Extract data from a true PDF page

The system may perform a method comprising analyzing metadata of a text layer of a page of a first pdf document to determine that the pdf document is a first true pdf document; receiving the first true pdf document, in response to the first pdf document being the first true pdf document; receiving a selection of a field including first data to be extracted from the first true pdf document; displaying the first data; creating a template including the coordinates corresponding to the selected field and the first data of the first true pdf document; and extracting from an accessible text layer of a second true pdf document, second data based on the template from the first true pdf document.

TECHNICAL FIELD

This disclosure generally relates to extracting text from a portable document format (PDF) document, and more particularly, to a system and method for using the position of data to extract the data from a true PDF document, without using optical character recognition (OCR).

BACKGROUND

PDF documents may be categorized in three different types. The types may include true PDF pages, image-only PDF pages (or scanned PDF pages) and searchable PDF pages. The PDF category may depend on the way the file was originally created. The way the document was originally created also defines whether the content of the PDF (e.g., text, images, tables) can be accessed or whether the content may be inaccessible (or “locked”) in an image of the page. The problem with image-only PDF pages is that the content cannot be extracted from the pages. The problem with the searchable PDF pages is that the content is often extracted with an OCR tool that may not be reliable. As such, a need exists to identify and extract content from true PDF pages.

SUMMARY

In various embodiments, the system may perform a method comprising analyzing, using a processor, metadata of a text layer of a page of a first pdf document to determine that the pdf document is a first true pdf document; receiving, at the processor, the first true pdf document, in response to the first pdf document being the first true pdf document; receiving, at the processor, a selection of a field including first data to be extracted from the first true pdf document; displaying, by the processor, the first data; creating, at the processor, a template including the coordinates corresponding to the selected field and the first data of the first true pdf document; and extracting, using the processor, from an accessible text layer of a second true pdf document, second data based on the template from the first true pdf document.

In various embodiments, the analyzing metadata of the text layer of the page of the first pdf document may include determining whether the text layer can be accessed for retrieval of data. The analyzing metadata may include extracting properties of a pdf file. The analyzing metadata may include determining that a hidden-text field of the metadata is set to false. The first data may include at least one of words, text, numbers, characters, symbols, regions, locations or empty spaces. The method may further comprise saving the layout of the template into a file format. The layout may include at least one of coordinates, template, field names, reference field values, region or quadrant information. The layout may be stored in JavaScript Object Notation (JSON) file format.

The receiving the selection of the field may be based on at least one of a selection of text elements or receiving coordinates of a bounding box drawn around the text elements. The receiving the selection of the field may be based on receiving coordinates of a bounding box drawn around text elements, wherein the coordinates are obtained by analyzing a number of pixels from each border of the first true pdf document to the text elements. The extracting from the accessible text layer the second data may include using logic to iterate through the accessible text layer and extracting the second data based on the coordinates. The receiving the selection of the field may be based on receiving coordinates of a bounding box drawn around text elements, and further comprising adjusting, by the processor, the bounding box to confirm the text elements are accurate. The receiving the selection of the field may be based on regex expressions extracting a field value from the field.

The method may further comprise suggesting a replacement value for a field value in the field. The method may further comprise applying a regex to clean up a field value from the field. The method may further comprise identifying a type of a page based on the template. The method may further comprise generating a tabular display of the field. The method may further comprise displaying, using a display screen, a listing of the second data and the coordinates corresponding to the selected field from the first true pdf document or the second true pdf document.

DETAILED DESCRIPTION

In general, as set forth in FIG. 1, in various embodiments, the system may perform a method 100 comprising analyzing, using a processor, metadata of a text layer of a page of a first pdf document to determine that the pdf document is a first true pdf document (step 105); receiving, at the processor, the first true pdf document, in response to the first pdf document being the first true pdf document (step 110); receiving, at the processor, a selection of a field including first data to be extracted from the first true pdf document (step 115); displaying, by the processor, the first data; (step 120); creating, at the processor, a template including the coordinates corresponding to the selected field and the first data of the first true pdf document (step 125); and extracting, using the processor, from an accessible text layer of a second true pdf document, second data based on the template from the first true pdf document (step 130).

