Exploiting the correlation between content classification and users' psychological profiles to provide an adaptable digital environment

Abstract

[0003] A system and method for identifying and utilizing correlations between content classifications and user psychological profiles to provide an adaptable digital environment is disclosed. An exemplary implementation can obtain interaction information, determine content-specific correlations between an individual user's psychological parameter value(s) and / or individual psychological profile and individual pieces of content based on the interaction information, determine content classes and content subclasses that characterize the individual pieces of content, determine master correlations between the content class(es) and / or content subclasses based on the content-specific correlations and strengths of one or more individual psychological parameter values ​​relative to other psychological parameter values ​​included in the psychological profile, determine individual predicted responses to the individual pieces of content based on the strengths of the master correlations, the psychological profile of the master correlations, and the interaction information, and identify and present potential pieces of content for the user based on the predicted responses.

Description

[Technical Field]

[0001] The present disclosure relates to systems and methods for identifying and utilizing correlations between content classifications and users' psychological profiles to provide an adaptable digital environment. [Background technology]

[0002] Existing systems may adapt a digital environment to a user based on how the user interacts with content presented through the digital environment, and these existing systems may not consider the content classes and content subclasses into which individual pieces of content may be classified. Summary of the Invention

[0003] One aspect of the present disclosure relates to a system configured to identify and utilize correlations between content classifications and a user's psychological profile to provide an adaptable digital environment. Individual pieces of content may be classified into various content classes and / or subclasses conforming to their own taxonomies. Based on how a user interacts with the pieces of content and the user's psychological parameter values ​​or psychological profile, content-specific correlations may be determined between the individual pieces of content and one or more psychological parameter values. Based on these content-specific correlations, a master correlation may be determined between the content class / subclass classifications of the pieces of content included in the content-specific correlations and the strength of one or more psychological parameter values. Based on the master correlations and the collected, monitored, and stored interaction information, a predicted response to the piece of content may be determined. Subsequently, based on the predicted response, active content in the user's digital environment, and the content class / subclass of the piece of content, potential content may be determined and appropriately presented to the user. Thus, a computer system may monitor multiple users' interactions with various pieces of content classified into content classes / subclasses and appropriately adjust the content presented to the users.

[0004] The system may include electronic storage devices, one or more hardware processors configured with machine-readable instructions, and / or other elements. The machine-readable instructions may include one or more instruction components. The instruction components may include computer program components. The instruction components may include one or more of an information acquisition component, a correlation determination component, a content class determination component, a master correlation determination component, a response determination component, a content identification component, a presentation component, and / or other instruction components.

[0005] The electronic storage device may store taxonomic classifications of individual pieces of content, psychological profiles of users of the digital environment, and / or other information. Individual classifications of the taxonomic classifications may include content classes and content subclasses for the content class of the individual pieces of content. The taxonomic classifications may conform to a taxonomy that defines a hierarchical system of content classes and content subclasses. Pieces of content may be characterized by their classification into content classes and content subclasses. The psychological profile may include psychological parameter values ​​for the psychological parameters.

[0006] The information acquisition component can be configured to acquire interaction information from an online platform providing the digital environment, where the individual interaction information can specify instances of interaction between individual users and / or individual psychological profiles of the individual users and one or more pieces of content and timing information of the instances via the digital environment, and / or other interaction information.

[0007] The correlation determination component may be configured to determine, based on the interaction information and / or other information, content-specific correlations between the individual user's psychological parameter values ​​and / or one or more of the individual user's individual psychological profile and the individual piece of content.

[0008] The content class determination component can be configured to determine content classes and content subclasses that characterize individual pieces of content involved in a content-specific correlation.

[0009] The master correlation determination component may be configured to determine, based on content-specific correlations and / or other information, master correlations between one or more of the content classes and / or content subclasses and the strength of individual ones or combinations of psychological parameter values ​​included in the psychological profile relative to other ones of the psychological parameter values ​​included in the psychological profile.

[0010] The response determination component may be configured to determine individual predicted responses for individual pieces of content classified into one or more content classes and / or content subclasses included in the master correlation based on the strength of the master correlation, psychological profiles included in the master correlation, interaction information, and / or other information.

[0011] The content identification component may be configured to identify potential pieces of content for the user based on predicted responses, pieces of content that are active in the digital environment, taxonomic classifications of individual pieces of content, a psychographic profile of the user, and / or other information.

[0012] The presentation component can be configured to facilitate the presentation of potential pieces of content to a user within a digital environment.

[0013] As used herein, the term "obtain" (and its derivatives) may include active and / or passive retrieval, determination, induction, transfer, uploading, downloading, submission, and / or exchange of information, and / or any combination thereof. As used herein, the term "effectuate" (and its derivatives) may include active and / or passive causation of any effect, both local and remote. As used herein, the term "determine" (and its derivatives) may include measuring, calculating, estimating, approximating, generating, and / or otherwise deriving, and / or any combination thereof.

