Intelligent recommendations for adjusting gameplay sessions

Abstract

SOLUTION: Systems and methods for monitoring gameplay are for monitoring real-time gameplay object data that is used to extract patterns in gameplay and generate notifications relating to control options based on the extracted patterns, in which: the notifications may indicate that selected conditions may be met based on activity data that may be analyzed based on learning models associated with the selected conditions; the learning models may include models relating to player behavior, such as bullying or harassing language; the extracted patterns may include the gameplay session length, type of game played, and in-game behavior; supervising accounts may receive recorded media segments associated with the activity data that meets the selected conditions; and user accounts engaging in such activities may be blocked and / or provided with suggestions relating to alternative activities.SELECTED DRAWING: None

Description

[Technical Field]

[0001] The present technology relates to analyzing gameplay of interactive content titles, and more particularly, to developing and customizing learning models about gameplay by players. [Background technology]

[0002] 2. Description of Related Art Players may exhibit behavioral patterns in virtual environments (e.g., during multiplayer gameplay sessions) that affect not only their own experience but also that of other players. Such patterns may be related to and affect physical health, mental and emotional health, and social interactions. For example, players who engage in gameplay for significant periods of time may spend that time indoors in a relatively sedentary and inactive state, while overlooking mealtimes, physical exercise, and other healthy behaviors. Other potentially harmful patterns, such as long periods of antisocial gameplay, bullying, harassment, aggressive or threatening behavior, may be related to social interactions or the lack thereof. Nevertheless, even otherwise healthy players may fall into the same gameplay traps. Thus, a lack of variety can lead to a poor gameplay experience.

[0003] Current gaming platforms may offer some simple settings, such as settings to limit game play time. However, such settings are typically not tailored to a variety of specific behaviors, and a one-size-fits-all approach of applying simple limits may ultimately be ineffective in suppressing or otherwise disrupting undesirable behavioral patterns. Thus, currently available systems not only lack the ability to identify such behavioral patterns as they emerge, but also lack the ability to address emerging patterns in a manner that addresses the specific situations and conditions under which they emerge.

[0004] Additionally, players may be from different demographics (as may their respective parent(s), guardian(s), caretaker(s), or other types of supervisor(s)). Individual players (and supervisors) may also exhibit unique habits, idiosyncrasies, and preferences related to gameplay. Thus, a one-size-fits-all approach to analyzing and adjusting gameplay cannot take into account such individual characteristics.

[0005] Therefore, there is a need in the art for improved systems and methods for developing and customizing learning models of gameplay by players to provide sets of recommendations or notifications that are tailored to different behavioral patterns in gameplay. Summary of the Invention

[0006] Disclosed are systems, devices, methods, computer-readable media, and circuits for developing and customizing learning models related to gameplay by players. According to at least one example, the method includes storing information regarding a user account and one or more selected conditions in a memory; monitoring activity data from multiple gameplay sessions of multiple interactive content titles, where the activity data is associated with the user account; analyzing the activity data to extract patterns in gameplay by the user account associated with the selected conditions, where the analyzing the activity data is based on one or more learning models associated with the selected conditions; and generating a notification including one or more recommendations regarding gameplay control options based on the extracted patterns indicating that one or more of the selected conditions are met by the activity data. For example, a monitoring server in a network environment stores information regarding a user account and one or more selected conditions in a memory, monitors activity data from multiple gameplay sessions of multiple interactive content titles, associates the activity data with the user account, analyzes the activity data to extract patterns in gameplay by the user account associated with the selected conditions, the analyzing the activity data is based on one or more learning models associated with the selected conditions, and generates a notification including one or more recommendations regarding gameplay control options based on the extracted patterns indicating that one or more of the selected conditions are met by the activity data.

[0007] In another example, a monitoring server of a network environment may be provided to develop and customize learning models related to gameplay by players. The monitoring server includes storage (e.g., a memory configured to store data such as virtual content data, one or more images, etc.) and one or more processors (e.g., implemented in circuitry). The one or more processors are coupled to the memory and, when executing instructions in combination with various components (e.g., a network interface, a display, an output device, etc.), the monitoring server of the network environment is configured to store information related to user accounts and one or more selected conditions in the memory; monitor activity data from multiple gameplay sessions of multiple interactive content titles; associate the activity data with the user accounts; analyze the activity data to extract patterns in gameplay by the user accounts associated with the selected conditions; analyze the activity data based on the one or more learning models associated with the selected conditions; and generate a notification including one or more recommendations related to gameplay control options based on the extracted patterns indicating that one or more of the selected conditions are satisfied by the activity data. [Brief explanation of the drawings]

