Patent Publication Number: US-11651138-B2

Title: Automated communication design construction system

Description:
TECHNICAL FIELD 
     This application relates to technologies for automatically constructing and producing communication designs to provide physical and electronic communication services. 
     BACKGROUND OF THE INVENTION 
     In modern economy, businesses must engage with their customers with frequent and purposeful communications. The content and the methods of the communications are often customized for individual customers, which may rely on data and logic to trigger and populate the appropriate custom messaging. Communications can be delivered in physical mails or electronic forms such as emails, social media, and mobile messages, and these various channels may share a large volume of common messaging, branding guidelines, and/or legal requirements compounding the complexity of managing and executing these communications. As businesses grow through mergers, acquisitions, or purely organically, the sizes of the communication materials they have to manage and control, grow exponentially. 
     There is a need for efficiently designing and analyzing communication content, and producing and distributing communications in physical and electronic forms. 
     SUMMARY OF THE INVENTION 
     In one aspect, the present application discloses system and methods that can allocate communication resources to satisfy business customers&#39; complex and varying needs in providing communications to their customers. The communications can be in the form of the mailed hardcopy print products in different sizes, form factors, materials, finishes, and packaging, as well as electronic channels such as emails, social media, and mobile messages. 
     The presently disclosed system can automatically monitor resources required for fulfilling physical and electronic communications, and can automatically evaluate the resource needs in customers&#39; requests for communications and allocate resources based on the availability of resources needed for the design, the production, and the distribution of physical or electronic communications. 
     In another aspect, the presently disclosed system and methods use machine learning to analyze communication content files in different formats from different sources. Objects in the communication content files are automatically recognized and sorted in different categories including static and variable objects, and local and global objects, or any combination thereof. The objects are quantized, normalized, which are defined by rules and stored in a unified data structure for all communication content. A communication design file can then be produced based on the unified data structure. 
     Moreover, the present disclosure provides systems and methods for automatically checking consistencies and logic relationships between objects in the communication content and designs. After the automated checking, the disclosed systems and methods also provide effective tools for users to verify consistencies between objects. 
     Furthermore, by constructing a unified data structure for all different communication types and formats, the disclosed system and methods can facilitate efficient reuse of variable design content such as style, layouts, backgrounds, colors, and text and image objects between different communications. 
     The disclosed system and method can perform deep analysis of several communication examples across multiple communication channels to generate an optimized library of content and logic for production execution. They can drastically reduce labor, time, and cost in formatting, proofing, and content building in conventional customer content on-boarding and pre-production processes. The automated systems also reduce human error and the need for rework in these processes therefore increasing quality and accuracy of communication execution. 
     In one general aspect, the present invention relates to a method for automatically analyzing and constructing communications to a plurality of recipients. The method includes in a system comprising one or more intelligent communication design servers, automatically separating communication content files into page groups, wherein each of the page groups is associated a recipient of the communications; inputting the communication content files into an intra-page machine prediction model to produce intra-page parameters; inputting the communication content files and the intra-page parameters into an intra-page machine prediction model to produce intra-group parameters and inter-group parameters; automatically constructing standard communication design files by an intelligent communication content learning and constructing engine based on the communication content files and the intra-page parameters, intra-group parameters, and inter-group parameters; and printing and finishing physical mailing pieces to be mailed to the recipients based on the standard communication design files. 
     Implementations of the system may include one or more of the following. The method can further include before the step of separating, automatically separating communication content files into page groups, wherein each of the page groups is associated a recipient of the communications. The method can further include automatically converting communication content files in different formats to normalized intermediate format files, wherein the communication content files define content of communications to the plurality of recipients. The method can further include sending the standard communication design files from the one or more intelligent communication design servers to one or more product fulfillment centers. The method can further include storing the intra-page parameters, the intra-group parameters, and inter-group parameters in a unified file structure for the normalized intermediate format files. The intra-page parameters can include zones, image objects, text objects, and paragraphs within individual pages. The intra-group parameters can include static global objects that are invariant between recipients. The static global objects can include text, logos, and images that are common to different recipients. The static global objects can include wireframes, page behaviors, and layout formatting that are common to different recipients. The inter-group parameters can include variable global objects. The variable global objects can include text, logos, and images that vary between recipients. The variable global objects can include wireframes, page behaviors, and paragraph styles that vary between recipients. The inter-group parameters can include data variables that vary between recipients. The data variables can include a recipient&#39;s personally identifiable information such as unique ID, name, address, or dates. The intra-page machine prediction model can be trained using historic communication files and associated intra-page parameters. The inter-page machine prediction model can be trained using historic communication files and associated intra-group parameters and inter-group parameters. The different formats of the communication content files can include fixed length, delimited, XML, Microsoft Excel, INDD, PDF, WORD, FONT, or JPEG. The method can further include automatically classifying the standard communication design files based on communication channel types, wherein the communication channel types include physical prints, e-mails, or web form. The method can further include automatically identifying a common wireframe in the normalized intermediate format files between different recipients based on locations of the zones and content in the zones. 