[0014] These and other features and characteristics of the present technology, as well as the method of operation and function of the associated elements of construction, and the combination of parts and economies of manufacture, will become more apparent from a study of the following description and appended claims, taken in conjunction with the accompanying drawings, all of which form a part of this specification, and in which like reference numerals refer to corresponding parts in the various views. It is to be expressly understood, however, that the drawings are for the purposes of illustration and description only and are not intended as a definition of the limits of the invention. As used in this specification and claims, the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise. [Brief explanation of the drawings]

[0015] [Figure 1] 1 illustrates a system configured to identify and utilize correlations between content classifications and user psychological profiles to provide an adaptable digital environment, according to one or more implementations. [Figure 2] Illustrates a method for identifying and utilizing correlations between content classifications and a user's psychological profile to provide an adaptable digital environment, according to one or more implementations. [Figure 3A]Illustrates an exemplary implementation of a system configured to identify and utilize correlations between content classifications and user psychological profiles to provide an adaptable digital environment, according to one or more implementations. [Figure 3B] Illustrates an exemplary implementation of a system configured to identify and utilize correlations between content classifications and user psychological profiles to provide an adaptable digital environment, according to one or more implementations. DETAILED DESCRIPTION OF THE INVENTION

[0016] 1 illustrates a system 100 configured to identify and utilize correlations between content classifications and user psychological profiles to provide an adaptable digital environment, according to one or more implementations. In some implementations, system 100 may include one or more servers 102, electronic storage devices 126, and / or other elements. Server(s) 102 may be configured to communicate with one or more client computing platforms 104 according to a client / server architecture and / or other architecture. Client computing platform(s) 104 may be configured to communicate with other client computing platforms via server(s) 102 and / or according to a peer-to-peer architecture and / or other architecture. Users may access system 100 via client computing platform(s) 104.

[0017] The electronic storage device 126 may store taxonomic classifications of individual pieces of content, psychological profiles of users of the digital environment, and / or other information. The individual taxonomic classifications may include content classes and content subclasses for the content class of the individual piece of content. The taxonomic classifications may conform to a taxonomy that defines a hierarchical system of content classes and content subclasses. Pieces of content may be characterized by their classification into content classes and content subclasses.

[0018] Pieces of content may include characters, games, game assets, video content, image content, and / or other content. A character may refer to an object (or group of objects) present in a virtual space that corresponds to an individual user (e.g., an avatar) and / or is controlled by a user. In some implementations, a character may not correspond to an individual user, but rather provide information (e.g., recommendations, suggestions) to a user. Game assets may include virtual items, virtual resources of in-game power (e.g., weapons, tools), in-game skills, in-game technology, and / or other game assets.

[0019] The digital environment may be hosted by or otherwise provided by an online platform. In some embodiments, the digital environment may be accessible via an application. The application may include mobile applications, desktop applications, console applications, television applications, and / or other applications accessible via the portable client computing platform 104. In some embodiments, the online platform may be directly accessible via a web browser and the Internet or may be offline. For example, digital environments and types thereof may include, by way of non-limiting example, gaming environments, educational environments, reading environments, music interfaces, social networking environments, entertainment environments, fitness environments, business environments, shopping environments, food and drink serving environments, among others, integrated with or connected to the system 100.

[0020] The digital environment and / or individual applications may provide a simulated space or view of a virtual space. Each simulated space may have a terrain, may represent continuous real-time interaction by one or more users, and / or may include one or more objects disposed within the terrain that can be moved within the terrain. In some instances, the terrain may be two-dimensional. In other instances, the terrain may be three-dimensional. The terrain may include the dimensions of the space and / or the surface features of surfaces or objects that "naturally" exist in the space. In some instances, the terrain may depict a surface (e.g., the ground) that spans at least a substantial portion of the space. In some instances, the terrain may depict a volume with one or more objects disposed within it (e.g., a simulation of gravity-deprived space with one or more celestial bodies disposed within it). Instances executed by computer components may be synchronous, asynchronous, and / or semi-synchronous.

[0021] Some content classes may be higher-level classes, and some content classes may be lower-level subclasses. That is, higher-level classes may contain more specific lower-level subclasses, and further content subclasses exist for the lower-level subclasses, so that categorization into subclasses more specifically describes the content. In some implementations, a given (sub)class may have one or more hierarchical orders within the taxonomy. Content classes may include genres, platform-specific genres, mechanics, themes, art styles and views, brand intellectual property, modes, churn, marketing assets, creative elements, and / or other content classes and subclasses. These content classes may be top-level classes of content classes and subclasses. As a non-limiting example, each of these content classes may contain one or more lower-level content subclasses.

[0022] A given genre may refer to a particular style, form, or set of content elements (e.g., action, adventure, sports, casino). A given platform-specific genre may be a genre specific to a platform and / or a real or virtual setting (e.g., arcade, music, party, racing, slots). A given mechanism may govern the rules for users and the responses to their actions and / or the actions of other content pieces within a digital (e.g., physical) environment. A given theme may refer to a particular subject or topic (e.g., crime / mystery, horror, vehicles) that a digital environment is related to and has evolved around. A given art style and perspective may refer to a visual style, rendering technique, perspective, and / or other art style and / or perspective. A given brand intellectual property may refer to a tangible or intangible concept (e.g., sports, game show, children's toys) that may be associated with a brand. A given mode may refer to the setting of a digital environment and the role or position of a user / player (e.g., player as manager, single player, player as actor) within the digital environment. A given churn may refer to how a user and / or content within a digital environment enters and exits the digital environment (e.g., intentionally). A given marketing asset may refer to an element (e.g., placement, emotional driver) that may facilitate the promotion or presentation of a piece of content. A given creative element may refer to an artistic element that facilitates the promotion of a piece of content (e.g., coin, flag, light bulb). Content classes and subclasses may be associated with binary numbers, Yes or No, and / or other types of values.