[0008] [Figure 1] 1 illustrates an exemplary network environment in which a system for developing and customizing learning models for game play by players may be implemented. [Figure 2] 1 illustrates an exemplary UDS system that monitors real-time gameplay object data used to develop and customize learning models about gameplay by players. [Figure 3] 1 shows an exemplary table of various objects and associated events. [Figure 4]1 is a flowchart of an exemplary method for developing and customizing learning models for player gameplay. [Figure 5] FIG. 1 is a block diagram of an exemplary electronic entertainment system that may be used with embodiments of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0009] Aspects of the present disclosure include systems and methods for developing and customizing learning models related to game play by players. A notification may be provided indicating that a selected condition may be met based on activity data, and that a learning model associated with the selected condition may be applied to identify behavioral patterns. The learning models may include models related to different types of player behavior, such as bullying or harassing language. The extracted patterns may include the length of the gameplay session, the type of game played, and in-game behavior. The administrative account may receive recorded media segments associated with the activity data that meets the selected conditions. The administrative account may further be provided with options for addressing user accounts that engage in such activities by blocking such user accounts from interacting with the administrative player, restricting game play with such user accounts, automatically recording interactions between the user account and the administrative player account, and / or making suggestions to the administrative player account regarding alternative activities.

[0010] 1 illustrates an exemplary network environment 100 in which a system for developing and customizing learning models for player gameplay may be implemented. The network environment 100 may include one or more interactive content source servers 110 that provide streaming content (e.g., interactive content titles, game titles, interactive videos, podcasts, etc.), one or more platform servers 120, one or more user devices 130, and one or more databases 140.

[0011] Interactive content source servers 110 (including game servers) can maintain, stream, and host interactive media available for streaming on user devices 130 over a communications network. Such interactive content source servers 110 can be implemented in the cloud (e.g., one or more cloud servers). Each media can include one or more sets of object data that can be available for user participation (e.g., viewing or interacting with an activity). Data about objects shown in the media can be stored in object files 216 ("object files") by the interactive content server 110, the platform server 120, and / or the user devices 130, as discussed in detail with respect to FIGS. 2 and 3.

[0012] The platform servers 120 can be responsible for communicating with different interactive content source servers 110, databases 140, and user devices 130. Such platform servers 120 can be implemented on one or more cloud servers. The platform servers 120 can include a pattern server 170 that interfaces with the notification server 120 to develop and customize learning models for gameplay by individual players.

[0013] In an exemplary embodiment, gameplay data may be provided by interactive content source server 110 and / or user device 130 for a particular player for a current gameplay session. The gameplay data may include object data, which may include activity data regarding in-game actions taken by the player's avatar relative to the virtual environment and virtual elements and objects therein. Pattern server 170 may be configured to analyze the activity data to extract patterns in gameplay by the user account. For example, gameplay patterns may identify that a player tends to perform certain types of actions under certain conditions.

[0014] In some embodiments, pattern server 170 can also access historical gameplay data from database 140 and order it to identify gameplay patterns. Such historical data may be associated with a particular player or with other players with similar characteristics as indicated in the player profile or other player data. In further embodiments, pattern server 170 can access and evaluate one or more learning models associated with selected conditions to identify whether such learning models may be applicable to the conditions of the current gameplay session. Similar learning models include patterns that match the gameplay data and the conditions of the current gameplay session. While learning models associated with other similarly situated players may be useful for analyzing the player of the current gameplay session, pattern server 170 can further use unique data about the player of the current gameplay session to customize the learning model. For example, if a player's gameplay data indicates divergence from a learning model identified as similar, the player's learning model can be customized to include the specific divergent behavior patterns.

[0015] As a player continues to play more games and generate more gameplay data from which new and different behavioral patterns can begin to emerge, the player's learning model can be further refined. Thus, such customized learning models may include a variety of different behavioral patterns that characterize a particular player's gameplay under different combinations of conditions, including different game titles, game genres, gameplay modes (e.g., individual, team-based), different peer players (e.g., competitors or teammates), and various in-game virtual environments and their elements. Meanwhile, gameplay data may include the types of activities the player engages in, the length of gameplay, gameplay achievements, interactions with peers in the virtual environment, etc.

[0016] The player's learning model may also include data regarding various actions that may be recommended when certain behavioral patterns emerge. For example, if an identified behavioral pattern indicates that a player's performance is beginning to decline during an extended gameplay session, the learning model may associate such pattern with an action involving notifying the player to take a break from gameplay, notifying a designated supervisor account, curbing gameplay time, or other action. Notifications may be generated and provided by a notification server 180, which sends or otherwise provides notifications to either the user device 130 of the player engaged in the gameplay session or another user device 130 associated with an administrative user account. Pattern server 170 may include or be distinct from notification server 180.

[0017] With respect to administrative accounts, the notification server 180 can send notifications to the administrative account related to recommendations regarding gameplay control options for user devices associated with the user account. If the administrative account chooses to implement a gameplay control option, such as requesting activity diversification, the notification server 180 can suggest one or more alternative activities currently available to the user account. Additionally, the user account can be blocked from engaging in a current activity for a predetermined period of time based on the gameplay control options implemented by the administrative user account. Other gameplay control options can include throttling game time or flagging in-game behaviors that meet certain selected conditions, such as by the administrative user account or the platform server 120, based on certain extracted patterns.

