Patent Publication Number: US-2022239965-A1

Title: Systems and methods for generating adapted content depictions

Description:
Cross-Reference to Related Applications 
     This application claims priority to U.S. Provisional Application No.  62 / 979 , 785  filed February  21 ,  2020 , the content of which is hereby incorporated by reference herein in its entirety. 
    
    
     Background 
     The present disclosure relates to systems and processes for generating image depictions of content based upon profiled preferences. 
     Summary 
     Depictions (e.g., posters, images) of content (e.g., movies) are commonly utilized to publicize and attract consumption of the content. Consumers with different consumer profiles may be attracted to content based upon different factors. For example, some consumption is based upon preferences toward comedic content, romantic content, action content, and/or particular actors (including their attributes). In one approach, a limited selection of depictions of the content is manually generated and distributed in order to attract and maximize consumption based upon a large, generalized set of consumer profiles. For example, on movie poster may be manually created for children and one for adults. In another example, one movie poster may be manually created for distribution in North America, and one for distribution in China. However, such manual creation of images representing content is expensive and time consuming because each image needs to be created manually. Furthermore, some users may not be attracted to any of the elements of generalized content depiction or may even be repelled by all or parts of the depictions. For this reason, such broad targeting is often ineffective because, for example, not every consumer in North America will have the same preferences. Thus, more effective systems and methods are needed for distributing exemplary depictions (e.g., posters) of content tailored to particular user profiles. 
     In some embodiments, machine learning (ML)/artificial intelligence (AI) methods and systems are implemented to generate content depictions (e.g., images/posters) based upon user profiles, metadata pertaining to the content being depicted, content structures and features (e.g., images extracted from the depicted content and/or other content) and related metadata. In an embodiment, a machine learning system is programmed to process and interpret user profiles (e.g., content browsing history, prior content consumption, social media patterns, etc. . .) into classifications of features and levels of preference for different kinds of features of content (e.g., particular actors or attributes of actors, scenery, comedic content, romantic content, action-based content, etc.) and utilizing a store of related feature depictions (e.g., images) from the content being depicted and/or other content (e.g., including images of the preferred actor(s), scenery, etc.) and generating a new depiction (e.g., image/poster) that may be distributed with respect to a particular user profile (e.g., an online user account). 
     After a depiction is distributed, data may be collected that is related to responses to the distribution (e.g., consumption history by user accounts to which the generated depictions were distributed to). This data may be received by the ML system, with which it may retrain its programming to further optimize output and subsequent outcomes (e.g., to increase consumption of content). For example, the ML may correlate a greater responsiveness by a particular user (or type of user) profile with certain features of the generated depictions (e.g., certain backgrounds, actors, etc.). As the ML system receives more feedback, it continues to “learn” and reprogram itself to optimize how to generate depictions and maximize outcomes (e.g., consumption). It&#39;s store (e.g., images) of features of content may also grow and certain features may be emphasized based upon the “learning.” 
     In some embodiments, the ML system includes a neural network with which it learns patterns and determines outputs (depictions). The neural network may include multiple nodes related to particular features of content and of user profiles. Connections between these nodes and the strengths of these connections may be programmed based upon historical metadata of user profiles as the data pertains to the preference for particular classified features of content. The neural network may learn to generate new nodes and connections based upon new data it receives and and/or based upon outcome data collected after content depictions are generated and distributed. 
     In some embodiments, a neural network is a generative adversarial network (GAN). The GAN may include a discriminator module that compares a generated depiction/image with “authentic,” approved, and/or previously distributed images/depictions. If the discriminator fails to “pass” the depiction, factors pertaining to the failure may be fed back into the ML system in order to improve or modify the depiction to more closely represent an approved or authentic depiction. For example, the “discriminator” module may determine if the features included in the generated depiction flow together naturally (e.g., an actor&#39;s depicted proportions are not oversized compared to an object or background scene in the depiction). In addition, the “discriminator” module itself may also be reprogrammed and/or modified via feedback loop. In some embodiments, both the ML system and the “discriminator” module may be fine tuned in parallel. 
