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
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
• • 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
  • G06Q20/00—Payment architectures, schemes or protocols
  • G06Q20/04—Payment circuits
  • G06Q20/047—Payment circuits using payment protocols involving electronic receipts
• • 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
  • G06Q20/00—Payment architectures, schemes or protocols
  • G06Q20/38—Payment protocols; Details thereof
  • G06Q20/40—Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
  • G06Q20/401—Transaction verification
• • 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
  • G06Q40/12—Accounting
  • G06Q40/123—Tax preparation or submission
• • 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
  • G06V30/14—Image acquisition
  • G06V30/148—Segmentation of character regions
  • G06V30/153—Segmentation of character regions using recognition of characters or words
• • 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
  • 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
  • G06Q20/00—Payment architectures, schemes or protocols
  • G06Q20/38—Payment protocols; Details thereof
  • G06Q20/389—Keeping log of transactions for guaranteeing non-repudiation of a transaction
• • 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

• the present disclosure relates generally to analyzing electronic documents, and more particularly to completing electronic documents with missing or unclear data.
• 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).
• a purchase order may be generated for the customer.
• the purchase order provides evidence of the order such as, for example, a purchase price, goods and/or services ordered, and the like.
• an invoice for the order may be generated. While the purchase order is usually used to indicate which products are requested and an estimate or offering for the price, the invoice is usually used to indicate which products were actually provided and the final price for the products. Frequently, the purchase price as demonstrated by the invoice for the order is different from the purchase price as demonstrated by the purchase order. As an example, if a guest at a hotel initially orders a 3-night stay but ends up staying a fourth night, the total price of the purchase order may reflect a different total price than that of the subsequent invoice.
• existing image recognition solutions may be unable to accurately identify some or all special characters (e.g., "!,” “@,” “#,” “$,” “ ⁇ ,” “%,” “&,” etc.). As an example, some existing image recognition solutions may inaccurately identify a dash included in a scanned receipt as the number “1 .” As another example, 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.
• the method comprises: analyzing the electronic document to determine at least one transaction parameter, wherein the electronic document includes at least partially unstructured data; creating a template for the electronic document, wherein the template is a structured dataset including at least one data element, wherein each transaction parameter is a value of one of the at least one data element; retrieving, based on the template, complementary data for one of the at least one data element when the data element is incomplete; generating, based on the complementary data and the incomplete data element, a complete data element; and associating the complete data element with the electronic document.
• 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 for completing an electronic document, the process comprising: analyzing the electronic document to determine at least one transaction parameter, wherein the electronic document includes at least partially unstructured data; creating a template for the electronic document, wherein the template is a structured dataset including at least one data element, wherein each transaction parameter is a value of one of the at least one data element; retrieving, based on the template, complementary data for one of the at least one data element when the data element is incomplete; generating, based on the complementary data and the incomplete data element, a complete data element; and associating the complete data element with the electronic document.
• Certain embodiments disclosed herein also include a system for completing an electronic document.
• the system comprises: a processing circuitry; and a memory, the memory containing instructions that, when executed by the processing circuitry, configure the system to: analyze the electronic document to determine at least one transaction parameter, wherein the electronic document includes at least partially unstructured data; create a template for the electronic document, wherein the template is a structured dataset including at least one data element, wherein each transaction parameter is a value of one of the at least one data element; retrieve, based on the template, complementary data for one of the at least one data element when the data element is incomplete; generate, based on the complementary data and the incomplete data element, a complete data element; and associate the complete data element with the electronic document.
• Figure 1 is a network diagram utilized to describe the various disclosed embodiments.
• Figure 2 is a schematic diagram of a validation system according to an embodiment.
• Figure 3 is a flowchart illustrating a method for completing an electronic document according to an embodiment.
• Figure 4 is a flowchart illustrating a method for creating a dataset based on at least one electronic document according to an embodiment.
• the various disclosed embodiments include a method and system for completing an electronic document including data of a transaction.
• a dataset is created based on the electronic document.
• a template of transaction attributes is created based on the dataset.
• one or more data sources are searched for complementary data for the incomplete data element.
• a complete data element is generated. The complete data element is associated with the electronic document.