     In another general aspect, the present invention relates to an automated communication design analysis and construction system that includes one or more intelligent communication design servers, comprising: a normalization module that can convert communication content files for different recipients to normalized intermediate format files; an objects identification and quantification module that can identify text objects and image objects in the normalized intermediate format files; a cross recipient group analysis module that can identify static global objects that are invariant between recipients, data variables, and variable global objects that vary between recipients in the normalized intermediate format files; and an intelligent communication content learning and constructing engine that can construct standard communication design files based on the static global objects, the data variables, and the variable global objects; and a data storage configured to store the communication content files and the standard communication design files; and a communication resource allocation server that can send the standard communication design files to one or more product fulfillment centers, wherein physical mailing pieces to be mailed to the recipients are printed and finished based on the standard communication design files. 
     Implementations of the system may include one or more of the following. The data storage can store a unified file structure for the normalized intermediate format files, wherein the unified file structure defines the static global objects, the data variables, and the variable global objects for communications to different recipients. The intelligent communication content learning and constructing engine can automatically recognize font type, font size, and font color of a text in the text objects. The automated communication design construction system can further include a zone classification module configured to automatically identify zones in the normalized intermediate format files according to the text object, logos, and the image objects identified by the machine learning and design construction engine in the normalized intermediate format files. The intelligent communication content learning and constructing engine can automatically identify wireframes, page behaviors, and paragraph styles within the zones. The static global objects can include wireframes, page behaviors, and paragraph styles that are common to different recipients. The variable global objects can include wireframes, page behaviors, and paragraph styles that vary between recipients. The data storage can store a unified file structure for the normalized intermediate format files, wherein the unified file structure defines the wireframes, the page behaviors, and the paragraph styles for communications to different recipients. The zone classification module can classify the zones into zone types based on behaviors across recipients, wherein the data storage comprises a content store configured to store standard content for a zone type. The standard content can be based on branding, legal, and regulatory requirements. The static global objects can include text, logos, and images that are common to different recipients. The variable global objects can include text, logos, and images that vary between recipients. The data variables can vary between recipients. The automated communication design construction system can further include a channel classification module configured to automatically classify the normalized intermediate format files based on communication channel types, wherein the communication channel types include physical prints, e-mails, or web form. The automated communication design construction system can further include a recipient matching module configured to automatically sort the normalized intermediate format files into groups each representing all communication documents for a single recipient, wherein the recipient matching module is configured to automatically match each of the groups of normalized intermediate format files to a recipient. The cross-recipient group analysis module can automatically analyze similar zones in the normalized intermediate format files between different recipients to identify static global objects, data variables, and variable global objects, wherein the similar zones have a same content type and locations in respective page layouts. The cross-recipient group analysis module can identify static global objects, data variables, and variable global objects based on whether content in the similar zones the normalized intermediate format files vary across recipients. The cross-recipient group analysis module can automatically identify a common wireframe in the normalized intermediate format files between different recipients based on locations of the zones and content in the zones. The automated communication design construction system can further include a cross recipient group analysis module configured to automatically identify white spaces in the normalized intermediate format files using multi-class classification machine learning, wherein pre-stored content can be inserted into the white spaces. 
     These and other aspects, their implementations and other features are described in detail in the drawings, the description and the claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a block diagram for a network-based communication fulfillment system in accordance with the present invention. 
         FIG.  2    is a detailed block diagram for a portion of the network-based communication fulfillment system in accordance with the present invention. 
         FIG.  3    is a flow diagram for allocating communication resources by the network-based communication fulfillment system in accordance with the present invention. 
         FIG.  4    is a block diagram of an automated communication design analysis and construction system in the network-based communication fulfillment system in  FIG.  1   . 
         FIG.  5    is a flow diagram for intelligently understanding and constructing communication content in the network-based communication fulfillment system in  FIG.  1   . 
         FIG.  6    is a schematic diagram illustrating conversion of communication content files received from clients in different formats into a normalized intermediate format to be index and tokenized as needed. 
         FIG.  7    is a schematic diagram illustrating automatic grouping and indexing of the normalized intermediate format files into groups each representing the sum total of all communication documents for a given recipient. 
         FIG.  8    is a schematic diagram illustrating automatic identification and indexing of image objects in the normalized intermediate format files. 
         FIG.  9    is a schematic diagram illustrating automatic identification and indexing of text zone types and wireframes in the normalized intermediate format files. 
         FIG.  10 A  is a schematic diagram illustrating modeling each zone in vector space. 
         FIG.  10 B  is a schematic diagram illustrating automatic identifications of font sizes and types in the text objects. 
         FIG.  11    is a schematic diagram illustrating automatic identification of data variables and static global objects. 
         FIG.  12    is schematic diagram illustrating automatic matching of entire groups of communication documents to each recipient in the accompanying data file. 
         FIGS.  13 A and  13 B  are a schematic diagram illustrating the automatic identification and indexing of additional static global objects found between zones within the same normalized intermediate format file. 
         FIG.  14    is a schematic diagram illustrating automatic quantification of paragraph styles within and between the text objects. 
         FIGS.  15 A and  15 B  are schematic diagrams illustrating automatic identifications of variable global objects in addition to static global objects and data variables in the normalized intermediate format files corresponding to different recipients. 
         FIG.  16    is a schematic diagram illustrating automatic identifications of base common messages across recipient groups in the normalized intermediate format files corresponding to different recipients. 
         FIG.  17    is a schematic diagram illustrating automatic identifications of base common wireframes in the normalized intermediate format files corresponding to different recipients. 
         FIG.  18    is a schematic diagram illustrating automatic identifications of variable white space behaviors in the normalized intermediate format file corresponding to different recipients where pages may be inserted into or appended to a communication. 