[0023] In some implementations, a given psychological profile may characterize and be for a single unique user. In some implementations, a given psychological profile may characterize and be for more than one user. A psychological profile may include psychological parameter values ​​for psychological parameters. A psychological profile may include a set of psychological parameter values ​​for psychological parameters for an individual user. As a non-limiting example, the psychological parameter values ​​for the psychological parameters may be a predetermined range of numeric scores, letter scores, and / or other types of values ​​specific to each psychological parameter that may overall characterize a particular user.

[0024] Parameters, such as psychological parameters, can designate measurable, recordable, and / or determined information, such as those described herein. A parameter value corresponding to a parameter may be a specific value, either numeric or non-numeric, that characterizes the content, user, or respective element with which the parameter value is associated and described. Psychological parameter values ​​can characterize a given user's feelings, emotions, perceptions, thoughts, and behaviors. As non-limiting examples, psychological parameter values ​​may characterize competitiveness, goal orientation, and learning style, among others.

[0025] The server(s) 102 may be configured with machine-readable instructions 106. The machine-readable instructions 106 may include one or more instruction components. The instruction components may include computer program components. The instruction components may include one or more of an information acquisition component 108, a correlation determination component 110, a content class determination component 112, a master correlation determination component 114, a response determination component 116, a content identification component 118, a presentation component 120, and / or other instruction components.

[0026] The information acquisition component 108 may be configured to acquire interaction information from an online platform providing the digital environment. The interaction information may characterize interactions between users and pieces of content through the digital environment and / or engagement by users with the pieces of content. Individual interaction information may specify i) instances of interactions between individual users and / or their individual psychological profiles and one or more pieces of content through the digital environment, ii) timing information for the instances, and / or other information. In some implementations, interaction information may be acquired on a continuous basis from multiple online platforms for multiple users at a time. As used herein, the term “continuously” may refer to continuing to perform (e.g., acquire) an action periodically (e.g., every 30 seconds, every minute, every hour, etc.) until receiving an instruction to terminate. An instruction to terminate may include powering off the system 100, shutting down the online platform, resetting the system 100, and / or other instructions to terminate. Accordingly, examples of interactions may be collected, acquired, received, monitored, and / or analyzed.

[0027] Examples of interactions may include content that users engage with, how individual users engage with content, content that users disengage or avoid, and / or other interaction information. In some implementations, the interaction information may identify a user. In some implementations, the interaction information may identify a user's psychographic profile. The content that individual users engage with may be related to the digital environment providing the content or the individual online platform. That is, for example, content provided by the digital environment may be related to an online game hosted by the digital environment (e.g., virtual goods, virtual mini-games, etc.). In some implementations, the content that individual users engage with may not be related to the digital environment providing the content. That is, the content may direct the user to a different digital environment.

[0028] How users engage with content, or engagement by individual users, with or based on the content may define a behavioral pattern for the individual user. A behavioral pattern may include a sequence of actions with the user in the digital environment, other users of the users in the digital environment, and content in the digital environment. A behavioral pattern may indicate a user's spending pattern, a task completed by the individual user, a task not completed by the individual user, a task failed by the individual user, a game mechanic initiated by the user, and / or other behavioral patterns. Actions may include one or more of: content-based purchases, sales, trades, donations, user content selections, content-based gameplay (e.g., mini-games, battles, competitions, etc.), individual user communication with other specific users or users, user task completions, frequent interaction with content, user alliance formation, and / or other actions. A spending pattern may indicate an amount of currency (e.g., real-world currency, virtual currency, points, etc.) spent, an amount of currency earned, an amount of currency donated, and / or other indications.

[0029] Game mechanics may include alternating turns of one or more games, action points, playing cards, capture, catch-up progression, dice, movement, resource management, risk and reward, role-playing, game modes (e.g., single player, multiplayer), and / or other new or known game mechanics. In some implementations, different games and / or content offered by the online platform through the digital environment may employ different game mechanics. Thus, a user may initiate and utilize different game mechanics within the digital environment. Conversely, a user may ignore some game mechanics by ignoring some of the games and / or content within the digital environment.

[0030] Timing information may include the time spent by individual users engaging / interacting with content, other users, and / or time spent within the digital environment, the frequency of subsequent re-interactions or the initiation of subsequent instances, the time of day of the instance, the time of week of the instance, the time of year of the instance, special occasions during or near the instance (e.g., the user's birthday, national holidays), and / or other timing information.

[0031] By way of non-limiting example, the interaction information acquisition component 108 may be configured to acquire first interaction information specifying at least a first instance of interaction between a first user and a first piece of content via a digital environment, timing information of the first instance, and / or other information. The first user may be associated with a first psychological profile. The first portion of the piece of content may be categorized with a first content class and a first content subclass of the second content class.

[0032] In some implementations, the information acquisition component 108 may be configured to receive the predicted response via a client computing platform 104 associated with an administrative user. The administrative user may be associated with a given online platform. The received predicted response may be a response elicited by a particular piece of content presented to the given user via the digital environment.

