Patent Publication Number: US-2023164403-A1

Title: Automated Generation of Personalized Content Thumbnails

Description:
BACKGROUND 
     Digital media content in the form of sports, news, movies, television (TV) programming, video games, and music, for example, are consistently sought out and enjoyed by consumers. Nevertheless, the popularity of a particular item of content, such as a particular movie, movie franchise, TV series, TV episode, or video game, can vary widely. In some instances, that variance in popularity may be due to fundamental differences in personal taste amongst consumers. However, in other instances, the lack of consumer interaction with digital media content may be due less to its inherent undesirability to those consumers than to their reluctance to explore content that is unfamiliar and appears unappealing. 
     One technique used to promote content to consumers includes displaying a thumbnail depicting characters, locations, or actions central to the storyline or theme of that content. In order for a thumbnail to effectively promote the content it represents, the images appearing in the thumbnail, as well as the composition of those images, should be both appealing and intuitively recognizable. However, what is in fact appealing and intuitively recognizable is in the eye of the beholder, and may be as individualized as any other personal taste or preference. Nevertheless, due to the resources often devoted to developing new content, the efficiency and effectiveness with which such new content can be promoted to consumers has become increasingly important to the producers, owners, and distributors of that content. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    shows an exemplary system for performing automated generation of personalized content thumbnails, according to one implementation; 
         FIG.  2    shows a diagram depicting exemplary parameters that may influence the generation and display of personalized content thumbnails, according to one implementation; 
         FIG.  3 A  shows a flowchart outlining an exemplary method for performing automated generation of personalized content thumbnails, according to one implementation; 
         FIG.  3 B  shows a flowchart providing a more detailed outline of action  310  in  FIG.  3 A , according to one implementation; 
         FIG.  3 C  shows a flowchart outlining additional exemplary actions for extending the method shown in  FIG.  3 A , according to one implementation; and 
         FIG.  3 D  shows a flowchart outlining yet more exemplary actions for extending the method shown in  FIG.  3 A . 
     
    
    
     DETAILED DESCRIPTION 
     The following description contains specific information pertaining to implementations in the present disclosure. One skilled in the art will recognize that the present disclosure may be implemented in a manner different from that specifically discussed herein. The drawings in the present application and their accompanying detailed description are directed to merely exemplary implementations. Unless noted otherwise, like or corresponding elements among the figures may be indicated by like or corresponding reference numerals. Moreover, the drawings and illustrations in the present application are generally not to scale, and are not intended to correspond to actual relative dimensions. 
     The present application discloses systems and methods for performing automated generation of personalized content thumbnails. It is noted that, as defined in the present application, the terms “automation,” “automated,” and “automating” refer to systems and processes that do not require the participation of a human editor or system administrator. Although, in some implementations, an editor or system administrator may review or modify the personalized content thumbnails generated by the automated systems and according to the automated methods described herein, that human involvement is optional. Thus, in some implementations, the methods described in the present application may be performed under the control of hardware processing components of the disclosed automated systems. 
       FIG.  1    shows an exemplary system for performing automated generation of personalized content thumbnails, according to one implementation. As shown in  FIG.  1   , system  100  includes computing platform  102  having processing hardware  104  and system memory  106  implemented as a computer-readable non-transitory storage medium. According to the present exemplary implementation, system memory  106  stores software code  110 , trained machine learning (ML) model  112 , content thumbnail generator  114 , and in some implementations, user profile database  124  including content consumption histories  126   a  and  126   b  of individual users, groups of users, or both. Also shown in  FIG.  1    is graphical user interface (GUI)  116  provided by software code  110 , content thumbnail  120  (hereinafter “thumbnail  120 ”) and other content thumbnails  122   a ,  122   b ,  122   c , and  122   d  (hereinafter “other content thumbnails  122   a - 122   d ”) displayed via GUI  116 . 
     As defined in the present application, the expression “machine learning model” or “ML model” may refer to a mathematical model for making future predictions based on patterns learned from samples of data or “training data.” Various learning algorithms can be used to map correlations between input data and output data. These correlations form the mathematical model that can be used to make future predictions on new input data. Such a predictive model may include one or more logistic regression models, Bayesian models, or neural networks (NNs). Moreover, a “deep neural network,” in the context of deep learning, may refer to an N that utilizes multiple hidden layers between input and output layers, which may allow for learning based on features not explicitly defined in raw data. As used in the present application, a feature identified as an NN refers to a deep neural network. In various implementations, NNs may be trained as classifiers and may be utilized to perform image processing, audio processing, or natural-language processing. 