         FIG.  19    is a schematic diagram illustrating automatic identification of additional variable white space behaviors in the normalized intermediate format file corresponding to different recipients where the body of variable length content could flow onto multiple pages and push other bodies of content into other positions. 
         FIG.  20    is a schematic diagram illustrating automatic identifications of zone types in the normalized intermediate format files based on corresponding to different recipients. 
         FIG.  21    illustrates another process for intelligently understanding and constructing communication content in the network-based communication fulfillment system in  FIG.  1   . 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     Referring to  FIG.  1   , a network-based communication fulfillment system  10  includes a communication production and control system  30  and one or more product fulfillment centers  40 ,  41 , which communicate with each other via a computer network  80 . The network-based communication fulfillment system  10  can be operated by a communication service provider such as Shutterfly Business Solutions. 
     The communication resource allocation servers  32  can power a website and mobile applications, which are accessible by business owners such as a business user  70  using a computer device  60  such as a mobile device, a desktop computer. The network-based communication fulfillment system  10  receives requests for communication services from the business user  70  via the Internet  50  or a wireless network  51 . 
     The requests can specify the types of communications, the content and design of the communications, the properties of the physical communications in the communication services, and recipients of the communications. The requests can also define the time(s) and frequencies of the communications to the recipients. The recipients are typically the current or potential customers of the business users. 
     The types of communications can include electronic forms such as emails, social media, and mobile messages, and physical mailing pieces of hardcopy print products. The content and design of the communications can include text, images, graphics, embellishments, colors and dimensions of all the design elements, layout, etc. for the electronic or physical communications. The properties of the physical mailing pieces can be defined by different sizes, form factors, materials, finishes, packaging, and shipping methods, etc. The recipient information can include the names and the physical and electronic addresses of the recipients and other additional demographic and/or behavioral data that a particular business user has accumulated about the recipients. 
     The product fulfillment center  40  includes a gateway server  42 , printers  45  for printing the communication designs on physical surfaces of substrates, finishing equipment  46  for finishing the physical mailing pieces after printing, and a shipping station  48  for confirming the completion of the orders and shipping the physical mailing pieces to recipients  190  and  195 . The gateway server  42  communicates with the communication production and control system  30  via the computer network  80  and facilitates the communications between different devices and stations in the printing and finishing facility  40 . The printers  45  receive digital image data and control data, and reproduce images on physical substrates made of paper, fabrics, plastic, metals, or other materials. Examples of the printers  45  can be digital printing presses, digital photographic printers, offset digital printers, inkjet printers, etc. The finishing equipment  46  perform finishing operations after printing, for example, cutting, folding, adding a cover to photo book, punching, stapling, gluing, binding, envelope printing and sealing, packaging, labeling, package weighing, and postage metering, etc. 
     The communication production and control system  30  includes one or more communication resource allocation servers  32  for communicating with the business users  70 , the product fulfillment centers  40 ,  41 , a data storage  34 , and other components within the communication production and control system  30 . The communication production and control system  30  also includes one or more communication servers  35 , one or more wireless routers  36 , and one or more intelligent communication design servers  37 . The communication production and control system  30  can be implemented in the cloud or with dedicated physical network equipment. 
     The data storage  34  stores information about the business customers and the recipients, the content and designs of the communications, and the types and timing of the communications. The servers  35  and the wireless routers  36  are configured to transmit electronic communications to recipients  170 ,  171  specified by the business users  70  in their requests. The electronic communications can be transmitted in wired or wireless communications to computers  160  or mobile devices  161  used by the recipients  170 ,  171 . 
     The intelligent communication design server  37  is configured to create a customized design for the electronic or physical communications based on the input from the business owners  70 . The content as well as transmission or shipping methods (timing, frequencies) are usually personalized for the recipients  170 ,  171 ,  190 ,  195  and based on the needs of the business owners. Customized design information can include text, images, graphics, embellishments, styles, product types, event types, information about the business user (i.e. sender of the communication), information about the recipient (names, special events/interests or occasion for the recipients, etc.). 
     Moreover, the designs of electronic communications such as emails, social media, and mobile messages can depend on the types of devices on which the recipients will view the electronic communications, the types of browsers for viewing the communications on webpages, the types of operating systems, and the types of social platforms. The page dimensions and form factors are usually different for different electronic communications; the communication designs need to be tailored according to the particular channels and the recipients&#39; devices. In other words, for each communication service of a given content, there can be hundreds of electronic versions. Thus, designing electronic communications can consume significant computation resources. 
     Similarly, physical mailing pieces have different product types, sizes, form factors, materials, finishes, packaging, and shipping methods. Examples of physical mailing pieces can include post cards, note cards, greeting cards, invitation cards, brochures, booklet, calendars, magnets, catalogs, coupons, banners, posters, totes, which can be printed with photos, text, and graphics, etc. Each physical mailing piece can include one or more pages, or one or more substrate surfaces. Creating customized designs for the physical products not only depend on the sizes, form factors, materials, finishes, but also the types of printing and finishing equipment employed to produce these physical mailing pieces. Each recipient  190 ,  195  can receive one or more physical mailing pieces in a communication service fulfilled by the network-based communication fulfillment system  10 . 
