WO2018027051A1 - Système et procédé d'analyse améliorée de documents électroniques non structurés concernant un voyage - Google Patents

Système et procédé d'analyse améliorée de documents électroniques non structurés concernant un voyage Download PDF

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Publication number
WO2018027051A1
WO2018027051A1 PCT/US2017/045333 US2017045333W WO2018027051A1 WO 2018027051 A1 WO2018027051 A1 WO 2018027051A1 US 2017045333 W US2017045333 W US 2017045333W WO 2018027051 A1 WO2018027051 A1 WO 2018027051A1
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WO
WIPO (PCT)
Prior art keywords
electronic document
refund amount
vat
data
template
Prior art date
Application number
PCT/US2017/045333
Other languages
English (en)
Inventor
Noam Guzman
Isaac SAFT
Original Assignee
Vatbox, Ltd.
M&B IP Analysts, LLC
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from US15/361,934 external-priority patent/US20170154385A1/en
Application filed by Vatbox, Ltd., M&B IP Analysts, LLC filed Critical Vatbox, Ltd.
Priority to GB1902675.6A priority Critical patent/GB2571636A/en
Publication of WO2018027051A1 publication Critical patent/WO2018027051A1/fr

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/04—Billing or invoicing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/10—Tax strategies
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/14—Travel agencies
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
    • G06V10/75—Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries
    • G06V10/751—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
    • G06V30/10—Character recognition
    • G06V30/19—Recognition using electronic means
    • G06V30/19007—Matching; Proximity measures
    • G06V30/19013—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
    • G06V30/40—Document-oriented image-based pattern recognition
    • G06V30/41—Analysis of document content
    • G06V30/412—Layout analysis of documents structured with printed lines or input boxes, e.g. business forms or tables
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
    • G06V30/10—Character recognition

