Description:

BACKGROUND

Software developers typically desire for their software to engage users for as long as possible. The longer a user is engaged with the software, the more likely that the software will be successful. The relationship between the length of engagement of the user and the success of the software is particularly true with respect to video games. The longer a user plays a particular video game, the more likely that the user enjoys the game and thus, the more likely the user will continue to play the game.

Often, games that are too difficult or too easy will result in less enjoyment for a user. Consequently, the user is likely to play the game less. Thus, one of the challenges of game development is to design a game with a difficulty level that is most likely to keep a user engaged for a longer period of time.

SUMMARY OF EMBODIMENTS

The systems, methods and devices of this disclosure each have several innovative aspects, no single one of which is solely responsible for the all of the desirable attributes disclosed herein. Details of one or more implementations of the subject matter described in this specification are set forth in the accompanying drawings and the description below.

In certain embodiments, a computer-implemented method is disclosed that may be implemented by an interactive computing system configured with specific computer-executable instructions to at least determine a user identifier of a user who is playing a video game on a user computing device. Further, the method may include accessing a set of input data associated with the user based at least in part on the user identifier of the user. The set of input data may comprise user interaction data associated with the user's interaction with the video game. In addition, based at least in part on the set of input data, the method may include determining a predicted churn rate for the user. The predicted churn rate may correspond to a probability that the user ceases to play the video game. Further, based at least in part on the predicted churn rate for the user, the method may include selecting a seed value for a knob associated with the video game. The knob may include a variable that when adjusted causes a modification to a state of the video game. Moreover, the method may include modifying execution of the video game by adjusting the knob based at least in part on the seed value.

In some implementations, modifying the execution of the video game comprises adjusting a difficulty of the video game. Further, the user interaction data may include recent user interaction data that is more recent than a threshold age time period and historical user interaction data that less recent than a threshold age time period. In some cases, the recent user interaction data is weighted more heavily than the historical user interaction data. Further, in some cases, the threshold age time period corresponds to a number of play sessions of the video game by the user.

For some embodiments, determining the predicted churn rate comprises providing the set of input data to a parameter function. The parameter function may be generated based at least in part on a machine learning algorithm. Further, the method may include determining the predicted churn rate based at least in part on an output of the parameter function. Moreover, generating the parameter function may include at least accessing a second set of input data. The second set of input data may be associated with a plurality of users who play the video game and may include data indicating churn rates for past game play for the plurality of users. In addition, the method may include using the machine learning algorithm to determine the parameter function based at least in part on the set of input data. Moreover, the method may include associating a penalty with the parameter function based at least in part on one or more of the following: a number of variables included in the parameter function, a complexity of a mathematical algorithm associated with the parameter function, or an accuracy of an output of the parameter function compared to the output data. Additionally, the method may include selecting the parameter function from a plurality of parameter functions based at least in part on a penalty value associated with at least some of the parameter functions from the plurality of parameter functions.

In some implementations, based at least in part on the set of input data, the method includes determining a reason for the predicted churn rate. In addition, based at least in part on the reason for the predicted churn rate, the method may include selecting the seed value for the knob associated with the video game. Further, modifying the execution of the video game may include providing the seed value to the user computing device.

In certain embodiments, a system comprising an electronic data store configured to store user interaction data with respect to a video game is disclosed. The system may further include a hardware processor in communication with the electronic data store. The hardware processor may be configured to execute specific computer-executable instructions to at least determine a user identifier of a user who is playing a video game and access a set of input data associated with the user based at least in part on the user identifier of the user. The set of input data may comprise user interaction data associated with the user's interaction with the video game. Further, based at least in part on the set of input data, the system can determine a retention probability associated with the probability that the user ceases to play the video game. Moreover, based at least in part on the retention probability for the user, the system can identify an adjustment value to a variable in the video game. The variable may be associated with a level of difficulty of the video game. In addition, the system may modify execution of the video game based at least in part on the adjustment value.

With some implementations, the modified execution of the video game is undetectable by the user. Moreover, in some cases, at least the accessing the set of input data, the determining the retention probability, the identifying the adjustment value, and the modifying the execution of the video game is repeated in response to a trigger event occurring during a play session of the video game by the user.

In addition, determining the retention probability may include providing the set of input data to a prediction model. The prediction model may be generated by a machine learning system. Further, the system can determine the retention probability based at least in part on an output of the prediction model. Moreover, in some cases, the hardware processor is further configured to generate the prediction model by executing specific computer-executable instructions to at least access a second set of input data. The second set of input data may be associated with a plurality of users who play the video game and may include data associated with a retention rate for the plurality of users. Further, the system may use the machine learning system to determine the prediction model based at least in part on the set of input data and the retention rate. In some cases, at least some prediction models from a plurality of prediction models are associated with a penalty. At least one prediction model from the plurality of prediction models may be associated with a different penalty than at least one other prediction model, and the hardware processor may be further configured to execute specific computer-executable instructions to at least select the prediction model from the plurality of prediction models based at least in part on the penalty associated with the plurality of prediction models.

Certain embodiments disclosed herein relate to a non-transitory computer-readable storage medium storing computer executable instructions that, when executed by one or more computing devices, configure the one or more computing devices to perform operations comprising accessing a set of input data associated with a user. The set of input data may include user interaction data associated with the user's interaction with the video game. Further, based at least in part on the set of input data, the operations may include determining a retention probability associated with the probability that the user ceases to play the video game. Moreover, based at least in part on the retention probability for the user, the operations may include identifying a difficulty level for the video game. In addition, based at least in part on the difficulty level for the video game, the operations may include identifying one or more seed values associated with the difficulty level.

In certain embodiments, the operations further comprise modifying the difficulty level of the video game by selecting a seed value from the one or more seed values to use during execution of the video game. Moreover, the operations may further comprise determining the retention probability by at least providing the set of input data to a prediction model. The prediction model may be generated by a machine learning system. Furthermore, the operations may include determining the retention probability based at least in part on an output of the prediction model.

Although certain embodiments and examples are disclosed herein, inventive subject matter extends beyond the examples in the specifically disclosed embodiments to other alternative embodiments and/or uses, and to modifications and equivalents thereof.

BRIEF DESCRIPTION OF THE DRAWINGS

Throughout the drawings, reference numbers are re-used to indicate correspondence between referenced elements. The drawings are provided to illustrate embodiments of the subject matter described herein and not to limit the scope thereof.

FIG. 1A illustrates an embodiment of a networked computing environment that can implement one or more embodiments of a dynamic difficulty adjustment system.

FIG. 1B illustrates an embodiment of a model generation system of FIG. 1A.

FIG. 1C illustrates an embodiment of a retention analysis system of FIG. 1A.

FIG. 2 presents a flowchart of an embodiment of a machine learning process.

FIG. 3 presents a flowchart of an embodiment of a difficulty based seed selection process.

FIG. 4 presents a flowchart of an embodiment of a cluster creation process.

FIG. 5 presents a flowchart of an embodiment of a cluster assignment process for a user.

FIG. 6 presents a flowchart of an embodiment of a difficulty setting process for an application.

FIG. 7 presents a flowchart of an embodiment of a seed evaluation process.

FIG. 8 illustrates an embodiment of a user computing system.

FIG. 9 illustrates an embodiment of a hardware configuration for the user computing system of FIG. 8.

