Patent Publication Number: US-11640440-B2

Title: Automation system and method

Description:
RELATED APPLICATION(S) 
     This application claims the benefit of U.S. Provisional Application No. 63/048,598 filed on 6 Jul. 2020, the entire contents of which are incorporated herein by reference. 
    
    
     TECHNICAL FIELD 
     This disclosure relates to automation systems and methods and, more particularly, to automation systems and methods that automatically process online web resources. 
     BACKGROUND 
     Conventional machine-to-machine communication is generally defined by specific communication protocols across various application, transport, and Internet layers (e.g., Hypertext Transfer Protocol (HTTP), Transmission Control Protocol (TCP), Internet Protocol (IP), etc.). However, for online web resources, communication between machines is generally limited to application programming interfaces (APIs) preprogrammed for particular purposes, and the presentation of webpages on a browser designed for a human user to navigate and perform operations thereon. Unfortunately, APIs are not standardized and are human-designed/coded for particular purposes, and websites are written to display a browser for human interpretability; not machine interpretability. Accordingly, conventional approaches to processing web resources and APIs do not allow machines to “learn” how to communicate with one another without human intervention. 
     SUMMARY OF DISCLOSURE 
     DataFi (General): 
     In one implementation, a computer-implemented method is executed on a computing device and includes: identifying one or more portions of a website structure of a specific website; and associating the one or more portions of the website structure with one or more descriptors of the specific website to define a specific data description model corresponding to the specific website. 
     One or more of the following features may be included. Identifying one or more portions of a website structure of a specific website may include: enabling a user to review the specific website to visually identify one or more spatial regions of the specific website; and associating the one or more spatial regions of the specific website with the one or more portions of the website structure. The one or more descriptors may include one or more of: a property descriptor; an attribute descriptor; and a value descriptor. The website structure may include one or more of: a HTML website structure; a javascript website structure; and a CSS website structure. The specific data description model may be processed to obtain useable information from the specific website. A database may be populated with at least a portion of the useable information. Processing the specific data description model to obtain useable information from the specific website may include: processing the specific data description model to obtain raw information from the specific website; and transforming the raw information into the useable information. Transforming the raw information into the useable information may include one or more of: amending the raw information; processing the raw information to normalize and/or homogenize one or more property descriptors; processing the raw information to normalize and/or homogenize one or more attribute descriptors; and processing the raw information to normalize and/or homogenize one or more value descriptors. A plurality of data description models corresponding to a plurality of websites may be defined. The plurality of data description models may include: the specific data description model corresponding to the specific website, and one or more additional data description models corresponding to one or more additional websites. The plurality of data description models corresponding to the plurality of websites may be provided to a machine learning process. Ontology data concerning the plurality of websites may be provided to the machine learning process. Target website data concerning a target website may be provided to the machine learning process. The plurality of data description models, ontology data and target website data may be processed using the machine learning process to generate a data description model for the target website. 
     In another implementation, a computer program product resides on a computer readable medium and has a plurality of instructions stored on it. When executed by a processor, the instructions cause the processor to perform operations including identifying one or more portions of a website structure of a specific website; and associating the one or more portions of the website structure with one or more descriptors of the specific website to define a specific data description model corresponding to the specific website. 
     One or more of the following features may be included. Identifying one or more portions of a website structure of a specific website may include: enabling a user to review the specific website to visually identify one or more spatial regions of the specific website; and associating the one or more spatial regions of the specific website with the one or more portions of the website structure. The one or more descriptors may include one or more of: a property descriptor; an attribute descriptor; and a value descriptor. The website structure may include one or more of: a HTML website structure; a javascript website structure; and a CSS website structure. The specific data description model may be processed to obtain useable information from the specific website. A database may be populated with at least a portion of the useable information. Processing the specific data description model to obtain useable information from the specific website may include: processing the specific data description model to obtain raw information from the specific website; and transforming the raw information into the useable information. Transforming the raw information into the useable information may include one or more of: amending the raw information; processing the raw information to normalize and/or homogenize one or more property descriptors; processing the raw information to normalize and/or homogenize one or more attribute descriptors; and processing the raw information to normalize and/or homogenize one or more value descriptors. A plurality of data description models corresponding to a plurality of websites may be defined. The plurality of data description models may include: the specific data description model corresponding to the specific website, and one or more additional data description models corresponding to one or more additional websites. The plurality of data description models corresponding to the plurality of websites may be provided to a machine learning process. Ontology data concerning the plurality of websites may be provided to the machine learning process. Target website data concerning a target website may be provided to the machine learning process. The plurality of data description models, ontology data and target website data may be processed using the machine learning process to generate a data description model for the target website. 
     In another implementation, a computing system includes a processor and a memory system configured to perform operations including identifying one or more portions of a website structure of a specific website; and associating the one or more portions of the website structure with one or more descriptors of the specific website to define a specific data description model corresponding to the specific website. 
     One or more of the following features may be included. Identifying one or more portions of a website structure of a specific website may include: enabling a user to review the specific website to visually identify one or more spatial regions of the specific website; and associating the one or more spatial regions of the specific website with the one or more portions of the website structure. The one or more descriptors may include one or more of: a property descriptor; an attribute descriptor; and a value descriptor. The website structure may include one or more of: a HTML website structure; a javascript website structure; and a CSS website structure. The specific data description model may be processed to obtain useable information from the specific website. A database may be populated with at least a portion of the useable information. Processing the specific data description model to obtain useable information from the specific website may include: processing the specific data description model to obtain raw information from the specific website; and transforming the raw information into the useable information. Transforming the raw information into the useable information may include one or more of: amending the raw information; processing the raw information to normalize and/or homogenize one or more property descriptors; processing the raw information to normalize and/or homogenize one or more attribute descriptors; and processing the raw information to normalize and/or homogenize one or more value descriptors. A plurality of data description models corresponding to a plurality of websites may be defined. The plurality of data description models may include: the specific data description model corresponding to the specific website, and one or more additional data description models corresponding to one or more additional websites. The plurality of data description models corresponding to the plurality of websites may be provided to a machine learning process. Ontology data concerning the plurality of websites may be provided to the machine learning process. Target website data concerning a target website may be provided to the machine learning process. The plurality of data description models, ontology data and target website data may be processed using the machine learning process to generate a data description model for the target website. 
     The details of one or more implementations are set forth in the accompanying drawings and the description below. Other features and advantages will become apparent from the description, the drawings, and the claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a diagrammatic view of a distributed computing network including a computing device that executes an automation process according to an embodiment of the present disclosure; 
         FIG.  2    is a diagrammatic view of a website for processing by the automation process of  FIG.  1    according to an embodiment of the present disclosure; 
         FIG.  3    is a flowchart of the automation process of  FIG.  1    according to an embodiment of the present disclosure; 
         FIG.  4    is another flowchart of the automation process of  FIG.  1    according to an embodiment of the present disclosure; 
         FIG.  5    is another diagrammatic view of a website for processing by the automation process of  FIG.  1    according to an embodiment of the present disclosure; 
         FIG.  6    is another flowchart of the automation process of  FIG.  1    according to an embodiment of the present disclosure; 
         FIG.  7    is another flowchart of the automation process of  FIG.  1    according to an embodiment of the present disclosure; 
         FIG.  8    is a diagrammatic view of a plurality of websites for processing by the automation process of  FIG.  1    according to an embodiment of the present disclosure; 
         FIG.  9    is a diagrammatic view of a complex task for processing by the automation process of  FIG.  1    according to an embodiment of the present disclosure; 
         FIG.  10    is another flowchart of the automation process of  FIG.  1    according to an embodiment of the present disclosure; 
         FIG.  11    is another flowchart of the automation process of  FIG.  1    according to an embodiment of the present disclosure; 
         FIG.  12    is another flowchart of the automation process of  FIG.  1    according to an embodiment of the present disclosure; 
         FIG.  13    is a diagrammatic view of a plurality of websites for processing by a cloud-based implementation of the automation process of  FIG.  1    according to an embodiment of the present disclosure; 
         FIG.  14    is another flowchart of the automation process of  FIG.  1    according to an embodiment of the present disclosure; 
         FIG.  15    is another flowchart of the automation process of  FIG.  1    according to an embodiment of the present disclosure; 
         FIG.  16    is another flowchart of the automation process of  FIG.  1    according to an embodiment of the present disclosure; 
         FIG.  17    is another flowchart of the automation process of  FIG.  1    according to an embodiment of the present disclosure; and 
         FIG.  18    is another flowchart of the automation process of  FIG.  1    according to an embodiment of the present disclosure; 
     
    
    
     Like reference symbols in the various drawings indicate like elements. 
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     System Overview 
     Referring to  FIG.  1   , there is shown automation process  10 . Automation process  10  may be implemented as a server-side process, a client-side process, or a hybrid server-side/client-side process. For example, automation process  10  may be implemented as a purely server-side process via automation process  10   s . Alternatively, automation process  10  may be implemented as a purely client-side process via one or more of automation process  10   c   1 , automation process  10   c   2 , automation process  10   c   3 , and automation process  10   c   4 . Alternatively still, automation process  10  may be implemented as a hybrid server-side/client-side process via automation process  10   s  in combination with one or more of automation process  10   c   1 , automation process  10   c   2 , automation process  10   c   3 , and automation process  10   c   4 . Accordingly, automation process  10  as used in this disclosure may include any combination of automation process  10   s , automation process  10   c   1 , automation process  10   c   2 , automation process  10   c   3 , and automation process  10   c   4 . 
     Automation process  10   s  may be a server application and may reside on and may be executed by computing device  12 , which may be connected to network  14  (e.g., the Internet or a local area network). Examples of computing device  12  may include, but are not limited to: a personal computer, a server computer, a series of server computers, a mini computer, a mainframe computer, a smartphone, or a cloud-based computing platform. 
     The instruction sets and subroutines of automation process  10   s , which may be stored on storage device  16  coupled to computing device  12 , may be executed by one or more processors (not shown) and one or more memory architectures (not shown) included within computing device  12 . Examples of storage device  16  may include but are not limited to: a hard disk drive; a RAID device; a random access memory (RAM); a read-only memory (ROM); and all forms of flash memory storage devices. 
     Network  14  may be connected to one or more secondary networks (e.g., network  18 ), examples of which may include but are not limited to: a local area network; a wide area network; or an intranet, for example. 
     Examples of automation processes  10   c   1 ,  10   c   2 ,  10   c   3 ,  10   c   4  may include but are not limited to a web browser, a game console user interface, a mobile device user interface, or a specialized application (e.g., an application running on e.g., the Android™ platform, the iOS™ platform, the Windows™ platform, the Linux™ platform or the UNIX cm platform). The instruction sets and subroutines of automation processes  10   c   1 ,  10   c   2 ,  10   c   3 ,  10   c   4 , which may be stored on storage devices  20 ,  22 ,  24 ,  26  (respectively) coupled to client electronic devices  28 ,  30 ,  32 ,  34  (respectively), may be executed by one or more processors (not shown) and one or more memory architectures (not shown) incorporated into client electronic devices  28 ,  30 ,  32 ,  34  (respectively). Examples of storage devices  20 ,  22 ,  24 ,  26  may include but are not limited to: hard disk drives; RAID devices; random access memories (RAM); read-only memories (ROM), and all forms of flash memory storage devices. 
     Examples of client electronic devices  28 ,  30 ,  32 ,  34  may include, but are not limited to, a smartphone (not shown), a personal digital assistant (not shown), a tablet computer (not shown), laptop computers  28 ,  30 ,  32 , personal computer  34 , a notebook computer (not shown), a server computer (not shown), a gaming console (not shown), and a dedicated network device (not shown). Client electronic devices  28 ,  30 ,  32 ,  34  may each execute an operating system, examples of which may include but are not limited to Microsoft Windows™, Android™, iOS™, Linux™, or a custom operating system. 
     Users  36 ,  38 ,  40 ,  42  may access automation process  10  directly through network  14  or through secondary network  18 . Further, automation process  10  may be connected to network  14  through secondary network  18 , as illustrated with link line  44 . 
     The various client electronic devices (e.g., client electronic devices  28 ,  30 ,  32 ,  34 ) may be directly or indirectly coupled to network  14  (or network  18 ). For example, laptop computer  28  and laptop computer  30  are shown wirelessly coupled to network  14  via wireless communication channels  44 ,  46  (respectively) established between laptop computers  28 ,  30  (respectively) and cellular network/bridge  48 , which is shown directly coupled to network  14 . Further, laptop computer  32  is shown wirelessly coupled to network  14  via wireless communication channel  50  established between laptop computer  32  and wireless access point (i.e., WAP)  52 , which is shown directly coupled to network  14 . Additionally, personal computer  34  is shown directly coupled to network  18  via a hardwired network connection. 
     WAP  52  may be, for example, an IEEE 802.11a, 802.11b, 802.11g, 802.11n, Wi-Fi, and/or Bluetooth device that is capable of establishing wireless communication channel  50  between laptop computer  32  and WAP  52 . As is known in the art, IEEE 802.11x specifications may use Ethernet protocol and carrier sense multiple access with collision avoidance (i.e., CSMA/CA) for path sharing. As is known in the art, Bluetooth is a telecommunications industry specification that allows e.g., mobile phones, computers, and personal digital assistants to be interconnected using a short-range wireless connection. 
     Automation Process Overview 
     As will be discussed below in greater detail, automation process  10  may be configured to allow for the automated processing of websites (generally) and ecommerce websites (specifically) so that these websites may effectuate the functionality of a database with respect to the products/services that are available for purchase through these websites. By enabling such functionality, complex tasks may be automatically effectuated at a holistic level, thus allowing automated searching to occur across multiple websites so that the purchases effectuated across these multiple websites may cumulatively satisfy the complex task. 
     DataFi (General): 
     Referring also to  FIGS.  2 - 3    and in order to enable such automated processing of websites, automation process  10  may enable a user (e.g., user  36 ) to review various websites (e.g., website  100 ), examples of which may include but are not limited to ecommerce websites that enable users to purchase various products/services. 
     For example, automation process  10  may identify  200  one or more portions of a website structure (e.g., website structure  54 ) of a specific website (e.g., website  100 ). Examples of such a website structure (e.g., website structure  54 ) may include one or more of: a HTML website structure; a javascript website structure; and a CSS website structure.
         HTML Website Structure: The HyperText Markup Language (i.e., HTML) is the standard markup language for documents designed to be displayed in a web browser. It may be assisted by technologies such as Cascading Style Sheets (CSS) and scripting languages. Web browsers may receive HTML documents from a web server or from local storage and render the documents into multimedia web pages. HTML may describe the structure of a web page semantically and originally included cues for the appearance of the document. HTML elements may be the building blocks of HTML pages. With HTML constructs, images and other objects such as interactive forms may be embedded into the rendered page. HTML may provide a means to create structured documents by denoting structural semantics for text such as headings, paragraphs, lists, links, quotes and other items. HTML elements may be delineated by tags, written using angle brackets. Tags such as &lt;img/&gt; and &lt;input/&gt; directly introduce content into the page. Other tags such as &lt;p&gt; may surround and provide information about document text and may include other tags as sub-elements. Browsers do not display the HTML tags, but use them to interpret the content of the page.   Javascript Website Structure: JavaScript (JS) is a programming language that conforms to the ECMAScript specification. JavaScript is high-level, often just-in-time compiled, and multi-paradigm. It may have curly-bracket syntax, dynamic typing, prototype-based object-orientation, and first-class functions. Alongside HTML and CSS, JavaScript is one of the core technologies of the World Wide Web. Over 97% of websites use it client-side for web page behavior, often incorporating third-party libraries. All major web browsers have a dedicated JavaScript engine to execute the code on the user&#39;s device. As a multi-paradigm language, JavaScript may support event-driven, functional, and imperative programming styles. It may have application programming interfaces (APIs) for working with text, dates, regular expressions, standard data structures, and the Document Object Model (DOM). DOM is a programming API for HTML and XML documents that defines the logical structure of documents and the way a document is accessed and manipulated. For example, DOM may treat an HTML or XML document as a tree structure where each node is an object representing a part of the document.   CSS Website Structure: Cascading Style Sheets (CSS) is a style sheet language used for describing the presentation of a document written in a markup language such as HTML. CSS is a cornerstone technology of the World Wide Web, alongside HTML and JavaScript. CSS is designed to enable the separation of presentation and content, including layout, colors, and fonts. This separation can improve content accessibility, provide more flexibility and control in the specification of presentation characteristics, enable multiple web pages to share formatting by specifying the relevant CSS in a separate .css file which reduces complexity and repetition in the structural content as well as enabling the .css file to be cached to improve the page load speed between the pages that share the file and its formatting. Separation of formatting and content may make it feasible to present the same markup page in different styles for different rendering methods, such as on-screen, in print, by voice (via speech-based browser or screen reader), and on Braille-based tactile devices. CSS may also have rules for alternate formatting if the content is accessed on a mobile device.       