The method may reduce the process to extract data from a PDF document to 0.52 seconds per page, whereas the OCR technology may take 4.32 seconds per page (based on a sample size of 23,719 pages). The system may also provide better accuracy and improved success rate (100% accuracy in extracting text) compared to OCR technology (99.5% accuracy, based on sample size of 23,719 pages). The system may also provide a cost savings of $0.002-$0.0003 per page.

This disclosure may describe the process implemented by a system, but the process contemplates being implemented by the system, a tool, an app, a website, a SaaS offering, an algorithm, artificial intelligence, machine learning, expert systems logic, fuzzy text matching, text layer extraction, a human user and/or any combination. With respect to the SaaS offering, third-party applications may connect to the system to perform the extraction process. A sample integration use case may include a user uploading a PDF document to the system and the user receiving an output of text extraction in an XML/j son format.

For more information related to text layer extraction, see U.S. patent application Ser. No. 16/047,346, entitled “System and Method for Automatic Detection and Verification of Optical Character Recognition Data,” filed Jul. 27, 2018 and U.S. patent application Ser. No. 15/922,821 entitled “System and Method for Automatic Detection and Verification of Optical Character Recognition Data,” filed Mar. 15, 2018, the contents of which are herein incorporated by reference in their entirety for all purposes. This disclosure may include other data extraction functions such as, for example, those functions set forth in U.S. Reissue Pat. No. RE45,007; U.S. Pat. Nos. 10,489,644; 8,655,075; 11,232,300 and U.S. Ser. No. 17/217,917, which are hereby incorporated by reference in their entireties for all purposes.

A PDF may include text, images, multimedia elements, web page links, execute JavaScript and include other content. A PDF may include different layers comprising a header, body, xref table and trailer. The header may be in the first line of the PDF file and include a version number of the PDF file format specification used by the document. The body of the PDF document may include objects. The objects may include text, text streams, images, other multimedia elements, etc. The body section may hold all the document's data visible by the user. The xref table may include a cross-reference table. The cross-reference table may include the references to all the objects in the document. The cross-reference table allows random access to objects in the file, so the entire PDF document does not need to be reviewed in order to locate the particular object Each object may be represented by one entry in the cross-reference table, which may be 20 bytes long. To access the cross-reference table, the system may open the PDF with a text editor. The cross-reference table may include four subsections including the object number, the number of objects in the subsection and the object represented by a number. The trailer provides information to the application reading the PDF document about how the application may find the cross-reference table and other special objects. The PDF may include incremental updates by appending objects to the end of the PDF file, without having to rewrite the entire file. Because of this process for updates, changes to a PDF document can be saved quickly.

The system may include a method of determining if a document is a true PDF document. A true PDF document may include a digitally created PDF that may consist of text and/or images. The PDF may be created using software such as Microsoft® Word®, Excel® or via the “print” function within a software application (virtual printer). To determine if the document is a true PDF, in various embodiments, the system may analyze the metadata of the PDF file. The metadata may be stored as part of the PDF file properties. The system may read the metadata by extracting the file properties of a given PDF file, wherein the PDF file properties may include, for example, the number of pages, dimensions of pages, details of images, text elements, etc. The system may also review a metadata field called hidden-text. If the value of this hidden-text field is set to false, the system marks the page as a true pdf page.