DETAILED DESCRIPTION OF EMBODIMENTS

Introduction

It is generally desirable for a video game to appeal to a large number of users. However, different users have different levels of skill and/or abilities when it comes to playing video games or video games of a particular genre or type. Further, different users have different desires with respect to how challenging a video game is to play. For example, some users prefer video games that are relatively challenging. These types of users may tend to be more engaged by a video game that may require a lot of practice to master and typically may not mind repeating the same portion of the video game numerous times before being successful. In contrast, some users prefer video games that are relatively easy. These types of users may tend to be more engaged by a video game where obstacles are easily overcome and the users rarely are required to repeat a portion of the video game to be successful.

One solution to the above challenges is for video game developers to incorporate multiple static difficulty levels within a particular video game. However, there are generally a limited number of difficulty levels that a developer can add due, for example, to storage constraints, development time constraints, and the challenge of predicting a large number of difficulty levels for a large number of user preferences. Further, these difficulty levels are generally coarse because, for example, the difficulty levels are typically created by adjusting a defined set of adjustable elements (which may sometimes be referred to as “knobs” herein). Thus, because a particular user may find a particular aspect of a video game challenging, but another aspect of the video game not challenging, selecting a static difficulty level may result in an inconsistent challenge throughout the video game for the particular user. Moreover, this problem of static difficulty levels is exacerbated by the fact that another user may find different aspects of the video game challenging or easy.

Another solution that may be used in some types of competitive video games, such as racing games, is to vary the ability of the user or the user's competitor based on the relationship between the user and the user's competitor. For example, supposing that the video game is a racing game, the user's car may be made faster when the user is doing poorly and may be made slower when the user is doing well. This solution may result in what is sometimes referred to as a “rubber band effect.” This solution is often noticeable by the user because the user's vehicle will behave inconsistently based on the location of the vehicle with respect to the user's competitor. As a result, instead of appealing to a user, the user may be driven away. Further, this solution can be challenging to implement with some types of video games that do not measure a user against a specific competitor.

Embodiments presented herein include a system and method for performing dynamic difficulty adjustment. Further, embodiments disclosed herein perform dynamic difficulty adjustment using processes that are not detectable or are more difficult to detect by users compared to static and/or existing difficulty adjustment processes. In some embodiments, historical user information is fed into a machine learning system to generate a prediction model that predicts an expected duration of game play, such as for example, an expected churn rate, a retention rate, the length of time a user is expected to play the game, or an indication of the user's expected game play time relative to a historical set of users who have previously played the game. Before or during game play, the prediction model is applied to information about the user to predict the user's expected duration of game play. Based on the expected duration, the system may then utilize a mapping data repository to determine how to dynamically adjust the difficulty of the game, such as, for example, changing the values of one or more knobs to make portions of the game less difficult.

In certain embodiments, systems disclosed herein monitor user activity with respect to one or more video games to determine a user's preferences regarding game difficulty and the user's skill level with respect to playing the video games. This information may be determined based at least in part on factors that are associated with a user's engagement level. For example, a user who plays a video game for an above average length of time and who spends money while playing the video game may have a higher level of engagement than a user who plays a video game for a short period of time. As another example, a user who plays a video game for a short period of time, but who plays an above average number of play sessions may be associated with a high level of engagement, but may be classified differently than the user of the previous example.

Further, in certain embodiments described herein, users may be grouped with other users who have similar preferences into clusters. The users may be grouped based on user behavior with respect to challenges or obstacles presented in the video game. Each of the groups or clusters of users may be associated with difficulty preferences or settings for one or more video games. Using this information, one or more aspects of the video game can be dynamically adjusted to present a user of the video game with a particular difficulty level that is most likely to engage the user, or more likely to engage the user than a static set of difficulty levels. As noted above and further herein, additional or alternative embodiments described herein may determine one or more seeds or knob values for adjusting the difficulty of the video game by using one or more parameter functions or prediction models. In some cases, the prediction models may be combined with clustering. In other embodiments, the prediction models may be used instead of clustering. It should be understood that clustering is one method that may be used with embodiments of the present disclosure. However, the present disclosure is not limited to the use of clustering and certain embodiments presented herein may omit the user of clustering. For example, certain embodiments disclosed herein may use a regression model to fit historical user data without the use of clustering. After obtaining an initial version of the regression model, it can be applied to additional players to facilitate the dynamic difficulty analysis and /or adjustment.

Moreover, in certain embodiments described herein, the user's activity with respect to the video game can be monitored or reviewed to determine the user's behavior with respect to the video game. This monitoring may occur substantially in real-time, or at some period of time after the user has completed a play session. The play session may be a period of time when the user plays the video game and/or a particular attempt to play the game that ends with the user completing or failing to complete the video game or a portion thereof. For example, one play session may begin with the user initiating a new instance of game play and end with the user running out of lives in the game. As another example, one play session may begin with the user initiating the video game and ending when the user exist the video game.

In some cases, monitoring the user's behavior with respect to the video game may enable a determination of the user's skill level and desired level of challenge. Based at least in part on this information, the difficulty of the video game, or portions of the video game, can be adjusted from the initial difficulty level determined based on the associated user cluster for the user.

Advantageously, in certain embodiments, by grouping users with similar characteristics with respect to playing video games and by adjusting difficulty levels based on individual user actions with respect to the video games, a more fine-grained management of difficulty level is possible compared to systems that do not monitor user behavior to determine a difficulty level. Further, although this disclosure focuses on adjusting settings of a video game that modify the difficulty level or challenge presented by the video game, this disclosure is not limited as such. Embodiments of the present disclosure can be used to modify various aspects of game state of a video game, which may or may not affect the difficulty level of the video game. For example, in a game where weapons are randomly dropped, if it is determined that a user prefers to play a game using a particular in-game weapon, the game may be adjusted to present the preferred weapon to the user more frequently. In some cases, such as when all weapons are evenly balanced, the type of weapon dropped may not impact the difficulty of the video game and thus, such an adjustment may be based on user play styles or preferences rather than difficulty level preferences. Some other non-limiting examples of features of the video game that can be modified, which may or may not be detectable by the user can include providing extra speed to an in-game character, improving throwing accuracy of an in-game character, improving the distance or height that the in-game character can jump, adjusting the responsiveness of controls, and the like. In some cases, the adjustments may additionally or alternatively include reducing the ability of an in-game character rather than improving the ability of the in-game character. For example, the in-game character may be made faster, but have less shooting accuracy.

Further, embodiments of the systems presented herein can adjust the difficulty level of the video game substantially in real time based at least in part on the user's skill level and whether the user is successfully completing challenges within the video game. However, the present disclosure is not limited as such. For example, the difficulty level of the video game may be adjusted based at least in part on user preferences, which may or may not correspond to a user's ability. For example, some users may prefer to play video games at the most difficult settings regardless of whether they are successful at completing the video game or objectives therein. By monitoring user actions with respect to playing a video game, the difficulty level of the video game can be adjusted to match a particular user's preferences and/or skill level.

To simplify discussion, the present disclosure is primarily described with respect to a video game. However, the present disclosure is not limited as such may be applied to other types of applications. For example, embodiments disclosed herein may be applied to educational applications or other applications that may be modified based on a history of user interactivity with the application. Further, the present disclosure is not limited with respect to the type of video game. The use of the term “video game” herein includes all types of games, including, but not limited to web-based games, console games, personal computer (PC) games, computer games, games for mobile devices (for example, smartphones, portable consoles, gaming machines, or wearable devices, such as virtual reality glasses, augmented reality glasses, or smart watches), or virtual reality games, as well as other types of games.