     As further shown in  FIG.  1   , system  100  is implemented within a use environment including communication network  108 , thumbnail database  128 , content database  144 , and user system  130  including display  132 . In addition,  FIG.  1    shows user  134   a  of user system  130  and system  100 , other users  134   b  of system  100 , human editor  137 , interaction data  136   a ,  136   b , and  136   c , quality assurance (QA) feedback data  138 , and user rating data  140 . Also shown in  FIG.  1    are network communication links  118  of communication network  108  interactively connecting system  100  with thumbnail database  128 , content database  144 , user system  130 , and other communication devices utilized by other users  134   b  and human editor  137  (other communication devices utilized by other users  134   b  and human editor  137  not shown in  FIG.  1   ). 
     It is noted that in some implementations, as shown in  FIG.  1   , user profile database  124  may be stored in system memory  106 . However, in other implementations, user profile database  124  may not be a component of system  100 , but may be remotely accessible to system  100  via communication network  108  and network communication links  118 . It is further noted that, in some implementations, as depicted in  FIG.  1   , thumbnail database  128  and content database  144  may be remote from but communicatively coupled to system  100  via communication network  108  and network communication links  118 . However, in other implementations, one or both of thumbnail database  128  and content database  144  may be assets of system  100 , and may be stored locally in system memory  106 . 
     Moreover, although  FIG.  1    shows user profile database  120  as storing two content consumption histories  126   a  and  126   b , that exemplary depiction is also provided merely in the interests of conceptual clarity. More generally, user profile database  120  may store more than two content consumption histories, such as hundreds, thousands, or millions of content consumption histories, for example. Furthermore, in various implementations, each of content consumption histories  126   a  and  126   b  may be a content consumption history dedicated to a particular user, such as user  134   a  or one of other users  134   b , to a group of users, such as other users  134   b , or to one or more distinct temporal sessions over which an individual user or group of users consumes content. 
     It is further noted that, in some implementations, content consumption histories  126   a  and  126   b  may be exclusive of personally identifiable information (PII) of user  134   a  or other users  134   b . Thus, in those implementations, although content consumption histories  126   a  and  126   b  may serve to distinguish one anonymous user or group of users from another anonymous user or group of users, user profile database  120  does not retain information describing the age, gender, race, ethnicity, or any other PII of any user or group. However, in some implementations, such as content subscription service applications, for example, a user of system  100  may be provided an opportunity to opt in to having their PII stored. 
     Although the present application refers to software code  110  as being stored in system memory  106  for conceptual clarity, more generally, system memory  106  may take the form of any computer-readable non-transitory storage medium. The expression “computer-readable non-transitory storage medium,” as used in the present application, refers to any medium, excluding a carrier wave or other transitory signal that provides instructions to processing hardware  104  of computing platform  102 . Thus, a computer-readable non-transitory storage medium may correspond to various types of media, such as volatile media and non-volatile media, for example. Volatile media may include dynamic memory, such as dynamic random access memory (dynamic RAM), while non-volatile memory may include optical, magnetic, or electrostatic storage devices. Common forms of computer-readable non-transitory storage media include, for example, optical discs, RAM, programmable read-only memory (PROM), erasable PROM (EPROM), and FLASH memory. 
     Moreover, although  FIG.  1    depicts software code  110 , trained (ML) model  112 , content thumbnail generator  114 , and optionally user profile database  124 , as being co-located in system memory  106  that representation is merely provided as an aid to conceptual clarity. More generally, system  100  may include one or more computing platforms  102 , such as computer servers for example, which may be co-located, or may form an interactively linked but distributed system, such as a cloud-based system, for instance. As a result, processing hardware  104  and system memory  106  may correspond to distributed processor and memory resources within system  100 . 
