implementing a semantic analysis algorithm on an expense report data submitted by an employee, wherein the expense report data is provided in a computer-readable format. The method includes the step of, with one or more machine learning algorithms, detecting an anomaly in expense report data. The method includes the step of obtaining an augmentation of the expense report data with a set of web scale data. The method includes the step of verifying receipts associated with an expense report. The method includes the step of determining that the employee or any employee has previously claimed an expense in the expense report data. The method includes the step of identifying an inappropriate expense in the expense report data.

公开(公告)号：US11126838B2

公开(公告)日：2021-09-21

申请号：US16785427

申请日：2020-02-07

申请人： AppZen, Inc.

发明人： Edris Naderan ,  Thomas James White ,  Deepti Chafekar ,  Debashish Panigrahi ,  Kunal Verma ,  Snigdha Purohit

IPC分类号： G06K9/00 ,  G06K9/62

摘要： A computer implemented method includes receiving a document with line item textual entries and an attachment containing images of different objects characterizing different transactions. The images of the different objects are split into individual image objects. Attributes from the individual image objects are extracted. The line item textual entries are matched with the individual image objects to form matched image objects. The matched image objects include ambiguous matches with multiple individual image objects assigned to a single line item textual entry or a single individual image object assigned to multiple line item textual entries. An assignment model is applied to resolve the ambiguous matches. The assignment model defines priority constraints, assigns pairs of line item textual entries and individual image objects that meet highest priority constraints, removes highest priority constraints when ambiguous matches remain, and repeats these operations until no ambiguous matches remain. One-to-one matches of line item textual entries and individual image objects are returned.