[0018] Pattern server 170 can continue to track not only gameplay data (which may be added to the historical data stored in database 140), but also subsequent control data by supervisor accounts and other player data to determine whether actions taken in response to identified pattern insights resulted in satisfactory outcomes. Analyses such as these can further be used to refine a particular player's learning model, which may result in different actions being taken in subsequent gameplay sessions associated with similar conditions. As gameplay continues, different learning models can be developed, customized, and further refined for different individual players in real time. Thus, pattern server 170 can also continue to monitor different learning models to identify similar behavior patterns, behavior patterns that begin to diverge or converge, and their associated actions and outcomes. Analyses such as these can also be used to further customize a player's learning model so that more tailored actions and recommendations can be made.

[0019] The interactive content source server 110 may communicate with multiple platform servers 120, although the interactive content source server 110 may be implemented on one or more platform servers 120. The platform server 120 may also execute instructions, such as receiving a user request from a user to stream streaming media (i.e., games, activities, videos, podcasts, user-generated content (“UGC”), publisher content, etc.). Additionally, the platform server 120 may execute instructions, such as streaming streaming media content titles. Such streaming media may have at least one object set associated with at least a portion of the streaming media. Each set of object data may have data about an object displayed during at least a portion of the streaming media (e.g., activity information, zone information, actor information, mechanics information, game media information, etc.).

[0020] The streaming media and at least one set of associated object data may be provided by an application programming interface (API) 160, allowing various types of interactive content source servers 110 to communicate with different platform servers 120 and different user devices 130. The API 160 may be specific to the particular computer programming language, operating system, protocol, etc. of the interactive content source server 110 providing the streaming media content titles, the platform server 120 providing the media and at least one set of associated object data, and the user device 130 receiving the same. In a network environment 100 including multiple different types of interactive content source servers 110 (or platform servers 120 or user devices 130), there may likewise be a corresponding number of APIs 160.

[0021] User devices 130 may include multiple different types of computing devices. For example, user devices 130 may include any number of different gaming consoles, mobile devices, laptops, and desktops. Such user devices 130 may also be configured to access data from other storage media, such as, but not limited to, memory cards or disk drives, which may be appropriate for downloaded services. Such devices 130 may include standard hardware computing components, such as, but not limited to, network interfaces, media interfaces, non-transitory computer-readable storage (memory), and processors for executing instructions that may be stored in the memory. These user devices 130 may also run a variety of different operating systems (e.g., iOS, Android), applications, or computing languages ​​(e.g., C++, JavaScript). An exemplary user device 130 is described in detail herein with respect to FIG. 5.

[0022] Database 140 can be stored on platform server 120, interactive content source server 110, any of servers 218 (examples shown in FIG. 2 ), on the same server, on different servers, on a single server, across different servers, or on user device 130. Such databases 140 can store media and / or associated object datasets. Such streaming media can depict one or more objects (e.g., activities) in which users can participate, and / or UGC (e.g., screenshots, videos, play-by-play commentaries, mashups, etc.) created by peers, publishers of media content titles, and / or third-party publishers. Such UGC can include metadata for searching such UGC. Such UGC can also include information about the media and / or peers. Such peer information can be derived from data collected during peer interactions with objects in interactive content titles (e.g., video games, interactive books, etc.) and can be “bound” to and stored with the UGC. Such binding extends the UGC because it can deep link (e.g., directly launch) to objects, provide information about the objects and / or peers of the UGC, and / or allow users to interact with the UGC. One or more user profiles can also be stored in database 140. Each user profile can include information about a user (e.g., activity and / or user progress within a media content title, user ID, user game avatar, etc.) and can be associated with media.

[0023] 2 illustrates an exemplary UDS system 200 that monitors real-time gameplay object data used to extract patterns in gameplay. As shown in FIG. 2, consoles 228 and servers 218 (e.g., streaming server 220, UGC server 224, and pattern server 170, and object server 226) are shown to receive object data and media files recorded by object recorder 206 and content recorder 202, respectively.

[0024] The console 228 may be implemented on any of the platform server 120, the cloud server, or the server 218. The console 228 may further include a content recorder 202 and an object recorder 210, described in more detail below, and content (e.g., media) may be recorded and output via the console 228. The game title 230 may be executed on the console 228. Alternatively, or in addition, the content recorder 202 may be implemented on any of the platform server 120, the cloud server, or the server 218.