[0033] The correlation determination component 110 can be configured to determine content-specific correlations. The content-specific correlations can be between i) one or more of the psychological parameter values ​​included in the individual user's psychological profile or the individual user's individual psychological profile and ii) individual pieces of content. The content-specific correlation determination can be based on interaction information and / or other information. That is, content-specific correlations can be determined based on the pieces of content with which the user engages, the manner in which the user engages, and timing information of the instances of interaction. Various interaction information can indicate similar timing information, similar behavioral patterns, similar majority of psychological profiles, identical psychological profiles for various users, pieces of content with a common content class / subclass, pieces of content with similar majority of taxonomic classifications, pieces of content with identical taxonomic classifications, and / or other similarities that provide the basis for the content-specific correlations. The majority similarity may include psychological parameter values ​​associated with different users for the same psychological parameter within a particular range, a particular amount of psychological parameter values ​​within a particular range or identical, a particular amount of the same content class / subclass, and / or other majority thresholds.

[0034] In some implementations, the content-specific correlation may be between a single psychological parameter value for a psychological parameter or a set of psychological parameter values ​​for a psychological parameter and an individual piece of content. In some implementations, the content-specific correlation may be between a psychological profile and an individual piece of content. That is, a set of psychological parameters and their psychological parameter values ​​that make up the entire psychological profile may be correlated with an individual piece of content. As a non-limiting example, the correlation determination component 110 may be configured to determine a first content-specific correlation between the first psychological profile and the first piece of content based on the first interaction information and other of the interaction information.

[0035] It should be understood that descriptions herein of a "correlation" between one or more psychological parameter values ​​or individual psychological profiles and pieces of content that are positively correlated are not intended to be limiting, and that negative correlations between one or more psychological parameter values ​​or individual psychological profiles and pieces of content are also contemplated and may be included in the general "correlation." A negative correlation may be determined when users who strongly exhibit a certain psychological parameter avoid a particular piece of content and / or when users who do not exhibit that psychological parameter interact with a particular piece of content relatively more (e.g., in frequency, total number of runs, etc.) than other users who strongly exhibit that psychological parameter.

[0036] The content class determination component 112 can be configured to determine a content class and a content subclass that characterize each piece of content included in the content-specific correlation. The content class and the content subclass can be determined based on information stored in the electronic storage device 126. As a non-limiting example, the content class determination component 112 can be configured to determine a content class and a content subclass that characterize a first piece of content. The first piece of content can be classified, for example, into a first content class and a first content subclass of a second class.

[0037] The master correlation determination component 114 may be configured to determine a master correlation. The master correlation may be determined based on a content-specific correlation. The master correlation may be between i) one or more of the content classes and / or content subclasses (included in the content-specific correlation) and ii) the strength of individual or combinations of psychological parameter values ​​included in the psychological profile (included in the content-specific correlation). The strength may be relative to other values ​​of the psychological parameter values ​​included in the psychological profile. In some implementations, the strength of a given psychological parameter value relative to a given psychological parameter relative to other psychological parameter values ​​may refer to a greater presence of the given psychological parameter value compared to other psychological parameter values ​​included in the given psychological profile. In some implementations, the strength of a given psychological parameter value relative to other psychological parameter values ​​may refer to a greater presence of the given psychological parameter value compared to psychological parameter values ​​for the given psychological parameter included in the psychological profile stored in the electronic storage device 126.

[0038] In some embodiments, the strength of a combination of psychological parameter values ​​relative to other psychological parameter values ​​may refer to a greater presence of the given combination compared to other psychological parameter values ​​included in a given psychological profile. In some embodiments, the strength of a given combination relative to other psychological parameter values ​​may refer to a greater presence of the given combination compared to the psychological parameter values ​​for a given psychological parameter included in a psychological profile stored in electronic storage 126.

[0039] The strength of a psychological parameter value or combination may be determined by analyzing the psychological parameter values ​​included in a given psychological profile or in all psychological profiles stored in electronic storage 126. The analysis may include determining whether the psychological profile or given psychological profile includes a psychological parameter value for a given psychological parameter or combination, and determining a strength from among the existing psychological parameter values. A maximum strength may be determined, or a minimum strength may be determined.

[0040] As a non-limiting example, the master correlation determination component 114 may be configured to determine a first master correlation based on the first content-specific correlation and other correlations of the content-specific correlations. The first master correlation may be between at least the first content class and the first content subclass that characterize the first piece of content and the strength of individual or combinations of psychological parameter values ​​associated with other values ​​of the psychological parameter values ​​included in the first psychological profile and the other psychological profile (e.g., the first psychological parameter value relative to the first psychological parameter).

[0041] The response determination component 116 can be configured to determine individual predicted responses for individual pieces of content categorized into one or more content classes and / or content subclasses included in the master correlation. The predicted responses can be responses expected from a user upon interaction with such individual pieces of content. By way of non-limiting example, the predicted responses can include purchasing a given piece of content upon discovery or presentation, collecting a given piece of content to give as a gift, viewing a given piece of visual content, sharing a given piece of visual content, playing a piece of game content until the game is won, and / or other responses.

[0042] The predicted response may be determined based on the strength of the master correlation, interaction information, psychological profiles included in the master correlation, and / or other information. The strength of the master correlation may be determined based on a particular frequency of determinations of the same master correlation, a majority of content classes / subclasses having the same and / or psychological parameter values, a majority of content classes / subclasses having the same and a majority of psychological parameter values ​​being in a particular range, and / or other determinations. The particular range may be predefined. Each content class and each content subclass may be associated with one or more predicted responses from users interacting with content in those classifications.