     Processing hardware  104  may include multiple hardware processing units, such as one or more central processing units, one or more graphics processing units, and one or more tensor processing units, one or more field-programmable gate arrays (FPGAs), custom hardware for machine-learning training or inferencing, and an application programming interface (API) server, for example. By way of definition, as used in the present application, the terms “central processing unit” (CPU), “graphics processing unit” (GPU), and “tensor processing unit” (TPU) have their customary meaning in the art. That is to say, a CPU includes an Arithmetic Logic Unit (ALU) for carrying out the arithmetic and logical operations of computing platform  102 , as well as a Control Unit (CU) for retrieving programs, such as software code  110 , from system memory  106 , while a GPU may be implemented to reduce the processing overhead of the CPU by performing computationally intensive graphics or other processing tasks. A TPU is an application-specific integrated circuit (ASIC) configured specifically for artificial intelligence (AI) processes such as machine learning. 
     In some implementations, computing platform  102  may correspond to one or more web servers, accessible over a packet-switched network such as the Internet, for example. Alternatively, computing platform  102  may correspond to one or more computer servers supporting a private wide area network (WAN), local area network (LAN), or included in another type of limited distribution or private network. In addition, or alternatively, in some implementations, system  100  may be implemented virtually, such as in a data center. For example, in some implementations, system  100  may be implemented in software, or as virtual machines. 
     Although user system  130  is shown as a desktop computer in  FIG.  1   , that representation is also provided merely as an example. More generally, user system  130  may be any suitable mobile or stationary computing device or system that implements data processing capabilities sufficient to enable use of GUI  116 , support connections to communication network  108 , and implement the functionality ascribed to user system  130  herein. For example, in other implementations, user system  130  may take the form of a laptop computer, tablet computer, smart TV, game platform, smartphone, or smart wearable device, such as a smartwatch, for example. 
     Display  132  of user system  130  may take the form of a liquid crystal display (LCD), a light-emitting diode (LED) display, an organic light-emitting diode (OLED) display, a quantum dot (QD) display, or any other suitable display screen that performs a physical transformation of signals to light. It is noted that, in some implementations, display  132  may be integrated with user system  130 , such as when user system  130  takes the form of a laptop or tablet computer for example. However, in other implementations, for example where user system  130  takes the form of a computer tower in combination with a desktop monitor, display  132  may be communicatively coupled to, but not physically integrated with user system  130 . 
       FIG.  2    shows diagram  200  depicting exemplary parameters that may influence the generation and display of personalized content thumbnails, according to one implementation. As shown in  FIG.  2   , the parameters influencing generation of a personalized content thumbnail may include content thumbnail features  250  including image layers  252  of the content thumbnail and features described by thumbnail metadata  254 . As further shown in  FIG.  2   , image layers of a content thumbnail may include its background, foreground, and title treatment text, such as text font, for example, as well as badging. Content thumbnail features described by thumbnail metadata  254  may include the color palette and average color of the content thumbnail, the mood it conveys, title text size, the prominence of the talent shown by the content thumbnail versus its title, and the scope or scale depicted, i.e., whether the content thumbnail depicts an intimate close-up image, an “epic” panoramic image, or something in between. In addition, content thumbnail features described by thumbnail metadata  254  may also include the setting, activities and events, and objects depicted by the content thumbnail, as well as attributes of the depicted talent, such as eyeline, whether the talent is a solo talent or an ensemble, and the gender, race, or ethnicity of the talent. 
     With respect to the expression “talent,” it is noted that as defined in the present application, “talent” refers to a featured performer or performers appearing in content being promoted by a content thumbnail. Thus, talent may include one or more human actors, one or more characters portrayed by human actors, one or more animated characters, participant, performer, reporter, or any combination thereof. Moreover, as defined herein, the expression “eyeline” refers to the gaze direction of talent depicted in a content thumbnail, such as whether the talent appears to be gazing outward at a user viewing the content thumbnail, at other talent or an object included in the content thumbnail, or in an adverted fashion into the distance, without direction toward other talent or an object. 
     In addition to content thumbnail features  250 , the parameters influencing generation of a personalized content thumbnail may further include content metadata  256  describing one or more of genre and keywords of the content promoted by the content thumbnail, the mood of the content, as well as its plot elements, characters, talent, setting, and activities and events. Other parameters influencing generation of a personalized content thumbnail may include media platform parameters  258  specific to the media platform on which content is to be promoted, such as techniques used by the media platform to generate recommendations and to provide personalization, the context in which content thumbnails are displayed by the media platform, marketing and promotional techniques used by the media platform, and localization based on regional differences in taste, sensibility, or regulation. 