       FIG.  2    illustrates detailed portions of the communication resource allocation servers  32  and the data storage  34  in the communication production and control system  30 . The communication resource allocation server  32  is in communications with the servers and storage in communication production and control system  30  and with the gateway servers  42  in the product fulfillment centers  40 ,  41 . In some embodiments, referring to  FIGS.  1  and  2   , the communication resource allocation server  32  includes a user request analysis module  210 , a printing resource tracking module  220 , an electronic communication resource tracking module  230 , a communication design resource tracking module  240 , a communication request resource matching module  250 , and a communication resource assignment module  260 . 
     The data storage  34  also includes a user database  270 , a content store  280 , and a recipient database  290 . The user data stored in the user database  270  can include account information, discount information, and order information associated with the business users  70 . The content store  280  stores the communication content specified in business users&#39; requests, and the designs automatically created by the intelligent communication design servers  37  for different types of electronic or physical communications. The recipient database  290  stores information for a large number of recipients such as names, family members&#39; names, their electronic and physical addresses, their anniversary or birthdays, their preferences and hobbies, communication histories, their response to past communications, etc. The recipient database  290  can also store the communication personalized for each recipient: communication delivery method, frequency, personalization, etc. For each business user  70 , each communication service can include a different set of recipients, whose numbers can range from hundreds to millions. 
     The user request analysis module  210  is configured to automatically analyze business users&#39; requests for communication services: determining the types of communications (physical vs. electronic, types of physical mailing pieces, electronic communication channels, etc.), determining the content (complexity text, image, graphics objects), accessing the amount of design work (e.g. level of customization, the number of variations of different electronic channels including device types, operating systems, types of viewing software, and viewing platforms, different types of physical mailing pieces, etc.), and assessing the amount of printing and finishing work for the physical mailing pieces. 
     The printing resource tracking module  220  monitors, via the gateway servers  42 , the workloads of the product fulfillment centers  40 ,  41  on their respective schedules, which include capacity forecast for the printers  45 , finishing equipment  46 , and shipping stations  48 . The electronic communication resource tracking module  230  monitors the workload of the communication servers  35  and wireless router  36 . The number of electronic communications can be much higher than the number of physical communications. Moreover, the communication servers  35  and wireless router  36  may need to handle response and feedback from the computer devices ( 160 ,  161 ) by the recipients  170 ,  171 . The communication design resource tracking module  240  monitors the anticipated workloads of the intelligent communication design servers  37  based on the communication services already committed to different business users  70 . As described above, the amount of design work is dependent on the types of communications. 
     The communication request resource matching module  250  is configured to find communication resources for the communication services specified in business users&#39; requests. For each communication request, the communication request resource matching module  250  receives all resource needs in the user request from the user request analysis module  210 . The communication request resource matching module  250  also receives resource workload and availability information from the printing resource tracking module  220 , the electronic communication resource tracking module  230 , and the communication design resource tracking module  240 . The communication request resource matching module  250  attempts to match the available design, printing, and transmission resources with resource needs in the user request. 
     Based the input information as above described, the communication request resource matching module  250  makes determination if a communication service can be fulfilled in the timeframe specified in the business user&#39;s request. It should be noted that there can be multiple product fulfillment centers  40 ,  41 , multiple communication design centers, and multiple electronic communication resources, which the communication request resource matching module  250  is configured to balance. The tasks of one communication service can be distributed across different such service resource centers. If so, the communication resource assignment module  260  assigns the appropriate resources and schedule them to perform various tasks: designing communication content by the intelligent communication design servers  37  for all versions and channels, transmitting electronic messages and receiving response by the communication servers  35  and wireless router  36 , and manufacturing and shipping physical mailing pieces by the product fulfillment centers  40 ,  41 . 
     Otherwise, the communication resource allocation server  32  replies to the business user  70  and possibly suggests a different schedule from the first request. For example, if the product fulfillment centers  40 ,  41  are expected to be short in capacity for the requested communication service, the communication resource allocation server  32  may suggest and renegotiate extra time for the deliveries of the physical mailing pieces. 
     It should be noted that the communication resource allocation server  32  is also configured to communicate pricing information to the business users  70 . The prices for electronic or physical communication depend on the complexities in content, volume, number of electronic channels and platform variations, and workloads in creating the design, transmission, and manufacturing the physical mailing pieces. The price can also depend on the urgency of the communication jobs. For example, communications services that can be schedule further into the future can be provided at discount prices. 
     The allocation of communication resources for a communication request can include one or more of the following steps in the disclosed network-based communication fulfillment system. Referring to  FIGS.  1 - 3   , a request for a communication service by a business customer is received (step  310 ) by a communication resource allocation server. 
     A user request analysis module automatically analyzes the resource needs for the design, printing production, and electronic transmissions for fulfilling the user request (step  320 ). The communication request can specify different types of communications such as electronic messages and physical mailing pieces. The printing communication resources in the product fulfillment centers are automatically monitored (step  330 ) by a printing resource tracking module, which includes the printing and finishing tasks already scheduled to fulfill scheduled communication services. The electronic communication resources are automatically monitored (step  340 ) by an electronic communication resource tracking module, which includes the electronic transmission tasks already scheduled to fulfill scheduled communication services. The communication design resources are automatically monitored (step  350 ) by a communication design resource tracking module, which includes the design tasks already scheduled to fulfill scheduled communication services. 
     Based on the input from the communication request resource matching module, the electronic communication resource tracking module, and the communication design resource tracking module, a communication request resource matching module automatically matches the user requested communication service to print and electronic communication and communication design resources (step  360 ). A communication resource assignment module then automatically assigns the communication resources to fulfill the user request (step  370 ). 