[0043] As a non-limiting example, the response determination component 116 may be configured to determine one or more predicted responses to the first piece of content based on the strength of the first master correlation, the first psychological profile, and the taxonomic classification of the first piece of content. That is, the strength of the first master correlation may be evident and / or conclusive, indicating that the pieces of content classified under the first content class and the first content subclass are strongly correlated with the first psychological parameter value for the first psychological parameter. The interaction information may include examples of interactions between the user with the psychological profile and the different pieces of content. Thus, interaction information may be determined that includes a psychological profile that includes the first psychological parameter value for the first psychological parameter. These specific interaction information may facilitate the determination of a predicted response by the user to the pieces of content classified under the first content class and the first content subclass.

[0044] The content identification component 118 may be configured to identify potential pieces of content for a user based on predicted responses, pieces of content that are active in the digital environment, taxonomic classifications of individual pieces of content, a user's psychological profile, and / or other information. A potential piece of content may be one or more pieces of content that are appropriate for the user and therefore may be presented to the user via the digital environment. Active content may be pieces of content that are presented to the user or that the user can discover using the digital environment. In some implementations, the identification of potential pieces of content may be based on predicted responses received from an administrative user. Thus, potential pieces of content may be determined taking into account the strength of psychological parameter values ​​included in the user's psychological profile, the various content classes and content subclasses into which each piece of content falls, the content already available to the user in the digital environment, and the predicted responses.

[0045] As a non-limiting example, identifying potential pieces of content for a user may be based on at least a first master correlation, a taxonomic classification of individual pieces of content, a psychological profile of the user, and / or other information.

[0046] The presentation component 120 may be configured to facilitate presentation of potential content pieces to a user within the digital environment. In some implementations, presenting potential content pieces may include presenting potential content integrated into the digital environment. That is, for example, if the digital environment is a simulation game, the potential content pieces may be presented or discoverable within the simulation game when appropriate. The appropriateness of presenting discoverability of potential content pieces may be based on active content pieces, the user's progress in the digital environment, the virtual location of the user or the user's character within the digital environment, the time of day within the digital environment, and / or other information.

[0047] In some implementations, presenting the potential pieces of content may include presenting a list of potential pieces of content within the digital environment. Thus, a user may select potential pieces of content from the list for integration into the digital environment, interact with the selection, remove them from the list, and / or take other actions related to the presentation. Selection for integration of one or more of the potential pieces of content from the list into the digital environment may result in such pieces being presented or made discoverable, where appropriate, within the digital environment.

[0048] 3A-3B illustrate the determined content-specific correlations, master correlations, and their utilization. FIG. 3A illustrates psychological profile 302a and psychological profile 302b including psychological parameter values ​​304 (for the same psychological parameter), e.g., high risk appetite, among other psychological parameter values. Psychological profiles 302a and 302b may be correlated with content 306a (e.g., wearable armor) based on acquired interaction information (illustrated in FIG. 3B), e.g., users with psychological profiles 302a and 302b frequently collect, purchase, or craft their own wearable armor. Accordingly, content-specific correlations 308a and 308b may be established. Content 306a may be classified into content class 310a (e.g., protection), content subclass 310b (e.g., wearable), and content class 310c (e.g., combat).

[0049] Figure 3B illustrates a master correlation 312 determined between the psychological parameter values ​​304 from Figure 3A and content class 310c, i.e., a taxonomic classification of the content 306 of Figure 3A. The content-specific correlations 308a and 308b of Figure 3A may indicate the frequency of the psychological parameter values ​​304 and therefore the strength of the psychological parameter values ​​304. Accordingly, such strength of the psychological parameter values ​​304 may be correlated with content class 310c to establish master correlation 312. Content 306a (same as Figure 3A), content 306b, and other content may be classified under content class 310c in addition to other content classes or content subclasses. The predicted response(s) 314 to the content 306a and 306b may be determined based on at least the strength of the master correlation 312 (e.g., the frequency of establishing this particular correlation), the psychological profiles 316, and the interaction information 318 stored in accessible electronic storage (similar to electronic storage 126 of FIG. 1, not illustrated). That is, specific ones of the psychological profiles 316 having psychological parameter values ​​304 may be determined, and the interaction information 318 including those specific psychological profiles 316 may be analyzed to determine the predicted response(s) 314.

[0050] Prospective content 306c may be identified and presented via digital environment 322 based on content 320a and 320b actively presented in digital environment 322, predicted response(s) 314, taxonomic classifications of content 320a, 320b, and 306c stored in electronic storage, and psychological profile 316. That is, as a non-limiting example, based on i) predicted response(s) 314 that predict that the user may engage in combat when combat begins, ii) content 320a and 320b (e.g., weapons and tools) that is actively presented to the user via digital environment 322, and iii) the content classes and / or content subclasses (i.e., taxonomic classifications) to which content 320a, 320b, 306c and other pieces of content are categorized and similar (e.g., content class 310a—combat, among others), potential content 306c (e.g., Kevlar® material) may be identified as suitable to be presented or discoverable via digital environment 322 in addition to content 320a and 320b.