[0025] Such a content recorder 202 may receive content (e.g., media) from an interactive content title 230 (e.g., game server 110) and record it in a content ring buffer 208. Such a ring buffer 208 may store multiple content segments (e.g., v1, v2, and v3), start times for each segment (e.g., V1_START_TS, V2_START_TS, V3_START_TS), and end times for each segment (e.g., V1_END_TS, V2_END_TS, V3_END_TS). Such segments may be stored by a console 228 as media files 212 (e.g., MP4, WebM, etc.). Such media files 212 (e.g., portions of streaming media) may be uploaded to a streaming server 220 for storage and subsequent streaming or use, although the media files 212 may be stored on any server, cloud server, any console 228, or any user device 130. The media files 212 may be uploaded periodically and / or in real time or near real time. The console 228 can store these start and end times for each segment as a content timestamp file 214. Such a content timestamp file 214 can also include a stream ID that matches the stream ID of the media file 212, thereby associating the content timestamp file 214 with the media file 212. Although such a content timestamp file 214 can be uploaded and stored on the UGC server 224, the content timestamp file 214 can be stored on any server, cloud server, any console 228, or any user device 130.

[0026] At the same time that the content recorder 202 receives and records content from the interactive content title 230, the object library 204 receives object data from the interactive content title 230, and the object recorder 206 tracks the object data to determine when an object begins and ends. Such object data can be uploaded periodically and / or in real time or near real time. The object library 204 and the object recorder 206 may be implemented on the platform server 120, a cloud server, or any server 218. When the object recorder 206 detects the start of an object, it receives object data (e.g., user interaction with the object, object ID, object start time, object end time, object result, object type, etc.) from the object library 204 and records the object data in the object ring buffer 208 (e.g., Object ID1, START_TS; Object ID2, START_TS; Object ID3, START_TS). Such object data recorded in the object ring buffer 208 can be stored in the object file 216.

[0027] Such object files 216 may also include object start times, object end times, object IDs, object results, object types (e.g., competitions, quests, tasks, etc.), and user or peer data associated with the object. For example, object files 216 may store data regarding activities, in-game items, zones, actors, mechanics, and game media, as described in more detail with respect to FIG. 3. Such object files 216 may be stored on an object server 226, although the object files 216 may be stored on any server, cloud server, any console 228, or any user device 130.

[0028] Such object data (e.g., object files 216) can be associated with content data (e.g., media files 212 and / or content timestamp files 214). In one example, the object server 226 stores the content timestamp files 214 with the object files 216 and associates the content timestamp files 214 with the object files 216 based on a match between the stream ID of the content timestamp files 214 and the corresponding activity ID of the object files 216. In another example, the object server 226 can store the object files 216 and receive queries for the object files 216 from the UGC server 224. Such queries can be performed by searching for the activity ID of the object files 216 that matches the stream ID of the content timestamp files 214 sent with the query. In yet another example, queries of stored content timestamp files 214 can be performed by matching the start and end times of the content timestamp files 214 with the start and end times of the corresponding object files 216 sent with the query. Such object files 216 can also be associated with matching content timestamp files 214 by the UGC server 224, although this association can be performed by any server, cloud server, any console 228, or any user device 130. In another example, the object files 216 and content timestamp files 214 can be associated by the console 228 during the creation of each file 214, 216.

[0029] The pattern server 170 can receive real-time object data from the object server 226 to monitor and analyze activity data associated with the user account. The object data, described in more detail below, can provide insight into what is currently happening during gameplay. The database 140 can store information about the user account and one or more selected conditions. The pattern server 170 can monitor activity data associated with the user account. The activity data can be received based on multiple gameplay sessions of multiple interactive content titles. The activity data can be further analyzed to extract patterns in gameplay by the user account that are associated with the selected conditions.

[0030] The activity data can be analyzed based on one or more learning models associated with selected conditions. The learning models can include an AI learning model that intelligently evaluates communications between players to identify bullying and harassing language based on input and history flags and natural language processing. For example, the learning model may determine a level of annoyance from other players based on behavior within the game environment, such as characters associated with other players leaving conversations or avoiding certain characters, and behaviors that form patterns associated with players communicating that may trigger such a reaction.

[0031] If appropriate, notifications may be generated and / or transmitted via notification server 180. The notifications may include one or more recommendations regarding gameplay control options based on extracted patterns that indicate one or more of the selected conditions are met by the activity data. For example, the gameplay control options may be associated with curtailing game time, requesting diversification of activity, or flagging in-game behavior that meets the selected conditions.

[0032] Additionally, the pattern server 170 can receive a content timestamp file 214 to determine which associated media files 212 may be appropriate to send to an administrative account as context for a flagged in-game behavior by a player in a gameplay session. For example, if a selected condition allows a player three chances before notifying the administrative user account, the administrative user account can receive recordings of media segments of the player's gameplay session engaging in suspicious behavior once the three chances are reached. The recorded media segments may be recorded based on one or more timestamps associated with activity data recorded by the content recorder 202. Another example may include the administrative user account setting a schedule or calendar to set different gameplay control modes for the user account based on a specified schedule or calendar. Gameplay patterns, which may be associated with the length of the gameplay session or the type of game played, may control the different gameplay control modes presented to the user account. For example, if the user account is playing more educational games or has increased social interaction, the specified schedule or calendar may reward more gameplay time or present more types of game titles to the user account.