In various embodiments, and as set forth in FIG. 2, to define a template, the system may include a tool that defines the target content elements 200 (e.g., text) that will be captured by the system (e.g., desired for extraction from the PDF page). While the target content elements may be described as text, the system may similarly capture any target content elements. For example, the target content elements may include one or more words, text, numbers, characters, symbols, regions, locations and/or empty spaces. The system may also use the tool to save the layout into a file format. The layout may include top, left, bottom, right coordinates apart from the template, field names, reference field values, region, and quadrant information. This layout information may be stored as a JavaScript Object Notation (JSON) file format. A JSON file is a file that stores simple data structures and objects in JSON format. The content of the JSON file format 300 may be hierarchical, as shown in FIG. 3. For example, the system may open a template PDF page. The system may open the template PDF by using an embedded component (e.g., PDFTron viewer) to open and view the PDF page in the system. The system may select target text elements on the PDF page and/or draw a bounding box (e.g., rectangle) around the target text. In various embodiments, a user may locate the area to be extracted. The system may receive input on where to put the boundaries of the rectangle based on a user using a mouse (by right click) to draw a rectangle on the display. While described as a bounding box, the system contemplates that the box may be any shape or configuration including, a circle or amorphous shape. The system may capture the coordinates of the bounding box (e.g., X-Y coordinates of the corners of the bounding box) to create the template, as shown in FIG. 3. The system, an algorithm and/or a human may obtain those X-Y coordinates from the PDF document using any type of element recognition system or optical character recognition system such as, for example, Amazon Textract. The location of the bounding box may include the four different coordinates corresponding to each corner of the bounding box. Such coordinates may be determined based on the pixel space. For example, the system may analyze the number of pixels from each border of the document to the target text.

The system may capture the target text in the bounding box from the text layer. The system may capture the text by using logic to iterate through the text layer and extract the text element based on the coordinates. To test the target text, the system may display the target text from the bounding box to the user. The user may verify the target text. To further test the target text, the system may re-select the rectangle and adjust the scope of the rectangle. The adjusting of the rectangle confirms the text is accurate because the coordinates will be different every time a rectangle is rescoped and/or adjusted.

As stated above, the template may include instructions for obtaining the coordinates of the bounding box around the target text. The template may also use associated regex expressions to extract the field value. A regex may search for text or patterns of text. For example, the system may select the value between brackets, etc. The system may identify a region and apply regex on the extracted value. The applying regex may help clean up the extracted text. The template may suggest a replacement value for the field value. Template may include a field called “ReferencedFieldValue” under each field definition, which can hold a suggested value that can be used to replace the extracted text. The template may also provide possible hints on where the bounding box or target text may exist. The template may include a field (called quadrant) that indicates a possible location of the field. This quadrant may help when the same or similar text is found in multiple locations. The system may also include business rules for determining the boundaries of the bounding box and/or for data transformation. A template's regex may be used to clean up the data. For example, if the field that is being extracted is an amount, then the regex can be applied to remove the locale/format of the extracted amount value. For example, the system may convert 2,340 to 2340 by removing the comma format.

In various embodiments, the template may be used as an identification or complete recognition. As set forth above, the system extracted the data using the coordinates from the template which may be called recognition. In various embodiments, the system may also use a template to identify the type of a given page. For example, the system may define a template to identify if a given page is a W-2 by reading a text from a targeted area and comparing that value to a suggested value (e.g., from field “ReferenceFieldValue”). The template may not be human readable. The template may be read by the system. The template may be saved into any storage medium. For example, the template may be saved into a public or private repository, which may be hosted locally or in the cloud. In various embodiments, and as set forth in FIG. 4, the system may test the template 400 against a single document or multiple documents. In particular, the system may submit one or more documents (e.g., W-2 pages) to test the accuracy of extraction. After a new page is loaded into (or recognized by) the system, the system uses the template to identify the text fields based on X-Y coordinates of the bounding box. In various embodiments, and as shown in FIG. 5, the system may also generate a tabular display of the identified text fields 500. The tabular display may include a field name, field value and X-Y coordinates. In various embodiments, the system may work in any of the Windows operating systems with .net framework (e.g., 4.5.2 and above), JSON and SQL server.