     A machine learning system may include a natural language processor (NLP) to interpret collected metadata pertaining to a user&#39;s account profile and/or content profile. For example, an NLP may interpret posts on a social media site which reflect that the user profile has a tendency to favor ocean scenes, car crashes, particular food items, etc. . . Likewise, an NLP may be used to interpret particular features of content (vocabulary) or its metadata with particular situations or themes (e.g., comedic, romantic, or hostile). 
     In some embodiments, the ML system utilizes deconstructed segments or features of content in order to learn which features/segments of the content are associated with particular themes, characters, scenes, etc. and/or for generating a content depiction tailored to a particular user profile or collection of user profiles. These segments/features may be classified as a content structure based on a content segment or other feature of content. 
     A content structure may include objects, where each object includes a set of attributes and corresponding mappings. For example, a movie may be deconstructed into a plurality of objects each having their own respective attributes and mappings. These structures may be assigned particular attributes that also correlate (e.g., to different levels of degree) to attributes of particular user profiles. The ML system may then identify a correlated structure and use it to generate a depiction or a part of a depiction of content tailored to a particular user profile. Exemplary content structures that can be used for generating new content structures and rendered into a content depiction are described by co-pending application No. 16/363,919 entitled “SYSTEMS AND METHODS FOR CREATING CUSTOMIZED CONTENT”, filed on Mar. 25, 2019 (“&#39;919 Application”), which is hereby expressly incorporated by reference herein in its entirety. 
     Generation of the tailored content structures and/or images helps overcome the limitations of generalized depictions for large audiences described above. For example, a user receiving content depictions tailored to their profile or similar profiles according to some embodiments will be apprised of the content features which match their preferences and thus is more likely to further consume the content being depicted. Generation will also be less time consuming, user intensive, and likely more predictive of positive outcomes than manual generation. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The above and other objects and advantages of the disclosure will be apparent upon consideration of the following detailed description, taken in conjunction with the accompanying drawings, in which like reference characters refer to like parts throughout, and in which: 
         FIG. 1  shows an illustrative flowchart of a machine learning system for generating tailored content depictions according to some embodiments of the disclosure. 
         FIG. 2  shows an illustrative flowchart of a generative adversarial neural network machine learning system for generating tailored content depictions according to some embodiments of the disclosure. 
         FIG. 3  shows an illustrative diagram of a neural network model node array according to some embodiments of the disclosure. 
         FIG. 4  is a diagram of an illustrative device for generating content depictions in accordance with some embodiments of the disclosure; 
       FIG,  5  shows an illustrative flowchart of a process for generating content depictions in accordance with some embodiments of the disclosure; 
         FIG. 6  shows an illustrative flowchart of a neural network process for generating content depictions in accordance with some embodiments of the disclosure; 
         FIG. 7  shows an illustrative process of combining image data to generate a content depiction in accordance with some embodiments of the disclosure; 
     
    
    
     DETAILED DESCRIPTION 
     In some embodiments of the present disclosure, a machine learning system utilizes profile input, content input, and a data store of content structures (e.g., content images, descriptions, etc.) to generate a content depiction tailored to the profile input.  FIG. 1  shows an illustrative flowchart of a machine learning system for generating tailored content depictions according to some embodiments of the disclosure. A machine learning engine  120  receives profile data  125  for which a content depiction  145  is generated. Profile data  125  can include content preferences, browsing history, and purchase history, such as may be collected in relation to an online account or profile. 
     Machine learning engine  120  also receives and/or has access to content data, including image data and content structure data relating to a particular content which is being depicted. Content data can include, for example, meta data identifying the title, actors, script, viewership, and other data pertaining to the depicted content or other content. Content structure data can include content structures defined by objects deconstructed from the content itself. The content structures may include attribute tables with attributes, such as, for example, the height, race, age, gender, hair color, eye color, body type, a facial pattern signature, a movement pattern, a relative location with other objects, an interaction with other objects, and/or the like. The attributes may be stored in an attribute table as a listing of data field names in the content structure. The attributes may also have associated mappings. Generation of such content structure may be performed, e.g., by deconstructing an existing content segment. Deconstruction of content segment and storage of resulting content structures is further described, for example, in the ‘919 Application referenced above. 