     When identifying  200  one or more portions of a website structure (e.g., website structure  54 ) of a specific website (e.g., website  100 ), automation process  10  may: enable  202  a user (e.g., user  36 ) to review the specific website (e.g., website  100 ) to visually identify one or more spatial regions of the specific website (e.g., website  100 ); and associate  204  the one or more spatial regions of the specific website (e.g., website  100 ) with the one or more portions of the website structure (e.g., website structure  54 ). For example, automation process  10  may enable  202  user  36  to review website  100  to visually identify spatial regions  102 ,  104  of website  100  (via selection with a mouse, not shown) and associate  204  spatial regions  102 ,  104  of website  100  with structure portions  106 ,  108  (respectively) of website structure  54 . Specifically, when user  36  visually identifies a spatial region (e.g., one of spatial regions  102 ,  104 ) of website  100 , automation process  10  may automatically associate  204  the identified spatial region (e.g., one of spatial regions  102 ,  104 ) with the corresponding portion (e.g., one of structure portions  106 ,  108  respectively) of the website structure (e.g., website structure  54 ) of the specific website (e.g., website  100 ). 
     Automation process  10  may associate  206  the one or more portions (e.g., structure portions  106 ,  108 ) of the website structure (e.g., website structure  54 ) with one or more descriptors (e.g., descriptors  56 ) of the specific website (e.g., website  100 ) to define a specific data description model (e.g., specific data description model  58 ) corresponding to the specific website (e.g., website  100 ). 
     The one or more descriptors (e.g., descriptors  56 ) may include one or more:
         Property Descriptors: A property descriptor may identify the field/area/region name of highly pertinent portion of a website, wherein these fields/areas/regions are common on a particular type of website. Accordingly, if website  100  is an ecommerce website, examples of such property descriptors may include but are not limited to: a title field/area/region; a picture field/area/region; a description field/area/region; and a price field/area/region. A property descriptor may be user-defined and/or automatically defined for a particular domain. For example, a domain may generally describe a type of website. Examples of domains may include but are not limited to: ecommerce websites; news websites; social media websites; and information websites. The property descriptor may be domain-specific such that each domain may include one or more property descriptors that represent highly pertinent portions of the website for that domain. The property descriptors for each domain may be defined in a domain ontology.   Attribute Descriptors: An attribute descriptor may identify the field/area/region name of supplemental portion of a website, wherein these fields/areas/regions supplement the above-described property descriptors. Accordingly, if website  100  is an ecommerce website, examples of such attribute descriptors may include but are not limited to: a size field/area/region; a color field/area/region; a material field/area/region; and a brand field/area/region.   Value Descriptors: A value descriptor may identify a value for one of the above-described property descriptors and/or attribute descriptors. For example and with respect to website  100 , the value descriptor for the “size” attribute descriptor may be “Large”; the value descriptor for the “color” attribute descriptor may be “California Blue”; the value descriptor for the “price” property descriptor may be “$19.98”; and the value descriptor for the “title” property descriptor may be “Synthetic Nitrile Blue Disposable Gloves”.       

     Automation process  10  may provide a user interface or overlay on a web browser as user  36  interacts with website  100 . For example, the user interface may be an extension of a web browser, built-into a web browser, and/or may be executed separately from a web browser that provides the ability to access websites. When identifying  200  one or more portions of website structure  54  of website  100 , the domain associated with website  100  may be determined. For example, a user  36  may provide (e.g., using the user interface) an indication or selection of the domain for website  100 . In another example, the domain may be automatically defined by automation process  10  e.g., when loading website  100 . In this example, suppose website  100  is an ecommerce website. Accordingly, website  100  may be associated with the ecommerce domain and automated process  10  may provide (e.g., within the user interface) a list of one or more property descriptors specific to the ecommerce domain for the user to visually identify within website  100 . 
     Associating  206  the structure portions  106 ,  108  of website structure  54  with descriptors  56  of website  100  to define a specific data description model may include defining, using the user interface, descriptors for structure portion corresponding to the identified spatial regions. For example, automation process  10  may provide, using the user interface, user  36  with the ability to define or select a descriptor type (e.g., a property descriptor, an attribute descriptor, or a value descriptor) for each structure portion corresponding to the identified spatial region(s). For example, automation process  10  may associate  206  structure portion  106  of website structure  54  with a price property descriptor and may associate  206  structure portion  108  of website structure  54  with a size attribute descriptor. In this manner, automation process  10  may define or generate the specific data description model for website  100  by associating or mapping particular specific structure portions of the website structure with one or more descriptors of the data description model corresponding to website  100 . 
     Associating  206  the structure portions  106 ,  108  of website structure  54  with descriptors  56  of website  100  to define a specific data description model may include defining, within the data description model, how to navigate between particular portions of the specific website (e.g., webpages of the specific website). For example and when defining specific data description model  58  corresponding to website  100 , automation process  10  may define a “home” webpage to initialize processing of website  100 . Suppose the home webpage of website  100  includes a list of webpages organized into a plurality of categories (i.e., on a category page). In this example, suppose the category page includes one or more links or other references to particular webpages based upon the category of each webpage. Automation process  10  may enable  202  a user to visually select the one or more spatial regions of the category page including the one or more links. Automation process  10  may associate  204  the selected spatial regions with the one or more corresponding portions of the webpage structure for the category page with the one or more links. Automation process  10  may associate  206  particular structure portions of the category page with one or more descriptors for the one or more links of the category webpage. The processing of a category page as described above may be repeated recursively for a plurality of category pages with links to each webpage of a website. Accordingly, defining the specific data description model with one or more category webpages may allow a computing device to navigate and process each webpage of website  100 . As will be discussed in greater detail below, automation process  10  may record the user&#39;s interactions within the website to define a functional description model configured to navigate and process webpages without human intervention. 
     When identifying  200  the one or more portions of the website structure of a specific website, the one or more portions (e.g., structure portions  106 ,  108 ) of website structure (e.g., website structure  54 ) may be generated or exposed in response to a user&#39;s interactions with the website (e.g., website  100 ). For example and as is known in the art, some websites may include portions of website structure or code that are generated dynamically as a user interacts with the website. Accordingly, automation process  10  may enable  202  a user (e.g., user  36 ) to interact with a website (e.g., website  100 ) to visually identify spatial regions of website  100  and associate  204  the spatial regions with the portions of webpage structure generated or exposed in response to user  36 &#39;s interaction with website  100 . Accordingly, automation process  10  may associate  206  the generated or exposed structure portions of the website structure with one or more descriptors of website  100  to define a specific data description model. As will be discussed in greater detail below, automation process  10  may define a function description model based, at least in part, upon the user&#39;s interactions with the website that result in the dynamic generation of website structure. 
     The specific data description model (e.g., specific data description model  58 ) corresponding to the specific website (e.g., website  100 ) may be configured to allow for the above-described automated accessing of (in this example) website  100 . For example and as discussed above, since specific data description model  58  locates the various data-related portions (e.g., structure portions  106 ,  108 ) within the website structure (e.g., website structure  54 ) of the specific website (e.g., website  100 ), the specific website (e.g., website  100 ) may be accessed and utilized in an automated fashion (since specific data description model  58  eliminates the need for a human being to visually-navigate website  100 ). 
     Once the user (e.g., user  36 ) and automation process  10  processes (in this example) webpage  110  of website  100 , the user (e.g., user  36 ) and automation process  10  may process (in this example) additional webpages (e.g., webpages  112 ,  114 ,  116 ) of website  100  to obtain additional data for inclusion within (and further refinement of) data description model  58 . For example, automation process  10  may enable  202  user  36  to review additional webpages (e.g., webpages  112 ,  114 ,  116 ) of website  100  to visually identify one or more spatial regions of these webpages (e.g., webpages  112 ,  114 ,  116 ) and associate  204  these spatial regions with one or more portions of the website structure (e.g., website structure  54 ) to obtain additional data for inclusion within (and further refinement of) the specific data description model (e.g., specific data description model  58 ) corresponding to the specific website (e.g., website  100 ). 
     When enabling  202  a user to review additional webpages, automation process  10  may provide, via the user interface, one or more suggestions for particular spatial regions of the additional webpages to identify as descriptors within the specific data description model. For example, suppose user  36  is reviewing webpage  112 . In this example, as user  36  hovers a mouse (not shown) adjacent to the same spatial region (e.g., spatial region  102 ) that was associated with e.g., a price property descriptor for webpage  110 , the user interface may display a hint or suggestion to associate  204  the same spatial region of webpage  112  with the structure portion of webpage  112  and to associate  206  the structure portion with the e.g., price property descriptor. Similarly, as user  36  hovers a mouse (not shown) adjacent to the same spatial region (e.g., spatial region  104 ) that was associated with e.g., a size attribute descriptor for webpage  110 , the user interface may display a hint or suggestion to associate  204  the same spatial region of webpage  112  with the structure portion of webpage  112  and to associate  206  the structure portion with the e.g., size attribute descriptor. In this manner, automation process  10  may provide automated suggestions for defining specific data description model  58  based, at least in part, upon a user&#39;s interactions with the webpages of website  100 . 
     Once a sufficient quantity of webpages (e.g., webpages  110 ,  112 ,  114 ,  116 ) of website  100  are processed (e.g., ten or more), automation process  10  may process  208  the specific data description model (e.g., specific data description model  58 ) to obtain useable information from the specific website (e.g., website  100 ). For example and when processing  208  the specific data description model (e.g., specific data description model  58 ) to obtain useable information from the specific website (e.g., website  100 ), automation process  10  may process  210  the specific data description model (e.g., specific data description model  58 ) to obtain raw information from the specific website (e.g., website  100 ); and transform  212  the raw information into the useable information. 
     As discussed above, automation process  10  may associate  206  the one or more portions (e.g., structure portions  106 ,  108 ) of the website structure (e.g., website structure  54 ) with one or more descriptors (e.g., descriptors  56 ) of the specific website (e.g., website  100 ) to define a specific data description model (e.g., specific data description model  58 ) corresponding to the specific website (e.g., website  100 ), wherein these descriptors (e.g., descriptors  56 ) may include property descriptors, attribute descriptors and value descriptors. As could be imagined, it is foreseeable that different webpages within a website may use different descriptors (e.g., descriptors  56 ). For example, some webpages within website  100  may use Small/Medium/Large, while other webpages within website  100  may use S/M/L. Further, some webpages within website  100  may use “Quantity”, while other webpages within website  100  may use “Count”. Additionally, some webpages within website  100  may use “Material”, while other webpages within website  100  may use “Construction”. Further still, some webpages within website  100  may use “Manufacturer”, while other webpages within website  100  may use “Brand”. 
     In order to properly utilize such data (e.g., descriptors  56 ), automation process  10  may process this data to transform  212  it from raw information (e.g., descriptors  56  in their original disjointed form) into useable information  60  (as will be described below). When transforming  212  the raw information (e.g., descriptors  56 ) into useable information  60 , automation process  10  may: amend  214  the raw information (e.g., descriptors  56 ); process  216  the raw information (e.g., descriptors  56 ) to normalize and/or homogenize one or more property descriptors; process  218  the raw information (e.g., descriptors  56 ) to normalize and/or homogenize one or more attribute descriptors; and/or process  220  the raw information (e.g., descriptors  56 ) to normalize and/or homogenize one or more value descriptors.
         Amend the Raw Information: Since it is foreseeable that different webpages within a website (e.g., website  100 ) may use data that is formatted differently, automation process  10  may amend such raw information (e.g., descriptors  56 ). For example, some webpages within website  100  may use data that has e.g., filler spaces inserted before a value, while other webpages within website  100  may not use such filler spaces. Accordingly, automation process  10  may amend this raw information (e.g., descriptors  56 ) so that e.g., all information defined within data description model  58  does not use filler spaces, thus generating useable information  60 .   Normalize/Homogenize the Property Descriptors: Since it is foreseeable that different webpages within a website (e.g., website  100 ) may use different property descriptors, automation process  10  may normalize/homogenize such property descriptors. For example, some webpages within website  100  may use the term “description” while other webpages within website  100  may use the term “details”. Accordingly, automation process  10  may normalize/homogenize this raw information (e.g., descriptors  56 ) such that e.g., all webpages defined within data description model  58  use the term “description”, thus generating useable information  60 . As will be discussed in greater detail below, automation process  10  may normalize/homogenize this raw information (e.g., descriptors  56 ) using an ontology defined for the website, multiple websites, and/or a domain, thus generating useable information  60 .   Normalize/Homogenize the Attribute Descriptors: Since it is foreseeable that different webpages within a website (e.g., website  100 ) may use different attribute descriptors, automation process  10  may normalize/homogenize such attribute descriptors. For example, some webpages within website  100  may use the term “Quantity” while other webpages within website  100  may use the term “Count”. Accordingly, automation process  10  may normalize/homogenize this raw information (e.g., descriptors  56 ) such that e.g., all webpages defined within data description model  58  use the term “Quantity”, thus generating useable information  60 . As will be discussed in greater detail below, automation process  10  may normalize/homogenize this raw information (e.g., descriptors  56 ) using an ontology defined for the website, multiple websites, and/or a domain, thus generating useable information  60 .   Normalize/Homogenize the Value Descriptors: Since it is foreseeable that different webpages within a website (e.g., website  100 ) may use different value descriptors, automation process  10  may normalize/homogenize such value descriptors. For example, some webpages within website  100  may use the terms “Small/Medium/Large” while other webpages within website  100  may use the term “S/M/L”. Accordingly, automation process  10  may normalize/homogenize this raw information (e.g., descriptors  56 ) such that e.g., all webpages defined within data description model  58  use the term “Small/Medium/Large”, thus generating useable information  60 . As will be discussed in greater detail below, automation process  10  may normalize/homogenize this raw information (e.g., descriptors  56 ) using an ontology defined for the website, multiple websites, and/or a domain, thus generating useable information  60 .       