[0033] Pattern server 170 can also block a user account from engaging in the current activity for a predetermined period of time. Additionally, pattern server 170 can provide suggestions for one or more alternative activities currently available to the user account. The available alternative activities can be configured by the administrative user account and / or platform server 120 to be limited to only approved interactive content titles.

[0034] 3 shows an example table of various objects and associated events. As shown in the example table 300 of FIG. 3, such object data (e.g., object file 216) can be associated with event information related to activity availability changes and can be related to other objects with associated object information. Media-object bindings can form telemetry between objects depicted in at least a portion of the streaming media and the streaming media. For example, such object data can be activity data 302, zone data 304, actor data 306, mechanics data 308, game media data 310, and other gameplay-related data.

[0035] Such object data (e.g., object files 216) may be categorized as in progress, open-ended, or in contention. Such activity data 302 may include optional properties, such as a longer description of the activity, an image associated with the activity, whether the activity is available to a player before launching the game, whether completion of the activity is required to complete the game, whether the activity can be played repeatedly in the game, and whether there are nested tasks or associated child activities. Such activity data 302 may include an activity availability change event that may indicate a list or array of activities currently available to the player. This may be used, for example, to determine which activities to display in a game plan.

[0036] Such zone data 304 may indicate an area of ​​the game world relative to a single coordinate system; a zone may have a 2D map associated with it and may be used to display locations within the zone. If applicable, each zone may include a zone ID and a short, localizable zone name. Such zone data 304 may be associated with a view projection matrix (4x4) to convert from 3D world coordinates to 2D map locations. Such zone data 304 may be associated with position change events that indicate updates to a player's current in-game location. Such position change events may be posted periodically or whenever a significant change occurs in the player's in-game location. The platform server 120 may store the latest values ​​in a "state." Such zone data 304 may include the x, y, and z positions of a player's avatar within the zone, as well as a, b, and c vectors that indicate the player's avatar's orientation or direction. Such zone data 304 can be associated with an activity start event and / or an activity end event, and in the case of an activity end event, a completion, failure, or abandon outcome can be associated with the activity (e.g., activity ID).

[0037] Such actor data 306 may be associated with entities related to in-game behavior, may control a player, or may control a game, and may change dynamically during gameplay. Such actor data 306 may include an actor's actor ID, a localizable name for the actor, an image of the actor, and / or a brief description of the actor. Such actor data 306 may be associated with an actor selection event that indicates a change in a player's selected actor(s). The selected actor(s) may represent the actors the player is controlling in the game and may be displayed in the player's profile and other spaces via the platform server 120. More than one actor may be selected at a time, and each game may replace the list of actors upon save data load.

[0038] Such mechanics data 308 may be associated with items, skills, or effects (e.g., bows, arrows, stealth attacks, fire damage) that can be used by a player or game to affect gameplay, and may exclude items that do not affect gameplay (e.g., collectibles). Such mechanics data 308 may include a mechanics ID for the mechanic, a short name for the mechanic, an image of the mechanic, and / or a short description of the mechanic. Such mechanics data 308 may be associated with a mechanics availability change event, which indicates that a mechanic available to a player has changed. Available may mean that the mechanic is available for use by a player in the game world, but may require the player to go through several steps to acquire the mechanic in their inventory (e.g., purchase it from a shop, acquire it from the world) before using it. Each game may replace the list of mechanics when loading a save.

[0039] Such mechanics data 308 may be associated with a mechanics inventory change event, indicating that a player's inventory has changed. Inventory may refer to mechanics that a player can immediately use without taking additional steps in the game before using the mechanics. Inventory information is used to gauge a player's readiness for various activities, which may be transferred to the platform server 120. A game may replace the list of mechanics inventory when loading a save. Cooldown mechanics may be considered part of the inventory. Mechanics counting any non-zero value (e.g., ammo, recovery points, etc.) may be treated as "in inventory." Inventory mechanics may be considered a subset of available mechanics.

[0040] Such mechanics data 308 may be associated with a mechanics-use event indicating that a mechanic was used by or against a player and may be used to display a mechanics usage amount within a UGC context. Such mechanics data 308 may include a list or array of mechanics used (e.g., flaming arrow, fire damage), or mechanics whether the initiator is a player, e.g., whether the mechanic was used by or against a player. Such mechanics data 308 may include an initiator actor ID, the initiator actor's current zone ID, and / or the initiator actor's current x, y, z position. Such mechanics data 308 may be associated with a mechanics-affect event indicating that a mechanic affected gameplay (e.g., an arrow hits an enemy) and may be used to display a mechanics image within a UGC context. The mechanics use event and the mechanics image event may not be linked. Such mechanics data 308 may include an initiator action ID, an initiator actor's current zone ID, the initiator actor's current x, y, z position, a target actor ID, the target actor's current zone ID, the target actor's current x, y, z position, and mitigation mechanics that may mitigate the initiator mechanic.