Example Networked Computing Environment

FIG. 1A illustrates an embodiment of a networked computing environment 100 that can implement one or more embodiments of a dynamic difficulty adjustment system. The networked computing environment 100 includes a user computing system 110 that can communicate with an interactive computing system 130 via a network 104. Further, the networked computing environment 100 may include a number of additional user computing systems 102. At least some of the user computing systems 102 may be configured the same as or similarly to the user computing system 110.

User computing system 110 may include or host a video game 112. In some cases, the video game 112 may execute entirely on the user computing system 110. In other cases, the video game 112 may execute at least partially on the user computing system 110 and at least partially on the interactive computing system 130. In some cases, the video game 112 may execute entirely on the interactive computing system 130, but a user may interact with the video game 112 via the user computing system 110. For example, the game may be a massively multiplayer online role-playing game (MMORPG) that includes a client portion executed by the user computing system 110 and a server portion executed by one or more application host systems (not shown) that may be included as part of the interactive computing system 130. As another example, the video game 112 may be an adventure game played on the user computing system 110 without interacting with the interactive computing system 130.

The video game 112 may include a number of knobs 114 that modify or affect the state of the video game 112. Typically, the knobs 114 are variables that affect the execution or operation of the video game 112. In some cases, the knobs 114 are state variables that directly modify the execution of the video game 112. In other cases, the knobs 114 are seeds or seed variables that may alter the probability of an occurrence within the video game 112 or a random (or pseudorandom) configuration or event within the video game 112. For example, one seed may correspond to and impact the generation of a level layout in the video game 112. As another example, one seed may correspond to and impact the number of occurrences of item drops or the types of items dropped as a user plays the video game 112. In some cases, the seed value is a value that initializes or influences a random or pseudorandom number generator. In some such cases, the random number generated based on the seed value may be utilized by one or more functions of the video game 112 to influence the operation of the video game 112. In some cases, the seed variables may be referred to as levers, and the seed variables are non-limiting examples of various types of knobs. It should be understood that the knobs 114, sometimes referred to as levers, are not limited to seeds, but can include any type of variable that may modify execution of the video game 112. The system may modify execution of the video game by adjusting any type of knob or lever that can change the video game 112 and may conduct a knob-level analysis of the churn rate. The system is not limited to adjusting the knob or lever based on a seed value.

Generally, the knobs 114 are variables that relate to a difficulty level of the video game 112. It should be understood that the knobs 114 typically include a subset of variables that modify the operation of video game 112 and that the video game 112 may include other variables not involved in the setting of the difficulty level of video game 112 and/or not available for modification. Further, the knobs 114 may include variables that modify the video game 112 in a manner that is not perceivable by a user or is difficult to perceive by the user. In some cases, whether or not the modification to the video game 112 is perceivable by the user may depend on the specific video game. For example, suppose that one knob 114 relates to the amount of life that an enemy in the video game 112 has. In some cases, modifying the value assigned to the knob 114 may be detectable by a user because, for example, the health of the enemy is numerically presented to the user. In such cases, the health of the enemy may remain unmodified in the difficulty level of video game 112, but the difficulty level of the video game 112 may be modified via a different knob 114. However, in some cases, modifying the health of the enemy may not be detectable by the user because, for example, the health of the enemy is not presented to the user and the amount of hits required to defeat the enemy varies within a range making it difficult for the user to determine that the health of the enemy may have been modified from one encounter with the enemy compared to another encounter with the enemy (for example, from one play through of the video game 112 compared to another play through of the video game 112).

In some cases, the video game 112 may include a user interaction history repository 116. The user interaction history repository 116 may store data or information relating to the user's interaction with the video game 112. This user interaction information or data may include any type of information that can be used to determine a user's level of engagement with the video game 112 and/or how difficult the video game 112 is for the user. For example, some non-limiting examples of the user interaction information may include information relating to actions taken by the user within the video game 112; a measure of the user's progress within the video game 112; whether the user was successful at performing specific actions within the video game 112 or completing particular objectives within the video game 112; how long it took the user to complete the particular objectives; how many attempts it took the user to complete the particular objectives; how much money the user spent with respect to the video game 112, which may include one or both of the amount of money spent to obtain access to the video game 112 and the amount of money spent with respect to the video game 112 exclusive of money spent to obtain access to the video game 112; how frequently the user accesses the video game 112; how long the user plays the video game 112; and the like. The user computing system 110 may share the user interaction information with the interactive computing system 130 via the network 104. In some embodiments, some or all of the user interaction information is not stored by the video game 112, but is instead provided to or determined by another portion of the user computing system 110 external to the video game 112 and/or by the interactive computing system 130. Thus, in some embodiments, the user interaction history repository 116 may be optional or omitted.

The user computing system 110 may include hardware and software components for establishing communications over a communication network 104. For example, the user computing system 110 may be equipped with networking equipment and network software applications (for example, a web browser) that facilitate communications via a network (for example, the Internet) or an intranet. The user computing system 110 may have varied local computing resources, such as central processing units and architectures, memory, mass storage, graphics processing units, communication network availability and bandwidth, and so forth. Further, the user computing system 110 may include any type of computing system. For example, the user computing system 110 may include any type of computing device(s), such as desktops, laptops, video game platforms, television set-top boxes, televisions (for example, Internet TVs), network-enabled kiosks, car-console devices, computerized appliances, wearable devices (for example, smart watches and glasses with computing functionality), and wireless mobile devices (for example, smart phones, PDAs, tablets, or the like), to name a few. In some embodiments, the user computing system 110 may include one or more of the embodiments described below with respect to FIG. 8 and FIG. 9.

As previously discussed, it may be desirable to maintain or increase a user's level of engagement with the video game 112. One solution for maintaining or increasing the user's level of engagement with the video game 112 includes setting or adjusting a difficulty level of the video game 112 based at least in part on a user's skill and a user's desired level of challenge when playing the video game 112. The interactive computing system 130 can determine a level of difficulty for the video game 112 for a particular user and can modify the difficulty of the video game 112 based on the determination. This determination, as will be described in more detail below, of the difficulty level may be made based at least in part on user interaction information with respect to the video game 112 and/or other video games accessible by the user.

Interactive computing system 130 may include a number of systems or subsystems for facilitating the determination of the difficulty level for the video game 112 for a particular user and the modification of the difficulty level based on the determination. These systems or subsystems can include a difficulty configuration system 132, a user clustering system 134, a seed evaluation system 136, a user data repository 138, a retention analysis system 140, a model generation system 146, and a mapping data repository 144. Each of these systems may be implemented in hardware, and software, or a combination of hardware and software. Further, each of these systems may be implemented in a single computing system comprising computer hardware or in one or more separate or distributed computing systems. Moreover, while these systems are shown in FIG. 1A to be stored or executed on the interactive computing system 130, it is recognized that in some embodiments, part or all of these systems can be stored and executed on the user computing system 110.

In some embodiments, when the user computing system 110 is connected or in communication with the interactive computing system 130 via the network 104, the interactive computing system 130 may perform the processes described herein. However, in some cases where the user computing system 110 and the interactive computing system 130 are not in communication, the user computing system 110 may perform certain processes described herein using recent game play of the user that may be stored in the user interaction history repository 116.