     Another group of parameters influencing generation of a personalized content thumbnail may include user behavior parameters  260 , such as the behavior of individual user  134   a , or group of other users  134   b . User behavior parameters  260  may include content or promotions that a user or group “clicks on” or otherwise selects or interacts with, content watched by a user or group, how long a user or members of a group tend to watch content they have selected, the speed with which a user selects a particular item of content, i.e. the time lapse from when a particular item of content is displayed until it is selected by the user, and favorites lists, watch lists, and ratings identified or provided by the user or group, to name a few examples. Referring to  FIGS.  1  and  2    in combination, it is noted that some of user behavior parameters  260  may be included in interaction data  136   a ,  136   b , and  136   c , while some others of user behavior parameters  260  may be included in user rating data  140 . Yet others of user behavior parameters  260  may be obtained from user profile database  124 . 
     It is further noted that, in some implementations, as described above by reference to content consumption histories  126   a  and  126   b , user behavior parameters  260  may be exclusive of PII of user  134   a  or other users  134   b . Thus, in those implementations, although user behavior parameters  260  may serve to distinguish one anonymous user or group of users from another anonymous user or group of users, user behavior parameters  260  do not retain information describing the age, gender, race, ethnicity, or any other PII of any user or group. However, in some implementations, such as content subscription service applications, for example, a user of system  100  may be provided an opportunity to opt in to having their PII stored. 
     It is also noted that although the methods and systems disclosed by the present application are described by reference to a specific use case in which personalized content thumbnails are generated to promote various types of digital media content to a user, such as content in the form of sports, news, movies, television (TV) programming, video games, and music, for example, the present concepts may be readily applied to the generation of personalized thumbnails for the promotion of a wide variety of other assets. Examples of other assets that may be promoted to users via the generation of personalized thumbnails include collectable items, including non-fungible tokens (NFTs), entertainment events, and real-world attractions, such as theme park attractions, to name a few. 
     The functionality of system  100  and software code  110 , in  FIG.  1   , will be further described by reference to  FIGS.  3 A,  3 B,  3 C, and  3 D  (hereinafter “ FIGS.  3 A- 3 D ”).  FIG.  3 A  shows flowchart  300  presenting an exemplary method for performing automated generation of personalized content thumbnails, according to one implementation, while  FIG.  3 B  shows flowchart  310  providing a more detailed outline of action  310  in  FIG.  3 A , according to one implementation. In addition,  FIGS.  3 C and  3 D  show respective flowcharts  330  and  340  outlining additional exemplary actions for extending the method shown in  FIG.  3 A . With respect to the actions outlined in  FIGS.  3 A- 3 D , it is noted that certain details and features have been left out of flowcharts  300 ,  310 ,  330 , and  340  in order not to obscure the discussion of the inventive aspects disclosed in the present application. 
     Referring to  FIG.  3 A  in combination with  FIG.  1   , flowchart  300  begins with receiving interaction data  136   a  describing interactions by user  134   a  with content thumbnails (action  302 ). For example, user  134   a  may utilize user system  130  to interact with, i.e., view, mouse over or otherwise partially activate, or click on or otherwise select one or more content thumbnails accessible on thumbnail database  128 , content database  144 , or both. As shown by  FIG.  1   , interaction data  136   a  may be received from user  134   a  by system  100  via user system  130 , communication network  108 , and network communication links  118 . In those implementations, interaction data  136   a  may be received by software code  110 , executed by processing hardware  104  of computing platform  102 . 
     Further referring to  FIG.  2   , interaction data  136   a  may include content thumbnail features  250  of various content thumbnails with which user  134   a  interacts, as well as content metadata  256  describing content associated with each such content thumbnail, and media platform parameters  258 , described above. In addition, interaction data  136   a  may include some of user behavior parameters  260 , such as which content thumbnails user  134   a  clicks on or otherwise selects, the speed with which user  134   a  selects a content thumbnail once it has been displayed, as well as, in some implementations, which content thumbnails are displayed to user  134   a  but are ignored by user  134   a.    