     Machine learning system  120  also receives sample depictions  110  from/with which to base and compare generated depiction  145 . These sample depictions  110  may include already generated and authenticated/approved depictions. Machine learning system  120  utilizes the input data as well as a database system  115  of image data to generate a new depiction  145 . The image data may include images and their attributes (e.g., particular actors, backgrounds, scenes, locations, objects, etc..). The image data may have been previously programmed into the database system  115  or obtained from content data  130  and sample depictions  110 . 
     Machine learning system  120  generates a new content depiction  145  of a content by combining and modifying elements of image data from content data  130  and/or content depictions  110  based upon profile data  125  and content data  130 . The machine learning system  120  is trained and programmed to combine and/or modify image data to reflect determined content preferences associated with profile  125 . Machine learning system  120  may include one or more machine learning models  123 . These models may employ, for example, linear regression, logistic regression, multivariate adaptive regression, locally weighted learning, Bayesian, Gaussian, Bayes, neural network, generative adversarial network (GAN), and/or others known to those of ordinary skill in the art. Multiple models may be used with results combined, weighted, and/or otherwise compared in order to determine an output depiction  145 . 
     Preferences associated with profile  125  may be determined such as by correlating profile data (e.g., browsing history, content preferences) with particular attributes of images (e.g., particular actors, actor attributes, themes of action, romance, comedy, etc.). For example, the machine learning system may determine that the profile consumes content (e.g., movies, television programs) with attributes of comedy to a greater degree than content with attributes of action or drama, The machine learning system  120  can, for example, analyze data (e.g., credits, reviews, scripts) associated with the consumed content that may be retrievable from local (e.g., local database systems) or online sources (e.g., websites) and include key words (e.g., “comedy,” “funny,” “hilarious”) that the system has been programmed or “learned” to ascribe with particular attributes (e.g., themes of comedy). In some embodiments, the machine learning system utilizes a natural language processor (NLP) to analyze the data and extract attributes of the content. 
     The machine learning system  120  may use one or more of content depictions  110  as a reference depiction. These may include presently approved/active depictions associated with the content and the attributes associated with the depictions (e.g., actors, scene description, background, location, etc. . . .). The machine learning system  120  may then tailor a reference depiction  110  or generate a substantially new depiction based upon the determined preferences associated with the profile  25 . For example, the machine learning system  20  may determine that most of the attributes of a content depiction  110  correspond to preferences of the profile  125  and thus may either minimally or decline to modify a selected content depiction. For example, the machine learning system  120  may simply substitute the background image of a depiction with a background image from the image database  115  with attributes (e.g., outdoor daytime scene) that more closely correspond to the preferences of profile  125 . 
     In some embodiments, the machine learning system  120  may generate a substantially new depiction (e.g., an image or content structure that represents an image) utilizing image data/content structure data from image database system  115 . For example, when a particular profile predilects to romance themes, and the selected depictions  110  include relatively little if any attributes of romance, the machine learning system  120  may pull images/content structure from image/content structure database  115  of two actors associated with the content and superimpose their images/content structure in an embrace over a background image/content structure with romantic attributes (e.g., as further shown in  FIG. 8 ). 
     Once a depiction  145  has been generated, it may be transmitted at block  150  to a destination associated with profile  125  (e.g., for display in a webpage downloaded using a browser using a “cookie” linked to the profile). The destination may include devices for personal displays of content (e.g., streaming media, live television) linked to profile  125 . For example, a user associated with profile  125  may login to a streaming or live content account or service (e.g., Tivo Edge™for Cable, Amazon Prime Video, Xfinity Cable, etc. . . .). During a broadcast of content using the associated device and/or service, an interval between or during periods of content delivery may include display of the generated depiction and may include providing information or an interface for accessing (e.g., viewing/recording) content (e.g., streaming/live broadcast content) associated with the depiction. 