     In some embodiments, the network-based communication fulfillment system  10  includes powerful capabilities to automatically extract, quantify, and normalize information from communication content files in different formats from different sources, organize and store the quantified communication content in a unified database suitable for many communication services, and automatically construct design files based on data stored in the unified database. 
     Referring to  FIG.  4   , an automated communication design analysis and construction system  400  that includes the one or more intelligent communication design servers  37  include a normalization module  410 , a recipient matching module  420 , an objects identification and quantification module  430 , a cross recipient group analysis module  440 , a global object and data variable identification module  450 , and a zone classification module  460 . Moreover, the one or more intelligent communication design servers  37  include a machine learning and design construction engine  470 . The automated communication design analysis and construction system  400  also includes the data storage  34  that includes a unified design database  480  in communication with the machine learning and design construction engine  470  and other modules in the one or more intelligent communication design servers  37 . 
     The automatic normalization, quantization, and standard construction of communication contents can include one or more of the following steps in the disclosed network-based communication fulfillment system. Referring to  FIGS.  1 ,  4 , and  5   , the normalization module  410  receives communication content files in different formats (step  510 ) from different users. These non-technical business users lead a marketing or communication campaign and are provided materials from a number of different internal or external teams such as art/creative, marketing, legal, governmental regulatory branches, and data sciences, etc. The materials provided to the user by such aforementioned teams may be of varying types and qualities due to different approaches and best practices within these teams and the legacy software tools they use to create the materials. These materials may be generated from a number of disparate technologies and systems that are not well integrated for direct transferal or ingestion into communication production execution. The types of the software tool and thus the types of output files can depend on many factors such as tradition and what&#39;s available in the particular firm, familiarity with the tools by the designers, file format of the historic communication design files, etc. Due to the competitive nature of vendors supplying these software tools that users and firms have chosen to leverage, the output from these tools takes many formats, which are distinct and not well integrated with other formats. Examples of different file formats, as shown in  FIG.  6    can include data files in a range of formats (e.g. fixed length, delimited, XML, Microsoft Excel, INDD, PDF, WORD, FONT, JPEG, etc.) containing varying recipient information (e.g. geographic, demographic, and/or behavioristic information). 
     Referring to  FIGS.  4 - 6   , the normalization module  410  automatically normalizes the communication content files  611 - 615  into normalized intermediate format files (NIFF)  620  with unique identifications (step  515 ). These NIFFs  620  allow for universal analysis and storage that is agnostic to any specific delivery process or product so that NIFF  620  can eventually be transformed into the required format based on which type of communication channel is deemed necessary for final communication execution. The NIFF  620  are stored in the content store  280 . 
     Business users who manage existing communication campaigns may already have a corpus of existing examples of previously executed communications, but the business rules and logic used to compose the communications are not well curated over time or lost altogether, so when a business user wants to switch vendors or communication channels it becomes difficult for the business user or their vendor to replicate the communication material and business logic. Thus, there is a need to analyze and compare communications from the existing corpus by identifying each unique group of communications for a given recipient. 
     Referring to  FIGS.  4 - 5 ,  7   , the recipient matching module  420  automatically organizes the normalized intermediate format files into groups each representing all communication documents for a single recipient (step  520 ) using document classification machine learning. The recipients  1 - 3  associated with normalized intermediate format files  711 - 714  are identified. The normalized intermediate format files  711 - 714  are grouped according to the recipients  1 - 3 . For example, the NIFF  712 ,  711  are single page communication files respectively associated with recipient  1  and  3 . The NIFF files  714 ,  713  are the first page and the second page of a communication file associated with recipient  2 . Now that each group of communications has been identified for further analysis of the communication content, styling, logic, variability, and communication channels used for execution can be compared and analyzed. 
     Referring to  FIGS.  4 - 5 ,  8   , the objects identification and quantification module  430  automatically identify image objects  815 ,  818 ,  825  and their location in the NIFF  810 ,  820  and record in a zone library  830  (step  525 ) using image recognition machine learning such as the Stroke Width Transform (SWT) algorithm. For example, image objects  815 ,  825 ,  818  are identified and stored respectively in zones  1 - 3  in the zone library  830 . Each object is defined by its properties such as size, type, and other uniquely identifiable digital features. 
     Referring to  FIGS.  4 - 5 ,  9   , the objects identification and quantification module  430  automatically identify text zones and their location from the remaining content not identified as an image using image recognition and classification machine learning. This results in the outlining of the wireframes in each of the NIFF, which are then indexed and recorded in the zone library ( 480  in  FIG.  4   ) (step  530 ). 
     Referring to  FIGS.  4 - 5 ,  10 A , the machine learning and design construction engine  470  automatically models each zone in vector space (step  540 ). For example, the text zone  1010  is selected and the text detected within the zone will undergo normalization process of tokening, converting words to all lower-case characters, removing stop-words (i.e. insignificant words), stemming, and then finally assigning values to the remaining words via Term Frequency-Inverse Document Frequency (TF-IDF). In the vectorized space within each zone, referring to  FIGS.  4 - 5 ,  10 B , the machine learning and design construction engine  470  automatically identifies text font size and type using font recognition machine learning using artificial neural networks and records it in the text style library (step  545 ). A text image  1040 , which has been recognized and stored in the zone library in step  530  and has been vectorized in step  540 , is analyzed by the machine learning and design construction engine  470 . Font type, size, and color are identified using convolutional artificial neural networks that were trained using a data set of already existing fonts within a range of sizes and colors. The convolutional artificial neural network will then be able to identify any font type from the training set within new text samples and will also be able to infer the additional colors and font sizes of new text sample based on the extrapolation of what was learned in the range of the colors and sizes in the training data set. The recognized font type name, font size, and font color are stored in a text style library  1060  (which can be stored in the unified design database shown  480  in  FIG.  4   ). 