     Continuing to refer to  FIGS.  1  and  3    in combination, flowchart  300  further includes identifying, using interaction data  136   a , an affinity by user  134   a  for at least one content thumbnail feature (action  304 ). Further referring to  FIG.  2   , the content thumbnail feature or features (hereinafter “content thumbnail feature(s)”) for which user  134   a  is identified as having an affinity in action  304  may include any of content thumbnail features  250  described above. That is to say, in some implementations, the content thumbnail feature(s) for which user  134   a  is identified as having an affinity may include one or more of a text font, text size, conveyed mood, or color palette of the content thumbnail, as well as one or more attributes of talent depicted by the content thumbnail, for example. 
     In addition, or alternatively, the content thumbnail feature(s) for which user  134   a  is identified as having an affinity may include an audio cue configured to be triggered by an interaction by the user with the content thumbnail. Examples of such an audio cue may include a character utterance, music, or a sound effect characteristic of the content represented by the content thumbnail, to name a few. 
     According to some implementations, identifying the affinity by user  134  for particular content thumbnail feature(s) may be further based on the content consumption history of user  134   a . For example, in implementations in which content consumption history  126   a  includes the content consumption history of user  134   a , identifying the affinity by user  134   a  for content thumbnail feature(s) in action  304  may further use content consumption history  126   a  of user  134   a . Content consumption history  126   a  may include some of user behavior parameters  260 , described above by reference to  FIG.  2   , such as content watched by  134   a , how long user  134   a  tends to watch content they have selected, and a favorites list, watch list, and ratings identified or provided by user  134   a.    
     As discussed above by reference to  FIG.  1   , in some implementations, system memory  106  may store user profile database  124  including content consumption history  126   a  of user  134   a . Identification of the affinity by user  134   a  for content thumbnail feature(s) in action  304  may be performed by software code  110 , executed by processing hardware  104  of computing platform  102 . 
     Referring to  FIGS.  1  and  3    in combination, flowchart  300  further includes determining, using at least one of interaction data  136   a  or a predetermined business rule, content for promotion to user  134   a  (action  306 ). For example, content represented by a content thumbnail which user  134   a  has clicked on or otherwise selected or interacted with may be identified, based on that/those interactions as likely to be desirable to user  134   a . That content may be determined to be worthwhile promoting to user  134   a  through use of a personalized content thumbnail generated specifically for user  134   a.    
     Alternatively, or in addition, in some implementations there may be a business reason to promote certain content, e.g., “content A,” over other content, because content A is new content, or a sequel or continuation of a content franchise, for example. In those implementations, content A may be promoted to all users, or to users having an affinity for the genre to which content A belongs, for example, based on a business rule that has been predetermined and programmed into software code  110  by an administrator of system  100 . A determination that user  134   a  has an affinity for the genre to which business rule promoted content A belongs may be made based on interaction data  136   a , a content consumption history of user  134   a  stored in user profile database  124 , or both. Thus, determination of the content for promotion to user  134   a  in action  306  may be performed by software code  110 , executed by processing hardware  104  of computing platform  102 , and using at least one of interaction data  136   a  or a predetermined business rule. 
     Continuing to refer to  FIGS.  1  and  3    in combination, flowchart  300  further includes providing a prediction, using trained ML model  112  and based on the affinity by user  134   a  identified in action  304 , the respective desirability of each of multiple candidate thumbnails for the content determined for promotion to user  134   a  in action  306 , to user  134   a  (action  308 ). For example, where user  134   a  is identified as having an affinity for three different content thumbnail features “A,” “B,” and “C,” candidate thumbnails including feature A alone, feature B alone, feature C alone, features A and B in combination, features A and C in combination, features B and C in combination, and features A, B, and C in combination may be evaluated and compared using trained ML model  112  to predict the desirability of each of those candidate thumbnails to user  134   a . Action  308  may be performed by software code  110 , executed by processing hardware  104  of computing platform  102 , using trained ML model  112 , as noted above. 
     Continuing to refer to  FIGS.  1  and  3    in combination, flowchart  300  further includes generating, using content thumbnail generator  114  and based on the prediction provided in action  308 , thumbnail  120  having features of one or more of the candidate thumbnails described above (action  310 ). By way of example, where one of the candidate thumbnails is predicted to be the most desirable to user  134   a , that candidate thumbnail may be generated as thumbnail  120  in action  310 . However, in implementations in which two or more alternative candidate thumbnails are predicted to be of substantially equal desirability to user  134   a , features from each of those alternative candidate thumbnails may be included in thumbnail  120  generated in action  310 . Generation of thumbnail  120  in action  310  may be performed by software code  110 , executed by processing hardware  104  of computing platform  102 , and using content thumbnail generator  114 , as noted above. 