Definitions

  • a customer may input credit card information pursuant to a payment, and the merchant may verify the credit card information in real-time before authorizing the sale. The verification typically includes determining whether the provided information is valid (i.e., that a credit card number, expiration date, PIN code, and/or customer name match known information).
  • existing image recognition solutions may be unable to accurately identify some or all special characters (e.g., "!,” “@,” “#,” “$,” “ ⁇ ,” “%,” “&,” etc.).
  • some existing image recognition solutions may inaccurately identify a dash included in a scanned receipt as the number “1 .”
  • some existing image recognition solutions cannot identify special characters such as the dollar sign, the yen symbol, etc.
  • such solutions may face challenges in preparing recognized information for subsequent use. Specifically, many such solutions either produce output in an unstructured format, or can only produce structured output if the input electronic documents are specifically formatted for recognition by an image recognition system. The resulting unstructured output typically cannot be processed efficiently. In particular, such unstructured output may contain duplicates, and may include data that requires subsequent processing prior to use.
  • MAPs Mileage allowance payments
  • VAT value- added tax
  • employees of businesses offering MAPs submit expense reports indicating information related to travel expenses in order to provide the information needed to refund the employee and seek any applicable VAT refunds.
  • the refund requirements vary based on country regulations, car types, and the like.
  • Certain embodiments disclosed herein include a method for improved analysis of travel-indicating unstructured electronic documents.
  • the method comprises: determining, based on data of a first electronic document, a mileage value-added tax (VAT) refund amount, wherein the first electronic document indicates at least one travel transaction; analyzing at least one second electronic document to determine at least one transaction parameter of each second electronic document, wherein each second electronic document includes at least partially unstructured data; creating a template for each of the at least one second electronic document, wherein each template is a structured dataset including the at least one transaction parameter determined for the respective electronic document; determining, based on the created at least one template, a fuel VAT refund amount; and determining, based on the mileage VAT refund amount and the fuel VAT refund amount, an entitled VAT refund amount.
  • VAT mileage value-added tax
  • Certain embodiments disclosed herein also include a non-transitory computer readable medium having stored thereon instructions for causing a processing circuitry to perform a process, the process comprising: determining, based on data of a first electronic document, a mileage value-added tax (VAT) refund amount, wherein the first electronic document indicates at least one travel transaction; analyzing at least one second electronic document to determine at least one transaction parameter of each second electronic document, wherein each second electronic document includes at least partially unstructured data; creating a template for each of the at least one second electronic document, wherein each template is a structured dataset including the at least one transaction parameter determined for the respective electronic document; determining, based on the created at least one template, a fuel VAT refund amount; and determining, based on the mileage VAT refund amount and the fuel VAT refund amount, an entitled VAT refund amount.
  • VAT mileage value-added tax
  • Certain embodiments disclosed herein also include a system for improved analysis of travel-indicating unstructured electronic documents.
  • the system comprises: a processing circuitry; and a memory, the memory containing instructions that, when executed by the processing circuitry, configure the system to: determine, based on data of a first electronic document, a mileage value-added tax (VAT) refund amount, wherein the first electronic document indicates at least one travel transaction; analyze at least one second electronic document to determine at least one transaction parameter of each second electronic document, wherein each second electronic document includes at least partially unstructured data; create a template for each of the at least one second electronic document, wherein each template is a structured dataset including the at least one transaction parameter determined for the respective electronic document; determine, based on the created at least one template, a fuel VAT refund amount; and determine, based on the mileage VAT refund amount and the fuel VAT refund amount, an entitled VAT refund amount.
  • VAT mileage value-added tax
  • Figure 1 is a network diagram utilized to describe the various disclosed embodiments.
  • Figure 3 is a flowchart illustrating a method for improved VAT refund analysis of travel- indicating unstructured electronic documents according to an embodiment.
  • the various disclosed embodiments include a method and system for improved analysis of travel-indicating unstructured electronic documents.
  • a mileage VAT refund amount is determined based on data extracted from a first expense report electronic document indicating information of a travel transaction.
  • a dataset is created for each of one or more second evidencing electronic documents indicating information evidencing travel transactions.
  • a template of transaction attributes is created based on each evidencing electronic document dataset.
  • a fuel VAT refund amount is determined for each evidencing electronic document. Based on the fuel VAT refund amount of the matching evidencing electronic document, the mileage VAT refund amount of the expense report electronic document, and one or more refund rules, it is determined which refund amount is valid. An electronic VAT reclaim may be generated based on the determined valid refund amount.
  • the disclosed embodiments allow for improved analysis of refunds based on travel transactions indicated in unstructured electronic documents. More specifically, the disclosed embodiments include providing structured dataset templates for electronic documents, thereby allowing for analyzing and comparing data of electronic documents that is unstructured, semi-structured, or otherwise lacking a known structure. Moreover, the structured templates may be used for more efficient comparison of portions of the unstructured electronic document for purposes of determining refunds. For example, the disclosed embodiments may include effectively analyzing images of scanned transaction evidencing documents such as receipts, thereby allowing for more accurate recognition of portions of the unstructured electronic documents indicating specific travel related expenses (e.g., fuel expenses) and, consequently, more accurate determination of refund eligibility.
  • specific travel related expenses e.g., fuel expenses
  • Fig. 1 shows an example network diagram 100 utilized to describe the various disclosed embodiments.
  • a travel document analyzer 120 an enterprise system 130, a database 140, and a plurality of data sources 150-1 through 150-N (hereinafter referred to individually as a data source 150 and collectively as data sources 150, merely for simplicity purposes), are communicatively connected via a network 1 10.
  • the network 1 10 may be, but is not limited to, a wireless, cellular or wired network, a local area network (LAN), a wide area network (WAN), a metro area network (MAN), the Internet, the worldwide web (WWW), similar networks, and any combination thereof.