The difficulty configuration system 132 sets or adjusts the difficulty level of a video game 112. In some cases, the difficulty configuration system 132 may set or adjust the difficulty level of the video game 112 by providing or adjusting values for one or more of the knobs 114, which are then fed into or provided to the video game 112. In some cases, the difficulty configuration system 132 sets or adjusts every available knob 114 each time a setting or an adjustment of a difficulty level is made. In other cases, the difficulty configuration system 132 may set or adjust less than every available knob 114 when setting or adjusting a difficulty level of the video game 112.

In some cases, the difficulty configuration system 130 may modify the difficulty of a portion of the video game 112 without constraint. However, in some other cases, the difficulty configuration system 132 may modify the difficulty of a portion of the video came 112 within a set of constraints that may be specified by a developer, a set of rules, or that are relative to other portions of the video game 112. For example, in some cases, the difficulty configuration system 132 may reduce the difficulty level of a particular portion of the video game 112 to no more than a difficulty threshold specified by a proceeding portion of the video game 112. Thus, in some cases, a reduction in the difficulty level of the particular portion of the video game 112 will not result in the particular portion of the video game 112 becoming easier than the proceeding portion 112. The difficulty adjustment may be turned off completely in some situations, such as during a tournament.

The model generation system 146 can use one or more machine learning algorithms to generate one or more prediction models or parameter functions. One or more of these prediction models may be used to determine an expected value or occurrence based on a set of inputs. For example, a prediction model can be used to determine an expected churn rate or a probability that a user will cease playing the video game 112 based on one or more inputs to the prediction model, such as, for example, historical user interaction information for a user. As another example, a prediction model can be used to determine an expected amount of money spent by the user on purchasing in-game items for the video game based on one or more inputs to the prediction model. In some cases, the prediction model may be termed a prediction model because, for example, the output may be or may be related to a prediction of an action or event, such as the prediction the user continues to play the video game 112. A number of different types of algorithms may be used by the model generation system 146. For example, certain embodiments herein may use a logistical regression algorithm. However, other algorithms are possible, such as a linear regression algorithm, a discrete choice algorithm, or a generalized linear algorithm.

The machine learning algorithms can be configured to adaptively develop and update the models over time based on new input received by the model generation system 146. For example, the models can be regenerated on a periodic basis as new user information is available to help keep the predictions in the model more accurate as the user information evolves over time. The model generation system 146 is described in more detail herein. After a model is generated, it can be provided to the retention analysis system 140.

Some non-limiting examples of machine learning algorithms that can be used to generate and update the parameter functions or prediction models can include supervised and non-supervised machine learning algorithms, including regression algorithms (such as, for example, Ordinary Least Squares Regression), instance-based algorithms (such as, for example, Learning Vector Quantization), decision tree algorithms (such as, for example, classification and regression trees), Bayesian algorithms (such as, for example, Naive Bayes), clustering algorithms (such as, for example, k-means clustering), association rule learning algorithms (such as, for example, Apriori algorithms), artificial neural network algorithms (such as, for example, Perceptron), deep learning algorithms (such as, for example, Deep Boltzmann Machine), dimensionality reduction algorithms (such as, for example, Principal Component Analysis), ensemble algorithms (such as, for example, Stacked Generalization), and/or other machine learning algorithms.

The retention analysis system 140 can include one or more systems for determining a predicted churn or retention rate for a user based on the application of user interaction data for the user to a prediction model generated by the model generation system 140. In some cases, the difficulty configuration system 132 may use the predicted retention rate determined by the retention analysis system 140 to determine an adjustment to the difficulty of the video game 112. In some embodiments the adjustments to the difficulty are determined using data in a mapping data repository 144 to determine which features of the game to change and how to change the features.

The mapping data repository 144 can include one or more mappings between the output of a prediction model and a difficulty level of the video game 112, which may be used by, for example, the difficulty configuration system 132 to determine how to modify the video game 112 to adjust the difficulty of the video game 112. For example, if the user's predicted level of churn is “high,” then the mapping data repository 144 may include a mapping between “high” and data to adjust the game to be “easy” such that one or more knobs or seeds associated with the game are set to values that make the game easier to play. As another example, if the user's predicted level of churn is 65%, then the mapping data repository 144 may include a mapping between “60-70%” to adjust a specific subset of the one or more knobs or seeds associated with the game to sets of values that make the game less difficult to play, but not the easiest to play. Alternatively, or in addition, the mapping may be between the output of the parameter function and one or more values for one or more knobs or seeds that can be used to modify the difficulty of the video game 112.

Further, generation and application of the parameter functions and their use in adjusting the difficulty level of the video game 112 will be described in further detail below with respect to the retention analysis system 140. In certain embodiments, the difficulty configuration system 132 may be or may include the model generation system 146. Moreover, in some cases, the difficulty configuration system 132 may be or may include the retention analysis system 140. As stated above, one non-limiting example of a machine learning algorithm that can be used herein is a clustering algorithm. The user clustering system 134 may facilitate execution of the clustering algorithm. The user clustering system 134 groups or divides a set of users into groups based at least in part on each user's skill level with respect to the video game 112 or other video games accessed by the users. Alternatively, or in addition, the user clustering system 134 may group or cluster the users based on one or more criteria associated with one or more of the users that impacts the users' engagement level with the video game 112 or other video games accessed by the users. Furthermore, the user clustering system 134 may identify or determine a set of difficulty preferences to associate with each user cluster identified or generated by the user clustering system 134.

The seed evaluation system 136, which may also be referred to as a lever evaluation system, evaluates a difficulty or a challenge provided by the video game 112 in response to a seed value. For example, the seed evaluation system 136 can determine how challenging a particular level or portion of the video game (such as a dungeon) generated in response to a particular seed value in a video game 112 is based on how well a group of users do playing the video game 112 when the particular seed value is utilized. Advantageously, in certain embodiments, by evaluating the challenge provided by a particular seed value, the difficulty level of the video game 112 can be refined by adding or removing the seed value to a set of available seed values for a particular difficulty level. For example, if it is determined that a seed value causes users to fail at an 80% rate, the seed value may be associated with the harder difficulty level than another seed value that causes users to fail at a 20% rate.

The user data repository 138 can store user interaction information associated with one or more users' interaction with the video game 112 and/or one or more other video games. This user interaction information can be obtained over one or more play sessions of the video game 112. Further, the user data repository 138 can store user cluster information associated with one or more user clusters generated by the user clustering system 134. In some cases, at least some of the data stored in the user data repository 128 may be stored at a repository of the user computing system 110. Each of the repositories described herein may include non-volatile memory or a combination of volatile and nonvolatile memory.

The network 104 can include any type of communication network. For example, the network 104 can include one or more of a wide area network (WAN), a local area network (LAN), a cellular network, an ad hoc network, a satellite network, a wired network, a wireless network, and so forth. Further, in some cases, the network 104 can include the Internet.