     Referring to  FIGS.  3 A,  3 B, and  1    in combination, flowchart  310  in  FIG.  3 B  provides a more detailed outline of action  310  in  FIG.  3 A . As shown by flowchart  310  in  FIG.  3 B , action  310  in  FIG.  3 A  may include identifying, using user profile database  124 , one or more other users  134   b  sharing the affinity by user  134   a  for the content thumbnail feature(s) identified in action  304  (action  312 ). In addition, action  310  may further include obtaining a predetermined thumbnail previously displayed to promote the content to user  134   a  in action  306 , to one or more other users  134   b  (action  314 ). In implementations in which action  310  includes action  312  and  314 , action  310  may also include generating thumbnail  120  further based on the predetermined thumbnail obtained in action  314  (action  316 ). When included in action  310 , actions  312 ,  314 , and  316  may be performed by software code  110 , executed by processing hardware  104  of computing platform  102 . 
     Referring once again to  FIGS.  1  and  3 A  in combination, in some implementations, flowchart  300  may further include sending thumbnail  120  to human editor  137  for QA review (action  318 ). For example, after initial training of trained ML model  112 , or in use cases in which thumbnail includes multiple features from multiple candidate thumbnails, it may be advantageous or desirable to obtain human validation of thumbnail  120  from human editor  137 . As shown by  FIG.  1   , thumbnail  120  may be sent to human editor  137  via communication network  108  and network communication links  118 . Action  318  may be performed by software code  110 , executed by processing hardware  104  of computing platform  102 . 
     In implementations in which the method outlined by flowchart  300  includes action  318 , flowchart  300  may further include receiving QA feedback data  138  (action  320 ). As shown by  FIG.  1   , QA feedback data  138  may be received from human editor  138  by system  100  via communication network  108  and network communication links  118 . In those implementations, QA feedback data  138  may be received by software code  110 , executed by processing hardware  104  of computing platform  102 . 
     Continuing to refer to  FIGS.  1  and  3 A  in combination, flowchart  300  further includes displaying thumbnail  120  generated in action  310  to promote the content to user  134   a  in action  306  (action  322 ). As shown in  FIG.  1   , thumbnail  120  may be displayed to user  134   a  via GUI  116  provided by software code  110 . Thus, the content determined for promotion to user  134   a  in action  306  may be promoted to user  134   a , by displaying thumbnail  120  in action  322 , by software code  110 , executed by processing hardware  104  of computing platform  102 . 
     It is noted that the involvement of human editor  137  in the method outlined by flowchart  300  is optional. That is to say, in some implementations, flowchart  300  may omit actions  318  and  320 , and may be performed as an automated process. In those implementations, action  310 , including in some implementations actions  312 ,  314 , and  316 , may be followed directly by action  322 . 
     In some implementations, the method outlined by flowchart  300  may conclude with action  322  described above. However, as shown by  FIG.  3 C , in some implementations, the actions outlined by flowchart  300  may be supplemented by one or more of the further actions outlined by flowchart  330 . Referring to  FIGS.  1  and  3 C  in combination, flowchart  330  may include displaying, via GUI  116 , thumbnail  120  among other content thumbnails  122   a - 122   d  for other content (action  332 ). Bearing in mind that thumbnail  120  represents the content being promoted to user  134   a , action  332  may include one or both of selecting other content thumbnails  122   a - 122   d  or arranging the display of other content thumbnails  122   a - 122   d , so as to attract user  134   a  to thumbnail  120 . 