  • LAN local area network
  • WAN wide area network
  • MAN metro area network
  • WWW worldwide web
  • the enterprise system 130 is associated with an enterprise, and may store data related to purchases made by the enterprise or representatives of the enterprise as well as data related to the enterprise itself.
  • the enterprise system 130 may further store data related to, for example, employee expense reports and receipts for expenses that may have been subject to value-added taxes (VATs).
  • VATs value-added taxes
  • the enterprise may be, but is not limited to, a business whose employees may purchase goods and services subject to VAT taxes while abroad.
  • the enterprise system 130 may be, but is not limited to, a server, a database, an enterprise resource planning system, a customer relationship management system, or any other system storing relevant data.
  • the data stored by the enterprise system 130 may include, but is not limited to, electronic documents (e.g., an image file showing, for example, a scan of an invoice, a text file, a spreadsheet file, etc.). Each electronic document may show, e.g., an invoice, a tax receipt, a purchase number record, a VAT reclaim request, an expense report, and the like. Data included in each electronic document may be structured, semi-structured, unstructured, or a combination thereof. The structured or semi-structured data may be in a format that is not recognized by the travel document analyzer 120 and, therefore, may be treated as unstructured data.
  • electronic documents e.g., an image file showing, for example, a scan of an invoice, a text file, a spreadsheet file, etc.
  • Each electronic document may show, e.g., an invoice, a tax receipt, a purchase number record, a VAT reclaim request, an expense report, and the like.
  • Data included in each electronic document may be structured, semi-structure
  • the expense report electronic document may be, for example, submitted by an employee of an enterprise to obtain a refund for travel expenses occurred in the course of his or her job.
  • the expense report electronic document may be a structured electronic document having defined fields for specific categories of information that are filled manually by the employee.
  • the expense report electronic document may be an unstructured electronic document such as an image of a scanned expense report form, and the travel document analyzer 120 may be configured to create a structured template for the expense report electronic document as described further herein below. Creating a template as described herein for an unstructured expense report allows for more efficient utilization of data therein and more accurate identification of specific data than, for example, via machine imaging alone.
  • the travel document analyzer 120 is configured to determine a mileage VAT refund amount for the travel transaction of the expense report electronic document.
  • the determination of the mileage VAT refund amount may be further based on one or more mileage VAT refund calculation rules, vehicle type data (e.g., a known engine size of the make and model indicated in the expense report), or both.
  • the mileage VAT refund calculation rules may be retrieved from, e.g., one of the data sources 150, for example a server of a tax authority of the country in which the travel transactions were made.
  • the mileage VAT refund rules may indicate that 20% of every 18 pence may be reclaimed for each mile traveled such that a mileage VAT refund of 54 pounds is determined.
  • the travel document analyzer 120 may be further configured to verify that each determined evidencing electronic document is eligible for use as a VAT reclaim receipt, for example, based on the seller indicated in a "seller" field of the created template and a list of registered VAT merchants for the country indicated in a "location" field of the created template. This allows for ensuring that each transaction submitted for VAT reclaim has a corresponding evidencing electronic document retained to provide subsequent evidence of the transaction. The verification may be further made with respect to a vehicle identifier indicated in the expense report electronic document. Specifically, in an example implementation, only travel transactions to refuel certain vehicles (e.g., vehicles owned by an enterprise, vehicles registered to a tax authority, etc.) may be eligible for refunds.
  • vehicles e.g., vehicles owned by an enterprise, vehicles registered to a tax authority, etc.
  • Determining the matching evidencing electronic documents may include comparing data in the created templates to corresponding data of the expense report electronic document. For example, an evidencing electronic document may match the expense report electronic document if a time of a travel transaction indicated in the expense report matches data in a "time" field of the created template for the evidencing electronic document and a vehicle type indicated in the expense report is compatible with a "fuel type” field of the created template. As another example, an evidencing electronic document may match the expense report electronic document if a transaction identifier indicated in a "transaction ID" field of the created template for the electronic document matches one of the transaction identifiers indicated in the expense report.
  • the travel document analyzer 120 is configured to create datasets based on electronic documents including data at least partially lacking a known structure (e.g., unstructured data, semi-structured data, or structured data having an unknown structure). To this end, the travel document analyzer 120 may be further configured to utilize optical character recognition (OCR) or other image processing to determine data in the electronic document.
  • OCR optical character recognition
  • the travel document analyzer 120 may therefore include or be communicatively connected to a recognition processor (e.g., the recognition processor 235, Fig. 2).
  • the travel document analyzer 120 is configured to analyze the created datasets to identify transaction parameters related to transactions indicated in the electronic documents and to create templates based on the created datasets.
  • Each template is a structured dataset including the identified transaction parameters for a transaction.
  • the travel document analyzer 120 is configured to determine a total mileage VAT refund amount and a total fuel VAT refund amount for a period of time.
  • the travel document analyzer 120 is further configured to compare the total mileage VAT refund amount to the total fuel VAT refund amount and to determine, based on the comparison, which VAT refund amount is entitled to a refund.
  • the determination of entitlement may be based on one or more regulatory rules stored in, for example, one of the data sources 150 associated with a regulatory authority. In an example implementation, the lower amount between the total fuel VAT refund amount and the total mileage VAT refund amount is entitled to a refund.
  • the travel document analyzer 120 may be further configured to generate, based on the VAT refund amount determined to be entitled to a refund, an electronic VAT reclaim.
  • the electronic VAT reclaim may be, for example, an electronic document including the entitled VAT refund amount and associated with the matching evidencing electronic documents for the expense report.
  • the travel document analyzer 120 may also be configured to send the generated electronic VAT reclaim to a tax authority server, e.g., one of the data sources 150.