Example Model Generation System

FIG. 1B illustrates an embodiment of the model generation system 146 of FIG. 1A. The model generation system 146 may be used to determine one or more prediction models 160 based on historical data 152 for a number of users. Typically, although not necessarily, the historical data 152 includes data associated with a large number of users, such as hundreds, thousands, hundreds of thousands, or more users. However, the present disclosure is not limited as such, and the number of users may include any number of users. Further, the historical data 152 can include data received from one or more data sources, such as, for example, an application host system (not shown) and/or one or more user computing systems 102. Further, the historical data 152 can include data from different data sources, different data types, and any data generated by one or more user's interaction with the video game 112. In some embodiments, the historical data 152 may include a very large number of data points, such as millions of data points, which may be aggregated into one or more data sets. In some cases, the historical data 152 may be accessed from a user data repository 138. In some embodiments, the historical data 152 is limited to historical information about the video game, but in other embodiments, the historical data 152 may include information from one or more other video games. Further, in some embodiments, one or more subsets of the historical data a limited by a date restriction, such as for example, limited to include only data from the last 6 months.

The model generation system 146 may, in some cases, also receive feedback data 154. This data may be received as part of a supervised model generation process that enables a user, such as an administrator, to provide additional input to the model generation system 146 that may be used to facilitate generation of the prediction model 160. For example, if an anomaly exists in the historical data 152, the user may tag the anomalous data enabling the model generation system 146 to handle the tagged data differently, such as applying a different weight to the data or excluding the data from the model generation process.

Further, the model generation system 146 may receive control data 156. This control data 156 may identify one or more features or characteristics for which the model generation system 146 is to determine a model. Further, in some cases, the control data 156 may indicate a value for the one or more features identified in the control data 156. For example, suppose the control data 156 indicates that a prediction model is to be generated using the historical data 152 to determine a length of time that the users played the video game 112. If the amount of time each user played the game is known, this data may be provided as part of the control data 156, or as part of the historical data 152. As another example, if the prediction model is to be generated to estimate a retention rate as determined, for example, based on whether the users played the video game 112 for a threshold period of time or continue to play the video game 112 after a particular threshold period of time, the control data 156 may include the retention rate for the users whose data is included in the historical data 152.

The model generation system 146 may generally include a model generation rule set 170 for generation of the prediction model 160. The rule set 170 may include one or more parameters 162. Each set of parameters 162 may be combined using one or more mathematical functions to obtain a parameter function. Further, one or more specific parameters may be weighted by the weights 164. In some cases, the parameter function may be obtained by combining a set of parameters with a respective set of weights 164. The prediction model 160 and/or the respective parameters 162 of the prediction models 160 may be derived during a training process based on particular input data, such as the historical data 152, feedback data 154, and control data 156, and defined output criteria, which may be included with the control data 156, used for training purposes. The model generation rule set 170 can define the specific machine learning rules and/or algorithms the model generation system 146 uses to generate the model based on a defined objective function, such as determining a churn rate. In some embodiments, initial parameters 162 and weights 164 can be manually provided during the initiation of the model generation process. The parameters 162 and weights 164 can be updated and modified during the model generation phase to generate the prediction model 160.

The model generation system 146 can filter and categorize the historical data sets according to various characteristics and parameters of the data. For example, the data can be categorized by the data source (such as, for example, game application data, host application data, or user profile data), information type (such as, for example, gameplay information, transaction information, interaction information, game account information), or other categories associated with the data. The model generation system 146 can filter the information to identify the information for further processing. In some embodiments, the model generation system 146 is configured to filter and separate the historical data 152 into a plurality of data types or categories before further processing. Moreover, in some cases, some of the historical data 152 may be filtered out or removed from the historical data 152 based on the data being associated with a relevance that does not satisfy a threshold relevance as determined by the model generation system 146.

Optionally, one or more of the prediction models 160 may be associated with a penalty 166. These penalties 166 may be used to facilitate the generation of or selection of a particular prediction model 160 based on one or more factors that are used to derive the penalty. For example, the mathematical complexity or the number of parameters included in a particular prediction model 160 may be used to generate a penalty for the particular prediction model 160, which may impact the generation of the model and/or a selection algorithm or a selection probability that the particular prediction model 160 is selected.

After the prediction model 160 has been generated, the model can be used during runtime of the retention analysis system 140 and/or the difficulty configuration system 132 to adjust the difficulty of the video game 112. In some cases, the adjustment of the difficulty may be dynamic and may occur during a user's interaction with the video game 112. Further, in some cases, the difficulty adjustment may occur in real-time or near real-time.

Example Retention Analysis System

FIG. 1C illustrates an embodiment of a retention analysis system 140 of FIG. 1A. The retention analysis system 140 can apply or use one or more of the prediction models 160 generated by the model generation system 146. Although illustrated as a separate system, in some cases, the retention analysis system 140 may be included as part of the difficulty configuration system 132. The retention analysis system 140 may use one or more prediction models 160A, 160B, 160N (which may be referred to collectively as “prediction models 160” or in the singular as “prediction model 160”) to process the input data 172 to obtain the output data 174.

The retention analysis system 140 may apply the prediction model(s) 160 during game play. In some embodiments, the prediction models 160 are applied at the beginning of the game to determine how to adjust the difficulty of the entire game. In other embodiments, the prediction models 160 are applied at different times during the game and/or at different stages in the game. During determination of a difficulty level for one or more portions of the video game 112, the retention analysis system 140 receives input data 172 that can be applied to one or more of the prediction models 160. The input data 172 can include one or more pieces of data associated with a user who is playing the video game 112. This data may include user interaction data for the video game 112, profile data for the user, and any other data that may be applied to the prediction model 160 to determine a retention or churn rate for the user. In some embodiments, the input data 172 can be filtered before it is provided to the retention analysis system 140.

In some embodiments, a single prediction model 160 may exist for the retention analysis system 140. However, as illustrated, it is possible for the retention analysis system 140 to include multiple prediction models 160. The retention analysis system 140 can determine which detection model, such as any of models 160A-N, to use based on input data 172 and/or additional identifiers associated with the input data 172. Additionally, the prediction model 160 selected may be selected based on the specific data provided as input data 172. The availability of particular types of data as part of the input data 172 can affect the selection of the prediction model 160. For example, the inclusion of demographic data (for example, age, gender, first language) as part of the input data may result in the use of prediction model 160A. However, if demographic data is not available for a particular user, then prediction model 160B may be used instead.

As mentioned above, one or more of the prediction models 160 may have been generated with or may be associated with a penalty 166. The penalty may be used to impact the generation of the model or the selection of a prediction model for use by the retention analysis system 140.

The output data 174 can be a retention rate or churn rate associated with a prediction that a user ceases to play the video game 112. For example, in some embodiments, the retention rate may be between 0 and 100 indicating the predicted percentage of users associated with similar or the same data as included as input data 172 who would cease to play the video game 112 within a threshold time period. In some cases, the output data 174 may also identify a reason for the retention rate. For example, the retention analysis system 140 may indicate that the 90% retention rate for a particular user is based at least in part on the amount of money spent while playing the video game 112. However, the retention analysis system 140 may indicate that the 90% retention rate for another user may be based at least in part on the below freezing temperature in the geographic region where the user is located. As another example, the retention analysis system 140 may indicate that the 20% retention rate for a user may be based at least in part on the below 25% win ratio.