     After the image and text objects are respectively recognized and stored in the zone library in steps  525 ,  530  and are respectively vectorized in step  540 , referring to  FIGS.  4 - 5 ,  11   , the global object and data variable identification module  450  automatically identifies data variables and static global objects in text zones and image objects (step  550 ). A global object is an image or text string (se  FIG.  11 ,  1130   ) that may be reused within a single zone (see  FIG.  11 ,  1140   ), across multiple different zones within a single wireframe, and/or across zones of multiple different wireframes and communication channels. Data variables are a text string that originates from a data file (see  FIGS.  11 ,  1150  and  1160   ) (e.g. XML, DAT, CSV, XLSX, etc.) and is placed into content on a communication output (see  FIGS.  11 ,  1110 , and  1120   ). Using Vector Space Modeling (VSM) machine learning model previously defined in step  540 , we can use the data values within the supplied data files to perform a nearest-neighbor comparison to match text within the VSM to the text in the data file to identify data variables with a 100% match or a match within a defined similarity tolerance. To identify global text objects the VSM defined in step  540  can be used to perform another nearest-neighbor comparison between text strings within a single zone of a single NIFF with a 100% match. 
     Referring to  FIGS.  4 - 5 ,  12   , the recipient matching module  420  automatically map communication content files to recipient data (step  555 ) by leveraging the results of the Recipient Matching Model (step  520 ) in  FIG.  7   . In step  520  illustrated in  FIG.  7   , the pages in the communication were grouped together based on clustering machine learning without identifying a recipient for a group of pages. In step  555  shown here in  FIG.  12   , recipients are identified for each group of pages of the communication. 
     Referring to  FIGS.  4 - 5 ,  13 A,  13 B , the content machine learning and design construction engine  470  automatically analyzes different zones ( FIG.  13 A,  1010  and  1020   ) within the same normalized intermediate format file to further identify static global objects ( FIG.  13 A,  1100  and  1120   ) for a single recipient (step  557 ) using Vector Space Modeling machine learning from step  540 , that could not have been identified previously since previous comparisons were only within a single zone. Additionally, the content machine learning and design construction engine  470  automatically analyzes different zones between different normalized intermediate format files ( FIG.  13 B,  1040  and  1050   ) grouped and associated with a recipient in step  555  to further identify static global objects ( FIG.  13 B,  1130  and  1140   ) for a single recipient (step  557 ) using Vector Space Modeling machine learning from step  540 . 
     In some embodiments, the data storage  34  ( FIG.  4   ) stores a unified file structure defines the static global objects, the data variables, and the variable global objects in the normalized intermediate format files for communications to different recipients. These communications can be delivered in different channels such as physical prints, email, or in a web form. 
     Referring to  FIGS.  4 - 5 ,  14   , the machine learning and design construction engine  470  automatically quantifies paragraph style (step  560 ). Text Line Localization (TLL) machine learning is used to identify lines of text and the distances between them, indentations, justifications, distances between and within paragraphs, etc. The paragraph styles can be stored in the unified design database shown  480  in  FIG.  4   ). In some embodiments, the unified file structure stored in the data storage  34  ( FIG.  4   ) defines the zones, wireframes, page behaviors, and paragraph styles in the normalized intermediate format files for communications to different recipients. The unified file structure can also define zones, zone types, and content to be inserted in white space (described below) on pages of the communications. 
     Referring to  FIGS.  4 - 5 ,  15   , the cross-recipient group analysis module  440  automatically analyzes NIFF across recipient groups (step  570 ). The global object and data variable identification module  450  automatically identifies static global objects, data variables and now variable global objects, as shown in  FIGS.  15 A and  15 B . By analyzing previously identified static global objects ( FIG.  11    step  550  and  FIGS.  13 A and  13 B  step  557 ), variable global objects ( FIGS.  15 A and  15 B,  1110  and  1120   ) can be identified through comparison in Vector Space Modeling (VSM) machine learning model previously defined in step  540 , as well as similar location within similar zones between similar normalized intermediate format files of different recipients ( FIG.  15 A,  1010  and  1020   ). If an accompanying data file was supplied, the system can automatically look at the various data values and identify possible values that drive the selection of the variable global objects. Lastly, whereas static global objects were previously only identified for a single recipient ( FIG.  11    step  550  and  FIGS.  13 A and  13 B  step  557 ) further static global objects ( FIG.  15 A,  1130   ) can now be identified between different recipients ( FIG.  15 A,  1010  and  1020   ). 
     Referring to  FIGS.  4 - 5 ,  16   , the cross-recipient group analysis module  440  automatically identifies base common messages (step  572 ) by suppressing all previously identified data variables as well as static and global objects to analyze the remaining content for uniqueness and recording the results. 