• the disclosed embodiments allow for automatic completion of, for example, documents providing evidentiary proof of transactions. More specifically, the disclosed embodiments include providing structured dataset templates for electronic documents, thereby allowing for more accurately identifying incomplete data elements in electronic documents that are unstructured, semi-structured, or otherwise lacking a known structure. For example, a price that appears smudged in an image of an invoice may be identified based on data in a "price" field of a structured template, and complementary price data may be used to generate a complete price data element.
• Fig. 1 shows an example network diagram 100 utilized to describe the various disclosed embodiments.
• a complete data generator 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 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, 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 complete data generator 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, and the like.
• Data included in each electronic document may be structured, semi-structured, unstructured, or a
• the database 140 may store complete data elements generated by the complete data generator 120 and associated electronic documents.
• the data sources 150 store at least potential complementary data related to transactions.
• the data sources 150 may include, but are not limited to, servers or devices of merchants, tax authority servers, accounting servers, a database associated with an enterprise, and the like.
• the complete data generator 120 is configured to create a template based on transaction parameters identified using machine vision of an electronic document indicating information related to a transaction.
• the complete data generator 120 may be configured to retrieve the electronic document from, e.g., the enterprise system 130. Based on the created template, the complete data generator 120 is configured to determine whether any of the data elements in the template are incomplete and, if so, to search for complementary data to be utilized for generating complete data elements.
• the complete data generator 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 complete data generator 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 complete data generator 120 may therefore include or be communicatively connected to a recognition processor (e.g., the recognition processor 235, Fig. 2).
• the complete data generator 120 is configured to analyze the created datasets to identify transaction parameters related to transactions indicated in the electronic documents.
• the complete data generator 120 is configured to create a template based on the created dataset for an electronic document.
• Each template is a structured dataset including the identified transaction parameters for a transaction. More specifically, each template may include a data element in each field, where each transaction parameter is a value of the data element.
• Using structured templates for completing electronic documents allows for more efficient and accurate completion than, for example, by utilizing unstructured data. Specifically, incomplete data elements may be identified with respect to fields of the structured templates, and complementary data may be searched with respect to fields that are missing data.
• the complete data generator 120 is configured to determine whether any of the data elements are incomplete.
• a data element may be incomplete if the data element is unclear or at least partially missing.
• the complete data generator 120 is configured to search in one or more of the data sources for complementary data. The search may be based on the values of the incomplete data elements, the respective fields of the template, other data in the template, or a combination thereof. Based on the complementary data found during the search and the incomplete data element, the complete data generator 120 is configured to generate a complete data element.
• the complete data generator 120 is configured to associate the complete data element with the electronic document.
• the complete data generator 120 may be further configured to store the complete data element in the template.
• the complete data generator 120 may be configured to generate a notification indicating the generated complete data element.
• Fig. 2 is an example schematic diagram of the complete data generator 120 according to an embodiment.
• the complete data generator 120 includes a processing circuitry 210 coupled to a memory 215, a storage 220, and a network interface 240.
• the complete data generator 120 may include an optical character recognition (OCR) processor 230.
• OCR optical character recognition
• the components of the complete data generator 120 may be communicatively connected via a bus 250.
• the processing circuitry 210 may be realized as one or more hardware logic components and circuits.
• illustrative types of hardware logic components include field programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), Application-specific standard products (ASSPs), system-on-a-chip systems (SOCs), general-purpose microprocessors, microcontrollers, digital signal processors (DSPs), and the like, or any other hardware logic components that can perform calculations or other manipulations of information.
• FPGAs field programmable gate arrays
• ASICs application-specific integrated circuits
• ASSPs Application-specific standard products
• SOCs system-on-a-chip systems
• DSPs digital signal processors
• the memory 215 may be volatile (e.g., RAM, etc.), non-volatile (e.g., ROM, flash memory, etc.), or a combination thereof.
• computer readable instructions to implement one or more embodiments disclosed herein may be stored in the storage 220.
• the memory 215 is configured to store software.
• Software shall be construed broadly to mean any type of instructions, whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise. Instructions may include code (e.g., in source code format, binary code format, executable code format, or any other suitable format of code).
• the instructions when executed by the one or more processors, cause the processing circuitry 210 to perform the various processes described herein. Specifically, the instructions, when executed, cause the processing circuitry 210 to complete electronic documents, as discussed herein.