     Once the raw information (e.g., the above-described property/attribute/value descriptors  56  in their original disjointed form) within data description model  58  are transformed  212  into useable information (i.e., useable information  60 ), automation process  10  may populate  222  a database (e.g., database  62 ) with at least a portion of this useable information (i.e., useable information  60 ), wherein database  62  may be included within and/or associated with data description model  58 . Useable information  60  stored within database  60  may (generally speaking) function as a roadmap that allows for automated navigation of (in this example) website  100 . 
     Continuing with the above-stated example, automation process  10  may utilize data description model  58  and useable information  60  to process additional webpages within website  100 . As could be imagined, a website (especially an ecommerce website) may include hundreds of thousands of webpages that correspond to the hundreds of thousands of products they sell. Accordingly, automation process  10  may allow a user to manually identify  200  one or more portions of a website structure (e.g., website structure  54 ) of a specific website (e.g., website  100 ) to define specific data description model  58  (albeit it in a rudimentary form). Automation process  10  may then use specific data description model  58  to automatically process (in the fashion described above) the remaining webpages within website  100  to further refine specific data description model  58 . 
     As discussed above and with specific data description model  58  defined for website  100 , automation process  10  may process  208  specific data description model  58  to obtain useable information from website  100  and populate  222  a database (e.g., database  62 ) with at least a portion of this useable information (i.e., useable information  60 ). In an example where website  100  is an ecommerce website, website  100  may include hundreds of thousands of webpages to correspond to the hundreds of thousands of products they sell. As such, automation process  10  may populate  222  database  62  with useable information pertaining to the products from the hundreds of thousands of webpages by defining and executing specific data description model  58  on the webpages of website  100 . In this manner, automation process  10  may allow for the generation or population of one or more databases representative of the useable information of the various webpages of a website. Accordingly, automation process  10  may automatically obtain useable information from a website and organize that information into a separate database utilizing the data description model without human intervention. 
     Automation process  10  may repeat the above described process for various other websites by defining data description models for respective websites, processing those data description models on the respective websites, and populating one or more databases with at least a portion of useable information from the respective websites. When processing each data description model, automation process  10  may populate the same database for each website, separate databases for each website, and/or certain databases for particular websites. For example, automation process  10  may populate one or more domain-specific databases based, at least in part, upon the domain of each data description model. However, it will be appreciated that information from any combination of websites may be used to populate any combination of databases within the scope of the present disclosure. In this manner, automation process  10  may process data description models for multiple websites to generate an aggregated database of information from each respective website. 
     DataFi (Data Models Generating Data Models): 
     Referring also to  FIG.  4    and once specific data description model  58  is completely defined (e.g., all of the webpages of website  100  have been processed), automation process  10  may define  224  a plurality of data description models (e.g., plurality of data description models  118 ) corresponding to a plurality of websites (e.g., plurality of websites  120 ), the plurality of data description models (e.g., plurality of data description models  118 ) including: the specific data description model (e.g., specific data description model  58 ) corresponding to the specific website (e.g., specific website  100 ), and one or more additional data description models corresponding to one or more additional websites. 
     Automation process  10  may provide  226  the plurality of data description models (e.g., plurality of data description models  118 ) corresponding to the plurality of websites (e.g., plurality of websites  120 ) to a machine learning (ML) process (e.g., machine learning process  122 ). 
     As is known in the art, machine learning (ML) is the study of computer algorithms that improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence. Machine learning algorithms may build a model based on sample data (known as “training data”) in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms may be used in a wide variety of applications, such as in medicine, email filtering, speech recognition, and computer vision, wherein it may be difficult or unfeasible to develop conventional algorithms to perform the needed tasks. Machine learning may involve computers discovering how they can perform tasks without being explicitly programmed to do so. It may involve computers learning from data provided so that they carry out certain tasks. 
     As discussed above, data description models locate the various data-related portions within a website structure of a website, thus eliminating the need for a human being to visually-navigate a website. Accordingly, machine learning process  122  may define data description models that represent a website in a machine-interpretable format. In this manner, computing devices may use the data description model defined for a website to navigate that website without human intervention. In this manner, machine learning process  122  may use the plurality of data description models (e.g., plurality of data description models  118 ) corresponding to the plurality of websites (e.g., plurality of websites  120 ) as training data to “learn” how to navigate other websites. 
     Additionally, automation process  10  may provide  228  ontology data (e.g., ontology data  124 ) concerning the plurality of websites (e.g., plurality of websites  120 ) to the machine learning process (e.g., machine learning process  122 ). 
     As will be discussed in greater detail below, in order to process different descriptors across different websites, automation process  10  may normalize descriptors (e.g., descriptors  56 ) within a master website dataset to generate ontology data  124 . When generating a data description model for a target website (e.g., www.targetwebsite.com) using machine learning process  122 , automation process  10  may utilize ontology data  124  to process the target website. For example, with ontology data  124 , automation process  10  may determine that e.g., “Small”, as shown on the target website, is a value descriptor of a “Size” attribute descriptor and/or e.g., “On hand”, as shown on the target website, is an attribute descriptor indicative of a stock-level. In this manner, ontology data  124  may provide a “dictionary” of descriptors used across the target website and the plurality of websites (e.g., plurality of websites  120 ). 
     Accordingly, this ontology data (e.g., ontology data  124 ) may function as a roadmap that allows for automated navigation of (in this example) the plurality of websites (e.g., plurality of websites  120 ). Accordingly, machine learning process  122  may use ontology data  124  as training data to “learn” how to navigate these websites (e.g., plurality of websites  120 ). 
     Further, automation process  10  may provide  230  target website data (e.g., target website data  126 ) concerning a target website (e.g., www.targetwebsite.com) to the machine learning process (e.g., machine learning process  122 ). Accordingly and using plurality of data description models  118  and ontology data  124  as training data, automation process  10  may allow a user (e.g., user  36 ) to provide  230  target website data (e.g., target website data  126 ) that identifies a target website (e.g., www.targetwebsite.com) for automated processing by automation process  10 . 
     Accordingly, automation process  10  may process  232  the plurality of data description models (e.g., plurality of data description models  118 ), ontology data (e.g., ontology data  124 ) and target website data (e.g., target website data  126 ) using the machine learning process (e.g., machine learning process  122 ) to generate a data description model (e.g., target data description model  128 ) for the target website (e.g., www.targetwebsite.com). For example, automation process  10  may automatically process webpages within www.targetwebsite.com to generate target data description model  128  (in the manner described above). 
     When processing  232  plurality of data description models  118 , ontology data  124 , and target website data  126  to generate target data description model  128 , machine learning process  122  may identify spatial regions and structure portions of the target website that correspond to the one or more descriptors from plurality of data description models  118 . For instance, machine learning process  122  may identify spatial regions and structure portions of the target website that correspond to one or more property descriptors and/or one or more attribute descriptors of plurality of data description models  118 . Accordingly, machine learning process  122  may associate  406  one or more structure portions of the website structure of the target website with one or more descriptors to define target data description model  128  based, at least in part, upon plurality of data description models  118 , ontology data  124 , and target website data  126 . 
     Once generated, target data description model  128  may be included within plurality of data description models  118  and ontology data  124  may be updated to homogenize the descriptors used within target data description model  128 ; thus enabling plurality of data description models  118  and ontology data  124  to be utilized by automation process  10  to automatically process additional target websites. 
     As discussed above, automation process  10  may process  208  the specific data description model (e.g., target data description model  128 ) to obtain useable information from the target website. For example, when processing  208  the specific data description model (e.g., target data description model  128 ) to obtain useable information from the specific website (e.g., website  100 ), automation process  10  may process  210  the specific data description model (e.g., target data description model  128 ) to obtain raw information from the target website; and transform  212  the raw information into the useable information. 
     As discussed above and once the raw information within target data description model  128  is transformed  212  into useable information, automation process  10  may populate  222  a database (e.g., database  62  or a separate database) with at least a portion of this useable information, where this may be included within and/or associated with data description model  128 . Accordingly, useable information  60  may be aggregated with information from other websites stored within a database (i.e., the same database for each website, separate databases for each website, and/or certain databases for particular websites) using the data description models automatically generated by automation process  10  for those websites. 
     ParaLogue (General): 
     While the above-discussion concerned automation process  10  processing websites to define data description models (i.e., models concerning data within webpages/websites), automation process  10  may also effectuate similar processes to define function description models (i.e., models concerning functions within webpages/websites; as will be discussed below in greater detail). 
     As discussed above, automation process  10  may enable a user (e.g., user  36 ) to review various websites (e.g., website  100 ). Referring also to  FIGS.  5 - 6   , automation process  10  may identify  300  one or more interactions with one or more portions of a website structure (e.g., website structure  54 ) of a specific website (e.g., website  100 ). For example, automation process  10  may identify  300  one or more actions performed on one or more portion of website structure  54  of website  100 . As discussed above, examples of such a website structure (e.g., website structure  54 ) may include one or more of: a HTML website structure; a javascript website structure; and a CSS website structure. 
     When identifying  300  one or more interactions with one or more portions of a website structure (e.g., website structure  54 ) of a specific website (e.g., website  100 ), automation process  10  may: enable  302  a user (e.g., user  36 ) to review the specific website (e.g., website  100 ) to visually interact with one or more spatial regions of the specific website (e.g., website  100 ); and associate  304  one or more interactions with the one or more spatial regions of the specific website (e.g., website  100 ) with the one or more portions of the website structure (e.g., website structure  54 ). For example, automation process  10  may enable  302  user  36  to review website  100  to visually interact with spatial regions  250 ,  252  of website  100  (via selection with a mouse, not shown) and associate  304  the user&#39;s interactions with spatial regions  250 ,  252  of website  100  with structure portions  254 ,  256  (respectively) of website structure  54 . Specifically, when user  36  visually interacts with a spatial region (e.g., one of spatial regions  250 ,  252 ) of website  100 , automation process  10  may automatically associate  304  the identified interactions or actions performed on the spatial region (e.g., one of spatial regions  250 ,  252 ) with the corresponding portion (e.g., one of structure portions  254 ,  256  respectively) of the website structure (e.g., website structure  54 ) of the specific website (e.g., website  100 ). 
     Automation process  10  may associate  306  the one or more interactions with the one or more portions (e.g., structure portions  254 ,  256 ) of the website structure (e.g., website structure  54 ) with one or more functions (e.g., functions  64 ) of the specific website (e.g., website  100 ) to define a specific function description model (e.g., specific function description model  66 ) corresponding to the specific website (e.g., website  100 ). 
     Automation process  10  may provide a user interface or overlay on a web browser as user  36  interacts with website  100 . For example, the user interface may be an extension of a web browser, built-into a web browser, and/or may be executed separately from a web browser that provides the ability to access websites. When identifying  300  one or more interactions with one or more portions of website structure  54  of website  100 , the domain associated with website  100  may be determined. For example, a user  36  may provide (e.g., using the user interface) an indication or selection of the domain for website  100 . In another example, the domain may be automatically defined by automation process  10  when loading website  100 . In this example, suppose website  100  is an ecommerce website. Accordingly, website  100  may be associated with the ecommerce domain and automated process  10  may provide (e.g., within the user interface) a list of one or more functions (e.g., functions  64 ) specific to the ecommerce domain for the user to visually identify within website  100 . 
     Enabling a user to review the specific website (e.g., website  100 ) to visually interact with the one or more spatial regions of the specific website (e.g., website  100 ) may include receiving one or more user interaction recordings or logs of one or more user interactions with the website. For example, automation process  10  may receive and process various clickstreams or other activity information indicating how the one or more users interact with the website (e.g., website  100 ). Automation process  10  may associate  306  the one or more interactions with the one or more portions (e.g., structure portions  254 ,  256 ) of the website structure (e.g., website structure  54 ) as defined in the one or more user interaction recordings with one or more functions (e.g., functions  64 ) of the specific website (e.g., website  100 ) to define a specific function description model (e.g., specific function description model  66 ) corresponding to the specific website (e.g., website  100 ). For example and as discussed above, automation process  10  may provide a list of one or more functions (e.g., functions  64 ) for the user to associate with the one or more interactions with the one or more portions (e.g., structure portions  254 ,  256 ) of the website structure (e.g., website structure  54 ) as defined in the one or more user interaction recordings. 
     The one or more functions (e.g., function  64 ) may include functionalities that are effectuated via (in this example) website  100 . For example, a function (e.g., function  64 ) may include one or more actions that are performed on a website. Accordingly, function  64  may include any number of discrete actions. Examples of such functionality may include, in an ecommerce domain for example, but are not limited to:
         Add to Cart Functionality: This functionality may add an item defined on the current webpage (e.g., webpage  110 ) to the shopping cart of this user (e.g., user  36 ), thus allowing the user (e.g., user  36 ) to continue shopping for additional products/services. This functionality may include specific actions corresponding to a user&#39;s interactions with webpage  110 . For example, the “Add to Cart” function may include actions or interactions associated with: e.g., navigating to a webpage; clicking a quantity field; typing a quantity input; clicking a button to add the quantity of products to a cart; and/or waiting for the “add to cart” process to complete on webpage  110 .   Buy Now Functionality: This functionality may enable the user (e.g., user  36 ) to immediately purchase the item defined on the current webpage (e.g., webpage  110 ), thus enabling the user (e.g., user  36 ) to bypass the shopping cart and make an immediate purchase. This functionality may include specific actions corresponding to a user&#39;s interactions with webpage  110  that enable the user to immediately purchase the item defined on the current webpage.   Quantity Selection Functionality: This functionality may enable the user (e.g., user  36 ) to select the quantity of the item defined on the current webpage (e.g., webpage  110 ) to be purchased, wherein selecting a quantity greater than one may result in the total cost being recalculated. This functionality may include specific actions corresponding to a user&#39;s interactions with webpage  110  that enable the user to select a quantity of items. For example, this function may include actions or interactions associated with: e.g., navigating to a webpage; clicking a quantity field; typing a quantity input; and/or checking for an updated quantity and price.       