     Referring to  FIGS.  4 - 5 ,  17   , the cross-recipient group analysis module  440  automatically identifies base common wireframes using Vector Space Modeling (VSM) machine learning (step  575 ) by analyzing the locations of the identified image zones ( FIG.  8    in step  525 ) and the identified text zone ( FIG.  9    step  530 ) as a composite Base Common Wireframe (BCM) for each uniquely identified normalized intermediate file format. For example, NIFF  2  ( 1100 ) is different from NIFF  3  and NIFF  14  ( 1200  and  1300 ) because while all 3 NIFFs have a zone in the top right ( 1110  and  1210  and  1310 ) the zone in NIFF  2  ( 1110 ) is a text zone whereas the zones in NIFF  3  ( 1210 ) and NIFF  14  ( 1310 ) are images. In addition, NIFF  2  does not have sidebar content that NFF  3  and NIFF  14  have ( 1220  and  1320 ), but it does has an additional image zone at the bottom ( 1140 ) that the other NIFFs do not have, so ultimately it is determined to be a unique BCW  1  ( 1500 ) from the other two ( 1600 ). As for NIFF  3  and NIFF  14  ( 1210  and  1310 ), they only have slight differences but maintain the overall structure. While the image for NIFF  3  ( 1210 ) and NIFF  4  ( 1310 ) as well as the side bar content ( 1220  and  1320 ) are different in content and length, both NIFFs can be generalized into the same BCW  2  ( 1600 ) which is still distinct from the other BCW  1  ( 1500 ). 
     Referring to  FIGS.  4 - 5 ,  18  and  19   , the cross-recipient group analysis module  440  automatically identifies variable white space management behaviors using multi-class classification machine learning (step  578 ). 
     Referring to  FIG.  18   , depending on various recipient data (e.g. current enrolled products/medical plans, preferences, demographic information, etc.) a given recipient might receive additional communication material targeted specifically to them such as a specific medical disclaimer based on their unique health plan or perhaps a specific product warranty based on the product they have shown interest in that another recipient may not receive. Variable information  1830  may be inserted or appended into a communication as an entire sheet or section. In some situations, referring to  FIG.  19   , additional content  1930  can be populated based on a list of qualifying offers or received products that can be repeated or are inserted in small chunks causing other zones to break and flow over to multiple pages. 
     Referring to  FIGS.  4 - 5 ,  20   , zone classification module  460  automatically classifies zones into zone types using Vector Space Modeling (VSM) machine learning (step  580 ). Each zone of a given NIFF may serve a specific communicative purpose such as an address block, call-to-action, disclaimer, etc., and the identification of these types of purposes can help more easily organize the content into like classes to be analyzed automatically for adherence to specific branding, legal, regulatory, and other requirements. The resulting classifications are recorded in a Zone Classification Library ( FIG.  20 ,  1010   ) which is stored in the Unified Design Database  480 . 
     Referring to  FIGS.  4 - 5 ,  20   , the channel classification module  490  automatically classifies NIFFs into communication channel types using Artificial Neural Networks (ANNs) machine learning (step  585 ). It is important to know which channel a communication will be ultimately delivered through be it e-mails, physical prints, web form, etc. as each channel has its own requirements and formatting for successful quality control and execution. Using features such as size, design, verbiage, and additional features uncovered through ANNs each collection of processed NIFFs can be categorized into its respective communication channel. 
     The machine learning and design construction engine  470  automatically construct standardized communication designs (step  590 ) using font sizes/color/types, paragraph styles, zone placement/measurements, static and variable global objects, base messages, data variables, images, variable whitespace behaviors, and business logic that have been defined and stored during the automated analyses outlined in this document to minimize redundant uses of these components. Then they are classified into their various required communication channels using multi-class classification machine learning (step  580 ) based on overall size, content types, required variable whitespace behaviors ( FIGS.  18  and  19    step  578 ). 
     The communication resource allocation server  32  determines available resources ( 220 - 240 ) based on the quantity and complexity of the standard communication designs, and assigns design, printing, and delivery tasks to different resources. The standardized communication designs are sent from the intelligent communication design server(s)  37  to product fulfillment centers  40 ,  41  ( FIG.  1   ) and/or the communication server  35  to fulfill communication service (step  595 ). As described above, the communication service can include printing and finishing physical communication materials to be mailed to the recipients. It can also include deliveries of electronic communication messages such as by the communication server  35 . 
     Exemplified operations of an automated communication design analysis and construction system have been described above in  FIGS.  1 ,  4 - 20   . Other aspects of the process of intelligently understanding and constructing communication content are now described in more detail. In some embodiments, referring to  FIG.  21    the communication content files in different formats are automatically normalized into normalized intermediate format files (NIFFs) with unique identifications (step  2100 ). Details of normalizing communication content files are also described above in relation to step  515  in  FIGS.  4 - 6   . 
     The NIFFs are then automatically separated into Page Groups PG 1  to PG N  (step  2110 ). Each page group include NIFF pages to be received by a specific recipient. The separation of NIFF pages into page groups are also described above in relation to step  520  in  FIGS.  4 - 5 ,  7   . 
     The NIFFs are next fed into an intra-page machine prediction model (step  2120 ), which outputs intra-page parameters for individual pages such as zones, image objects, text objects, text significance, and paragraphs (step  2125 ). The intra-page parameters can include text properties such as font type, font size, and font color. The intra-page parameters can describe wireframes, and paragraph styles within the zones, and zone types. Intra-page parameters are also described in steps  525 - 545 ,  560  and  FIGS.  8 - 10 B,  13 A, and  14    above. 