[0051] 1 , in some implementations, the server(s) 102, the client computing platform(s) 104, and / or the external resources 124 may be operatively linked via one or more electronic communications links. For example, such electronic communications links may be established, at least in part, via a network, such as the Internet and / or other networks. While this is not intended to be limiting, it should be understood that the scope of the present disclosure includes implementations in which the server(s) 102, the client computing platform(s) 104, and / or the external resources 124 may be operatively linked via some other communications medium.

[0052] A given client computing platform 104 may include one or more processors configured to execute computer program components that may be configured to enable a professional or user associated with a given client computing platform 104 to interface with system 100 and / or external resources 124 and / or provide other functionality attributed to the client computing platform(s) 104 herein. By way of non-limiting example, a given client computing platform 104 may include one or more of a desktop computer, a laptop computer, a handheld computer, a tablet computing platform, a netbook, a smartphone, a game console, and / or other computing platform.

[0053] External resources 124 may include information sources external to system 100, external entities participating with system 100, and / or other resources. In some implementations, some or all of the functionality attributed to external resources 124 herein may be provided by resources included in system 100.

[0054] The server(s) 102 may include electronic storage 126, one or more processors 128, and / or other components. The server(s) 102 may include communication lines or ports to enable the exchange of information with a network and / or other computing platforms. The illustration of the server(s) 102 in FIG. 1 is not intended to be limiting. The server(s) 102 may include multiple hardware, software, and / or firmware components operating together to provide the functionality attributed to the server(s) 102 herein. For example, the server(s) 102 may be implemented by a cloud of computing platforms operating together as the server(s) 102.

[0055] The electronic storage 126 may include non-transitory storage media that electronically store information. The electronic storage media of the electronic storage 126 may include one or both of a system storage device that is provided integrally with the server(s) 102 (i.e., substantially non-removable) and / or a removable storage device that is removably connectable to the server(s) 102 via, for example, a port (e.g., a USB port, a firewire port, etc.) or a drive (e.g., a disk drive, etc.). The electronic storage 126 may include one or both of an optically readable storage medium (e.g., an optical disk, etc.), a magnetically readable storage medium (e.g., a magnetic tape, a magnetic hard drive, a floppy drive, etc.), a charge-based storage medium (e.g., an EEPROM, RAM, etc.), a solid-state storage medium (e.g., a flash drive, etc.), and / or other electronically readable storage media. The electronic storage 126 may include one or more virtual storage resources (e.g., cloud storage, a virtual private network, and / or other virtual storage resources). The electronic storage device 126 may store software algorithms, information determined by the processor(s) 128, information received from the server(s) 102, information received from the client computing platform(s) 104, and / or other information that enables the server(s) 102 to function as described herein.

[0056] Processor(s) 128 may be configured to provide information processing capabilities in server(s) 102. Thus, processor(s) 128 may include one or more of a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a state machine, and / or other mechanisms for electronically processing information. Although processor(s) 128 are shown in FIG. 1 as a single entity, this is for illustrative purposes only. In some implementations, processor(s) 128 may include multiple processing units. These processing units may be physically located within the same device, or processor(s) 128 may represent the processing functionality of multiple devices acting in concert. Processor(s) 128 may be configured to execute components 108, 110, 112, 114, 116, 118, and / or 120 and / or other components. Processor(s) 128 may be configured to execute components 108, 110, 112, 114, 116, 118, and / or 120 and / or other components by software, hardware, firmware, some combination of software, hardware, and / or firmware, and / or other mechanisms for configuring processing power on processor 128. As used herein, the term "component" may refer to any component or set of components that perform the functionality attributed to the component. This may include one or more physical processors in execution of processor-readable instructions, processor-readable instructions, circuitry, hardware, storage media, or any other component.

[0057] 1 as being implemented within a single processing unit, it should be understood that in implementations in which processor(s) 128 include multiple processing units, one or more of components 108, 110, 112, 114, 116, 118, and / or 120 may be implemented remotely from the other components. The description of functionality provided by various components 108, 110, 112, 114, 116, 118, and / or 120 described below is for illustration purposes and is not intended to be limiting, as any of components 108, 110, 112, 114, 116, 118, and / or 120 may provide more or less functionality than described. For example, one or more of components 108, 110, 112, 114, 116, 118, and / or 120 may be eliminated, and some or all of its functionality may be provided by others of components 108, 110, 112, 114, 116, 118, and / or 120. As another example, processor(s) 128 may be configured to execute one or more additional components capable of performing some or all of the following functions attributed to one of components 108, 110, 112, 114, 116, 118, and / or 120:

[0058] 2 illustrates a method 200 for identifying and utilizing correlations between content classifications and a user's psychological profile to provide an adaptable digital environment, according to one or more implementations. The operations of method 200 presented below are intended to be illustrative. In some implementations, method 200 may be achieved with one or more additional operations not described and / or without one or more of the operations described. Additionally, the order in which the operations of method 200 are illustrated in FIG. 2 and described below is not intended to be limiting.

[0059] In some implementations, method 200 can be implemented in one or more processing devices (e.g., digital processors, analog processors, digital circuits designed to process information, analog circuits designed to process information, state machines, and / or other mechanisms for electronically processing information). The one or more processing devices may include one or more devices that perform some or all of the operations of method 200 in response to instructions electronically stored on an electronic storage medium. The one or more processing devices may include one or more devices configured through hardware, firmware, and / or software to be specifically designed to perform one or more of the operations of method 200.