     Associating  306  the interactions with structure portions  254 ,  256  of website structure  54  with functions  64  of website  100  to define a specific function description model may include defining, using the user interface, functions for structure portions corresponding to the identified spatial regions. For example, automation process  10  may provide, using the user interface, user  36  with the ability to define or select a function for each structure portion corresponding to the user&#39;s actions and identified spatial region(s). For example, automation process  10  may associate  306  user  36 &#39;s interactions with structure portion  254  of website structure  54  with e.g., an “Add to Cart” function and may associate  306  user  36 &#39;s interactions with structure portion  256  of website structure  54  with e.g., a “Buy Now” function. In this manner, automation process  10  may define or generate the specific function description model for website  100  by associating or mapping particular specific structure portions of the website structure with one or more functions of the function description model corresponding to website  100 . 
     The specific function description model (e.g., specific function description model  66 ) corresponding to the specific website (e.g., website  100 ) may be configured to allow for the above-described automated accessing of (in this example) website  100 . For example and as discussed above, since specific function description model  66  locates the various function-related portions (e.g., structure portions  254 ,  256 ) within the website structure (e.g., website structure  54 ) of the specific website (e.g., website  100 ), the specific website (e.g., website  100 ) may be accessed and utilized in an automated fashion (since specific function description model  66  eliminates the need for a human being to visually-navigate website  100 ). In this manner and with the specific function description model, automation process  10  may generate machine-readable or machine-executable application programming interfaces (APIs) directly from the above-described association of portions of website structure with one or more functions. 
     The specific function description model may be both machine-interpretable and human-interpretable. For example, with specific function description model  66  corresponding to website  100 , automation process  10  may generate one or more machine-executable scripts capable of performing the one or more functions described above for website  100 . In this manner, specific function description model  60  is machine interpretable. Additionally, with specific function description model  66  corresponding to website  100 , automation process  10  may generate one or more natural language descriptions of the one or more functions described above. For example, automation process  10  may process specific function description model  66  corresponding to website  100  with one or more predefined translation rules to generate a natural language description of the one or more functions defined by specific function description model  66 . For example, automation process  10  may use a translator (e.g., translator  71 ) to: translate the functions of the function description model to a natural language description; and to translate a natural language description of a function description model to a function description model. In this manner, a user (e.g., user  36 ) can interpret what functions that specific function description model  66  is capable of performing on website  100  and a machine can interpret a natural language description of a function for performing on website  100 . 
     As discussed above, automation process  10  may identify  200  one or more portions of a website structure (e.g., website structure  54 ) of a specific website (e.g., website  100 ) and associate  206  the one or more portions (e.g., structure portions  106 ,  108 ) of the website structure (e.g., website structure  54 ) with one or more descriptors (e.g., descriptors  56 ) of the specific website (e.g., website  100 ) to define a specific data description model (e.g., specific data description model  58 ) corresponding to the specific website (e.g., website  100 ). When identifying  200  the one or more portions of the website structure of a specific website, the one or more portions (e.g., structure portions  106 ,  108 ) of website structure (e.g., website structure  54 ) may be generated or exposed in response to a user&#39;s interactions with the website (e.g., website  100 ). For example and as is known in the art, some websites may include portions of website structure or code that are generated dynamically as a user interacts with the website. Accordingly, automation process  10  may associate  306  the user&#39;s recorded interactions that generate the additional website structure (e.g., structure portions  106 ,  108 ) with one or more functions (e.g., functions  64 ) of the specific website (e.g., website  100 ). In this manner, specific function description model  66  may locate the various function-related portions within the website structure of the specific website (e.g., website  100 ) that generate or expose additional website structure. With a specific function description model that describes how to generate the additional website structure, the dynamically generated or dynamically accessible portions of the website may be identified and utilized in an automated fashion (since specific function description model  66  eliminates the need for a human being to visually-navigate website  100 ). 
     Once the user (e.g., user  36 ) and automation process  10  processes (in this example) webpage  110  of website  100 , the user (e.g., user  36 ) and automation process  10  may process (in this example) additional webpages (e.g., webpages  112 ,  114 ,  116 ) of website  100  to obtain additional functions for inclusion within (and further refinement of) function description model  66 . For example, automation process  10  may enable  302  user  36  to review additional webpages (e.g., webpages  112 ,  114 ,  116 ) of website  100  to visually interact with one or more spatial regions of these webpages (e.g., webpages  112 ,  114 ,  116 ) and associate  304  these interactions with the spatial regions with one or more portions of the website structure (e.g., website structure  54 ) to obtain additional functions for inclusion within (and further refinement of) the specific function description model (e.g., specific function description model  66 ) corresponding to the specific website (e.g., website  100 ). 
     Once a sufficient quantity of webpages (e.g., webpages  110 ,  112 ,  114 ,  116 ) of website  100  are processed (e.g., ten or more), automation process  10  may process the specific function description model (e.g., specific function description model  66 ) to obtain useable information from the specific website (e.g., website  100 ). For example and when processing the specific function description model (e.g., specific function description model  66 ) to obtain useable information from the specific website (e.g., website  100 ), automation process  10  may process the specific function description model (e.g., specific function description model  66 ) to obtain raw information from the specific website (e.g., website  100 ) and transform this raw information into useable information. 
     In another example, automation process  10  may process the specific function description model (e.g., specific function description model  66 ) to perform particular functions on the specific website (e.g., website  100 ). For example, suppose the specific function description model (e.g., specific function description model  66 ) includes e.g., adding a product to a shopping cart. In this example, automation process  10  may process specific function description model  66  to perform the one or more actions associated with the “Add to Cart” function (e.g., function  64 ). In this example, automation process  10  may perform the actions specified in the “Add to Cart” function to e.g., navigate to a webpage; click on a quantity field; type in a quantity input; click on a button to add the quantity of products to a shopping cart; and waiting for the products to be added to the shopping cart. As will discussed in greater detail below, with function description model  66  defined for website  100 , automation process  10  may automatically perform various functions on website  100  without requiring human intervention. 
     As discussed above, automation process  10  may associate  306  the one or more interactions (e.g., user&#39;s  36  interactions on website  100 ) with the one or more portions (e.g., structure portions  254 ,  256 ) of the website structure (e.g., website structure  54 ) with one or more functions (e.g., functions  64 ) of the specific website (e.g., website  100 ) to define a specific function description model (e.g., specific function description model  66 ) corresponding to the specific website (e.g., website  100 ), wherein these functions (e.g., functions  64 ) may include functionalities that are effectuated via (in this example) website  100 . As could be imagined, it is foreseeable that different webpages within a website may use different functions (e.g., functions  64 ). For example, some webpages within website  100  may use “Add to Cart” functionality, while other webpages within website  100  may use “Place in Cart” functionality. Further, some webpages within website  100  may use “Buy Now” functionality, while other webpages within website  100  may use “Check Out” functionality. 
     In order to properly utilize such functionality (e.g., functions  64 ), automation process  10  may process these functions to transform them from raw information (e.g., functions  64  in their original disjointed form) into useable information  68  (as will be described below). When transforming the raw information (e.g., functions  64 ) into useable information  68 , automation process  10  may: amend/normalize/homogenize the raw information (e.g., functions  64 ). Specifically, since it is foreseeable that different webpages within a website (e.g., website  100 ) may use functions that are formatted differently, automation process  10  may amend/normalize/homogenize such raw information (e.g., functions  64 ) to standardize the formatting. 
     Once the raw information (e.g., the above-described functions  64  in their original disjointed form) within function description model  66  are transformed into useable information (i.e., useable information  68 ), automation process  10  may populate a database (e.g., database  70 ) with at least a portion of this useable information (i.e., useable information  68 ), wherein database  70  may be included within and/or associated with function description model  66 . Useable information  68  stored within database  70  may (generally speaking) function as a roadmap that allows for automated navigation of (in this example) website  100 . 
     Continuing with the above-stated example, automation process  10  may utilize function description model  66  and useable information  68  to process additional webpages within website  100 . As could be imagined, a website (especially an ecommerce website) may include hundreds of thousands of webpages that correspond to the hundreds of thousands of products they sell. Accordingly, automation process  10  may allow a user to manually identify  300  one or more portions of a website structure (e.g., website structure  54 ) of a specific website (e.g., website  100 ) to define specific function description model  66  (albeit it in a rudimentary form). Automation process  10  may then use specific function description model  66  to automatically process (in the fashion described above) the remaining webpages within website  100  to further refine specific function description model  66 . 
     ParaLogue (Function Models Generating Function Models): 
     Referring also to  FIG.  7    and once specific function description model  66  is completely defined (e.g., all of the webpages of website  100  have been processed), automation process  10  may define  308  a plurality of function description models (e.g., plurality of function description models  258 ) corresponding to a plurality of websites (e.g., plurality of websites  120 ), the plurality of function description models (e.g., plurality of function description models  258 ) including: the specific function description model (e.g., specific function description model  66 ) corresponding to the specific website (e.g., specific website  100 ), and one or more additional function description models corresponding to one or more additional websites. 
     Automation process  10  may provide  310  the plurality of function description models (e.g., plurality of function description models  258 ) corresponding to the plurality of websites (e.g., plurality of websites  120 ) to a machine learning (ML) process (e.g., machine learning process  122 ). 
     As discussed above, function description models locate the various function-related portions within a website structure of a website, thus eliminating the need for a human being to visually-navigate a website. Accordingly, machine learning process  122  may use the plurality of function description models (e.g., plurality of function description models  258 ) corresponding to the plurality of websites (e.g., plurality of websites  120 ) as training data to “learn” how to navigate other websites. 
     Additionally, automation process  10  may provide  312  ontology data (e.g., ontology data  260 ) concerning the plurality of websites (e.g., plurality of websites  120 ) to the machine learning process (e.g., machine learning process  122 ). 
     As discussed above, being different webpages within a website use different functions (e.g., functions  64 ), in order to properly utilize such functions (e.g., functions  64 ), automation process  10  processes these functions to transform them from raw information (e.g., functions  64  in their original disjointed form) into useable information  68  (in a normalized/homogenized form). As could be imagined, it is foreseeable that different websites may use different functions (e.g., functions  64 ) within their webpages. For example, a first website (www.abc.com) may define the function “Buy Now”, while another website (www.xyz.com) may define the function “Check Out”. Additionally, different websites may define functions with different sequences of actions. For example, one website (www.abc.com) may define a function for entering quantity information with a text box that can receive text while another website (www.xyz.com) may define the function for entering quantity information with a drop-down list with multiple values where selection of a value, inputs the value into a text field. Therefore and in order to properly utilize such functions (e.g., functions  64 ) across multiple websites (e.g., www.abc.com &amp; www.xyz.com), automation process  10  may process these functions to transform them from their original disjointed form into useable (e.g., normalized/homogenized) information (e.g., ontology data  260 ). Accordingly and when generating ontology data  260 , automation process  10  may process the useable information included within each of the plurality of function description models (e.g., plurality of function description models  258 ) to amend/normalize/homogenize this useable information across the plurality of websites (e.g., plurality of websites  120 ). 
     In a similar fashion, this ontology data (e.g., ontology data  260 ) may function as a roadmap that allows for automated navigation of (in this example) the plurality of websites (e.g., plurality of websites  120 ). Accordingly, machine learning process  122  may use ontology data  260  as training data to “learn” how to navigate these websites (e.g., plurality of websites  120 ). 
     Automation process  10  may also provide website data (e.g., website data  130 ) concerning the plurality of websites (e.g., plurality of websites  120 ) to the machine learning process (e.g., machine learning process  122 ). An example of website data  130  may include, but is not limited to, website usage data describing one or more user interactions with the plurality of websites. For example, website usage data may include one or more user interaction recordings or logs of one or more user interactions with plurality of websites  120 . For example and as discussed above, website usage data may include various clickstreams or other activity information indicating how the one or more users interact with plurality of websites  120 . Accordingly, automation process  10  may allow a user (e.g., user  36 ) to provide website data (e.g., website data  130 ) concerning plurality of websites  120  for automated processing of other websites by automation process  10 . 
     Returning to the above example, suppose plurality of websites  120  includes one website (www.abc.com) that provides e.g., a text box that can receive text. As discussed above, while a user interacts with the text box on the website, automation process  10  may associate  306  the user&#39;s various interactions with the text box (e.g., click: text_box, type: &lt;quantity input&gt;) with a text box interaction function. On a different website (www.xyz.com), suppose that the website provides e.g., a drop-down list with values “1”, “2”, “3”, “4”, and “5+.” Suppose the user selects (e.g., by clicking) the value “5+”, the website creates a text input field. Accordingly, automation process  10  may associate  306  the user&#39;s various interactions with the drop-down list that creates a text input field (e.g., click: quantity_dropdown, click: quantity_5_plus, click: quantity_box, type: &lt;quantity input&gt;) with a text box interaction function. Accordingly, automation process  10  may provide website data (e.g., website data  130 ) concerning the plurality of websites (e.g., plurality of websites  120 ) to the machine learning process (e.g., machine learning process  122 ) for automated processing of other websites by automation process  10 . 
     Further, automation process  10  may provide  314  target website data (e.g., target website data  126 ) concerning a target website (e.g., www.targetwebsite.com) to the machine learning process (e.g., machine learning process  122 ). An example of target website data  126  may include, but is not limited to, target website usage data describing one or more user interactions with the target website. For example, target website usage data may include one or more user interaction recordings or logs of one or more user interactions with the target website. For example and as discussed above, target website usage data may include various clickstreams or other activity information indicating how the one or more users interact with the target website. Accordingly and using plurality of function description models  258  and ontology data  260  as training data, automation process  10  may allow a user (e.g., user  36 ) to provide  314  target website data (e.g., target website data  126 ) concerning a target website (e.g., www.targetwebsite.com) for automated processing by automation process  10 . 
     Accordingly, automation process  10  may process  316  the plurality of function description models (e.g., plurality of function description models  258 ), ontology data (e.g., ontology data  260 ) and target website data (e.g., target website data  126 ) using the machine learning process (e.g., machine learning process  122 ) to generate a function description model (e.g., target function description model  264 ) for the target website (e.g., www.targetwebsite.com). For example, automation process  10  may automatically process webpages within www.targetwebsite.com to generate target function description model  264  (in the manner described above). Once generated, target function description model  264  may be included within plurality of function description models  258  and ontology data  260  may be updated to homogenize the functions used within target function description model  264 ; thus enabling plurality of function description models  258  and ontology data  260  to be utilized by automation process  10  to automatically process additional target websites. 
     ParaFlow: 
     As discussed above, through the use of a data description model (e.g., data description model  58 ) and a function description model (e.g., function description model  66 ), a website (e.g., website  100 ) may be navigated without human intervention. Specifically and as discussed above, a data description model (e.g., data description model  58 ) may define the data defined within a website (e.g., website  100 ), while a function description model (e.g., function description model  66 ) may define the functions available via the website (e.g., website  100 ). 
     Referring also to  FIG.  8   , automation process  10  may be capable of navigating and/or effectuating the functionality of a plurality of websites through the use of such data description models and function description models. For the following example, assume that:
         Since data description model  58  and function description model  66  were defined for website  100 , automation process  10  may navigate and/or effectuate the functionality of website  100 . Accordingly and in the event that website  100  is an ecommerce website, automation process  10  may e.g., access website  100 , locate one or more products/services available via website  100 , and effectuate the purchase of the same.   Since data description model  350  and function description model  352  were defined for website  112 , automation process  10  may navigate and/or effectuate the functionality of website  112 . Accordingly and in the event that website  112  is an ecommerce website, automation process  10  may e.g., access website  112 , locate one or more products/services available via website  112 , and effectuate the purchase of the same.   Since data description model  354  and function description model  356  were defined for website  114 , automation process  10  may navigate and/or effectuate the functionality of website  114 . Accordingly and in the event that website  114  is an ecommerce website, automation process  10  may e.g., access website  114 , locate one or more products/services available via website  114 , and effectuate the purchase of the same.   Since data description model  358  and function description model  360  were defined for website  116 , automation process  10  may navigate and/or effectuate the functionality of website  116 . Accordingly and in the event that website  116  is an ecommerce website, automation process  10  may e.g., access website  116 , locate one or more products/services available via website  116 , and effectuate the purchase of the same.       

     While in this example, automation process  10  is shown to be capable of navigating and/or effectuating the functionality of four websites (e.g., websites  100 ,  112 ,  114 ,  116 ), this is for illustrative purposes only and is not intended to be a limitation of this disclosure, as other configurations are possible and are considered to be within the scope of this disclosure. For example, automation process  10  may be capable of navigating and/or effectuating the functionality of many additional websites (e.g., additional websites  362 ) provided the appropriate data description model(s) (e.g., data description model  364 ) and a function description model(s) (e.g., function description model  366 ) are defined. 
     Referring also to  FIG.  9   , automation process  10  may be configured to process complex tasks (e.g., complex task  400 ). Complex task  400  may include a plurality of discrete tasks, such as discrete tasks  402 ,  404 ,  406 ,  408 , wherein each of these discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) may include a plurality of sub-discrete tasks. For example: discrete task  402  may include sub-discrete tasks  410 ; discrete task  404  may include sub-discrete tasks  412 ; discrete task  406  may include sub-discrete tasks  414 ; and discrete task  408  may include sub-discrete tasks  416 . Each of these sub-discrete tasks (e.g., sub-discrete tasks  410 .  412 ,  414 ,  416 ) may define discrete requirements for the related subtask that concern e.g., price, delivery data, tracking of shipping and tracking of delivery. 
     Generally speaking, a complex task (e.g., complex task  400 ) may be a task that is traditionally executed across multiple websites (e.g., websites  100 ,  112 ,  114 ,  116 ). For example, complex task  400  may be the task of planning a vacation that includes multiple subtasks, such as: arranging air travel to a destination, booking a hotel at the destination, arranging car travel from a destination airport to the hotel, arranging car travel from the hotel to the destination airport, and arranging air travel from the destination. Additionally, a complex task (e.g., complex task  400 ) may be a task that is traditionally executed on a single website (e.g., website  100 ), but ends up being executed across one or more additional websites (e.g., websites  112 ,  114 ,  116 ) due to the scope of the complex task (e.g., complex task  400 ). Complex task  400  may be user-defined (e.g., by user  36 ) or automatically generated (e.g., by a machine). For example, automation process  10  may provide a user interface for a user to provide complex task  400 . Additionally, complex task  400  may be defined by one or more computing devices. In this manner, it will be appreciated that complex task  400  may be received from various sources. 
     For example, assume that a user (e.g., user  42 ) wishes to purchase 1,000,000 pair of surgical gloves, wherein the purchase price of these surgical gloves must be less than $20 per hundred pair. Additionally, these surgical gloves need to be received by 1 Jan. 2022. 
     This complex task (e.g., complex task  400 ) may be a portion of an overarching defined goal (e.g., define goal  418 ), wherein defined goal  418  may include a plurality of complex tasks (e.g., complex tasks  400 ,  420 ,  422 ,  424 ). For example, defined goal  418  may be the outfitting of a new hospital in West Virginia, wherein complex task  400  may be configured to obtain surgical gloves, complex task  420  may be configured to obtain syringes, complex task  422  may be configured to obtain pharmaceuticals, and complex task  424  may be configured to obtain surgical instruments/supplies. 
     Referring also to  FIG.  10   , automation process  10  may receive  450  a complex task (e.g., complex task  400 ). As discussed above, the complex task (e.g., complex task  400 ) in this illustrative example concerns the purchase of 1,000,000 pair of surgical gloves that need to be received by 1 Jan. 2022 and must cost less than $20 per hundred pair. Automation process  10  may process  452  the complex task (e.g., complex task  400 ) to define a plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) each having a discrete goal. In this example, the plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) concern the purchasing of the same product from multiple websites, wherein additional surgical gloves are purchased from additional websites until a total of 1,000,000 pair of surgical gloves are purchased. However, it is understood that the plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) may concern the purchasing of different products/services from different websites (e.g., purchasing air travel from an airline website and purchasing hotel lodging from a hotel website). 
     The plurality of discrete tasks may be formed from one or more functions defined in one or more function description models as described above. Returning to the above example, discrete tasks  402 ,  404 ,  406 ,  408  may be formed from one or more functions of respective function description models (e.g., function description models  66 ,  352 ,  356 ,  360 ) defined for various websites (e.g., websites  100 ,  112 ,  114 ,  116 ). Automation process  10  may utilize a predefined label associated with a function to form the plurality of discrete tasks. For example, suppose one or more functions are labeled as e.g., “Add product to Cart”. In this example, automation process  10  may process the label for the function to define which functions are capable of performing discrete tasks  402 ,  404 ,  406 ,  408 . Accordingly, when processing  452  complex task  400  to define plurality of discrete tasks  402 ,  404 ,  406 ,  408 , automation process  10  may identify one or more functions from function description models  66 ,  352 ,  356 ,  360  defined for websites  100 ,  112 ,  114 ,  116  that perform at least a portion of complex task  400 . 
     Additionally, automation process  10  may form the plurality of discrete tasks from one or more application programming interfaces (APIs) predefined for one or more websites. For example, automation process  10  may have access to one or more APIs predefined for a respective website (e.g., websites  100 ,  112 ,  114 ,  116 ) and may define discrete tasks  402 ,  404 ,  406 ,  408  using the one or more APIs. Accordingly, automation process  10  may form the plurality of discrete tasks from any combination of one or more functions from one or more function description models defined for one or more websites, and one or more application programming interfaces (APIs) predefined for the one or more websites. 
     Concerning the plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) included within complex task  400 , these discrete tasks  402 ,  404 ,  406 ,  408  (and their related discrete goals) may be conditional in nature and may generally mimic that of a workflow. Generally speaking:
         Discrete task  402  may concern accessing website  100  to purchase surgical gloves, wherein the discrete goal of discrete task  402  is the purchase 1,000,000 pair of surgical gloves that need to be received by 1 Jan. 2022 and must cost less than $20 per hundred pair.   Discrete task  404  may be conditional in nature. Specifically, if 1,000,000 pair of surgical gloves were already obtained, discrete task  404  may not be needed. However and assuming that it is needed, discrete task  404  may concern accessing website  112  to purchase surgical gloves, wherein the discrete goal of discrete task  404  is the purchase of whatever surgical gloves are still needed to satisfy complex task  400  (i.e., the purchase of 1,000,000 pair of surgical gloves that need to be received by 1 Jan. 2022 and must cost less than $20 per hundred pair).   Discrete task  406  may be conditional in nature. Specifically, if 1,000,000 pair of surgical gloves were already obtained, discrete task  406  may not be needed. However and assuming that it is needed, discrete task  406  may concern accessing website  114  to purchase surgical gloves, wherein the discrete goal of discrete task  406  is the purchase of whatever surgical gloves are still needed to satisfy complex task  400  (i.e., the purchase of 1,000,000 pair of surgical gloves that need to be received by 1 Jan. 2022 and must cost less than $20 per hundred pair).   Discrete task  408  may be conditional in nature. Specifically, if 1,000,000 pair of surgical gloves were already obtained, discrete task  408  may not be needed. However and assuming that it is needed, discrete task  408  may concern accessing website  116  to purchase surgical gloves, wherein the discrete goal of discrete task  408  is the purchase of whatever surgical gloves are still needed to satisfy complex task  400  (i.e., the purchase of 1,000,000 pair of surgical gloves that need to be received by 1 Jan. 2022 and must cost less than $20 per hundred pair).       