The detailed description of various embodiments herein makes reference to the accompanying drawings, which show various embodiments by way of illustration. While these various embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, it should be understood that other embodiments may be realized and that logical and mechanical changes may be made without departing from the spirit and scope of the disclosure. Thus, the detailed description herein is presented for purposes of illustration only and not of limitation. For example, the steps recited in any of the method or process descriptions may be executed in any order and are not limited to the order presented. Moreover, any of the functions or steps may be outsourced to or performed by one or more third parties. Modifications, additions, or omissions may be made to the systems, apparatuses, and methods described herein without departing from the scope of the disclosure. For example, the components of the systems and apparatuses may be integrated or separated. Moreover, the operations of the systems and apparatuses disclosed herein may be performed by more, fewer, or other components and the methods described may include more, fewer, or other steps. Additionally, steps may be performed in any suitable order. As used in this document, “each” refers to each member of a set or each member of a subset of a set. Furthermore, any reference to singular includes plural embodiments, and any reference to more than one component may include a singular embodiment. Although specific advantages have been enumerated herein, various embodiments may include some, none, or all of the enumerated advantages.

Terms and phrases similar to “associate” and/or “associating” may include tagging, flagging, correlating, using a look-up table or any other method or system for indicating or creating a relationship between elements, such as, for example, (i) a transaction account and (ii) an item (e.g., offer, reward, discount) and/or digital channel. Moreover, the associating may occur at any point, in response to any suitable action, event, or period of time. The associating may occur at predetermined intervals, periodically, randomly, once, more than once, or in response to a suitable request or action. Any of the information may be distributed and/or accessed via a software enabled link, wherein the link may be sent via an email, text, post, social network input, and/or any other method known in the art.

In various embodiments, software may be stored in a computer program product and loaded into a computer system using a removable storage drive, hard disk drive, or communications interface. The control logic (software), when executed by the processor, causes the processor to perform the functions of various embodiments as described herein. In various embodiments, hardware components may take the form of application specific integrated circuits (ASICs). Implementation of the hardware so as to perform the functions described herein will be apparent to persons skilled in the relevant art(s).

The system and method are described herein with reference to screen shots, block diagrams and flowchart illustrations of methods, apparatus, and computer program products according to various embodiments. It will be understood that each functional block of the block diagrams and the flowchart illustrations, and combinations of functional blocks in the block diagrams and flowchart illustrations, respectively, can be implemented by computer program instructions.

In various embodiments, the software elements of the system may also be implemented using a JAVASCRIPT® run-time environment configured to execute JAVASCRIPT® code outside of a web browser. For example, the software elements of the system may also be implemented using NODE.JS® components. NODE.JS® programs may implement several modules to handle various core functionalities. For example, a package management module, such as NPM®, may be implemented as an open source library to aid in organizing the installation and management of third-party NODE.JS® programs. NODE.JS® programs may also implement a process manager, such as, for example, Parallel Multithreaded Machine (“PM2”); a resource and performance monitoring tool, such as, for example, Node Application Metrics (“appmetrics”); a library module for building user interfaces, and/or any other suitable and/or desired module.

The computers discussed herein may provide a suitable website or other internet-based graphical user interface which is accessible by users. In one embodiment, MICROSOFT® company's Internet Information Services (IIS), Transaction Server (MTS) service, and an SQL SERVER® database, are used in conjunction with MICROSOFT® operating systems, WINDOWS NT® web server software, SQL SERVER® database, and MICROSOFT® Commerce Server. Additionally, components such as ACCESS® software, SQL SERVER® database, ORACLE® software, SYBASE® software, INFORMIX® software, MYSQL® software, INTERBASE® software, etc., may be used to provide an Active Data Object (ADO) compliant database management system. In one embodiment, the APACHE® web server is used in conjunction with a LINUX® operating system, a MYSQL® database, and PERL®, PHP, Ruby, and/or PYTHON® programming languages.