     By way of example, in some implementations, user  134   a  may be preferentially attracted to thumbnail  120  over other content thumbnails  122   a - 122   d  using color effects, lighting effects, or the positioning of thumbnail  120  among other content thumbnails  122   a - 122   d . That is to say, in various implementations, thumbnail  120  may appear “center stage” in the middle of an array of content thumbnails including thumbnail  120  and other content thumbnails  122   a - 122   d , or at the top of a list of content thumbnails including thumbnail  120  and other content thumbnails  122   a - 122   d . In addition, or alternatively, thumbnail  120  may be brightly colored, while other content thumbnails  122   a - 122   b  may be displayed using muted color palettes. As yet another alternative, or in addition, thumbnail  120  may be displayed having a spotlight lighting effect, while other content thumbnails may lack such a lighting enhancement, or may be shown to be in shadow. When included in flowchart  330 , action  332  may be performed by software code  110 , executed by processing hardware  104  of computing platform  102 . 
     Alternatively, or in addition, flowchart  330  may further include receiving one or both of user rating data  140  for the content displayed to user  134   a  in action  322 , or interaction data  136   c  describing an interaction by user  134   a  with one or more of thumbnail  120  and other content thumbnails  122   a - 122   d  (action  334 ). That is to say, in various implementations, flowchart  330  may include action  332  but omit action  334 , may include action  334  but omit action  332 , or may include both of actions  332  and  334 . 
     User rating data  140  may rate the enjoyment, by user  134   a , of the content represented and promoted by thumbnail  120 , as a result of selection and consumption or partial consumption of that content by user  134   a . Interaction data  136   c  may reveal the extent to which user  134   a  interacts with one or more content thumbnails included among other content thumbnails  122   a - 122   d , in lieu of, or in addition to, selection or other interaction by user  134   a  with thumbnail  120 . As shown by  FIG.  1   , interaction data  136   c , user rating data  140 , or both, may be received from user  134   a  by system  100  via user system  130 , communication network  108 , and network communication links  118 . In those implementations, one or both of interaction data  136   c  and user rating data  140  may be received by software code  110 , executed by processing hardware  104  of computing platform  102 . 
     It is noted that interaction data  136   c , user rating data  140 , or both, provide feedback to system  100  regarding the predictive performance of trained ML model  112 . Accordingly, in some implementations in which flowchart  330  includes action  334 , flowchart  330  may further include training trained ML model  112  using one or more of user rating data  140  and interaction data  136   c  (action  336 ). The ongoing training of trained ML model  112  using instances of one or more of user rating data  140  and interaction data  136   c  received from multiple users, including user  134   a , can advantageously serve to improve the performance of trained ML model  112 , as well as that of system  100  as a whole, over time. Training of trained ML model  112  using one or more of user rating data  140  and interaction data  136   c  in action  336  may be performed by software code  110 , executed by processing hardware  104  of computing platform  102 . 
     Referring to  FIG.  3 D , in some implementations, the method outlined by flowchart  300 , in  FIG.  3 A , may be further extended by the actions outlined by flowchart  340 . Referring to  FIGS.  1  and  3 D  in combination, flowchart  340  includes receiving interaction data  136   b  describing interactions by one or more of other users  134   b  with other content thumbnails (action  342 ). As shown by  FIG.  1   , interaction data  136   b  may be received from one or more of other user  134   b  by system  100  via communication network  108  and network communication links  118 . In those implementations, interaction data  136   b  may be received by software code  110 , executed by processing hardware  104  of computing platform  102 . 
     Continuing to refer to  FIGS.  1  and  3 D  in combination, flowchart  340  further includes identifying, using interaction data  136   b , an affinity by one or more other users  134   b  for one or more other content thumbnail features (action  344 ). Action  344  may be performed by software code  110 , executed by processing hardware  104  of computing platform  102 , in a manner analogous to that described above by reference to action  304 . 
     Flowchart  340  further includes providing another prediction, using trained ML model  112  and based on the affinity by one or more of other users  134   b  identified in action  344 , of the respective desirability of each of other candidate thumbnails for the content determined for promotion to user  134   a  in action  306 , to one or more other users  134   b  (action  346 ). Action  346  may be performed by software code  110 , executed by processing hardware  104  of computing platform  102 , in a manner analogous to that described above by reference to action  308 . 
     Flowchart  340  further includes generating, using content thumbnail generator  114  and based on the prediction provided in action  346 , another thumbnail using features of one or more of the other candidate thumbnails evaluated by trained ML model  112  in action  346  (action  348 ). Action  348  may be performed by software code  110 , executed by processing hardware  104  of computing platform  102 , in a manner analogous to that described above by reference to action  310 . Moreover, in some implementations, action  348  may include additional actions analogous to and corresponding in general to actions  312 ,  314 , and  316  (hereinafter “actions  312 - 314 ”) outlined by flowchart  310 , in  FIG.  3 B . 