[0041] Such game media data 310 may include a game media ID for the game media, a localizable name for the game media, a media format (e.g., image, audio, video, text, etc.), a media category or type (cutscene, audio log, poster, developer commentary, etc.), a URL or server-provisioned media file, and / or whether the game media is associated with a particular activity. Such game media data 310 may be associated with a game media start event, which indicates that a particular piece of game media has just started in the game, and a game media end event, which indicates that a particular piece of game media has ended.

[0042] 4 is a flowchart of an example method 400 for evaluating a player's in-game actions by monitoring real-time gameplay object data, extracting patterns in the gameplay data, and generating notifications regarding control options based on the extracted patterns. While the example method 400 depicts a particular sequence of operations, this sequence may be modified without departing from the scope of the present disclosure. For example, some of the depicted operations may be performed in parallel or in a different sequence without substantially affecting the functionality of the method 400. In other examples, different components of an example device or system implementing the method 400 may perform functions substantially simultaneously or in a particular sequence.

[0043] According to some examples, the method includes storing information about the user account and the one or more selected conditions in a memory at step 405. For example, database 140 shown in FIG. 1 may store information about the user account and the one or more selected conditions in a memory.

[0044] According to some examples, the method includes, at step 410, monitoring activity data from multiple game play sessions of multiple interactive content titles, where the activity data is associated with a user account. For example, pattern server 170 shown in Figures 1 and 2 may monitor activity data from multiple game play sessions of multiple interactive content titles, where the activity data may be associated with a user account.

[0045] According to some examples, the method includes, at step 415, analyzing the activity data to extract patterns in game play by the user account associated with the selected condition. For example, pattern server 170 shown in FIGS. 1 and 2 may analyze the activity data to extract patterns in game play by the user account associated with the selected condition. In some examples, the one or more learning models include a model related to player behavior including bullying or harassing language. In some examples, analyzing the activity data is based on the one or more learning models associated with the selected condition. In some examples, the extracted patterns are associated with at least one of the length of the gameplay session, the type of game played, and in-game behavior.

[0046] According to some examples, the method includes, at step 420, generating a notification having one or more recommendations regarding gameplay control options based on the extracted pattern indicating that one or more of the selected conditions are met by the activity data. For example, pattern server 170 and / or notification server 180 shown in Figures 1 and 2 can generate the notification including one or more recommendations regarding gameplay control options based on the extracted pattern indicating that one or more of the selected conditions are met by the activity data. In some examples, the selected condition includes a schedule or calendar specified by an administrative user account, and further includes toggling between different gameplay control modes for the user account based on the specified schedule or calendar.

[0047] According to some examples, the method includes sending a notification to a user device associated with an administrative user account, the notification including recommendations regarding gameplay control options. For example, the platform server 120 shown in FIG. 1 may send a notification to a user device associated with the administrative user account, the notification including recommendations regarding gameplay control options. In some examples, the notification, recommendations, and gameplay control options may be accessible via a mobile application (e.g., a Playstation® app) on the user device. In some examples, the gameplay control options include at least one of throttling game time, requesting activity diversification, or flagging in-game behavior that meets selected conditions. In some examples, the selected conditions include a schedule or calendar specified by the administrative user account, and further include toggling between different gameplay control modes for the user account based on the specified schedule or calendar.

[0048] In some examples, the administrative user account selects to implement a gameplay control option that requests activity diversification. As a result, the method may include blocking the user account from engaging in the current activity for a predetermined period of time. For example, pattern server 170 shown in FIGS. 1 and 2 may block the user account from engaging in the current activity for a predetermined period of time. Further, the method may include suggesting one or more alternative activities currently available to the user account. For example, pattern server 170 shown in FIGS. 1 and 2 may suggest one or more alternative activities currently available to the user account.

[0049] According to some examples, the method includes recording media segments of game play by the user account based on one or more timestamps associated with the activity data that meets the selected condition. For example, the content recorder 202 using the content ring buffer 208 that generates the media files 212 and the content timestamp file 214 as shown in FIG. 2 may generate the recorded media segments of game play by the user account based on one or more timestamps associated with the activity data that meets the selected condition.

[0050] According to some examples, the method includes providing the recorded media segments to an administrative user account. For example, the pattern server 170 shown in Figures 1 and 2 can provide the recorded media segments to the administrative user account.

[0051] Figure 5 is a block diagram of an exemplary electronic entertainment system that may be used with embodiments of the present invention. The entertainment system 500 of Figure 5 includes a main memory 505, a central processing unit (CPU) 510, a vector unit 515, a graphics processing unit 520, an input / output (I / O) processor 525, an I / O processor memory 530, a peripheral interface 535, a memory card 540, a universal serial bus (USB) interface 545, and a communication network interface 550. The entertainment system 500 further includes an operating system read-only memory (OS ROM) 555, an audio processing unit 560, an optical disc control unit 570, and a hard disk drive 565, which are connected to the I / O processor 525 via a bus 575.

[0052] Entertainment system 500 may be an electronic game console. Alternatively, entertainment system 500 may be implemented as a general-purpose computer, a set-top box, a handheld gaming device, a tablet computing device, a virtual reality device, an augmented reality device, or a mobile computing device or phone. Entertainment systems may include more or fewer operating components depending on the particular form factor, purpose, or design.