     After transmission of content depiction  145  at block  150 , feedback data (e.g., metadata) may be collected at block  155  in connection with its transmission. Data reflecting consumption of the content (e.g., consumption in response to or proximate to the display of the content depiction) may be collected and transmitted back to the machine learning engine  120 . For example, a Tivo Edge™device may be programmed to store records of consumption of the content before and immediately after display of the generated content depiction and also consumption of the content in response to other content depictions and/or consumption of content absent a proximate display of any content depiction. 
     After receiving the feedback data collected at block  155 , machine learning system  120  may use the feedback data to further program itself for purposes of generating further content depictions. For example, machine learning system may correlate certain content depictions or aspects thereof with greater consumption of the content by specific profiles or profiles with particular characteristics (e.g., predilections for romance, action, etc.). 
       FIG. 2  shows an illustrative flowchart of a generative adversarial neural network (GAN) machine learning system for generating tailored content depictions according to some embodiments of the disclosure. A content depiction generator  230  network module receives data  215  for a particular content, profile data  200 , and collected metadata  210  pertaining to the particular content and other content. Data  215  may include data describing the content including, for example, its actors, themes, story summary, etc. Data  215  may also include content structures such as described herein including content objects that may be used to generate the content or variations thereof. Data  215  may include content depictions, content structures, and images from which new depictions may be based as described herein. 
     Collected metadata  210  may include, for example, content consumption statistics for the content to be depicted and/or other related content. For example, the metadata  210  may include data pertaining to the actors of the content, their relative popularity, the success of particular content they have been involved in, the success of particular content depictions related to the content, and other data that may be used to tailor a content depiction using generator module  230 . 
     Profile data  200  may include content preferences and consumption history associated with a particular profile. Profile data  200  may include internet browsing history, social media posts, content “likes” or “dislikes,” and other data that may be analyzed by generator  230  to determine content preferences associated with a profile such as further described herein. 
     Generator module  230  may also be programmed to generate tailored content utilizing a store  235  of image data and model content depictions  250 . Such as described with respect to  FIG. 1 , image data may include images of particular actors, backgrounds, scenes, objects, etc., and their attributes. Content depictions  250  may include previously generated and approved (model) content depictions. 
     Generator module  230  includes a neural network of nodes and node connections programmed to determine and generate a tailored content depiction based upon content data  215 , profile data  200  and metadata  210 . An exemplary network of nodes and connections is shown and described with respect to  FIG. 3 . The nodes and connections, store  235  of images, and model depictions  250  may be pre-programmed to a certain level as a basis for generating initial content depictions. As will be described further, generator module  230  is programmed to generate new nodes and connections for content depiction generation based upon feedback and fine tuning from block  265  and a discriminator module  240 . Discriminator module  240  may include a neural network which is programmed with nodes and connections to discriminate between passable depictions and those that fail discrimination. 
     Generator module  230  may pre-process profile data  200  and metadata  210  to determine particular preferences associated with a profile. For example, generator module  230  can compare content consumption history provided in profile data  200  to metadata  210  or content data  215  relating to the content consumed (e.g., keywords, actors, descriptions of the content) to determine a profile preference for particular content attributes. Profile data  200  may also include predetermined profile preferences. 
     Using determined profile preferences and content data as an input, a neural network of generator  230  operates to modify an existing content depiction or generate a new depiction from various image data elements from image data store  235 . For example, an input reflecting a high degree of preference for a particular content attribute (e.g., a particular actor or content theme) may cause the neural network to apply a node and strong connection for incorporating an image/content structure attribute with that particular attribute (e.g., an image/content structure of a particular actor or content backdrop). The neural network may utilize numerous such nodes and connections balanced against each other to modify or create a depiction with various attributes. 