     While four discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) are shown, this is for illustrative purposes only and is not intended to be a limitation of this disclosure, as it is understood that the quantity of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) may be increased or decreased as needed. 
     When processing  452  the complex task (e.g., complex task  400 ) to define a plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) each having a discrete goal, automation process  10  may gather  454  information concerning the complex task (e.g., complex task  400 ). As stated above, complex task  400  defines a maximum purchase price of $20 per hundred pair and a delivery date of no later than 1 Jan. 2022. Accordingly, automation process  10  may inquire from an actor (e.g., a user and/or a machine) as to whether there are any additional task-based restrictions/requirements (e.g., country of manufacture, sustainability, material, color, packaging) to gather  454  information concerning the complex task (e.g., complex task  400 ). Additionally, while the four discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) are described as sequentially executed task, it will be appreciated that discrete tasks may be executed in parallel and/or based, at least in part, upon various conditions or information concerning the complex task and/or concerning the plurality of discrete tasks. Continuing with the above example, discrete tasks  402 ,  404 ,  406 ,  408  may execute in parallel with each task checking the availability and/or price of surgical gloves required by complex task  400  on each respective website. Automation process  10  may determine which discrete task to execute to purchase the surgical gloves based, at least in part, upon the availability and/or price information gathered from the plurality of websites. 
     In addition, gathering  454  information concerning the complex task (e.g., complex task  400 ) may include executing a description model (e.g., data description model  58  and/or function description model  66 ) on one or more websites. As discussed above and once fully defined, data description model  58  and/or function description model  66  may enable automation process  10  to autonomously navigate and/or effectuate the functionality of e.g., website  100  without any human intervention, as data description model  58  and/or function description model  66  may (generally speaking) function as a roadmap that allows for automated navigation of (in this example) website  100 . Additionally, automation process  10  may execute data description model  58  and/or function description model  66  to populate one or more databases with at least a portion of data from a website. Accordingly, automation process  10  may execute data description model  58  and/or function description model  66  on one or more websites based, at least in part, upon the complex task (e.g., complex task  400 ). For example, automation process  10  may execute data description model  58  and/or function description model  66  on one or more websites to e.g., determine a product price and/or product availability from the one or more websites based, at least in part, upon the complex task. 
     Further and when processing  452  the complex task (e.g., complex task  400 ) to define a plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) each having a discrete goal, automation process  10  may gather  456  information concerning the plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ). For example, automation process  10  may inquire from an actor (e.g., a user and/or a machine) as to whether there are subtask-based restrictions/requirements (e.g., prohibited websites, minimum shipment size, shipper location) to gather  456  information concerning the plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ). 
     When processing  452  the complex task (e.g., complex task  400 ) to define a plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) each having a discrete goal, automation process  10  may optimize  458  the plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ). For example, suppose that several complex tasks (e.g., complex tasks  400 ,  420 ) within defined goal  418  are simultaneously being processed by automation process  10 , wherein website  100  is providing products in each of these complex tasks (e.g., surgical gloves for complex task  400  and syringes for complex task  420 ). Accordingly and to optimize  458  the plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ), automation process  10  may combine these two orders (e.g., surgical gloves and syringes) to e.g., save on transportation costs. 
     When processing  452  the complex task (e.g., complex task  400 ) to define a plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) each having a discrete goal, automation process  10  may perform one or more of the following operations:
         Process  460  the complex task (e.g., complex task  400 ) to define a plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) each having a discrete goal via one or more predefined rules. For example, one or more predefined rules may exist concerning e.g., preferred vendors, blacklisted vendors, transportation requirements, country of manufacture, country of origination/operation, etc., all of which may be applied when processing  460  the complex task (e.g., complex task  400 ) to define a plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ).   Process  462  the complex task (e.g., complex task  400 ) to define a plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) each having a discrete goal via one or more ML-defined rules. For example, as orders are processed by automation process  10 , information may be gathered concerning the processing of these orders. Machine learning process  122  may process this order information to define ML-defined rules concerning e.g., preferred vendors, blacklisted vendors, transportation requirements, country of manufacture, country of origination/operation, etc., all of which may be applied when processing  462  the complex task (e.g., complex task  400 ) to define a plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ).   Process  464  the complex task (e.g., complex task  400 ) to define a plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) based, at least in part, upon human intervention. For example, the user (e.g., user  42 ) or a third-party may be consulted to define rules/preferences concerning e.g., preferred vendors, blacklisted vendors, transportation requirements, country of manufacture, country of origination/operation, etc., all of which may be applied when processing  464  the complex task (e.g., complex task  400 ) to define a plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ). Defining a plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) based, at least in part, upon human intervention may include determining that the one or more functions defined for the plurality of function description models for the plurality of websites are unable to perform one of the discrete tasks. Accordingly, automation process  10  may provide a request for human intervention for input regarding the discrete task. For example, automation process  10  may provide a request (e.g., using various communication applications) to a human (e.g., user  42 ) to perform a particular function on the website to supplement the function description model for that website. In this manner, automation process  10  may identify missing functionality in one or more function description models and may associate user interactions with missing functions to define or update a function description model for the website. In addition to performing functions of a website, automation process  10  may request that a human perform certain tasks that a machine cannot perform (e.g., prepare an electronic signature). In this manner, a machine and a human may collaborate to define and/or accomplish the plurality of discrete tasks of the complex task.       

     Once defined, automation process  10  may execute  466  the plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) on a plurality of machine-accessible public computing platforms (e.g., machine-accessible public computing platforms  370 ,  372 ,  374 ,  376 ), wherein examples of this plurality of machine-accessible public computing platforms (e.g., machine-accessible public computing platforms  370 ,  372 ,  374 ,  376 ) may include but is not limited to a plurality of ecommerce computing platforms coupled to the internet. 
     As discussed above, a data description model (e.g., data description model  58 ,  350 ,  354 ,  358 ) may be defined for at least one of the plurality of machine-accessible public computing platforms (e.g., machine-accessible public computing platforms  370 ,  372 ,  374 ,  376 ) and a function description model (e.g., function description model  66 ,  352 ,  356 ,  360 ) may be defined for at least one of the plurality of machine-accessible public computing platforms (e.g., machine-accessible public computing platforms  370 ,  372 ,  374 ,  376 ); wherein automation process  10  may navigate and/or effectuate the functionality of e.g., websites  100 ,  112 ,  114 ,  116  through the use of such data description models (e.g., data description models  58 ,  350 ,  354 ,  358 ) and function description models (e.g., function description models  66 ,  352 ,  356 ,  360 ). Accordingly, automation process  10  may execute the plurality of discrete tasks on a plurality of machine-accessible public computing platforms by executing the plurality of discrete tasks using a data description model (e.g., data description model  58 ,  350 ,  354 ,  358 ) and/or a function description model (e.g., function description model  66 ,  352 ,  356 ,  360 ) defined for the plurality of machine-accessible public computing platforms (e.g., machine-accessible public computing platforms  370 ,  372 ,  374 ,  376 ). 
     As discussed above, some of the discrete tasks (e.g., discrete tasks  404 ,  406 ,  408 ) may be conditional in nature and, therefore, may not be needed. For example, if the 1,000,000 pair of surgical gloves were obtained from website  100 , discrete tasks  404 ,  406 ,  408  may not be needed. Additionally, if the 1,000,000 pair of surgical gloves were obtained from websites  100 ,  112  (cumulatively), discrete tasks  406 ,  408  may not be needed. Further, if the 1,000,000 pair of surgical gloves were obtained from websites  100 ,  112 ,  114  (cumulatively), discrete task  408  may not be needed. Accordingly, automation process  10  may monitor the discrete and cumulative progress of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) to ensure that the complex task (e.g., complex task  400 ) is successfully effectuated. 
     Automation process  10  may determine  468  if any of the plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) failed to achieve its discrete goal, wherein “failing to achieve its discrete goal” may include one or more of:
         An immediate total failure of the discrete goal: For example, if automation process  10  accesses website  100  to purchase the 1,000,000 pair of surgical gloves . . . only to find out that website  100  does not have any surgical gloves available, automation process  10  may consider this to be an immediate total failure of the discrete goal.   An immediate partial failure of the discrete goal: For example, if automation process  10  accesses website  100  to purchase the 1,000,000 pair of surgical gloves . . . only to find out that website  100  only has 500,000 pair of surgical gloves available, automation process  10  may consider this to be an immediate partial failure of the discrete goal.   A retroactive total failure of the discrete goal: For example, if automation process  10  accesses website  100  to purchase the 1,000,000 pair of surgical gloves and purchases the same . . . only to find out that website  100  fails to ship any surgical gloves (e.g., due to them being backordered or systemic failure), automation process  10  may consider this to be a retroactive total failure of the discrete goal.   A retroactive partial failure of the discrete goal: For example, if automation process  10  accesses website  100  to purchase the 1,000,000 pair of surgical gloves and purchases the same . . . only to find out that website  100  shipped only 500,000 surgical gloves (e.g., due to them being backordered or systemic failure), automation process  10  may consider this to be a retroactive partial failure of the discrete goal.       

     While the above examples refer to failures of discrete goals when performing discrete tasks on a website, it will be appreciated that failing to achieve a discrete goal may include failure of a discrete goal associated with non-website resources (e.g., an API or other external service). 
     As will be explained below in greater detail, if a specific discrete task (e.g., one or more of discrete tasks  402 ,  404 ,  406 ,  408 ) failed to achieve its discrete goal, automation process  10  may define  470  a substitute discrete task having a substitute discrete goal, wherein automation process  10  may execute  472  the substitute discrete task. As will be clear from the discussion below, a substitute discrete task may be any discrete task that is modified as a result of the previously-executed discrete task. 
     Continuing with the above-stated example in which user  42  wishes to purchase 1,000,000 pair of surgical gloves for less than $20 per hundred pair and they are needed by 1 Jan. 2022, this complex task (e.g., complex task  400 ) may include a plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ). Assume that automation process  10  attends to the sequential execution of discrete tasks  402 ,  404 ,  406 ,  408  and the following operations occur:
         Automation process  10  may effectuate discrete task  402  and attempt to purchase the 1,000,000 pair of surgical gloves from website  100 . Assume that website  100  does not have any surgical gloves available. Accordingly, automation process  10  may consider this to be an immediate total failure of the discrete goal associated with discrete task  402 . As complex task  400  is still not satisfied (i.e., 0 of the 1,000,000 pair have been purchased), automation process  10  may continue on to the next discrete task.   Automation process  10  may effectuate discrete task  404  and attempt to purchase the 1,000,000 pair of surgical gloves from website  112 . Assume that website  112  has 500,000 pair of surgical gloves available (that satisfy the price and delivery requirements). Accordingly, automation process  10  may purchase this 500,000 pair of surgical glove but may consider this to be an immediate partial failure of the discrete goal associated with discrete task  404 . As complex task  400  is still not satisfied (i.e., 500,000 of the 1,000,000 pair have been purchased), automation process  10  may continue on to the next discrete task.   Automation process  10  may effectuate discrete task  406  and attempt to purchase the remaining 500,000 pair of surgical gloves from website  114 . Assume that website  114  has 200,000 pair of surgical gloves available (that satisfy the price and delivery requirements). Accordingly, automation process  10  may purchase this 200,000 pair of surgical glove but may consider this to be an immediate partial failure of the discrete goal associated with discrete task  406 . As complex task  400  is still not satisfied (i.e., 700,000 of the 1,000,000 pair have been purchased), automation process  10  may continue on to the next discrete task.   Automation process  10  may effectuate discrete task  408  and attempt to purchase the remaining 300,000 pair of surgical gloves from website  116 . Assume that website  112  has 300,000 pair of surgical gloves available (that satisfy the price and delivery requirements). Accordingly, automation process  10  may purchase this 300,000 pair of surgical glove and may consider this to not be a failure of the discrete goal associated with discrete task  408 . As complex task  400  is now satisfied (i.e., 1,000,000 of the 1,000,000 pair have been purchased), automation process  10  may not continue on to the next discrete task.       

     Grokit System (General): 
     As will be discussed below in greater detail, the above-described discrete systems (e.g., DataFi, ParaLogue &amp; ParaFlow) may be combined to form an end-to-end platform that enables the navigation of a plurality of websites (e.g., websites  100 ,  112 ,  114 ,  116 ) without the need for human intervention, thus enabling the automated &amp; distributed execution of complex tasks (e.g., complex task  400 ). As discussed above, complex task  400  may include the purchase of 1,000,000 pair of surgical gloves for less than $20 per hundred pair and delivered by 1 Jan. 2022. 
     Referring also to  FIG.  11    and as discussed above, automation process  10  may define  500  a data description model (e.g., data description models  58 ,  350 ,  354 ,  358 ) and a function description model (e.g., function description models  66 ,  352 ,  356 ,  360 ) corresponding to a website (e.g., websites  100 ,  112 ,  114 ,  116 ) on one or more of a plurality of machine-accessible public computing platforms (e.g., machine-accessible public computing platforms  370 ,  372 ,  374 ,  376 ). This plurality of machine-accessible public computing platforms (e.g., machine-accessible public computing platforms  370 ,  372 ,  374 ,  376 ) may include a plurality of ecommerce computing platforms coupled to the internet. 
     Automation process  10  may process  502  a complex task (e.g., complex task  400 ) to define a plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) each having a discrete goal, wherein this complex task (e.g., complex task  400 ) may be based upon a defined goal (e.g., defined goal  418 ). 
     As discussed above and when processing  502  the complex task (e.g., complex task  400 ) to define a plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) each having a discrete goal, automation process  10  may:
         gather  504  information concerning the complex task (e.g., complex task  400 ), in the manner described above;   gather  506  information concerning the plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ), in the manner described above; and/or   optimize  508  the plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ), in the manner described above.       

     Additionally/alternatively and when processing  502  the complex task (e.g., complex task  400 ) to define a plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) each having a discrete goal, automation process  10  may:
         Process  510  the complex task (e.g., complex task  400 ) to define a plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) each having a discrete goal via one or more predefined rules. For example, one or more predefined rules may exist concerning e.g., preferred vendors, blacklisted vendors, transportation requirements, country of manufacture, country of origination/operation, etc., all of which may be applied when processing  510  the complex task (e.g., complex task  400 ) to define a plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ).   Process  512  the complex task (e.g., complex task  400 ) to define a plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) each having a discrete goal via one or more ML-defined rules. For example, as orders are processed by automation process  10 , information may be gathered concerning the processing of these orders. Machine learning process  122  may process this order information to define ML-defined rules concerning e.g., preferred vendors, blacklisted vendors, transportation requirements, country of manufacture, country of origination/operation, etc., all of which may be applied when processing  512  the complex task (e.g., complex task  400 ) to define a plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ).   Process  514  the complex task (e.g., complex task  400 ) to define a plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) based, at least in part, upon human intervention. For example, the user (e.g., user  42 ) or a third-party may be consulted to define rules/preferences concerning e.g., preferred vendors, blacklisted vendors, transportation requirements, country of manufacture, country of origination/operation, etc., all of which may be applied when processing  514  the complex task (e.g., complex task  400 ) to define a plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ). As discussed above, defining a plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) based, at least in part, upon human intervention may include determining that the one or more functions defined for the plurality of function description models for the plurality of websites are unable to perform one of the discrete tasks. Accordingly, automation process  10  may provide a request for human intervention for input regarding the discrete task. For example, automation process  10  may provide a request to a human (e.g., user  42 ) to perform a particular function on the website to supplement the function description model for that website. In this manner, automation process  10  may identify missing functionality in one or more function description models and may associate user interactions with missing functions to define or update a function description model for the website. In addition to performing functions of a website, automation process  10  may request that a human perform certain tasks that a machine cannot perform (e.g., prepare an electronic signature). In this manner, a machine and a human may collaborate to define and/or accomplish the plurality of discrete tasks of the complex task.       

     As discussed above, automation process  10  may execute  516  the plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) on the plurality of machine-accessible public computing platforms (e.g., machine-accessible public computing platforms  370 ,  372 ,  374 ,  376 ) and may determine  518  if any of the plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) failed to achieve its discrete goal. As discussed above, “failing to achieve its discrete goal” may include one or more of:
         An immediate total failure of the discrete goal: For example, if automation process  10  accesses website  100  to purchase the 1,000,000 pair of surgical gloves . . . only to find out that website  100  does not have any surgical gloves available, automation process  10  may consider this to be an immediate total failure of the discrete goal.   An immediate partial failure of the discrete goal: For example, if automation process  10  accesses website  100  to purchase the 1,000,000 pair of surgical gloves . . . only to find out that website  100  only has 500,000 pair of surgical gloves available, automation process  10  may consider this to be an immediate partial failure of the discrete goal.   A retroactive total failure of the discrete goal: For example, if automation process  10  accesses website  100  to purchase the 1,000,000 pair of surgical gloves and purchases the same . . . only to find out that website  100  fails to ship any surgical gloves (e.g., due to them being backordered or systemic failure), automation process  10  may consider this to be a retroactive total failure of the discrete goal.   A retroactive partial failure of the discrete goal: For example, if automation process  10  accesses website  100  to purchase the 1,000,000 pair of surgical gloves and purchases the same . . . only to find out that website  100  shipped only 500,000 surgical gloves (e.g., due to them being backordered or systemic failure), automation process  10  may consider this to be a retroactive partial failure of the discrete goal.       