[0053] The CPU 510, vector unit 515, graphics processing unit 520, and I / O processor 525 of FIG. 5 communicate via a system bus 585. Additionally, the CPU 510 of FIG. 5 may communicate with main memory 505 via a dedicated bus 580, and the vector unit 515 and graphics processing unit 520 may communicate via a dedicated bus 590. The CPU 510 of FIG. 5 executes programs stored in the OS ROM 555 and the main memory 505. The main memory 505 of FIG. 5 may include pre-stored programs and programs transferred via the I / O processor 525 from a CD-ROM, DVD-ROM, or other optical disk (not shown) using the optical disk control unit 570. The I / O processor 525 of FIG. 5 may also enable the introduction of content transferred via wireless or other communication networks (e.g., 4G, LTE, 1G, etc.). The I / O processor 525 of FIG. 5 primarily controls the exchange of data between various devices of the entertainment system 500, including the CPU 510, the vector unit 515, the graphics processing unit 520, and the peripheral interface 535.

[0054] The graphics processing unit 520 of Figure 5 executes graphics instructions received from the CPU 510 and the vector unit 515 to generate images for display on a display device (not shown). For example, the vector unit 515 of Figure 5 may convert an object from three-dimensional coordinates to two-dimensional coordinates and send the two-dimensional coordinates to the graphics processing unit 520. Additionally, the audio processing unit 560 executes instructions to generate audio signals, which are output to an audio device such as a speaker (not shown). Other devices may be connected to the entertainment system 500 via the USB interface 545 and a communications network interface 550, such as a wireless transceiver, which may be incorporated within the system 500 or as part of some other component, such as a processor.

[0055] 5 provides instructions to CPU 510 via peripheral interface 535, which enables the use of a variety of different available peripheral devices (e.g., controllers) known in the art. For example, the user may instruct CPU 510 to store certain game information on memory card 540 or other non-transitory computer-readable storage medium, or to instruct a game avatar to perform some specified action.

[0056] The present disclosure relates to applications that may be operable by a variety of end-user devices. For example, the end-user device may be a personal computer, a home entertainment system (e.g., Sony PlayStation2® or Sony PlayStation3® or Sony PlayStation4® or Sony PlayStation5®), a portable gaming device (e.g., Sony PSP® or Sony Vita®), or a home entertainment system from a different, but subordinate, manufacturer. It is fully contemplated that the methods described herein are operable on a variety of devices. Additionally, aspects of the present disclosure may be implemented in a cross-title neutral manner and / or may be utilized across a variety of titles from a variety of publishers.

[0057] Aspects of the present disclosure may be implemented in applications that may be operable using a variety of devices. A non-transitory computer-readable storage medium refers to any medium or media that participates in providing instructions to a central processing unit (CPU) for execution. Such media can take many forms, including but not limited to non-volatile and volatile media, such as optical or magnetic disks and dynamic memory, respectively. Common forms of non-transitory computer-readable media include, for example, floppy disks, flexible disks, hard disks, magnetic tape, any other magnetic media, CD-ROM disks, digital video disks (DVDs), any other optical media, RAM, PROM, EPROM, FLASHEPROM, and any other memory chips or cartridges.

[0058] Various forms of transmission media may be involved in carrying one or more sequences of one or more instructions to the CPU for execution. A bus carries the data to system RAM, and the CPU retrieves and executes the instructions from the system RAM. The instructions received by the system RAM may optionally be stored on a fixed disk either before or after execution by the CPU. Various forms of storage as well as other network interfaces and network topologies that implement the same may be implemented as well.

[0059] In some aspects of the present disclosure, computer-readable storage devices, media, and memories may include cables or wireless signals containing bitstreams, etc. However, when referred to, non-transitory computer-readable storage media explicitly excludes media such as energy, carrier signals, electromagnetic waves, and the signals themselves.

[0060] The above detailed description of the present technology has been presented for purposes of illustration and description. It is not intended to be exhaustive or to limit the present technology to the precise form disclosed. Many modifications and variations are possible in light of the above teachings. The described embodiments were selected to best explain the principles of the technology, its practical application, and to enable those skilled in the art to utilize the technology in various embodiments and with various modifications suitable for the particular uses contemplated. It is intended that the scope of the present technology be defined by the claims.

Claims

1. 1. A method for developing and customizing a learning model for gameplay, comprising: storing in memory information regarding the user account and conditions relating to one or more selected current game play sessions; monitoring activity data from multiple game play sessions of multiple interactive content titles, the activity data being associated with the user account; analyzing the activity data to extract one or more gameplay patterns exhibited by the user account associated with the selected condition, wherein analyzing the activity data is based on one or more learning models associated with the selected condition; generating a notification, including one or more gameplay control options, based on the learning model associated with the extracted patterns indicating that one or more of the selected conditions are satisfied by the activity data; and determining, from the activity data, behavioral patterns that diverge from the one or more learned models; customizing the one or more learning models to include the divergent behavioral patterns; refining the one or more learning models based on one or more selected gameplay control options and one or more outcomes of each gameplay control option; Including, the one or more learning models learn player behavior patterns; The method, wherein the customization is associated with the user account.