     After a depiction is generated by generator  230 , discriminator module  240  compares the generated depiction to one or more model content depictions  250  at  255 . The discriminator  240  may apply analysis and comparisons, including the use of a neural network, to determine if the generated depiction satisfies particular criteria pertaining to authentic/approved content depictions. Analysis/comparisons may include, for example, determining whether features (e.g., images/content structures of actors, objects, backgrounds) sufficiently resemble features of the model depictions. Various image/content structure processing functions (e.g., facial/object recognition, pattern matching) may be employed to perform the analysis/comparisons. Based upon the analysis/comparisons, a determination is made about whether the generated depiction satisfies the criteria/comparisons to a sufficient degree at block  245 . 
     If, at block  245 , the generated depiction doesn&#39;t satisfy the tests performed by discriminator  240  and/or other examinations/criteria (e.g., approval/rejection through an external process/operator), feedback data regarding the rejection may be received by the generator  230  and the discriminator  240 . Feedback data may include, for example, rejections of particular identified actors, scenes, backgrounds, and/or objects within the content depiction. Feedback data may include data indicating attributes that should be introduced, removed, and/or modified in the depiction. Based upon the feedback, generator module  230  may generate/modify a content depiction and again output the newly generated depiction for further processing by discriminator module  240 . The cycle may continue until a satisfactory depiction is generated and/or a particular threshold of rejections is exceeded. 
     At block  260 , the generated depiction is distributed such as across a computer network and to a content platform. In some embodiments, the depiction is distributed in a manner that is linked with a particular account profile (e.g., a content distribution platform linked to the profile) or type of profile. As described herein, the feedback data pertaining to the distribution of the depiction and related content consumption may be collected and received at block  265  and used to update medadata store  210  and/or profile data  200 . The feedback data may be fed back into generator module  230  or discriminator module  240  and result in reprogramming of the generator  230 /discriminator  240  such as based upon analysis of the generated depiction(s), related content consumption, and profile data. 
       FIG. 3  shows an illustrative diagram of a neural network model node array  300  according to some embodiments of the disclosure. An input layer  310  may include various input nodes  350  matched to particular profile attributes (e.g., particular types of content preferences). The input nodes may also include inputs to various image data, content structures, and content data. These input nodes may be connected, designated by varying degrees of connection strength, to other nodes within a processing layer  320  of nodes. The processing layer  320  of nodes directs the neural network to modify or generate a content depiction based upon connections to the input layer and to other nodes within the processing layer  320 . The processing layer processes the input depending on the current state of the network&#39;s adaptive programming. The processing layer may have direct access to an image/content structure data store (e.g., data/content structure store  235  of  FIG. 2 ) from which image data is used to generate and/or modify content depictions. Model node array  300  may be used within a neural network generator module such as generator module  230  of  FIG. 2 . 
     Based upon the processing in the processing layer  320 , an output depiction is generated through the output layer  330 . The output layer  330  produces an output content depiction with various attributes determined through the input and processing layers  310  and  320 . The output depiction may be further forwarded to a discriminator module (e.g., module  240  of  FIG. 2 ) and/or distributed such as further described herein. After a depiction is forwarded to a discriminator and/or distributed, the neural network may be (re-)programmed based upon feedback received in response. For example, feedback data may indicate a greater relative positive response (e.g., consumption of content) from particular profile types to particular image/content structure attributes. The neural network may thus be reprogrammed to strengthen a connection (association) between a particular profile and image/content structure attribute. 
       FIG. 4  is a diagram of an illustrative device  400  used for generating, distributing, and displaying content depictions in accordance with some embodiments of the disclosure. A system for generating and distributing content depictions may include, for example, servers, data storage devices, communication devices, display devices, and/or other computer devices. Control circuitry  404  may be based on any suitable processing circuitry such as processing circuitry  406 . As referred to herein, processing circuitry should be understood to mean circuitry based on one or more microprocessors, microcontrollers, digital signal processors, programmable logic devices, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), etc., and may include a multi-core processor (e.g., dual-core, quad-core, hexa-core, or any suitable number of cores) or supercomputer. 
     In some embodiments, processing circuitry  406  may be distributed across multiple separate processors or processing units, for example, multiple of the same type of processing units (e.g., two Intel Core i 7  processors) or multiple different processors (e.g., an Intel Core i 5  processor and an Intel Core i 7  processor). A network interface  410  may be used to communicate with other devices in a machine learning system (e.g., an image database system  15  of  FIG. 1 ) or with devices to which content depictions are distributed (e.g., content servers or content display devices). 