     As discussed above, if a specific discrete task failed to achieve its discrete goal, automation process  10  may define  520  a substitute discrete task having a substitute discrete goal and may execute  522  the substitute discrete task. 
     Grokit System (SaaS): 
     As will be discussed below in greater detail, the above-described discrete systems (e.g., DataFi, ParaLogue &amp; ParaFlow) may be combined to form an end-to-end Software-as-a-Service (SaaS) platform that enables the navigation of a plurality of websites (e.g., websites  100 ,  112 ,  114 ,  116 ) without requiring human intervention, thus enabling the automated &amp; distributed execution of complex tasks (e.g., complex task  400 ). As discussed above, complex task  400  may include the purchase of 1,000,000 pair of surgical gloves for less than $20 per hundred pair and delivered by 1 Jan. 2022. 
     Referring also to  FIGS.  12 - 13    and as discussed above, automation process  10  may define  550  a data description model (e.g., data description models  58 ,  350 ,  354 ,  358 ) and a function description model (e.g., function description models  66 ,  352 ,  356 ,  360 ) for one or more of a plurality of machine-accessible public computing platforms (e.g., machine-accessible public computing platforms  370 ,  372 ,  374 ,  376 ) on a cloud-based computing resource (e.g., cloud-based computing resource  600 ). The plurality of machine-accessible public computing platforms (e.g., machine-accessible public computing platforms  370 ,  372 ,  374 ,  376 ) may include a plurality of ecommerce computing platforms coupled to the internet. 
     An example of cloud-based computing resource  600  may include but is not limited to a system that provides on-demand availability of computer system resources, especially data storage (e.g., cloud storage) and computing power, without direct active management by the user. The term is generally used to describe data centers available to many users over the Internet. Large clouds often have functions distributed over multiple locations from central servers. If the connection to the user is relatively close, it may be designated an edge server. Clouds may be limited to a single organization (enterprise clouds), or be available to multiple organizations (public cloud). Cloud computing may rely on sharing of resources to achieve coherence and economies of scale. Benefits of public and hybrid clouds include allowing companies to avoid (or minimize) up-front IT infrastructure costs while getting applications up and running faster with improved manageability and less maintenance. Cloud computing may enable IT teams to more rapidly adjust resources to meet fluctuating and unpredictable demand, while providing burst computing capability (i.e., high computing power at certain periods of peak demand). 
     As discussed above, automation process  10  may process  552  the complex task (e.g., complex task  400 ) on the cloud-based computing resource (e.g., cloud-based computing resource  600 ) to define a plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) each having a discrete goal, wherein this complex task (e.g., complex task  400 ) may be based upon a defined goal (e.g., defined goal  418 ). 
     As discussed above and when processing  552  a complex task (e.g., complex task  400 ) on the cloud-based computing resource (e.g., cloud-based computing resource  600 ) to define a plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) each having a discrete goal, automation process  10  may:
         gather  554  information concerning the complex task (e.g., complex task  400 ), in the manner described above;   gather  556  information concerning the plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ), in the manner described above; and/or   optimize  558  the plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ), in the manner described above.       

     Additionally/alternatively and when processing  552  a complex task (e.g., complex task  400 ) on the cloud-based computing resource (e.g., cloud-based computing resource  600 ) to define a plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) each having a discrete goal, automation process  10  may:
         Process  560  the complex task (e.g., complex task  400 ) to define a plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) each having a discrete goal via one or more predefined rules. For example, one or more predefined rules may exist concerning e.g., preferred vendors, blacklisted vendors, transportation requirements, country of manufacture, country of origination/operation, etc., all of which may be applied when processing  560  the complex task (e.g., complex task  400 ) to define a plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ).   Process  562  the complex task (e.g., complex task  400 ) to define a plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) each having a discrete goal via one or more ML-defined rules. For example, as orders are processed by automation process  10 , information may be gathered concerning the processing of these orders. Machine learning process  122  may process this order information to define ML-defined rules concerning e.g., preferred vendors, blacklisted vendors, transportation requirements, country of manufacture, country of origination/operation, etc., all of which may be applied when processing  562  the complex task (e.g., complex task  400 ) to define a plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ).   Process  564  the complex task (e.g., complex task  400 ) to define a plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) based, at least in part, upon human intervention. For example, the user (e.g., user  42 ) or a third-party may be consulted to define rules/preferences concerning e.g., preferred vendors, blacklisted vendors, transportation requirements, country of manufacture, country of origination/operation, etc., all of which may be applied when processing  564  the complex task (e.g., complex task  400 ) to define a plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ). As discussed above, defining a plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) based, at least in part, upon human intervention may include determining that the one or more functions defined for the plurality of function description models for the plurality of websites are unable to perform one of the discrete tasks. Accordingly, automation process  10  may provide a request for human intervention for input regarding the discrete task. For example, automation process  10  may provide a request to a human (e.g., user  42 ) to perform a particular function on the website to supplement the function description model for that website. In this manner, automation process  10  may identify missing functionality in one or more function description models and may associate user interactions with missing functions to define or update a function description model for the website. In addition to performing functions of a website, automation process  10  may request that a human perform certain tasks that a machine cannot perform (e.g., prepare an electronic signature). In this manner, a machine and a human may collaborate to define and/or accomplish the plurality of discrete tasks of the complex task.       

     As discussed above, automation process  10  may execute  566  the plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) on the plurality of machine-accessible public computing platforms (e.g., machine-accessible public computing platforms  370 ,  372 ,  374 ,  376 ) via the cloud-based computing resource (e.g., cloud-based computing resource  600 ). 
     When executing  566  the plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) on the plurality of machine-accessible public computing platforms (e.g., machine-accessible public computing platforms  370 ,  372 ,  374 ,  376 ) via the cloud-based computing resource (e.g., cloud-based computing resource  600 ), automation process  10  may define  568  a plurality of discrete computing processes (e.g., computing processes  602 ) on the cloud-based computing resource (e.g., cloud-based computing resource  600 ). 
     Examples of the plurality of discrete computing processes (e.g., computing processes  602 ) may include: one or more virtual machines; one or more containers; and one or more unikernels.
         Virtual Machines: As is known in the art, a virtual machine (VM) is the virtualization/emulation of a computer system. Virtual machines may be based on computer architectures and may provide functionality of a physical computer, wherein their implementations may involve specialized hardware, software, or a combination. Virtual machines may differ and are organized by their function. For example, system virtual machines (also termed full virtualization VMs) may provide a substitute for a real machine. System virtual machines may provide functionality needed to execute entire operating systems. A hypervisor may use native execution to share and manage hardware, allowing for multiple environments that are isolated from one another, yet exist on the same physical machine. Modern hypervisors may use hardware-assisted virtualization, virtualization-specific hardware, primarily from the host CPUs. Process virtual machines may be designed to execute computer programs in a platform-independent environment.   Containers: As is known in the art, virtualization is an operating system paradigm in which the kernel allows the existence of multiple isolated user space instances. Such instances (called containers, zones, virtual private servers, partitions, virtual environments, virtual kernels, or jails) may look like real computers from the point of view of programs running in them. A computer program running on an ordinary operating system may see all resources (e.g., connected devices, files and folders, network shares, CPU power, quantifiable hardware capabilities) of that computer. However, programs running inside of a container can only see the container&#39;s contents and devices assigned to the container.   Unikernels: As is known in the art, a unikernel is a specialized, single address space machine image constructed by using library operating systems. A developer selects, from a modular stack, the minimal set of libraries that correspond to the OS constructs required for the application to run. These libraries may then be compiled with the application and configuration code to build sealed, fixed-purpose images (unikernels) that run directly on a hypervisor or hardware without an intervening OS such as Linux or Windows. Thousands of unikernels may run on the same hardware, thus meeting the aspirational objective of the triple-order of magnitude (e.g., trillions of machines operating over the web versus the billions of machines that operate today). Unikernels have the security of a virtual machine and are stripped down to the bare essentials of running code inside processes without the overhead of an entire OS′ device support. With a reduced memory footprint, a unikernal can startup in less than 125 milliseconds. Further, unikernels may be managed by Kubernetes (automatic orchestration).       

     When executing  566  the plurality of discrete tasks on the plurality of machine-accessible public computing platforms (e.g., machine-accessible public computing platforms  370 ,  372 ,  374 ,  376 ) via the cloud-based computing resource (e.g., cloud-based computing resource  600 ), automation process  10  may utilize  570  the plurality of discrete computing processes (e.g., computing processes  602 ) to execute the plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) on the plurality of machine-accessible public computing platforms (e.g., machine-accessible public computing platforms  370 ,  372 ,  374 ,  376 ). 
     As discussed above, automation process  10  may determine  572  if any of the plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) failed to achieve its discrete goal, wherein “failing to achieve its discrete goal” may include one or more of:
         An immediate total failure of the discrete goal: For example, if automation process  10  accesses website  100  to purchase the 1,000,000 pair of surgical gloves . . . only to find out that website  100  does not have any surgical gloves available, automation process  10  may consider this to be an immediate total failure of the discrete goal.   An immediate partial failure of the discrete goal: For example, if automation process  10  accesses website  100  to purchase the 1,000,000 pair of surgical gloves . . . only to find out that website  100  only has 500,000 pair of surgical gloves available, automation process  10  may consider this to be an immediate partial failure of the discrete goal.   A retroactive total failure of the discrete goal: For example, if automation process  10  accesses website  100  to purchase the 1,000,000 pair of surgical gloves and purchases the same . . . only to find out that website  100  fails to ship any surgical gloves (e.g., due to them being backordered or systemic failure), automation process  10  may consider this to be a retroactive total failure of the discrete goal.   A retroactive partial failure of the discrete goal: For example, if automation process  10  accesses website  100  to purchase the 1,000,000 pair of surgical gloves and purchases the same . . . only to find out that website  100  shipped only 500,000 surgical gloves (e.g., due to them being backordered or systemic failure), automation process  10  may consider this to be a retroactive partial failure of the discrete goal.       

     As discussed above, if a specific discrete task failed to achieve its discrete goal, automation process  10  may define  574  a substitute discrete task having a substitute discrete goal and may execute  576  the substitute discrete task. 
     Grokit System (Supply Chain): 
     As will be discussed below in greater detail, the above-described discrete systems (e.g., DataFi, ParaLogue &amp; ParaFlow) may be combined to form an end-to-end supply chain management platform that enables the navigation of a plurality of websites (e.g., websites  100 ,  112 ,  114 ,  116 ) without requiring human intervention, thus enabling the automated &amp; distributed execution of complex tasks (e.g., complex task  400 ). For example, the end-to-end supply chain management platform may allow machines to do human work and for complex tasks to be interactively delegated between humans and machines. As discussed above, complex task  400  may include the purchase of 1,000,000 pair of surgical gloves for less than $20 per hundred pair and delivered by 1 Jan. 2022. 
     Referring also to  FIG.  14    and as discussed above, automation process  10  may define  600  a data description model (e.g., data description models  58 ,  350 ,  354 ,  358 ) and a function description model (e.g., function description models  66 ,  352 ,  356 ,  360 ) for one or more of a plurality of machine-accessible supply-chain computing platforms (e.g., machine-accessible public computing platforms  370 ,  372 ,  374 ,  376 ). The plurality of machine-accessible supply-chain computing platforms (e.g., machine-accessible public computing platforms  370 ,  372 ,  374 ,  376 ) may include a plurality of ecommerce computing platforms coupled to the internet. 
     As discussed above, automation process  10  may process  602  a complex supply-chain task (e.g., complex task  400 ) to define a plurality of discrete supply-chain tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) each having a discrete goal, wherein the complex supply-chain task (e.g., complex task  400 ) may be based upon a defined goal (e.g., defined goal  418 ). The complex supply-chain task (e.g. complex task  400 ) may concern one or more of obtaining a large quantity of a product; tracking a shipping status of the product; and processing one or more invoices. However, it will be appreciated that the complex supply-chain task (e.g. complex task  400 ) may concern any supply-chain-related task within the scope of the present disclosure. 
     As discussed above and when processing  602  the complex supply-chain task (e.g., complex task  400 ) to define a plurality of discrete supply-chain tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) each having a discrete goal, automation process  10  may:
         gather  604  information concerning the complex supply-chain task (e.g., complex task  400 ), in the manner described above;   gather  606  information concerning the plurality of discrete supply-chain tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ), in the manner described above; and/or   optimize  608  the plurality of discrete supply-chain tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ), in the manner described above.       

     Additionally/alternatively and when processing  602  the complex supply-chain task (e.g., complex task  400 ) to define a plurality of discrete supply-chain tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) each having a discrete goal, automation process  10  may:
         Process  610  the complex supply-chain task (e.g., complex task  400 ) to define a plurality of discrete supply-chain tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) each having a discrete goal via one or more predefined rules. For example, one or more predefined rules may exist concerning e.g., preferred vendors, blacklisted vendors, transportation requirements, country of manufacture, inventory restocking, competitive pricing, country of origination/operation, etc., all of which may be applied when processing  610  the complex supply-chain task (e.g., complex task  400 ) to define a plurality of discrete supply-chain task (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ).   Process  612  the complex supply-chain task (e.g., complex task  400 ) to define a plurality of discrete supply-chain tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) each having a discrete goal via one or more ML-defined rules. For example, as orders are processed by automation process  10 , information may be gathered concerning the processing of these orders. Machine learning process  122  may process this order information to define ML-defined rules concerning e.g., preferred vendors, blacklisted vendors, transportation requirements, country of manufacture, inventory restocking, competitive pricing, country of origination/operation, etc., all of which may be applied when processing  612  the complex supply-chain task (e.g., complex task  400 ) to define a plurality of discrete supply-chain task (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ).   Process  614  the complex supply-chain task (e.g., complex task  400 ) to define a plurality of discrete supply-chain tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) based, at least in part, upon human intervention. For example, the user (e.g., user  42 ) or a third-party may be consulted to define rules/preferences concerning e.g., preferred vendors, blacklisted vendors, transportation requirements, country of manufacture, inventory restocking, competitive pricing, country of origination/operation, etc., all of which may be applied when processing  614  the complex supply-chain task (e.g., complex task  400 ) to define a plurality of discrete supply-chain task (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ). As discussed above, defining a plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) based, at least in part, upon human intervention may include determining that the one or more functions defined for the plurality of function description models for the plurality of websites are unable to perform one of the discrete tasks. Accordingly, automation process  10  may provide a request for human intervention for input regarding the discrete task. For example, automation process  10  may provide a request to a human (e.g., user  42 ) to perform a particular function on the website to supplement the function description model for that website. In this manner, automation process  10  may identify missing functionality in one or more function description models and may associate user interactions with missing functions to define or update a function description model for the website. In addition to performing functions of a website, automation process  10  may request that a human perform certain tasks that a machine cannot perform (e.g., prepare an electronic signature). In this manner, a machine and a human may collaborate to define and/or accomplish the plurality of discrete supply-chain tasks of the complex supply-chain task.       

     As discussed above, automation process  10  may execute  616  the plurality of discrete supply-chain task (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) on the plurality of machine-accessible supply-chain computing platforms (e.g., machine-accessible public computing platforms  370 ,  372 ,  374 ,  376 ). 
     When executing  616  the plurality of discrete supply-chain task (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) on the plurality of machine-accessible supply-chain computing platforms (e.g., machine-accessible public computing platforms  370 ,  372 ,  374 ,  376 ), automation process  10  may: execute  618  a first discrete supply-chain task (e.g., discrete task  402 ) on a first of the machine-accessible supply-chain computing platforms (e.g., machine-accessible public computing platforms  370 ) to obtain a first portion of the large quantity of the product (e.g., the 1,000,000 pair of surgical gloves); and execute  620  at least a second discrete supply-chain task (e.g., one or more of discrete task  404 ,  406 ,  408 ) on at least a second of the machine-accessible supply-chain computing platforms (e.g., machine-accessible public computing platforms  372 ,  374 ,  376 ) to obtain at least a second portion of the large quantity of the product (e.g., the 1,000,000 pair of surgical gloves). 
     Accordingly, the complex supply-chain task (e.g., complex task  400 ) may be broken down into a plurality of smaller tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) that may be executed across a plurality of computers (e.g., machine-accessible public computing platforms  370 ,  372 ,  374 ,  376 ), thus enabling a task (e.g., the purchase of 1,000,000 pair of surgical gloves) that would likely not be executable on a single ecommerce website (e.g., website  100 ) to be executed in a distributed fashion across a plurality of ecommerce websites (e.g., websites  100 ,  112 ,  114 ,  116 ). 
     As discussed above, automation process  10  may determine  622  if any of the plurality of discrete supply-chain task (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) failed to achieve its discrete goal, wherein “failing to achieve its discrete goal” may include one or more of:
         An immediate total failure of the discrete goal: For example, if automation process  10  accesses website  100  to purchase the 1,000,000 pair of surgical gloves . . . only to find out that website  100  does not have any surgical gloves available, automation process  10  may consider this to be an immediate total failure of the discrete goal.   An immediate partial failure of the discrete goal: For example, if automation process  10  accesses website  100  to purchase the 1,000,000 pair of surgical gloves . . . only to find out that website  100  only has 500,000 pair of surgical gloves available, automation process  10  may consider this to be an immediate partial failure of the discrete goal.   A retroactive total failure of the discrete goal: For example, if automation process  10  accesses website  100  to purchase the 1,000,000 pair of surgical gloves and purchases the same . . . only to find out that website  100  fails to ship any surgical gloves (e.g., due to them being backordered or systemic failure), automation process  10  may consider this to be a retroactive total failure of the discrete goal.   A retroactive partial failure of the discrete goal: For example, if automation process  10  accesses website  100  to purchase the 1,000,000 pair of surgical gloves and purchases the same . . . only to find out that website  100  shipped only 500,000 surgical gloves (e.g., due to them being backordered or systemic failure), automation process  10  may consider this to be a retroactive partial failure of the discrete goal.       