The prediction models 160A, 160B, 160N may generally include a set of one or more parameters 162A, 162B, 162N, respectively (which may be referred to collectively as “parameters 162”). Each set of parameters 162 (such as parameters 162A) may be combined using one or more mathematical functions to obtain a parameter function. Further, one or more specific parameters from the parameters 162A, 162B, 162N may be weighted by the weights 164A, 164B, 164N (which may be referred to collectively as “weights 164”). In some cases, the parameter function may be obtained by combining a set of parameters (such as the parameters 162A) with a respective set of weights 164 (such as the weights 164A). Optionally, one or more of the prediction models 160A, 160B, 160N may be associated with a penalty 166A, 166B, 166N, respectively (which may be referred to collectively as “penalties 166”).

Example Machine Learning Process

FIG. 2 presents a flowchart of an embodiment of a machine learning process 200. The process 200 can be implemented by any system that can generate one or more parameter functions or prediction models that include one or more parameters. In some cases, the process 200 serves as a training process for developing one or more parameter functions or prediction models based on historical data or other known data. The process 200, in whole or in part, can be implemented by, for example, an interactive computing system 130, a difficulty configuration system 132, a user clustering system 134, a retention analysis system 140, a model generation system 146, or a user computing system 110, among others. Although any number of systems, in whole or in part, can implement the process 200, to simplify discussion, the process 200 will be described with respect to particular systems. Further, it should be understood that the process 200 may be updated or performed repeatedly over time. For example, the process 200 may be repeated once per month, with the addition or release of a new video game, or with the addition of a threshold number of new users available for analysis or playing a video game 112. However, the process 200 may be performed more or less frequently.

The process 200 begins at block 202 where the model generation system 146 receives historical data 152 comprising user interaction data for a number of users of the video game 112. This historical data 152 may serve as training data for the model generation system 146 and may include user demographics or characteristics, such as age, geographic location, gender, or socioeconomic class. Alternatively, or in addition, the historical data 152 may include information relating to a play style of one or more users; the amount of money spent playing the video game 112; user success or failure information with respect to the video game 112 (for example, a user win ratio); a play frequency of playing the video game 112; a frequency of using particular optional game elements (for example, available boosts, level skips, in-game hints, power ups, and the like); the amount of real money (for example, U.S. dollars or European euros) spent purchasing in-game items for the video game 112; and the like. Further, in some cases, the historical data 152 may include data related to the video game 112, such as one or more seed values used by users who played the video game 112. Additional examples of data related to the video game 112 that may be received as part of the historical data 152 may include settings for one or more knobs or state variables of the video game 112, the identity of one or more difficulty levels for the video game 112 used by the users, the type of the video game 112, and the like.

At block 204, the model generation system 146 receives control data 156 indicating a desired prediction for the number of users corresponding to the historical data. This control data 156 may indicate one or more features or characteristics for which the model generation system 146 is to determine a model. Alternatively, or in addition, the control data 156 may include a value for the features or characteristics that are associated with the received historical data 152. For example, the control data 156 may identify churn rate, or retention rate, as the desired feature to be predicted by the model that is to be generated by the model generation system 146. The churn rate or retention rate may correspond to a percentage of users associated with the historical data 152 that ceased playing the video game 112. Further, the control data 156 may identify a retention rate associated with the historical data. For example, the control data 156 may indicate that the retention rate is 60% for certain of the users whose data is included in the historical data 152. In some embodiments, the control data 156 may include multiple characteristics or features to be predicted by the model to be generated by the model generation system 146. For example, the control data 156 may identify both a retention rate and a reason for the retention rate (such as the difficulty of the video game 112 being too low or too high for the users whose data was provided as part of the historical data 152 at block 202), or a retention rate and an average monetary amount spent by the users whose data was provided as the historical data 152.

At block 206, the model generation system 146 generates one or more prediction models 160 based on the historical data 152 and the control data 156. The prediction models 160 may include one or more variables or parameters 162 that can be combined using a mathematical algorithm or model generation ruleset 170 to generate a prediction model 160 based on the historical data 152 and, in some cases, the control data 156. Further, in certain embodiments, the block 206 may include applying one or more feedback data 154. For example, if the prediction model 160 is generated as part of a supervised machine learning process, a user (for example, an administrator) may provide one or more inputs to the model generation system 146 as the prediction model 160 is being generated and/or to refine the prediction model 160 generation process. For example, the user may be aware that a particular region or geographic area had a power outage. In such a case, the user may supply feedback data 154 to reduce the weight of a portion of the historical data 152 that may correspond to users from the affected geographic region during the power outage. Further, in some cases, one or more of the variables or parameters may be weighted using, for example, weights 164. The value of the weight for a variable may be based at least in part on the impact the variable has in generating the prediction model 160 that satisfies, or satisfies within a threshold discrepancy, the control data 156 and/or the historical data 152. In some cases, the combination of the variables and weights may be used to generate a prediction model 160.

Optionally, at block 208, the model generation system 146 applies a penalty to or associates a penalty 166 with at least some of the one or more prediction models 160 generated at block 206. The penalty associated with each of the one or more prediction models 160 may differ. Further, the penalty for each of the prediction models 160 may be based at least in part on the model type of the prediction model 160 and/or the mathematical algorithm used to combine the parameters 162 of the prediction model 160, and the number of parameters included in the parameter function. For example, when generating a prediction model 160, a penalty may be applied that disfavors a very large number of variables or a greater amount of processing power to apply the model. As another example, a prediction model 160 that uses more parameters or variables than another prediction model may be associated with a greater penalty 166 than the prediction model that uses fewer variables. As a further example, a prediction model that uses a model type or a mathematical algorithm that requires a greater amount of processing power to calculate than another prediction model may be associated with a greater penalty than the prediction model that uses a model type or a mathematical algorithm that requires a lower amount of processing power to calculate.

The model generation system 146, at block 210, based at least in part on an accuracy of the prediction model 160 and any associated penalty, and selects a prediction model 160. In some embodiments, the model generation system 146 selects a prediction model 160 associated with a lower penalty compared to another prediction model 160. However, in some embodiments, the model generation system 146 may select a prediction model associated with a higher penalty if, for example, the output of the prediction model 160 is a threshold degree more accurate than the prediction model associated with the lower penalty. In certain embodiments, the block 210 may be optional or omitted. For example, in some cases, the prediction models 160 may not be associated with a penalty. In some such cases, a prediction model may be selected from a plurality of prediction models based on the accuracy of the output generated by the prediction model.

Example Difficulty Based Seed Selection Process

FIG. 3 presents a flowchart of an embodiment of a difficulty based seed selection process 300. The process 300 can be implemented by any system that can select a seed value for adjusting a difficulty of a video game based at least in part on the output of a prediction function or a parameter function. The process 300, in whole or in part, can be implemented by, for example, an interactive computing system 130, a difficulty configuration system 132, a user clustering system 134, a model generation system 146, a retention analysis system 140, or a user computing system 110, among others. Although any number of systems, in whole or in part, can implement the process 300, to simplify discussion, the process 300 will be described with respect to particular systems. Further, it should be understood that the process 300 may be updated or performed repeatedly over time. For example, the process 300 may be repeated for each play session of a video game 112, for each round of the video game 112, each week, each month, for every threshold number of play sessions, for every threshold number of times a user loses or fails to complete an objective, each time a win ratio drops below a threshold level, and the like. However, the process 300 may be performed more or less frequently.