[0060] Operation 202 may include obtaining interaction information from an online platform providing the digital environment. The individual interaction information specifies instances of interactions between individual users and / or the individual users' individual psychological profiles and one or more pieces of content via the digital environment, as well as timing information for the instances. Taxonomic classifications of the individual pieces of content and the psychological profiles of users of the digital environment may be stored in electronic storage. The individual taxonomic classifications may include content classes and content subclasses for the content classes of the individual pieces of content. The taxonomic classifications may conform to a taxonomy defining a hierarchical system of content classes and content subclasses. The pieces of content may be characterized by their classification into content classes and content subclasses. The psychological profile may include psychological parameter values ​​for the psychological parameters. Operation 202 may be performed by one or more hardware processors configured with machine-readable instructions, including components the same as or similar to information acquisition component 108, according to one or more embodiments.

[0061] Operation 204 may include determining content-specific correlations between one or more of the individual user's psychological parameter values ​​and / or the individual user's individual psychological profile and the individual piece of content based on the interaction information. Operation 204 may be performed by one or more hardware processors configured with machine-readable instructions, including components the same as or similar to correlation determination component 110, according to one or more implementations.

[0062] Operation 206 may include determining a content class and a content subclass that characterize the individual pieces of content involved in the content-specific correlation. Operation 206 may be performed by one or more hardware processors configured with machine-readable instructions, including components the same as or similar to content class determination component 112, according to one or more implementations.

[0063] Operation 208 may include determining a master correlation between one or more of the content classes and / or content subclasses and the strength of individual values ​​or combinations of psychological parameter values ​​included in the psychological profile relative to other values ​​of the psychological parameter values ​​included in the psychological profile based on the content-specific correlations. Operation 208 may be performed by one or more hardware processors configured with machine-readable instructions including components the same as or similar to master correlation determination component 114, according to one or more implementations.

[0064] Operation 210 may include determining individual predicted responses to individual pieces of content classified into one or more content classes and / or content subclasses included in the master correlation based on the strength of the master correlation, the psychographic profile included in the master correlation, interaction information, and / or other information. Operation 210 may be performed by one or more hardware processors configured with machine-readable instructions including components the same as or similar to response determination component 116, according to one or more implementations.

[0065] Operation 212 may include identifying potential pieces of content for the user based on the predicted responses, pieces of content active in the digital environment, classifications of individual pieces of content, a user's psychological profile, and / or other information. Operation 212 may be performed by one or more hardware processors configured with machine-readable instructions, including components the same as or similar to content identification component 118, according to one or more embodiments.

[0066] Operation 214 may include effectuating presentation of the piece of potential content to a user within a digital environment. Operation 214 may be performed by one or more hardware processors configured with machine-readable instructions, including components the same as or similar to presentation component 120, according to one or more implementations.

[0067] While the present technology has been described in detail for purposes of illustration based on what are presently considered to be the most practical and preferred embodiments, it should be understood that such detail is for that purpose only, and that the present technology is not limited to the disclosed embodiments, but on the contrary, is intended to cover modifications and equivalent arrangements within the spirit and scope of the appended claims. For example, it should be understood that the present technology contemplates that, to the extent possible, one or more features of any embodiment can be combined with one or more features of any other embodiment.

Claims

1. 1. A system configured to identify and utilize correlations between content classifications and a user's psychological profile to provide an adaptable digital environment, the system comprising: an electronic storage device storing i) a taxonomic classification of individual pieces of content, each taxonomic classification comprising a content class and a content subclass for a content class of said individual piece of content; and ii) a psychological profile of a user of a digital environment, said taxonomic classification conforming to a taxonomy defining a hierarchical system of said content classes and content subclasses, said pieces of content being characterized by said classification into said content classes and content subclasses, and said psychological profile comprising psychological parameter values ​​of psychological parameters; one or more processors, wherein the one or more processors execute, by machine-readable instructions: obtaining, from an online platform providing the digital environment, interaction information, each interaction information specifying i) an instance of interaction via the digital environment between a respective one of the users and / or a respective one of the psychological profiles of the respective user and one or more of the respective pieces of content, and ii) timing information of the instance; determining, based on the interaction information, a content-specific correlation between i) the psychological profile of the individual user or one or more of the psychological parameter values ​​included in the individual psychological profile of the individual user, and ii) the individual piece of content; determining the content classes and content subclasses that characterize the individual pieces of content involved in the content-specific correlation; determining, based on the content-specific correlations, master correlations between i) one or more of the content classes and / or content subclasses and ii) the strength of individual ones of the psychological parameter values ​​or combinations of the psychological parameter values ​​included in the psychological profile relative to other ones of the psychological parameter values ​​included in the psychological profile; determining individual predicted responses to the individual pieces of content classified into the one or more content classes and / or content subclasses included in the master correlation based on the strength of the master correlation, the psychological profile included in the master correlation, and the interaction information; identifying potential pieces of content for the user based on the predicted responses, the pieces of content active in the digital environment, the taxonomic classifications of the individual pieces of content, and the psychographic profile of the user; A system configured to facilitate presentation of the pieces of potential content to the user within the digital environment.