• the storage 220 may be magnetic storage, optical storage, and the like, and may be realized, for example, as flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVDs), or any other medium which can be used to store the desired information.
• flash memory or other memory technology
• CD-ROM Compact Discs
• DVDs Digital Versatile Disks
• the OCR processor 230 may include, but is not limited to, a feature and/or pattern recognition processor (RP) 235 configured to identify patterns, features, or both, in unstructured data sets. Specifically, in an embodiment, the OCR processor 230 is configured to identify at least characters in the unstructured data. The identified characters may be utilized to create a dataset including data required for verification of a request.
• the network interface 240 allows the complete data generator 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 completing an electronic document according to an embodiment.
• the method may be performed by a complete data generator (e.g., the complete data generator 120).
• the electronic document may be an electronic receipt (e.g., an image showing a scanned receipt).
• a dataset is created based on the electronic document including information related to a transaction.
• the electronic document 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.
• S310 may further include analyzing the 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
• analyzing the dataset may include, but is not limited to, determining transaction parameters such as, but not limited to, at least one entity identifier (e.g., a consumer enterprise identifier, a merchant enterprise identifier, or both), information related to the transaction (e.g., a date, a time, a price, a type of good or service sold, etc.), or both.
• entity identifier e.g., a consumer enterprise identifier, a merchant enterprise identifier, or both
• information related to the transaction e.g., a date, a time, a price, a type of good or service sold, etc.
• analyzing the dataset may also include identifying the transaction based on the first dataset.
• a template is created based on the dataset.
• the template may be, but is not limited to, a data structure including a plurality of fields.
• the fields may include the identified transaction parameters.
• the fields may be predefined.
• Creating templates from electronic documents allows for faster processing due to the structured nature of the created templates. For example, query and manipulation operations may be performed more efficiently on structured datasets than on datasets lacking such structure. Further, identifying incomplete date elements in structured templates may result in more accurate identification of incomplete data elements based on unstructured data. Additionally, searching for complementary data based on incomplete data elements identified in structured templates may be performed with respect to fields of the templates, thereby more accurately identifying complementary data.
• a data element may be incomplete if the data element is unclear or is missing in whole or in part. For example, if a supplier ID data element is missing from the "Supplier ID" field of the template, the data element may be determined to be incomplete. As another example, if a value of a supplier name data element is unclear, the data element may be determined to be incomplete.
• Each data element defines a unit of data stored in the field of the created template.
• Whether a data element is incomplete may be determined based on one or more completeness rules and the value of the data unit in the template that is defined by the data element.
• the completeness rules may vary depending on the field of the data element.
• Example data elements include, but are not limited to, supplier identifier, time pointer, VAT amount, price, and the like.
• complementary data is retrieved for the incomplete data element.
• the complementary data may be retrieved based on the value of the incomplete data element, the field of the template in which the value is stored, other data in the template, or a combination thereof.
• S350 may include comparing the value of the incomplete data element to values of one or more potential complementary data elements.
• the complementary data may include, but is not limited to, a character, a series of characters, a word, a sentence, a portion of a sentence, a numerical value, and the like.
• the supplier ID number may be retrieved and utilized as complementary data.
• the complementary data supplier ID number may be retrieved based on a partially missing supplier ID number and previous electronic documents including the full supplier ID number.
• S360 based on the incomplete data element and the complementary data, a complete data element is generated.
• S360 may further include generating a notification indicating the complete data element.
• the complete data element is associated with the electronic document.
• S370 may further include storing the value of the complete data element in the respective field of the incomplete data element in the template.
• Fig. 4 is an example flowchart S310 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 electronic document is analyzed.
• the analysis may include, but is not limited to, using optical character recognition (OCR) to determine characters in the electronic document.
• OCR optical character recognition
• 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 phrase "at least one of” followed by a listing of items means that any of the listed items can be utilized individually, or any combination of two or more of the listed items can be utilized. For example, if a system is described as including "at least one of A, B, and C," the system can include A alone; B alone; C alone; A and B in combination; B and C in combination; A and C in combination; or A, B, and C in combination.
• 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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• 2017
  • 2017-08-03 CN CN201780060456.9A patent/CN109791537A/zh active Pending
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