  • the travel document analyzer 120 may also be configured to validate each analyzed unstructured electronic document based on its respective template.
  • the validation may include, but is not limited to, determining whether each the electronic document is complete and accurate.
  • Each electronic document may be determined to be complete if, for example, one or more predetermined reporting requirements is met (e.g., for a VAT, reporting requirements may include requiring each of type of goods or services purchased, country of seller, country of buyer, and amount of VAT paid).
  • predetermined reporting requirements e.g., for a VAT, reporting requirements may include requiring each of type of goods or services purchased, country of seller, country of buyer, and amount of VAT paid).
  • Each electronic document may be determined to be accurate based on data stored in at least one external source.
  • the at least one electronic source may include, but is not limited to, the enterprise system 130, one or more of the web sources 150, the database 140, or a combination thereof. Examples of determining accuracy follow.
  • the network interface 240 allows the travel document analyzer 120 to communicate with the enterprise system 130, the database 140, the data sources 150, or a combination of, for the purpose of, for example, collecting metadata, retrieving data, storing data, and the like.
  • Fig. 3 is an example flowchart 300 illustrating a method for improved VAT refund analysis of travel-indicating unstructured electronic documents according to an embodiment.
  • the method may be performed by the travel document analyzer 120.
  • a first expense report electronic document is received.
  • the expense report electronic document indicates information related to one or more travel transactions such as, for example, transactions for purchases of fuel.
  • the expense report indicates at least a mileage (i.e., a distance traveled), a type of vehicle, and identifying information for the transaction.
  • the expense report electronic document may be a structured document from which data is extracted with respect to particular fields, or may be an unstructured electronic document such as an image of a scanned expense report form.
  • extracting the data may further include creating a template for the expense report electronic document as described further herein 4.
  • datasets for one or more second evidencing electronic documents are created.
  • Each dataset is created based on an evidencing electronic document that may include, but is not limited to, unstructured data, semi-structured data, structured data with structure that is unanticipated or unannounced, or a combination thereof.
  • the evidencing electronic documents may be images of scanned receipts of gasoline purchases.
  • S340 may further include analyzing each evidencing electronic document using optical character recognition (OCR) to determine data in the electronic document, identifying key fields in the data, identifying values in the data, or a combination thereof.
  • OCR optical character recognition
  • the fuel VAT refund amount may be a total fuel VAT refund amount determined based on fuel costs in each evidencing electronic document indicating information related to a corresponding travel transaction of the expense report electronic document.
  • S370 may include comparing data of the evidencing electronic documents to the expense report electronic document to determine one or more matching evidencing electronic documents, where each matching evidencing electronic document provides evidence of one of the travel transactions indicated in the expense report electronic document. The comparison may be with respect to, for example, particular fields of the created template including transaction identifying information (e.g., transaction identifier number, price, fuel type purchased, combinations thereof, etc.).
  • the fuel VAT refund amount may be a total fuel VAT refund amount for transactions made during the period of time of the mileage VAT refund amount.
  • an entitled VAT refund amount is determined based on the determined mileage and fuel VAT refund amounts. The determination may be based on, for example, travel VAT refund rules for a country in which the travel transactions indicated in the expense report were made. In an embodiment, S380 includes comparing the mileage VAT refund amount to the fuel VAT refund amount, where the determination is based on the comparison. For example, the lower VAT refund amount between the mileage and fuel VAT refund amounts may be determined to be the entitled VAT refund amount.
  • Fig. 4 is an example flowchart S340 illustrating a method for creating a dataset based on an electronic document according to an embodiment.
  • the electronic document is obtained.
  • Obtaining the electronic document may include, but is not limited to, receiving the electronic document (e.g., receiving a scanned image) or retrieving the electronic document (e.g., retrieving the electronic document from a consumer enterprise system, a merchant enterprise system, or a database).
  • the key field may include, but are not limited to, merchant's name and address, date, currency, good or service sold, a transaction identifier, an invoice number, and so on.
  • An electronic document may include unnecessary details that would not be considered to be key values. As an example, a logo of the merchant may not be required and, thus, is not a key value.
  • a list of key fields may be predefined, and pieces of data that may match the key fields are extracted. Then, a cleaning process is performed to ensure that the information is accurately presented. For example, if the OCR would result in a data presented as "121 1212005", the cleaning process will convert this data to 12/12/2005. As another example, if a name is presented as "Mo$den”, this will change to "Mosden”.
  • the cleaning process may be performed using external information resources, such as dictionaries, calendars, and the like.
  • S430 results in a complete set of the predefined key fields and their respective values.
  • a structured dataset is generated.
  • the generated dataset includes the identified key fields and values.
  • any reference to an element herein using a designation such as "first,” “second,” and so forth does not generally limit the quantity or order of those elements. Rather, these designations are generally used herein as a convenient method of distinguishing between two or more elements or instances of an element. Thus, a reference to first and second elements does not mean that only two elements may be employed there or that the first element must precede the second element in some manner. Also, unless stated otherwise, a set of elements comprises one or more elements.
  • the various embodiments disclosed herein can be implemented as hardware, firmware, software, or any combination thereof.
  • the software is preferably implemented as an application program tangibly embodied on a program storage unit or computer readable medium consisting of parts, or of certain devices and/or a combination of devices.
  • the application program may be uploaded to, and executed by, a machine comprising any suitable architecture.
  • the machine is implemented on a computer platform having hardware such as one or more central processing units (“CPUs"), a memory, and input/output interfaces.
  • CPUs central processing units
  • the computer platform may also include an operating system and microinstruction code.
  • a non-transitory computer readable medium is any computer readable medium except for a transitory propagating signal.