     In some embodiments, control circuitry  404  executes instructions for execution of a machine learning system stored in memory (i.e., storage  408 ). The instructions may be stored in either a non-volatile memory  414  and/or a volatile memory  412  and loaded into processing circuitry  406  at the time of execution. A system for generating content depictions (e.g., the systems described in reference to  FIGS. 1, 2, and 3 ) may be a stand-alone application implemented on a media device and/or a server or distributed across multiple devices in accordance with device  400 . The system may be implemented as software or a set of executable instructions. The instructions for performing any of the embodiments discussed herein of content depiction generation may be encoded on non-transitory computer-readable media (e.g., a hard drive, random-access memory on a DRAM integrated circuit, read-only memory on a BLU-RAY disk, etc.) or transitory computer-readable media (e.g., propagating signals carrying data and/or instructions). For example, instructions in accordance with the processes of  FIGS. 5, 6, and 7  may be stored in storage  408 , and executed by control circuitry  404  of device  400 . 
       FIG. 5  shows an illustrative flowchart of a process for generating content depictions in accordance with some embodiments of the disclosure. At block  510 , profile data is received at a machine learning system (e.g., machine learning system  FIGS. 1 and 2 ). As described herein, profile data can include content preferences, browsing history, content consumption history, and social media history. At block  520 , profile preferences are identified such as based upon analyzing the profile data. A set of resulting profile preference inputs is then further processed by the machine learning system for generating an output depiction. 
     At block  530 , the machine learning system receives and/or accesses content structures and/or image data associated with a content to be depicted or related to other content. At block  540 , the machine learning system may classify the received/accessed content structures and/or image data according to content categories. In some embodiments, accessed content structures and/or image data may already be classified within the machine learning system. For example, images and/or content structures of particular actors, objects, background scenes, etc., may be accessible within an image database and/or a content structure store (e.g., image/content structure database store  15  of  FIG. 1 ). 
     At block  550 , the machine learning system may use one or more trained models for correlating profile preferences with content structures, images, or image features. These models may employ, for example, linear regression, logistic regression, multivariate adaptive regression, locally weighted learning, Bayesian, Gaussian, Bayes, neural network, generative adversarial network (GAN), and/or others known to those of ordinary skill in the art. Multiple models may be used with results combined, weighted, and/or otherwise compared. 
     At block  560 , the model(s) are utilized to generate a content structure/image depiction of identified content based upon the profile preferences and correlated content structures, images, and/or image/content structure features as further described herein. The resulting content structure/image depiction may be further analyzed and/or modified, and/or the model(s) reprogrammed, such as described with respect to the GAN of  FIG. 2 . The generated depiction may be in the form of an image and/or a content structure represented by one or more objects (e.g., images, image attributes, vector graphic commands, etc.) that can be employed or converted for example, to generate an image depiction. After generation, the depiction may be distributed such as to a target audience (e.g., an account associated with the profile) and may be presented in the context of a promotion or link to consumption of content associated with the content depiction (e.g., by way of a web page or content guidance/selection/viewing system). An image depiction or an image based upon the generated content structure depiction may be created for display on a screen to a target audience such as using the techniques described in the ‘919 Application. Image conversion from the content structure/depiction may occur in whole or part using devices including those which are used to generate the content structure/depiction and device(s) from which the image is displayed. 
     At block  570 , in response to distribution of the content depiction at block  560 , feedback data may be collected. The feedback data may include consumption of content, ratings, and/or social media posts pertaining to the content depiction structure and image depictions generated therefrom. At block  580 , the model(s) of the machine learning system may be reprogrammed based upon the feedback such as to improve correlation and generation of content depictions that induce increased content consumption as further described herein. After reprogramming, the machine learning system may receive further profile data at block  510  for generating a new depiction based upon the reprogramming. 