     As discussed above, if a specific discrete supply-chain task failed to achieve its discrete goal, automation process  10  may define  624  a substitute discrete supply-chain task having a substitute discrete goal and may execute  626  the substitute discrete supply-chain task. 
     Grokit System (Automated Navigation): 
     As will be discussed below in greater detail, the above-described discrete systems (e.g., DataFi, ParaLogue &amp; ParaFlow) may be combined to form an end-to-end platform that enables the navigation of a plurality of websites (e.g., websites  100 ,  112 ,  114 ,  116 ) via an orchestrating computing system (i.e., removing the exclusive reliance on humans from the equation), thus enabling the automated &amp; distributed execution of complex tasks (e.g., complex task  400 ). For example, this end-to-end platform may allow for automated orchestration of a complex task among machines and any number of humans (or without humans). As discussed above, complex task  400  may include the purchase of 1,000,000 pair of surgical gloves for less than $20 per hundred pair and delivered by 1 Jan. 2022. 
     Referring also to  FIG.  15    and as discussed above, automation process  10  may define  650  a data description model (e.g., data description models  58 ,  350 ,  354 ,  358 ) and a function description model (e.g., function description models  66 ,  352 ,  356 ,  360 ) for one or more of a plurality of machine-accessible computing platforms (e.g., machine-accessible public computing platforms  370 ,  372 ,  374 ,  376 ). The plurality of machine-accessible computing platforms (e.g., machine-accessible public computing platforms  370 ,  372 ,  374 ,  376 ) may include a plurality of ecommerce computing platforms coupled to the internet. 
     As discussed above, automation process  10  may process  652  a complex task (e.g., complex task  400 ) to define a plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) each having a discrete goal, wherein the complex task (e.g., complex task  400 ) may be based upon a defined goal (e.g., defined goal  418 ). 
     As discussed above and when processing  652  the complex task (e.g., complex task  400 ) to define a plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) each having a discrete goal, automation process  10  may:
         gather  654  information concerning the complex task (e.g., complex task  400 ), in the manner described above;   gather  656  information concerning the plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ), in the manner described above; and/or   optimize  658  the plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ), in the manner described above.       

     Additionally/alternatively and when processing  652  the complex task (e.g., complex task  400 ) to define a plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) each having a discrete goal, automation process  10  may:
         Process  660  the complex task (e.g., complex task  400 ) to define a plurality of discrete task (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) each having a discrete goal via one or more predefined rules. For example, one or more predefined rules may exist concerning e.g., preferred vendors, blacklisted vendors, transportation requirements, country of manufacture, country of origination/operation, etc., all of which may be applied when processing  660  the complex task (e.g., complex task  400 ) to define a plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ).   Process  662  the complex task (e.g., complex task  400 ) to define a plurality of discrete task (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) each having a discrete goal via one or more ML-defined rules. For example, as orders are processed by automation process  10 , information may be gathered concerning the processing of these orders. Machine learning process  122  may process this order information to define ML-defined rules concerning e.g., preferred vendors, blacklisted vendors, transportation requirements, country of manufacture, country of origination/operation, etc., all of which may be applied when processing  662  the complex task (e.g., complex task  400 ) to define a plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ).   Process  664  the complex task (e.g., complex task  400 ) to define a plurality of discrete task (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) based, at least in part, upon human intervention. For example, the user (e.g., user  42 ) or a third-party may be consulted to define rules/preferences concerning e.g., preferred vendors, blacklisted vendors, transportation requirements, country of manufacture, country of origination/operation, etc., all of which may be applied when processing  664  the complex task (e.g., complex task  400 ) to define a plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ). As discussed above, defining a plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) based, at least in part, upon human intervention may include determining that the one or more functions defined for the plurality of function description models for the plurality of websites are unable to perform one of the discrete tasks. Accordingly, automation process  10  may provide a request for human intervention for input regarding the discrete task. For example, automation process  10  may provide a request to a human (e.g., user  42 ) to perform a particular function on the website to supplement the function description model for that website. In this manner, automation process  10  may identify missing functionality in one or more function description models and may associate user interactions with missing functions to define or update a function description model for the website. In addition to performing functions of a website, automation process  10  may request that a human perform certain tasks that a machine cannot perform (e.g., prepare an electronic signature). In this manner, a machine and a human may collaborate to define and/or accomplish the plurality of discrete tasks of the complex task.       

     As discussed above, automation process  10  may execute  666  the plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) on the plurality of machine-accessible public computing platforms (e.g., machine-accessible public computing platforms  370 ,  372 ,  374 ,  376 ) and may determine  668  if any of the plurality of discrete tasks (e.g., discrete tasks  402 ,  404 ,  406 ,  408 ) failed to achieve its discrete goal. As discussed above, “failing to achieve its discrete goal” may include one or more of:
         An immediate total failure of the discrete goal: For example, if automation process  10  accesses website  100  to purchase the 1,000,000 pair of surgical gloves . . . only to find out that website  100  does not have any surgical gloves available, automation process  10  may consider this to be an immediate total failure of the discrete goal.   An immediate partial failure of the discrete goal: For example, if automation process  10  accesses website  100  to purchase the 1,000,000 pair of surgical gloves . . . only to find out that website  100  only has 500,000 pair of surgical gloves available, automation process  10  may consider this to be an immediate partial failure of the discrete goal.   A retroactive total failure of the discrete goal: For example, if automation process  10  accesses website  100  to purchase the 1,000,000 pair of surgical gloves and purchases the same . . . only to find out that website  100  fails to ship any surgical gloves (e.g., due to them being backordered or systemic failure), automation process  10  may consider this to be a retroactive total failure of the discrete goal.   A retroactive partial failure of the discrete goal: For example, if automation process  10  accesses website  100  to purchase the 1,000,000 pair of surgical gloves and purchases the same . . . only to find out that website  100  shipped only 500,000 surgical gloves (e.g., due to them being backordered or systemic failure), automation process  10  may consider this to be a retroactive partial failure of the discrete goal.       

     As discussed above, if a specific discrete task failed to achieve its discrete goal, automation process  10  may define  670  a substitute discrete task having a substitute discrete goal and may execute  672  the substitute discrete task. 
     Grokit System Features: 
     As discussed above, the above-described discrete systems (e.g., DataFi, ParaLogue &amp; ParaFlow) may be combined to form an end-to-end platform that enables the navigation of a plurality of websites (e.g., websites  100 ,  112 ,  114 ,  116 ) without the need for human intervention, thus enabling the automated &amp; distributed execution of complex tasks (e.g., complex task  400 ). 
     As will be discussed below in greater detail, automation process  10  may include various additional features that may enhance the functionality of the platform discussed above. 
     Ontology: 
     Discussed below is the manner in which automation process  10  may generate the above-described ontology (e.g., ontology data  124 ). 
     As discussed above, being different webpages within a website may use different descriptors (e.g., descriptors  56 ), in order to properly utilize such data (e.g., descriptors  56 ), automation process  10  may process this data to transform it from raw information (e.g., descriptors  56  in their original disjointed form) into useable information  60  (in a normalized/homogenized form). Further and as discussed above, being different websites (e.g., websites  100 ,  112 ,  114 ,  116 ) may use different descriptors (e.g., descriptors  56 ) within their webpages, automation process  10  may process this data (e.g., descriptors) to transform it from its original disjointed form into useable (e.g., normalized/homogenized) information (e.g., ontology data  124 ). Accordingly and when generating ontology data  124 , automation process  10  may process the useable information included within each of the plurality of data description models (e.g., plurality of data description models  118 ) to amend/normalize/homogenize this useable information across the plurality of websites (e.g., plurality of websites  120 ). 
     The following discussion concerns one particular example of the manner in which ontology data  124  may be generated. Referring also to  FIG.  16   , automation process  10  may normalize  700  descriptors (e.g., descriptors  56 ) within a master website dataset (e.g., a website dataset associated with website  100 ), wherein these descriptors (e.g., descriptors  56 ) within the master website dataset (e.g., a website dataset associated with website  100 ) may be obtained  702  via a description model (e.g., a data description model and/or a function description model). 
     As discussed above, the one or more descriptors (e.g., descriptors  56 ) may include one or more of: a property descriptor; an attribute descriptor; and a value descriptor.
         Property Descriptors: A property descriptor may identify the field/area/region name of highly pertinent portion of a website, wherein these fields/areas/regions are common on a particular type of website. Accordingly, if website  100  is an ecommerce website, examples of such property descriptors may include but are not limited to: a title field/area/region; a picture field/area/region; a description field/area/region; and a price field/area/region.   Attribute Descriptors: An attribute descriptor may identify the field/area/region name of supplemental portion of a website, wherein these fields/areas/regions supplement the above-described property descriptors. Accordingly, if website  100  is an ecommerce website, examples of such attribute descriptors may include but are not limited to: a size field/area/region; a color field/area/region; a material field/area/region; and a brand field/area/region.   Value Descriptors: A value descriptor may identify a value for one of the above-described property descriptors and/or attribute descriptors. For example and with respect to website  100 , the value descriptor for the “size” attribute descriptor may be “Large”; the value descriptor for the “color” attribute descriptor may be “California Blue”; the value descriptor for the “price” property descriptor may be “$ 19 . 98 ”; and the value descriptor for the “title” property descriptor may be “Synthetic Nitrile Blue Disposable Gloves”.       

     For the following example, assume that the master website dataset (e.g., a website dataset associated with website  100 ) includes useable information  60  stored within database  62  that is included within and/or associated with data description model  58  associated with website  100  (i.e., the master website), wherein useable information  60  was generated by automation process  10  normalizing  700  descriptors  56 . As discussed above, being different websites may use different descriptors within their webpages, automation process  10  may process this data (e.g., descriptors) to transform it from its original disjointed form into useable (e.g., normalized/homogenized) information (e.g., ontology data  124 ) that is useable across a plurality of websites (e.g., plurality of websites  120 ). When normalizing  700  descriptors  56 , automation process  10  may compare pairs of descriptors for similarity. For example, automation process  10  may generate a user interface configured to present a listing of the comparison of descriptors  56 . The user interface may enable a user (e.g., user  36 ) to select which pairs of descriptors to normalize (e.g., based upon a similarity score or other comparison metric). In this example, a user may selectively approve or reject the normalization of descriptors within the master website dataset. In another example, automation process  10  may utilize one or more thresholds to determine when to and/or when to not automatically normalize descriptors  56  within website dataset associated with website  100 . 
     Accordingly and in order to homogenize this data (e.g., descriptors) for use across multiple websites (e.g., plurality of websites  120 ), automation process  10  may compare  704  descriptors (e.g., descriptors  72 ) within a subordinate website dataset (e.g., a website dataset associated with website  112 ) to descriptors (e.g., useable information  60 ) within the master website dataset (e.g., a website dataset associated with website  100 ) to define a similarity score (e.g., similarity scores  74 ) for each descriptor (e.g., each of descriptors  72 ) within the subordinate website dataset (e.g., a website dataset associated with website  112 ). Each similarity score (e.g., each of similarity scores  74 ) may be one or more of: a value set similarity score (e.g., the similarity between values/value sets, wherein a value set is a domain of possible values); a type similarity score (e.g., integer versus character string); and a string similarity score (e.g., edit distance . . . how many characters would have to change to make one string identical to the second?). Automation process  10  may obtain  706  the descriptors (e.g., descriptors  72 ) within the subordinate website dataset (e.g., a website dataset associated with website  112 ) via a description model (e.g., a data description model and/or a function description model). 
     When comparing  704  descriptors (e.g., descriptors  72 ) within a subordinate website dataset (e.g., a website dataset associated with website  112 ) to descriptors (e.g., useable information  60 ) within the master website dataset (e.g., a website dataset associated with website  100 ) to define a similarity score (e.g., similarity score  74 ) for each descriptor (e.g., each of descriptors  72 ) within the subordinate website dataset (e.g., a website dataset associated with website  112 ), automation process  10  may determine  708  a Cartesian product of the descriptors (e.g., descriptors  72 ) within a subordinate website dataset (e.g., a website dataset associated with website  112 ) and the descriptors (e.g., useable information  60 ) within the master website dataset (e.g., a website dataset associated with website  100 ) to define the similarity score (e.g., similarity score  74 ) for each descriptor (e.g., each of descriptors  72 ) within the subordinate website dataset (e.g., a website dataset associated with website  112 ). 
     As is known in the art, a Cartesian product is a binary operation on two sets, denoted by the symbol “×”, where a set is an unordered collection of unique elements. Given two sets A and B, the Cartesian product (read “A×B”) is a set comprising all ordered pairs (a, b) where a is an element of A and b is an element of B. It has many applications in computer science and mathematics. A table can be created by taking the Cartesian product of a set of rows and a set of columns, where the rows and columns may represent values from different domains. The cells of the produced table will be ordered pairs of the form (row value, column value). Note, that the rows and columns need not be of the same length. As will be discussed in greater detail below, the Cartesian product may be used to produce a set of pairs of values of the form (subordinate website value, master website value), which automation process  10  may use to define a similarity score between the two values of each pair. 
     Automation process  10  may normalize  710  one of more descriptors (e.g., descriptors  72 ) of the subordinate website dataset (e.g., a website dataset associated with website  112 ) if the similarity score (e.g., similarity score  74 ) is above a similarity threshold (e.g., 95%). For example, the descriptor “Sz” and the descriptor “Size” may have a high similarity score (e.g., 96%); while the descriptor “Title” and the descriptor “Size” may have a low similarity score (e.g., 58%). 
     As discussed above, normalizing  710  one or more descriptors (e.g., descriptors  72 ) of the subordinate website dataset (e.g., a website dataset associated with website  112 ) may include transforming descriptors  72  by: amending descriptors  72 ; normalizing and/or homogenizing one or more property descriptors; normalizing and/or homogenizing one or more attribute descriptors; and/or normalizing and/or homogenizing one or more value descriptors. As discussed above, amending descriptors  72  may include performing various operations on the descriptors (e.g., removing extra spaces or uncommon characters). 
     When normalizing  710  one of more descriptors (e.g., descriptors  72 ) of the subordinate website dataset (e.g., a website dataset associated with website  112 ) if the similarity score (e.g., similarity score  74 ) is above a similarity threshold (e.g., 95%), automation process  10  may map  712  the one or more descriptors (e.g., descriptors  72 ) of the subordinate website dataset (e.g., a website dataset associated with website  112 ) to the one or more descriptors (e.g., descriptors  56 ) within the master website dataset (e.g., a website dataset associated with website  100 ). For example, as, the descriptor “Sz” and the descriptor “Size” has a high similarity score (e.g., 96%) that exceeds the similarity threshold (e.g., 95%), automation process  10  may map  712  the “Sz” descriptor of the subordinate website dataset (e.g., a website dataset associated with website  112 ) to the “Size” descriptor within the master website dataset (e.g., a website dataset associated with website  100 ), thus indicating that the two descriptors are synonymous. 
     Automation process  10  may generate  714  a plurality of mappings (e.g., plurality of mappings  76 ) between one or more descriptors of a plurality of subordinate website datasets (e.g., website datasets associated with websites  112 ,  114 ,  116 ) and the one or more descriptors of the master website dataset (e.g., a website dataset associated with website  100 ). Accordingly, automation process  10  may process the descriptors associated with (in this example) websites  100 ,  112 ,  114 ,  116  to generate  714  mappings (e.g., plurality of mappings  76 ) between the descriptors associated with websites  112 ,  114 ,  116 ) and the descriptors associated with website  100 , thus forming an end-to-end platform that enables the navigation of websites  100 ,  112 ,  114 ,  116  without the need for human intervention and the automated &amp; distributed execution of complex tasks (e.g., complex task  400 ). 
     The above-described normalization may be accomplished in an automated fashion via machine learning process  122 . For example, automation process  10  may:
         provide  716  the plurality of mappings (e.g., plurality of mappings  76 ) between one or more descriptors of a plurality of subordinate website datasets (e.g., website datasets associated with websites  112 ,  114 ,  116 ) and the one or more descriptors of the master website dataset (e.g., a website dataset associated with website  100 ) to a machine learning process (e.g., machine learning process  122 );   provide  718  target subordinate website data (e.g., descriptors  78 ) concerning a target subordinate website (e.g., www.targetwebsite.com) to the machine learning process (e.g., machine learning process  122 ); and   normalize  720  one or more descriptors of the target website data (e.g., descriptors  78 ) to the master website dataset (e.g., a website dataset associated with website  100 ) using the machine learning process (e.g., machine learning process  122 ).
 