In various embodiments, the system and various components may integrate with one or more smart digital assistant technologies. For example, exemplary smart digital assistant technologies may include the ALEXA® system developed by the AMAZON® company, the GOOGLE HOME® system developed by Alphabet, Inc., the HOMEPOD® system of the APPLE® company, and/or similar digital assistant technologies. The ALEXA® system, GOOGLE HOME® system, and HOMEPOD® system, may each provide cloud-based voice activation services that can assist with tasks, entertainment, general information, and more. All the ALEXA® devices, such as the AMAZON ECHO®, AMAZON ECHO DOT®, AMAZON TAP®, and AMAZON FIRE® TV, have access to the ALEXA® system. The ALEXA® system, GOOGLE HOME® system, and HOMEPOD® system may receive voice commands via its voice activation technology, activate other functions, control smart devices, and/or gather information. For example, the smart digital assistant technologies may be used to interact with music, emails, texts, phone calls, question answering, home improvement information, smart home communication/activation, games, shopping, making to-do lists, setting alarms, streaming podcasts, playing audiobooks, and providing weather, traffic, and other real time information, such as news. The ALEXA®, GOOGLE HOME®, and HOMEPOD® systems may also allow the user to access information about eligible transaction accounts linked to an online account across all digital assistant-enabled devices.

The present system or any part(s) or function(s) thereof may be implemented using hardware, software, or a combination thereof and may be implemented in one or more computer systems or other processing systems. However, the manipulations performed by embodiments may be referred to in terms, such as matching or selecting, which are commonly associated with mental operations performed by a human operator. No such capability of a human operator is necessary, or desirable, in most cases, in any of the operations described herein. Rather, the operations may be machine operations or any of the operations may be conducted or enhanced by artificial intelligence (AI) or machine learning. AI may refer generally to the study of agents (e.g., machines, computer-based systems, etc.) that perceive the world around them, form plans, and make decisions to achieve their goals. Foundations of AI include mathematics, logic, philosophy, probability, linguistics, neuroscience, and decision theory. Many fields fall under the umbrella of AI, such as computer vision, robotics, machine learning, and natural language processing. Useful machines for performing the various embodiments include general purpose digital computers or similar devices.

The computer system also includes a main memory, such as random access memory (RAM), and may also include a secondary memory. The secondary memory may include, for example, a hard disk drive, a solid-state drive, and/or a removable storage drive. The removable storage drive reads from and/or writes to a removable storage unit in a well-known manner. As will be appreciated, the removable storage unit includes a computer usable storage medium having stored therein computer software and/or data.

In various embodiments, secondary memory may include other similar devices for allowing computer programs or other instructions to be loaded into a computer system. Such devices may include, for example, a removable storage unit and an interface. Examples of such may include a program cartridge and cartridge interface (such as that found in video game devices), a removable memory chip (such as an erasable programmable read only memory (EPROM), programmable read only memory (PROM)) and associated socket, or other removable storage units and interfaces, which allow software and data to be transferred from the removable storage unit to a computer system.

The terms “computer program medium,” “computer usable medium,” and “computer readable medium” are used to generally refer to media such as removable storage drive and a hard disk installed in hard disk drive. These computer program products provide software to a computer system.

The computer system may also include a communications interface. A communications interface allows software and data to be transferred between the computer system and external devices. Examples of such a communications interface may include a modem, a network interface (such as an Ethernet card), a communications port, etc. Software and data transferred via the communications interface are in the form of signals which may be electronic, electromagnetic, optical, or other signals capable of being received by communications interface. These signals are provided to communications interface via a communications path (e.g., channel). This channel carries signals and may be implemented using wire, cable, fiber optics, a telephone line, a cellular link, a radio frequency (RF) link, wireless and other communications channels.

As used herein an “identifier” may be any suitable identifier that uniquely identifies an item. For example, the identifier may be a globally unique identifier (“GUID”). The GUID may be an identifier created and/or implemented under the universally unique identifier standard. Moreover, the GUID may be stored as 128-bit value that can be displayed as 32 hexadecimal digits. The identifier may also include a major number, and a minor number. The major number and minor number may each be 16-bit integers.

The firewall may include any hardware and/or software suitably configured to protect CMS components and/or enterprise computing resources from users of other networks. Further, a firewall may be configured to limit or restrict access to various systems and components behind the firewall for web clients connecting through a web server. Firewall may reside in varying configurations including Stateful Inspection, ProX-Y based, access control lists, and Packet Filtering among others. Firewall may be integrated within a web server or any other CMS components or may further reside as a separate entity. A firewall may implement network address translation (“NAT”) and/or network address port translation (“NAPT”). A firewall may accommodate various tunneling protocols to facilitate secure communications, such as those used in virtual private networking. A firewall may implement a demilitarized zone (“DMZ”) to facilitate communications with a public network such as the internet. A firewall may be integrated as software within an internet server or any other application server components, reside within another computing device, or take the form of a standalone hardware component.