2. The system of claim 1 , wherein the presenting the pieces of potential content comprises presenting the potential content integrated into the digital environment.

3. The system of claim 1 , wherein the presentation of the potential pieces of content comprises presenting a list of the potential pieces of content within the digital environment.

4. The system of claim 1 , wherein the individual pieces of content are characterized by at least a mode of user interaction, an interactivity type, a theme, and one or more subject matters.

5. The one or more processors are caused by the machine-readable instructions to:

10. The system of claim 1, further configured to receive the predicted response via a client computing platform of an administrative user, and wherein the identification of the potential piece of content is based on the received predicted response.

6. The system of claim 1 , wherein the pieces of content that are active are the pieces of content that are presented to the user or that are discoverable by the user using the digital environment.

7. The one or more processors are caused by the machine-readable instructions to: obtaining first interaction information specifying i) a first instance of an interaction between a first user having a first psychographic profile and a first piece of content categorized in a first content class and a first content subclass, via the digital environment, and ii) timing information of the first instance; determining a first content-specific correlation between the first psychological profile and the first piece of content based on the first interaction information and other information of the interaction information; determining the content class and the content subclass that characterize the first piece of content; determining, based on the first content-specific correlation and other of the content-specific correlations, a first master correlation between at least i) the first content class and the first content sub-class characterizing the first piece of content and ii) strengths of individual values ​​of psychological parameter values ​​or combinations of psychological parameter values ​​associated with other values ​​of the psychological parameter values ​​included in the first psychological profile and other of the psychological profiles; 2. The system of claim 1, further configured to determine one or more predicted responses to the first piece of content based on the first master correlation, the first psychological profile, and the taxonomic classification of the first piece of content, and wherein identifying the potential piece of content for the user is based on at least the strength of the first master correlation, the taxonomic classification of the individual piece of content, and the psychological profile of the user.

8. 1. A method for identifying and utilizing correlations between content classifications and a user's psychological profile to provide an adaptable digital environment, the method comprising: obtaining interaction information from an online platform providing the digital environment, the individual interaction information specifying i) instances of interaction between individual users and / or individual psychological profiles of the individual users and one or more pieces of content, via the digital environment, and ii) timing information of the instances, wherein a) taxonomic classifications of the individual pieces of content and b) the psychological profiles of users of the digital environment are stored in an electronic storage device, the individual taxonomic classifications including the content classes and content subclasses for the content classes of the individual pieces of content, the taxonomic classifications conforming to a taxonomy defining a hierarchical system of the content classes and content subclasses, the pieces of content being characterized by the classifications into the content classes and content subclasses, and the psychological profiles including psychological parameter values ​​for psychological parameters; determining, based on the interaction information, a content-specific correlation between i) one or more of the psychological parameter values ​​included in the psychological profile of the individual user or the individual psychological profile of the individual user, and ii) the individual piece of content; determining the content classes and content subclasses that characterize the individual pieces of content involved in the content-specific correlation; determining, based on the content-specific correlations, a master correlation between i) one or more of the content classes and / or content subclasses and ii) the strength of individual ones of the psychological parameter values ​​or combinations of the psychological parameter values ​​included in the psychological profile relative to other ones of the psychological parameter values ​​included in the psychological profile; determining an individual predicted response to the individual pieces of content classified into the one or more content classes and / or content subclasses included in the master correlation based on the strength of the master correlation, the psychological profile included in the master correlation, and the interaction information; identifying potential pieces of content for the user based on the predicted responses, the pieces of content active in the digital environment, the taxonomic classifications of the individual pieces of content, and the psychographic profile of the user; and enabling presentation of the piece of potential content to the user within the digital environment.

9. The method of claim 8 , wherein the presenting the piece of potential content comprises presenting the potential content integrated into the digital environment.

10. The method of claim 8 , wherein the presenting the pieces of potential content comprises presenting a list of the pieces of potential content within the digital environment.

11. The method of claim 8 , wherein the individual pieces of content are characterized by at least a mode of user interaction, an interactivity type, a theme, and one or more subject matters.

12. 8. The method of claim 7, further comprising receiving the predicted response via a client computing platform of an administrative user, and wherein the identification of the potential piece of content is based on the received predicted response.

13. The method of claim 8 , wherein the pieces of content that are active are the pieces of content that are presented to the user or that are discoverable by the user using the digital environment.

14. obtaining first interaction information specifying i) a first instance of an interaction between a first user having a first psychographic profile and a first piece of content categorized in a first content class and a first content subclass, via the digital environment, and ii) timing information of the first instance; determining a first content-specific correlation between the first psychological profile and the first piece of content based on the first interaction information and other ones of the interaction information; determining the content class and the content subclass that characterize the first piece of content; determining, based on the first content-specific correlation and other of the content-specific correlations, a first master correlation between at least i) the first content class and the first content sub-class characterizing the first piece of content and ii) strengths of individual ones of the psychological parameter values ​​or combinations of the psychological parameter values ​​associated with other ones of the psychological parameter values ​​included in the first psychological profile and other ones of the psychological profiles; 9. The method of claim 8, further comprising: determining one or more predicted responses to the first piece of content based on the first master correlation, the first psychological profile, and the taxonomic classification of the first piece of content; and identifying the potential piece of content for the user based on at least the strength of the first master correlation, the taxonomic classification of the individual piece of content, and the psychological profile of the user.

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