The process 300 begins at block 302 where the retention analysis system 140 receives a set of input data (such as the input data 172) comprising user interaction data for a user of the video game 112. This input data 172 is typically, although not necessarily, user specific data. Further, the set of input data 172 may include both historical user interaction data and recent user interaction data for the user. Historical user interaction data may include user interaction data from a noncurrent play session and/or user interaction data that satisfies a threshold age or that is older than a particular threshold time period. For example, the historical user interaction data may include user interaction data that is at least a week or a month old. Alternatively, or in addition, the historical user interaction data may include data from play sessions that are more than 5 or 10 play sessions old.

Conversely, the recent user interaction data may include user interaction data that satisfies a threshold age or that is more recent than a particular threshold time period. For example, the recent user interaction data may include user interaction data that is less than a week or a month old. Alternatively, or in addition, the recent user interaction data may include data from play sessions that are less than 3, 5, or 10 play sessions old.

In some embodiments, the historical user data and the recent user data may be weighted differently within the prediction model 160. In some cases, each parameter 162 within the prediction model 160 may be repeated. For example, one version of the parameter may be based on the historical user data and may be associated with one weight 164 and another version of the parameter may be based on the recent user data and may be associated with a different weight 164. Moreover, in some implementations, the weight may be applied on a sliding scale or a graduated basis. For example, more recent historical user data may be associated with a higher weight 164 and less recent historical user data.

At block 304, the retention analysis system 140, using a parameter function or a prediction model 160, may determine a predicted churn rate for the user based at least in part on the set of input data 172 received at block 302. The predicted churn rate for the user may be determined at the beginning of game play, and/or at various stages or at various time frames during game play. The determined or calculated output 174 of the prediction model 160 may be a predicted churn rate or an expected churn for users with the same or substantially similar input data as the set of input data received at block 302. Further, the churn rate may indicate the percentage of users or the likelihood that a user will continue to play or not continue to play the video game 112 based on the set of input data received at block 302. For example, if a churn rate of 75% is output by the retention analysis system 140, then it may be estimated that there is a 75% chance that the user will not continue to play the video game 112. Alternatively, or in addition, the churn rate of 75% may indicate that 75% of users with the same or similar user interaction data that is associated with the set of input data received at the block 302 will cease playing the video game 112 generally, after a certain amount of time, or at a specific point in the game. In some embodiments, the prediction model 160 may predict the amount of time the user is predicted to play the video game 112 over a particular time period or until ceasing to play the video game 112. This determination may be for a particular play session or for a number of play sessions and may be instead of or in addition to determining the retention rate.

Optionally, at block 306, using the prediction model 160, the retention analysis system 140 determines a predicted reason for the predicted churn rate for the user based on the set of input data 172. For example, the parameter function may determine based on an input data indicating a win ratio below a threshold that the churn rate calculated by the parameter function is based at least in part on the difficulty of the video game 112.

At block 308, based on the predicted churn rate for the user determined at block 304, the difficulty configuration system 132 selects a seed associated with a particular level of difficulty for a portion of the video game. Selecting the seed may include accessing mapping data at the mapping data repository 144. This mapping data may indicate a mapping between one or more churn rates and one or more difficulty levels for the video game 112. In some cases, the churn rate above a particular threshold level may be mapped to one or more seed values that may reduce the difficulty of the video game 112. Further, in some cases, a churn rate may be mapped to different sets of seed values associated with different difficulty levels. In such cases, the set of seed values associated with a particular difficulty level may be selected based on an additional output 174 of the retention analysis system 140. For example, the retention analysis system 140 may output a churn rate and a reason for the churn rate for a particular user. Such an example, a churn rate above a particular threshold may cause a new seed value to be selected. Further, the reason for the churn rate may be used to select a plurality of seed values associated with different difficulty levels. The reason for the churn rate may be associated with particular numeric values. For example, a churn rate above a particular threshold caused by the video game 112 being too easy may be associated with one numeric value in a churn rate above a particular threshold caused by the video game 112 being too hard may be associated with another numeric value.

In some embodiments, the retention analysis system 140 may output the one or more factors or data from the input data 172 that had the most impact or a threshold amount of impact in determining the retention rate for the user. Using this information, the difficulty configuration system 132 may identify one or more changes to state variables of the video game 112, or one or more seed values, that correspond to the factors used to generate the retention rate.

In some cases, the mapping between the output from the retention analysis system 140 and the seed values may be a multistage mapping. For example, a first mapping may be between the churn rate output by the retention analysis system 140 and a plurality of seed values, and a second mapping may be between another output of the retention analysis system 140 (such as a predicted reason for the churn rate) and a particular seed value or subset of seed values from the plurality of seed values. In another implementation, the multistage mapping may include a first stage mapping between the churn rate and a particular difficulty level, and a second stage mapping between the particular difficulty level and one or more seed values or knob values.

In some cases, the seeds are adjustments to knobs were state variables used to modify execution of the video game 112. In other cases, the seeds are values used in random or pseudorandom number generators that are used to modify the probability or the occurrence rate of one or more events within the video game 112. Further, in some cases, the seeds are values used to affect the generation of a level or a portion of the video game 112 and/or the location of a user playable character within the video game 112.

In some cases, one or more of the above embodiments may be combined with clustering to facilitate identifying users who may prefer to play video games that are more or less difficult. Some example embodiments of clustering the may be used with the present disclosure are described in more detail below.

Example Clustering Embodiments

In some embodiments, a clustering process may be used to group users who share one or more characteristics that may be used to identify difficulty preferences for a video game 112. Clusters may include one or more users that share one or more characteristics. Difficulty preferences can be associated with each cluster to facilitate adjusting the difficult of the video game 112. A user whose characteristics match a particular cluster, may be assigned to the particular cluster. A difficulty level of the video game 112 can be adjusted for the user based on difficulty preferences associated with the cluster. Certain non-limiting example processes are described below that enable difficult adjustment using clustering.

Example Cluster Creation Process

FIG. 4 presents a flowchart of an embodiment of a cluster creation process 400. The process 400 can be implemented by any system that can create a plurality of clusters or groups of users based on each user's interaction with a video game and the level of engagement associated with each user. For example, the process 400, in whole or in part, can be implemented by an interactive computing system 130, a difficulty configuration system 132, a user clustering system 134, or a user computing system 110, among others. Although any number of systems, in whole or in part, can implement the process 400, to simplify discussion, the process 400 will be described with respect to particular systems. Further, it should be understood that the process 400 may be updated or performed repeatedly over time. For example, the process 400 may be repeated once per month, with the addition or release of a new video game, or with the addition of a threshold number of new users available for analysis or playing a video game 112. However, the process 400 may be performed more or less frequently.

The process 400 begins at block 402 where the user clustering system 134 identifies a set of users of the video game 112. To simplify discussion, the process 400 is primarily described with respect to a single video game, such as the video game 112. However, this disclosure is not limited as such, and the process 400 can be implemented for a plurality of video games. In some cases, each of the plurality of video games may be of the same genre or may share one or more characteristics in common. In other cases, the plurality of video games may be distributed across a number of genres. The genres may be based on theme and/or game type (for example, an open world game, a role-playing game, a first-person shooter, a side scrolling game, a simulation, a space fighter, a western, and the like). Further, in some cases, the process 400 includes analyzing data across additional video games to confirm or refine the clusters created based on the analysis of a video game.