     The intra-page machine prediction model is based on machine learning, which can be implemented by a single model consisting of a hybrid implementation of both convolutional neural networks (CNN) and recurrent neural networks (RNN). A neural network includes multiple inter-connected layers each made of a plurality of nodes, such as an input layer, convolutional layers, max pooling layers, an output layer, and optionally a subsampling layer. The intra-page machine prediction model is trained by intra-page machine learning (step  2135 ) using historic communication files and known associated intra-page parameters ( 2130 ). The historic communication files can be stored in a unified communication data structure as described above. For each page of a historic communication file, the intra-page machine prediction model produces predicted intra-page parameters. The predicted intra-page parameters are compared with the known intra-page parameters associated with that page to produce errors. The errors are distributed via backpropagation through the layers. The intra-page machine prediction model is optimized by training with a large number of samples (i.e. pages of historic communication files). 
     The steps  2120 ,  2125 ,  2135  can be primarily performed by the machine learning and design construction engine  470  and the objects identification and quantification module  430  in  FIG.  4   . 
     The NIFFs and the intra-page parameters are next fed into an inter-page machine prediction model (step  2150 ), which outputs inter-page parameters across different pages. The inter-page parameters include intra-group parameters (step  2160 ,  FIGS.  13 A and  13 B ) such as static global objects and inter-group parameters (step  2165 ,  FIGS.  15 A,  15 B,  18 ,  19   ) such as static global objects, variable global objects, variable whitespace behaviors, and data variables. The generation of the inter-group parameters in step  2165  can be dependent in part on intra-group parameters produced in step  2160 . As described in steps  550  and  557  ( FIG.  5   ) above, the static global objects include text, logos, and images that are common to different recipients. The variable global objects can include text, logos, and images that vary between recipients. Data variables ( FIG.  11   ) include a recipient&#39;s personally identifiable information such as unique ID, name, address, dates (e.g. birthdays, doctor visits) etc., but may also include personally identifiable information shared between recipients such as household IDs, tiered reward membership, geographic region, demographic information, etc. Variable white space behaviors include instances where the communication fluctuates between total page count (e.g. 1 page to 2 pages) due to variable content that changes in length and requires more pages or items to be appended/inserted into the total page count ( FIGS.  18  and  19   ). 
     The inter-page machine prediction model is also based on machine learning, which for example can be implemented by an artificial neural network comprising multiple inter-connected layers each made of a plurality of nodes. The inter-page machine prediction model is trained by inter-page machine learning (step  2155 ) using historic communication files with and known associated intra-group parameters and inter-group parameters ( 2130 ), both of which relate to properties across pages. The historic communication files can be stored in a unified communication data structure as described above. For a set of historic communication files, the inter-page machine prediction model produces predicted intra-group parameters and inter-group parameters. The predicted intra-group parameters and inter-group parameters are compared with the known intra-group parameters and inter-group parameters associated with that page to produce errors. The errors are distributed via backpropagation through the layers. The inter-page machine prediction model is optimized by training with a large number of historic communication files (i.e. pages of historic communication files). 
     The steps  2150 ,  2155  can be primarily performed by the machine learning and design construction engine  470  and the objects identification and quantification module  430  in  FIG.  4   . 
     The base-line composition of the inter-group parameters and intra-group parameters is determined (step  2170 ) and consists of base common messages ( FIG.  16   ) and base common wireframes ( FIG.  17   ). The standard content is stored in the content store  280  ( FIG.  4   ). The standard content may be the most updated version based on branding, legal, and regulatory requirements. Some aspects of the base-line composition are also described above in relation to step  572  and  575  in  FIG.  5   . Their consistencies and logic relationships between these parameters are automatically checked, and necessary corrections are made to ensure consistencies (step  2170 ). 
     The NIFFs, the intra-page parameters and the inter-page parameters consisting of the intra-group parameters and inter-group parameters are stored in the unified communication data structure (step  2180 ), which can be added to the historic communication files ( 2130 ) and used for training the intra-page machine prediction model and the inter-page machine prediction model in the future. 
     The intelligent communication content learning and constructing engine ( 470 ,  FIG.  4   ) automatically constructs standard communication design files based on the normalized intermediate format files and the intra-page parameters, intra-group parameters, and inter-group parameters (step  2190 . Standard communication design files), as illustrated in  FIGS.  8 - 20   , are the recorded collection and relationship of the various global objects both static and global, their placement, font information, paragraph styling, base common messages, base common wireframes, and variable behaviors of the aforementioned information combined. The standard communication design files can be stored in the unified design database  480  ( FIG.  4   ). 
     The standard communication design files can then be classified into communication channel types using machine learning (step  585  in  FIG.  5   ), and automatically constructed in standardized communication designs based on the intra-page parameters, intra-group parameters, and inter-group parameters (step  590  in  FIG.  5   ). The standardized communication designs are sent from the intelligent communication design server(s)  37  to product fulfillment centers  40 ,  41  ( FIG.  1   ) and/or the communication server  35  to fulfill communication service (step  595 ). 
     It should be understood that the presently disclosed systems and methods can be compatible with different devices or applications other than the examples described above. For example, the disclosed method is compatible with different computer devices and network configurations, different forms of physical and electronic communication methods other than the ones described above, and different printing and finishing equipment for reproducing information on physical substrates.