       FIG. 6  shows an illustrative flowchart of a neural network process for generating content depictions in accordance with some embodiments of the disclosure. At block  610 , profile data reflecting preferences of a profile (e.g., user account profile) is received at a neural network system such as described, for example, with respect to  FIGS. 2 and 3 . At block  620 , content preferences for the profile are identified (e.g., based upon content consumption history, browsing history, social media posts, etc.,) such as further described herein. 
     At block  630 , a neural network further accesses/receives content structures and/or image data that are or can be classified with particular attributes (e.g., particular actors, backgrounds, themes, etc.). For example, the neural network may utilize a deconstruction engine as described above to break down content into content structures and objects having particular attributes (e.g., as described in the ‘919 Application). At block  640 , the nodes and connections of the neural network may be programmed (or reprogrammed) according to classified content structures and/or image data received or accessed at block  630 , using profile data, and/or feedback data received in response to content depiction distribution (e.g., described below in reference to block  690 ). 
     At block  650 , the neural network nodes and connections process the profile preferences and utilize available content structures and/or image data to generate a content depiction at block  660 . The content depiction may be an image depiction and/or a content structure which may be used to generate an image depiction for optimal induction of content consumption based upon the profile preferences. At block  670 , the content depiction is processed by a discriminator (e.g., discriminator module  240  of  FIG. 2 ). Such as described herein, a discriminator may compare the depicted content to one or more model depictions or depiction properties/attributes. If the comparison fails particular criteria (e.g., such as learned by the discriminator to determine passable/acceptable depictions), the neural network may reprogram itself based upon the failing attributes and regenerate another depiction at block  540  in order to address the failed criteria. The neural network may reprogram itself also based upon passing depictions and “learn” to more efficiently generate passing content depictions. 
     At block  680 , if the content depiction passes discrimination at block  670 , the content depiction is distributed such as across a computer network for display in a device associated with the user profile. At block  690 , feedback data collected in response to the generated and distributed content depiction is received by the neural network system and used to reprogram the nodes and connections of the network at block  640 . For example, connections in the neural network may be modified or reinforced based upon a negative or positive degree of consumption of content in relation to the content depiction. 
       FIG. 7  shows an illustrative process of combining image/content structure data to generate a content depiction in accordance with some embodiments of the disclosure. In an embodiment, a machine learning/artificial intelligence system generates a content depiction  730  of a content. Data associated with a particular profile is used by the machine learning/artificial intelligence system to tailor the depiction to reflect preferences of the profile. As described in various embodiments herein, a machine learning/artificial intelligence system accesses and/or receives content structures and image data including image data  710 A,  710 B, and  710 C and associated object structures  715 A,  715 B, and  715 C, respectively, for generating a content depiction  730 . 
     Image data/content structures may include image data objects that represent particular characters/actors such as images  710 B and  710 C, respectively. Image data objects/content structures may include or be defined by associated object data structures  715 B and  715 C including character attributes or other attributes associated with the images including character roles in a content, gender, relative scales of the images, etc. Object data structures are further described, for example, within the ‘919 application referenced above. 
     The input profile data may reflect a preference for one or more of these object attributes, based upon which the system may be directed to generate a depiction including these characters. Additional profile data may reflect a preference for romantic themes, for example, which may further direct the machine learning/artificial intelligence system to generate a depiction with the characters in an embrace and a background representing a romantic theme (e.g., a moonlit night) such as exemplified in image data  710 A and associated object structure  715 A. An exemplary depiction  730  combining these various attributes may then result from the system to reflect preferences of the profile. 
     The above-described embodiments of the present disclosure are presented for purposes of illustration and not of limitation, and the present disclosure is limited only by the claims which follow. Furthermore, it should be noted that the features and limitations described in any one embodiment may be applied to any other embodiment herein, and flowcharts or examples relating to one embodiment may be combined with any other embodiment in a suitable manner, done in different orders, or done in parallel. In addition, the systems and methods described herein may be performed in real time. It should also be noted, the systems and/or methods described above may be applied to, or used in accordance with, other systems and/or methods.