Any databases discussed herein may include relational, hierarchical, graphical, blockchain, object-oriented structure, and/or any other database configurations. Any database may also include a flat file structure wherein data may be stored in a single file in the form of rows and columns, with no structure for indexing and no structural relationships between records. For example, a flat file structure may include a delimited text file, a CSV (comma-separated values) file, and/or any other suitable flat file structure. Common database products that may be used to implement the databases include DB2® by IBM® (Armonk, NY), various database products available from ORACLE® Corporation (Redwood Shores, CA), MICROSOFT ACCESS® or MICROSOFT SQL SERVER® by MICROSOFT® Corporation (Redmond, Washington), MYSQL® by MySQL AB (Uppsala, Sweden), MONGODB®, Redis, APACHE CASSANDRA®, HBASE® by APACHE®, MapR-DB by the MAPR® corporation, or any other suitable database product. Moreover, any database may be organized in any suitable manner, for example, as data tables or lookup tables. Each record may be a single file, a series of files, a linked series of data fields, or any other data structure.

As used herein, big data may refer to partially or fully structured, semi-structured, or unstructured data sets including millions of rows and hundreds of thousands of columns. A big data set may be compiled, for example, from a history of purchase transactions over time, from web registrations, from social media, from records of charge (ROC), from summaries of charges (SOC), from internal data, or from other suitable sources. Big data sets may be compiled without descriptive metadata such as column types, counts, percentiles, or other interpretive-aid data points.

Association of certain data may be accomplished through any desired data association technique such as those known or practiced in the art. For example, the association may be accomplished either manually or automatically. Automatic association techniques may include, for example, a database search, a database merge, GREP, AGREP, SQL, using a key field in the tables to speed searches, sequential searches through all the tables and files, sorting records in the file according to a known order to simplify lookup, and/or the like. The association step may be accomplished by a database merge function, for example, using a “key field” in pre-selected databases or data sectors. Various database tuning steps are contemplated to optimize database performance. For example, frequently used files such as indexes may be placed on separate file systems to reduce In/Out (“I/O”) bottlenecks.

The data may be big data that is processed by a distributed computing cluster. The distributed computing cluster may be, for example, a HADOOP® software cluster configured to process and store big data sets with some of nodes comprising a distributed storage system and some of nodes comprising a distributed processing system. In that regard, distributed computing cluster may be configured to support a HADOOP® software distributed file system (HDFS) as specified by the Apache Software Foundation at www.hadoop.apache.org/docs.

“Cloud” or “Cloud computing” includes a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. Cloud computing may include location-independent computing, whereby shared servers provide resources, software, and data to computers and other devices on demand.

Any database discussed herein may comprise a distributed ledger maintained by a plurality of computing devices (e.g., nodes) over a peer-to-peer network. Each computing device maintains a copy and/or partial copy of the distributed ledger and communicates with one or more other computing devices in the network to validate and write data to the distributed ledger. The distributed ledger may use features and functionality of blockchain technology, including, for example, consensus-based validation, immutability, and cryptographically chained blocks of data. The blockchain may comprise a ledger of interconnected blocks containing data. The blockchain may provide enhanced security because each block may hold individual transactions and the results of any blockchain executables. Each block may link to the previous block and may include a timestamp. Blocks may be linked because each block may include the hash of the prior block in the blockchain. The linked blocks form a chain, with only one successor block allowed to link to one other predecessor block for a single chain. Forks may be possible where divergent chains are established from a previously uniform blockchain, though typically only one of the divergent chains will be maintained as the consensus chain. In various embodiments, the blockchain may implement smart contracts that enforce data workflows in a decentralized manner. The system may also include applications deployed on user devices such as, for example, computers, tablets, smartphones, Internet of Things devices (“IoT” devices), etc. The applications may communicate with the blockchain (e.g., directly or via a blockchain node) to transmit and retrieve data. In various embodiments, a governing organization or consortium may control access to data stored on the blockchain. Registration with the managing organization(s) may enable participation in the blockchain network.