     Flowchart  340  further includes displaying, to one or more other users  134   b  using the other thumbnail generated in action  348 , the same content displayed to user  134   a  using thumbnail  120  in action  322  (action  350 ). That is to say the same content promoted to user  134   a  using thumbnail  120  specifically generated and personalized for user  134   a  is displayed to one or more other users  134   b  using another thumbnail generated and personalized for one or more other users  134   b , in action  350 . Action  350  may be performed by software code  110 , executed by processing hardware  104  of computing platform  102 , in a manner analogous to that described above by reference to action  322 . In addition, in some implementations, actions  342 ,  344 ,  346 ,  348 , and  350  (hereinafter “actions  342 - 350 ”) outlined by flowchart  340  may be followed by actions analogous to and corresponding in general to action  332 , action  334 , actions  332  and  334 , actions  334  and  336 , or actions  332 ,  334 , and  336  (hereinafter “actions  332 - 336 ”) outlined by flowchart  330 , in  FIG.  3 C . 
     With respect to the method outlined by flowchart  300 , in  FIG.  3 A , and the additional actions outlined by flowcharts  310 ,  330 , and  340  in respective  FIGS.  3 B,  3 C, and  3 D , it is emphasized that actions  302 ,  304 ,  306 , and  308  (hereinafter “actions  302 - 308 ”) and actions  310  and  322 , or actions  302 - 308 , action  310  including actions  312 - 316 , and action  322 , or actions  302 - 308  and actions  310 ,  322 , and  332 , or actions  302 - 308 , action  310  including actions  312 - 316 , and actions  322 , and  332 , or actions  302 - 308  and actions  310 ,  322 , and  334 , or actions  302 - 308 , action  310  including actions  312 - 316 , and actions  322 , and  334 , or actions  302 - 308  and actions  310 ,  322 ,  332 , and  334 , or actions  302 - 308 , action  310  including actions  312 - 316 , and actions  322 ,  332  and  334 , or actions  302 - 308  and actions  310 ,  322 ,  334 , and  336 , or actions  302 - 308 , action  310  including actions  312 - 316 , and actions  322 ,  334  and  336 , or actions  302 - 308  and actions  310 ,  322 , and actions  332 - 336 , or actions  302 - 308 , action  310  including actions  312 - 316 , and actions  322 , and  332 - 36 , or actions  302 - 308  and actions  310 ,  322 ,  332 , and actions  342 - 350 , or actions  302 - 308 , action  310  including actions  312 - 316 , action  322 , and actions  332  and  342 - 350 , or actions  302 - 308  and actions  310 ,  322 ,  334 , and actions  342 - 350 , or actions  302 - 308 , action  310  including actions  312 - 316 , action  322 , and actions  334  and  342 - 350 , or actions  302 - 308  and actions  310 ,  322 ,  332 ,  334 , and actions  342 - 350 , or actions  302 - 308 , action  310  including actions  312 - 316 , action  322 , and actions  332 ,  334  and  342 - 350 , or actions  302 - 308  and actions  310 ,  322 ,  334 ,  336 , and actions  342 - 350 , or actions  302 - 308 , action  310  including actions  312 - 316 , action  322 , and actions  334 ,  336  and  342 - 350 , or actions  302 - 308  and actions  310 ,  322 ,  332 - 336 , and actions  342 - 350 , or actions  302 - 308 , action  310  including actions  312 - 316 , action  322 , and actions  332 - 336  and  342 - 350 , may be performed in an automated process from which human involvement may be omitted. 
     Thus, the present application discloses systems and methods for performing automated generation of personalized content thumbnails. From the above description it is manifest that various techniques can be used for implementing the concepts described in the present application without departing from the scope of those concepts. Moreover, while the concepts have been described with specific reference to certain implementations, a person of ordinary skill in the art would recognize that changes can be made in form and detail without departing from the scope of those concepts. As such, the described implementations are to be considered in all respects as illustrative and not restrictive. It should also be understood that the present application is not limited to the particular implementations described herein, but many rearrangements, modifications, and substitutions are possible without departing from the scope of the present disclosure.