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Abstract

Cette invention concerne un système et un procédé d'analyse de remboursement de documents électroniques non structurés concernant un voyage Le procédé comprend : la détermination, sur la base de données d'un premier document électronique, d'un montant de remboursement de taxe sur la valeur ajoutée (TVA) de kilométrage, le premier document électronique concernant au moins une transaction de voyage; l'analyse d'au moins un second document électronique pour déterminer au moins un paramètre de transaction de chaque second document électronique, chaque second document électronique comprenant des données au moins partiellement non structurées; la création d'un modèle pour chacun dudit/desdits second(s) document(s) électronique(s), chaque modèle étant un ensemble de données structuré comprenant ledit/lesdits paramètre(s) de transaction déterminé(s) pour le document électronique respectif; la détermination, sur la base dudit/desdits modèle(s) créé(s), d'un montant de remboursement de TVA de carburant; et la détermination, sur la base du montant de remboursement de TVA de kilométrage et du montant de remboursement de TVA de carburant, d'un montant de remboursement de TVA dû.
PCT/US2017/045333 2016-08-05 2017-08-03 Système et procédé d'analyse améliorée de documents électroniques non structurés concernant un voyage WO2018027051A1 (fr)

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GB1902675.6A GB2571636A (en) 2016-08-05 2017-08-03 System and method for improved analysis of travel-indicating unstructured electronic documents

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US201662371235P 2016-08-05 2016-08-05
US62/371,235 2016-08-05
US15/361,934 US20170154385A1 (en) 2015-11-29 2016-11-28 System and method for automatic validation
US15/361,934 2016-11-28

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Citations (5)

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US20030088562A1 (en) * 2000-12-28 2003-05-08 Craig Dillon System and method for obtaining keyword descriptions of records from a large database
US7299408B1 (en) * 2002-04-01 2007-11-20 Fannie Mae Electronic document validation
US20100161616A1 (en) * 2008-12-16 2010-06-24 Carol Mitchell Systems and methods for coupling structured content with unstructured content
US20140153830A1 (en) * 2009-02-10 2014-06-05 Kofax, Inc. Systems, methods and computer program products for processing financial documents
US20150242832A1 (en) * 2014-02-21 2015-08-27 Mastercard International Incorporated System and method for recovering refundable taxes

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030088562A1 (en) * 2000-12-28 2003-05-08 Craig Dillon System and method for obtaining keyword descriptions of records from a large database
US7299408B1 (en) * 2002-04-01 2007-11-20 Fannie Mae Electronic document validation
US20100161616A1 (en) * 2008-12-16 2010-06-24 Carol Mitchell Systems and methods for coupling structured content with unstructured content
US20140153830A1 (en) * 2009-02-10 2014-06-05 Kofax, Inc. Systems, methods and computer program products for processing financial documents
US20150242832A1 (en) * 2014-02-21 2015-08-27 Mastercard International Incorporated System and method for recovering refundable taxes

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