Data transfers performed through the blockchain-based system may propagate to the connected peers within the blockchain network within a duration that may be determined by the block creation time of the specific blockchain technology implemented. For example, on an ETHEREUM®-based network, a new data entry may become available within about 13-20 seconds as of the writing. On a HYPERLEDGER® Fabric 1.0 based platform, the duration is driven by the specific consensus algorithm that is chosen, and may be performed within seconds. In that respect, propagation times in the system may be improved compared to existing systems, and implementation costs and time to market may also be drastically reduced. The system also offers increased security at least partially due to the immutable nature of data that is stored in the blockchain, reducing the probability of tampering with various data inputs and outputs. Moreover, the system may also offer increased security of data by performing cryptographic processes on the data prior to storing the data on the blockchain. Therefore, by transmitting, storing, and accessing data using the system described herein, the security of the data is improved, which decreases the risk of the computer or network from being compromised.

In various embodiments, the system may also reduce database synchronization errors by providing a common data structure, thus at least partially improving the integrity of stored data. The system also offers increased reliability and fault tolerance over traditional databases (e.g., relational databases, distributed databases, etc.) as each node operates with a full copy of the stored data, thus at least partially reducing downtime due to localized network outages and hardware failures. The system may also increase the reliability of data transfers in a network environment having reliable and unreliable peers, as each node broadcasts messages to all connected peers, and, as each block comprises a link to a previous block, a node may quickly detect a missing block and propagate a request for the missing block to the other nodes in the blockchain network.

The particular blockchain implementation described herein provides improvements over conventional technology by using a decentralized database and improved processing environments. In particular, the blockchain implementation improves computer performance by, for example, leveraging decentralized resources (e.g., lower latency). The distributed computational resources improves computer performance by, for example, reducing processing times. Furthermore, the distributed computational resources improves computer performance by improving security using, for example, cryptographic protocols.

Any communication, transmission, and/or channel discussed herein may include any system or method for delivering content (e.g., data, information, metadata, etc.), and/or the content itself. The content may be presented in any form or medium, and in various embodiments, the content may be delivered electronically and/or capable of being presented electronically. For example, a channel may comprise a website, mobile application, or device (e.g., FACEBOOK®, YOUTUBE®, PANDORA®, APPLE TV®, MICROSOFT® XBOX®, ROKU®, AMAZON FIRE®, GOOGLE CHROMECAST™, SONY® PLAYSTATION®, NINTENDO® SWITCH®, etc.) a uniform resource locator (“URL”), a document (e.g., a MICROSOFT® Word or EXCEL™, an ADOBE® Portable Document Format (PDF) document, etc.), an “ebook,” an “emagazine,” an application or microapplication (as described herein), an short message service (SMS) or other type of text message, an email, a FACEBOOK® message, a TWITTER® tweet, multimedia messaging services (MMS), and/or other type of communication technology. In various embodiments, a channel may be hosted or provided by a data partner. In various embodiments, the distribution channel may comprise at least one of a merchant website, a social media website, affiliate or partner websites, an external vendor, a mobile device communication, social media network, and/or location based service. Distribution channels may include at least one of a merchant website, a social media site, affiliate or partner websites, an external vendor, and a mobile device communication. Examples of social media sites include FACEBOOK®, FOURSQUARE®, TWITTER®, LINKEDIN®, INSTAGRAM®, PINTEREST®, TUMBLR®, REDDIT®, SNAPCHAT®, WHATSAPP®, FLICKR®, VK®, QZONE®, WECHAT®, and the like. Examples of affiliate or partner websites include AMERICAN EXPRESS®, GROUPON®, LIVINGSOCIAL®, and the like. Moreover, examples of mobile device communications include texting, email, and mobile applications for smartphones.