ERR (Ephemeral Random Retry):
       

     Discussed below is the manner in which automation process  10  may react in response to a failure associated with the execution of the description model (e.g., data description model  58  and/or function description model  66 ). 
     As discussed above and once initially defined (in the manner described above), automation process  10  may utilize data description model  58  and/or function description model  66  to automatically process the vast majority of webpages within e.g., website  100 , as a website (especially an ecommerce website) may include hundreds of thousands of webpages that correspond to the hundreds of thousands of products they sell. As discussed above and once fully defined, data description model  58  and/or function description model  66  may enable automation process  10  to autonomously navigate and/or effectuate the functionality of e.g., website  100  without any human intervention, as data description model  58  and/or function description model  66  may (generally speaking) function as a roadmap that allows for automated navigation of (in this example) website  100 . Additionally, automation process  10  may execute data description model  58  and/or function description model  66  to populate one or more databases with at least a portion of data from a website. 
     Unfortunately and as could be imagined, website  100  may change over time, wherein: existing webpages/products may be removed; existing webpages/products may be revised; and/or new webpages/products may be added. As could be imagined, such changes may complicate the ability of automation process  10  to autonomously navigate and effectuate e.g., website  100 . 
     Referring also to  FIG.  17   , automation process  10  may execute  750  a description model (e.g., data description model  58  and/or function description model  66 ) when utilizing a website (e.g., website  100 ). As discussed above, ontology data (e.g., ontology data  124 ) may define acceptable values for data description model  58  and/or function description model  66 . Specifically, useable data  60  within database  62  included within and/or associated with data description model  58  may define the descriptors of website  10 , while useable data  68  within database  70  included within and/or associated with function description model  66  may define the functions of website  10 . 
     Generally speaking, automation process  10  may utilize data description model  58  and/or function description model  66  to autonomously navigate and/or effectuate the functionality of e.g., website  100 . Unfortunately and as would be expected, navigation failures may occur due to changes made to (in this example) website  100  (e.g., the adding/removing/modifying of webpages). 
     Accordingly, automation process  10  may detect  752  a failure associated with the execution of the description model (e.g., data description model  58  and/or function description model  66 ), which may include: detecting  754  a failure of the description model (e.g., data description model  58 /function description model  66 ); detecting  756  a failure of the website (e.g., website  100 ); detecting  758  the unavailability of the website (e.g., website  100 ); detecting  760  data incongruities with respect to the website (e.g., website  100 ); detecting  762  missing data with respect to the website (e.g., website  100 ); and detecting  764  unacceptable data with respect to the website (e.g., website  100 ).
         A failure of the description model may include but is not limited to a general/system failure of the description model (e.g., data description model  58 /function description model  66 ). For example and for some unspecific reason, the description model (e.g., data description model  58 /function description model  66 ) may no longer function properly or has been corrupted.   A failure of the website may include but is not limited to the website (e.g., website  100 ) no longer responding. For example, the website (e.g., website  100 ) may not be responding due to a technical issue with the website (e.g., website  100 ).   Unavailability of the website may include but is not limited to the website (e.g., website  100 ) no longer being available. For example, the website (e.g., website  100 ) may have been taken offline (due to e.g., a company ceasing operations) and/or the server effectuating website  100  (e.g., machine-accessible public computing platform  370 ) may have gone down.   Data incongruities with respect to the website may include but is not limited to various data inconsistencies/inaccuracies. For example, data inconsistences/inaccuracies may be detected between what is defined within the description model (e.g., data description model  58 /function description model  66 ) and what is actually present/defined within the website (e.g., website  100 ). Data inconsistencies may also include descriptor anomalies (e.g., property descriptors, attribute descriptors, and/or value descriptors) between what is defined within the description model (e.g., data description model  58 /function description model  66 ) and what is actually present/defined within the website (e.g., website  100 ). In another example, data inconsistencies may also include inconsistencies between different portions of the website. For example, suppose an ecommerce website includes various categories of products and a total number of products for a particular category is listed on the website. Now, suppose that after executing the description model (e.g., data description model  58 /function description model  66 ), automation process  10  executes the description model on a different number of products (e.g., more or less than the displayed total number of products). As the number of products processed from the website and the listed number of products do not match (e.g., the listed number of products includes duplicate products that are only processed by the description model once), automation process  10  may detect  760  a data incongruity.   Missing data with respect to the website may include but is not limited to data that is no longer present within the website (e.g., website  100 ). For example and as discussed above, products/webpages may be removed/revised from the website (e.g., website  100 ), resulting in the description model (e.g., data description model  58 /function description model  66 ) identifying data that is not currently present within the website (e.g., website  100 ).   Unacceptable data with respect to the website (e.g., website  100 ) may include but is not limited to data that is damaged/nonresponsive within the website (e.g., website  100 ). For example, some of the data and/or functionality within the website (e.g., website  100 ) may become corrupt, resulting in the website (e.g., website  100 ) not functioning/responding properly.       

     In the event that a failure is detected  752  concerning the execution of the description model (e.g., data description model  58  and/or function description model  66 ), automation process  10  may re-execute  766  the description model (e.g., data description model  58  and/or function description model  66 ) one or more times in an attempt to utilize the website (e.g., website  100 ). For example, automation process  10  may attempt to re-execute  766  data description model  58  and/or function description model  66  e.g., three more times in an attempt to utilize the website (e.g., website  100 ). 
     If a failure is detected for these (in this example) three additional times, automation process  10  may report  768  the failure to a user (e.g., user  42 ). When such a failure is reported, automation process  10  may reacquire data (e.g., descriptors  56  and functions  64 ) so that the description model (e.g., data description model  58  and/or function description model  66 ) may be updated to address such a failure. 
     Sleuth: 
     Discussed below is the manner in which automation process  10  may maintain the description model (e.g., data description model  58  and/or function description model  66 ). 
     As discussed above, being different webpages within a website may use different descriptors (e.g., descriptors  56 ), in order to properly utilize such data (e.g., descriptors  56 ), automation process  10  may process this data to transform it from raw information (e.g., descriptors  56  in their original disjointed form) into useable information  60  (in a normalized/homogenized form). Further and as discussed above, being different websites (e.g., websites  100 ,  112 ,  114 ,  116 ) may use different descriptors (e.g., descriptors  56 ) within their webpages, automation process  10  may process this data (e.g., descriptors) to transform it from its original disjointed form into useable (e.g., normalized/homogenized) information (e.g., ontology data  124 ). Accordingly and when generating ontology data  124 , automation process  10  may process the useable information included within each of the plurality of data description models (e.g., plurality of data description models  118 ) to amend/normalize/homogenize this useable information across the plurality of websites (e.g., plurality of websites  120 ). 
     As also discussed above, websites (e.g., website  100 ) may change over time, wherein: existing webpages/products may be removed; existing webpages/products may be revised; and/or new webpages/products may be added. Accordingly and when such changes occur, the description model (e.g., data description model  58  and/or function description model  66 ) associated with the website may no longer be accurate, as the underlying data (e.g., descriptors  56  and functions  64 ) has changed. 
     Additionally and as discussed above, automation process  10  may execute data description model  58  and/or function description model  66  to populate one or more databases with at least a portion of data from a website. For example, with description models (e.g., data description model  58  and/or function description model  66 ) defined for website  100 , automation process  10  may execute data description model  58  and/or function description model  66  to obtain useable information from website  100  and populate a database (e.g., database  62  and/or database  70 ) with at least a portion of this useable information. In an example where website  100  is an ecommerce website, website  100  may include hundreds of thousands of webpages to correspond to the hundreds of thousands of products they sell. As such, automation process  10  may populate database  62  and/or database  70  with useable information pertaining to the products from the hundreds of thousands of webpages by defining and executing data description model  58  and/or function description model  66  on the webpages of website  100 . In this manner, automation process  10  may allow for the population of one or more databases representative of the useable information of the various webpages of a website. 
     Accordingly and as will be discussed below in greater detail, automation process  10  may be configured to periodically refresh such underlying data (e.g., descriptors  56  and functions  64 ) so that the description model (e.g., data description model  58  and/or function description model  66 ) associated with the website (e.g., website  100 ) remains fresh and accurate. Referring also to  FIG.  18    and as discussed above, automation process  10  may acquire  800  data (e.g., descriptors  56  and functions  64 ) associated with a particular portion of website (e.g., website  100 ), wherein the particular portion of website (e.g., website  100 ) may be associated with a product/service offered for sale. As discussed above, automation process  10  may systematically process the various webpages included within (in this example) website  100  to acquire  800  data (e.g., descriptors  56  and functions  64 ). As also discussed above, when acquiring  800  data (e.g., descriptors  56  and functions  64 ) associated with a particular portion of website (e.g., website  100 ), automation process  10  may acquire  802  data (e.g., descriptors  56  and functions  64 ) associated with a particular portion of website (e.g., website  100 ) via the description model (e.g., data description model  58  and/or function description model  66 ). 
     As stated above, over time this data (e.g., descriptors  56  and functions  64 ) may grow stale due to age. Accordingly, automation process  10  may determine  804  if the data (e.g., descriptors  56  and functions  64 ) associated with the particular portion of the website (e.g., website  100 ) should be reacquired. For example, automation process  10  may balance how often the underlying data (e.g., descriptors  56  and functions  64 ) should be reacquired. As discussed above, acquiring data associated with a website may include executing the description model (e.g., data description model  58  and/or function description model  66 ) defined for the website on hundreds or thousands of webpages. As this may consume significant computing resources and processing time for a website host and/or computing devices executing the description model (data description model  58  and/or function description model  66 ), automation process  10  may determine  804  when to reacquire the data (e.g., descriptors  56  and functions  64 ) associated with the particular portion of the website (e.g., website  100 ). 
     When determining  804  if the data (e.g., descriptors  56  and functions  64 ) associated with the particular portion of the website (e.g., website  100 ) should be reacquired, automation process  10  may: determine  806  a popularity level of the particular portion of the website (e.g., website  100 ); determine  808  an importance level of the particular portion of the website (e.g., website  100 ); and determine  810  if the particular portion of the website (e.g., website  100 ) is too fresh to reacquire.
         When determining  806  a popularity level of the particular portion of the website (e.g., website  100 ), automation process  10  may monitor how often a portion of the website (e.g., website  100 ) is accessed. For example, automation process  10  may receive information indicating when and/or how often the particular portion of website  100  is accessed (e.g., how often users access particular portions of website  100 ). The information indicating when and/or how often the particular portion of website  100  is accessed may include a reference to a particular portion of the website as defined by the description model (e.g., data description model  58  and/or function description model  66 ) and/or a reference to the particular portion of website  100  as defined in a database (e.g., database  62  and/or database  70 ) populated with usable information from the particular portion of website  100 . As discussed above, in the event that the data (e.g., descriptors  56  and functions  64 ) associated with the particular portion of the website (e.g., website  100 ) is inconsistent/inaccurate/missing/unacceptable, a failure will occur (as described above) and, if the failure is persistent, the inconsistent/inaccurate/missing/unacceptable will be reacquired. In another example, if a portion of the website (e.g., website  100 ) is popular and accessed often, the data (e.g., descriptors  56  and functions  64 ) associated with the particular portion of the website (e.g., website  100 ) may be reacquired more frequently to ensure consistent data. When determining a popularity level of the particular portion of the website, automation process  10  may define a weighting for the popularity level. The weighting for the popularity level may be user-defined and/or automatically defined by automation process  10 . Accordingly, automation process  10  may determine if the data (e.g., descriptors  56  and functions  64 ) associated with the particular portion of the website (e.g., website  100 ) should be reacquired based upon the weighting defined for the popularity level.   When determining  808  an importance level of the particular portion of the website (e.g., website  100 ), automation process  10  may monitor how important a portion of the website (e.g., website  100 ) is. For example and as would be expected, website  100  may offer several products that are their best sellers, wherein these best seller may have a higher level of important assigned to them. Accordingly and when a higher level of importance is assigned to a particular product, automation process  10  may more frequently reacquire the data (e.g., descriptors  56  and functions  64 ) associated with that particular portion of the website (e.g., website  100 ). When determining an importance level of the particular portion of the website, automation process  10  may define a weighting for the importance level. The weighting for the importance level may be user-defined and/or automatically defined by automation process  10 . Accordingly, automation process  10  may determine if the data (e.g., descriptors  56  and functions  64 ) associated with the particular portion of the website (e.g., website  100 ) should be reacquired based upon the weighting defined for the importance level.   When determining  810  if the particular portion of the website (e.g., website  100 ) is too fresh to reacquire, automation process  10  may not reacquire data (e.g., descriptors  56  and functions  64 ) if that data has been recently acquired. For example and when data (e.g., descriptors  56  and functions  64 ) is acquired, information (e.g., metadata) may be defined for the acquired data (e.g., descriptors  56  and functions  64 ) that identifies when the data (e.g., descriptors  56  and functions  64 ) was last acquired. For example, if the data (e.g., descriptors  56  and functions  64 ) associated with the particular portion of the website (e.g., website  100 ) was last acquired 10 minutes ago, automation process  10  may determine  804  that the data (e.g., descriptors  56  and functions  64 ) should not be reacquired. Conversely, if the data (e.g., descriptors  56  and functions  64 ) associated with the particular portion of the website (e.g., website  100 ) was last acquired 10 days ago, automation process  10  may determine  804  that the data (e.g., descriptors  56  and functions  64 ) should be reacquired. Automation process  10  may receive a time-based threshold for reacquiring data from a particular portion of the website and/or may automatically define the time-based threshold for reacquiring data from the particular portion of the website. Accordingly, automation process  10  may determine  810  if the particular portion of the website (e.g., website  100 ) is too fresh to reacquire based, at least in part, upon the time-based threshold.       

     Accordingly and if the data (e.g., descriptors  56  and functions  64 ) associated with the particular portion of the website (e.g., website  100 ) should be reacquired, automation process  10  may reacquire  812  the data (e.g., descriptors  56  and functions  64 ) associated with the particular portion of the website (e.g., website  100 ) via a description model (e.g., data description model  58  and/or function description model  66 ) in the fashion described above. 
     General 
     As will be appreciated by one skilled in the art, the present disclosure may be embodied as a method, a system, or a computer program product. Accordingly, the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module” or “system.” Furthermore, the present disclosure may take the form of a computer program product on a computer-usable storage medium having computer-usable program code embodied in the medium. 
     Any suitable computer usable or computer readable medium may be utilized. The computer-usable or computer-readable medium may be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium. More specific examples (a non-exhaustive list) of the computer-readable medium may include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a transmission media such as those supporting the Internet or an intranet, or a magnetic storage device. The computer-usable or computer-readable medium may also be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted, or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory. In the context of this document, a computer-usable or computer-readable medium may be any medium that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The computer-usable medium may include a propagated data signal with the computer-usable program code embodied therewith, either in baseband or as part of a carrier wave. The computer usable program code may be transmitted using any appropriate medium, including but not limited to the Internet, wireline, optical fiber cable, RF, etc. 
     Computer program code for carrying out operations of the present disclosure may be written in an object oriented programming language such as Java, Smalltalk, C++ or the like. However, the computer program code for carrying out operations of the present disclosure may also be written in conventional procedural programming languages, such as the “C” programming language or similar programming languages. The program code may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through a local area network/a wide area network/the Internet (e.g., network  14 ). 
     The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, may be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer/special purpose computer/other programmable data processing apparatus, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     These computer program instructions may also be stored in a computer-readable memory that may direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function/act specified in the flowchart and/or block diagram block or blocks. 
     The computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     The flowcharts and block diagrams in the figures may illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, may be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. 
     The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. 
     The corresponding structures, materials, acts, and equivalents of all means or step plus function elements in the claims below are intended to include any structure, material, or act for performing the function in combination with other claimed elements as specifically claimed. The description of the present disclosure has been presented for purposes of illustration and description, but is not intended to be exhaustive or limited to the disclosure in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the disclosure. The embodiment was chosen and described in order to best explain the principles of the disclosure and the practical application, and to enable others of ordinary skill in the art to understand the disclosure for various embodiments with various modifications as are suited to the particular use contemplated. 
     A number of implementations have been described. Having thus described the disclosure of the present application in detail and by reference to embodiments thereof, it will be apparent that modifications and variations are possible without departing from the scope of the disclosure defined in the appended claims.