2. The method of claim 1 , wherein the one or more learning models relate to player behaviors that include bullying or harassing language.

3. The method of claim 1 , wherein the extracted gameplay patterns are associated with at least one of a length of a gameplay session, a type of game played, and an in-game behavior.

4. sending the notification to a user device associated with an administrative user account; The method of claim 1 , wherein the gameplay control options are selectable via a mobile application on the user device.

5. The method of claim 1 , wherein the gameplay control options include at least one of throttling game time, requesting diversification of activity, or flagging in-game behavior that meets the selected conditions.

6. the selected conditions include a schedule or calendar specified by an administrative user account; 2. The method of claim 1, further comprising toggling between different gameplay control modes for the user account with the one or more selected gameplay control options based on the specified schedule or calendar.

7. recording media segments of game play by the user account based on one or more timestamps associated with the activity data that meet the selected condition; providing the recorded media segments to an administrative user account; The method of claim 1 further comprising:

8. the administrative user account selects to implement gameplay control options requiring activity diversification; blocking the user account from engaging in current activity for a predetermined period of time; The method of claim 7 , further comprising: suggesting one or more alternative activities currently available to the user account.

9. At least one of the learning models is associated with the user account; The method of claim 1 , further comprising customizing the at least one learning model based on the extracted patterns.

10. 1. A system for developing and customizing learning models for game play, comprising: a memory for storing information regarding a user account and conditions relating to one or more selected current game play sessions; a communications interface for receiving activity data transmitted over a communications network from multiple game play sessions of multiple interactive content titles, the activity data being associated with the user account; and a processor for executing instructions stored in said memory; Including, The processor: monitoring the activity data; analyzing the activity data to extract one or more gameplay patterns exhibited by the user account associated with the selected condition, wherein analyzing the activity data is based on one or more learning models associated with the selected condition; generating a notification, including one or more gameplay control options, based on the learning model associated with the extracted patterns indicating that one or more of the selected conditions are satisfied by the activity data; and determining, from the activity data, behavioral patterns that diverge from the one or more learned models; customizing the one or more learning models to include the divergent behavioral patterns; refining the one or more learning models based on one or more selected gameplay control options and one or more outcomes of each gameplay control option; Executing the instructions to the one or more learning models learn player behavior patterns; The customization is associated with the user account. system.

11. The system of claim 10 , wherein the one or more learning models relate to player behaviors that include bullying or harassing language.

12. The system of claim 10 , wherein the extracted patterns are associated with at least one of a length of a gameplay session, a type of game played, and an in-game behavior.

13. the communication interface further transmits the notification to a user device associated with an administrative user account via the communication network; The system of claim 10 , wherein the gameplay control options are selectable via a mobile application on the user device.

14. 11. The system of claim 10, wherein the gameplay control options include at least one of throttling game time, requesting diversification of activity, or flagging in-game behavior that meets the selected condition.

15. the selected conditions include a schedule or calendar specified by an administrative user account; 11. The system of claim 10, further comprising toggling between different gameplay control modes for the user account with the one or more selected gameplay control options based on the specified schedule or calendar.

16. The processor: recording media segments of game play by the user account based on one or more timestamps associated with the activity data that meet the selected condition; providing the recorded media segments to an administrative user account; The system of claim 10 , further comprising instructions to:

17. the administrative user account selects to implement gameplay control options requiring activity diversification; The processor: blocking the user account from engaging in current activity for a predetermined period of time; suggesting one or more alternative activities currently available to the user account; and 20. The system of claim 16, further comprising instructions to:

18. At least one of the learning models is associated with the user account; The system of claim 10 , wherein the processor executes further instructions for customizing the at least one learning model based on the extracted patterns.

19. A non-transitory computer-readable medium containing instructions, The instructions, when executed by a computing system, cause the computing system to: storing in memory information regarding the user account and conditions relating to one or more selected current game play sessions; monitoring activity data from multiple game play sessions of multiple interactive content titles, the activity data being associated with the user account; analyzing the activity data to extract one or more gameplay patterns exhibited by the user account associated with the selected condition, wherein analyzing the activity data is based on one or more learning models associated with the selected condition; generating a notification, including one or more gameplay control options, based on the learning model associated with the extracted patterns indicating that one or more of the selected conditions are satisfied by the activity data; and determining, from the activity data, behavioral patterns that diverge from the one or more learned models; customizing the one or more learning models to include the divergent behavioral patterns; and refining the one or more learning models based on one or more selected gameplay control options and one or more outcomes of each gameplay control option. Let them do this, the one or more learning models learn player behavior patterns; The customization is associated with the user account. The non-transitory computer-readable medium.

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