At block 404, for each user identified at the block 402, the user clustering system 134 monitors the user's interaction with the video game 112 over time to obtain user interaction data for the user. This monitoring can be done by reviewing sets of user interaction data for the user from different time periods or by pulling data from the video game 112 in real time and storing the data for later review. This user interaction data can include any of the information previously described with respect to FIG. 1A. Further, the user interaction data may include data relating to the users progress within the video game; the action taken by the user when the user succeeds in completing a level or objective; the action taken by the user when the user does not succeed in completing a level or objective; differences in actions taken by the user based on the length of time it takes the user to succeed at an objective; the length of time the user plays the video game each time or on average when the user plays the video game; whether the user typically plays the game for short periods of time or long periods of time; whether the user spends real world currency (as opposed to in-game currency) when playing the video game, which may be used as a factor to identify the users level of engagement (for example, a user spends money while playing a video game is more likely to play the game again compared to a user who does not spend money or playing a video game); and any other criteria that can be used to measure the users level of engagement with the video game and/or the user's action in response to the level of challenge that the video game presents to the user. Further, user interaction data may also include information relating to the type of user computing system 110 used by the user to access the video game; differences, if any, and how the user interacts with the video game based on the type of user computing system 110 used to access the video game; whether the user uses multiple user computing systems 110 to access the video game; and the like.

At block 406, the user clustering system 134 filters out users whose user interaction data does not satisfy a minimum set of interaction criteria. This minimum set of interaction criteria may be related to the length of time that the user played the game or to whether the user played the video game for multiple play sessions. For example, a user who played the video game less than a threshold amount of time or for less than a threshold number of play sessions may not have provided sufficient data to determine whether the video game's difficulty impacted the user's level of engagement. Further, the minimum set of interaction criteria may be related to the type of actions the user has taken in the video game 112 or the progress the user has made in the video game 112. In some cases, users whose user interaction data does not satisfy the minimum set of interaction criteria may be retained, but weighted lower compared to user interaction data for users that does satisfy the minimum set of interaction criteria. In some embodiments, the block 406 may be optional or omitted.

For each remaining user from the set of users, the user clustering system 134 determines a level of engagement for the user based on the user interaction data for that user at block 408. Determining the level of engagement for the user may include determining whether the user continues to play or stops playing a video game based on the user's progress within the video game. Further, determining the level of engagement for the user may include determining whether and/or how much money the user spends while playing the video game. In some cases, determining the level of engagement for the user may include determining a probability that the user will play the video game again based on the user interaction data collected for the user. In some cases, determining the level of engagement for the user may include determining the user's skill with respect to the video game. Further, performing the operations associated with the block 408 may include applying one or more machine learning algorithms using the user interaction data as input to determine a probability that the user continues to play or stops playing the video game based on the amount of challenge presented to the user by the video game.

At block 410, the user clustering system 134 determines a number of user clusters based on the user interaction data and the level of engagement for each user of the remaining users. Determining the user clusters may include grouping users based on their behavior as determined from the user interaction data monitored at the block 404. Further, grouping the users into the user clusters may include identifying characteristics associated with each user based on the user interaction data collected for the users that indicate a level of engagement with the video game. For example, suppose that the system determines that a number of users typically play the video game for less than 30 minutes and tend to succeed at an objective in less than two attempts over the course of a threshold number of play sessions and/or objectives. Further, suppose that these users typically stop playing the video game after reaching some number of objectives that require more than two attempts to complete the objectives. This number of users may be clustered together in a user cluster for users who tend to play short game sessions and tend to prefer video games that are not challenging. In contrast, another group of players who tend to play video games for two hours at a time and continue to play the video game despite objectives taking several attempts to complete may be clustered together in a separate user cluster. One or more machine learning algorithms may be used to identify the clusters of users based at least in part on the user interaction data for the set of users.

In some cases, block 410 may include generating subclusters within each cluster. For example, one cluster may include users who tend to prefer video games that are particularly challenging. Within this cluster, there may be two subclusters. One subcluster may be for users who tend to prefer video games that remain challenging throughout the course of the entire game. Another subcluster may be for users who tend to prefer video games that are challenging in the beginning, but may become less challenging over time as may occur with video games that are difficult to master, but are less difficult once the user masters the skills required by the video game.

At block 412, the difficulty configuration system 132 associates difficulty preferences with each user cluster based on the user interaction data associated with the users in the user cluster. In some embodiments, a machine learning model may be generated by, for example, the model generation system 146, to determine which values for difficulty preferences are associated with longer game play compared to other values for the difficulty preferences. The difficulty preferences may identify whether users associated with the user cluster prefer to play a video game that is difficult, easy, or some gradation in between. Further, the difficulty preferences may identify one or more values resetting one or more knobs were game state variables associated with the video game.

Example Cluster Assi nment Process

FIG. 5 presents a flowchart of an embodiment of a cluster assignment process 500 for a user. The process 500 can be implemented by any system that can identify a user cluster with which to associate a user based on the user's interaction with a video game. For example, the process 500, in whole or in part, can be implemented by an interactive computing system 130, a difficulty configuration system 132, a user clustering system 134, or a user computing system 110, among others. Although any number of systems, in whole or in part, can implement the process 500, to simplify discussion, the process 500 will be described with respect to particular systems. Further, it should be understood that the process 500 may be updated or performed repeatedly over time. For example, the process 500 may be repeated once per month, after a threshold number of play sessions by the user since a prior performance of the process 500, or after the user plays a new video game. However, the process 500 may be performed more or less frequently. Furthermore, the process 500 may be performed in real time or may be performed in advance of a particular event. For example, the process 500 may be performed upon user registration with a game deployment system or upon the user accessing the video game 112.

The process 500 begins at block 502 where the user clustering system 134 identifies a user of the video game 112. The user may be identified based on user account information, such as a user login, or based on information associated with an avatar of the user, such as a screen name. Alternatively, or in addition, a user may be identified based on information associated with a user computing system 110 of the user, such as an Internet protocol (IP) address.

At block 504, the user clustering system 134 monitors the user's interaction with the video game 112 over a time period to obtain user interaction data for the user. This monitoring can be done by reviewing sets of user interaction data for the user from different time periods or by pulling data from the video game 112 in real time and storing the data for later review. Typically, the time period is a historical time period that may include the user's interaction with the video game 112 over multiple play sessions. Further, the length of the time period may be selected to satisfy or exceed a minimum time threshold. For example, the time period may be selected to be at least, or to exceed, one month, two months, half a year, and the like. In some cases, instead of or in addition to monitoring the user's interaction with the video game over a time period, the user clustering system 134 may be configured to monitor the user's interaction over a threshold number of play sessions. For example, the user clustering system 134 may be configured to monitor the first number (for example, five, ten, twelve, and the like) of play sessions of the user or the most recent number of play sessions of the user. In some cases, the block 504 may include monitoring the user's interaction with a plurality of video games. The plurality of video games may be video games of the same type as the video game 112. In other cases plurality of video games may not be limited to a particular type of video game.

The user clustering system 134 accesses cluster definitions for a set of clusters at block 506. Accessing the cluster definitions for the set of clusters may include accessing a user data repository 138. The cluster definitions may include a set of characteristics that correlate to or are derived from user interaction data for a set of users.

Using the cluster definitions accessed at the block 506 and the user interaction data obtained at the block 504, the user clustering system 134 identifies a cluster from the set of clusters at the block 508. Identifying the cluster from the set of clusters may include matching characteristics of the user interaction data with characteristics associated with each of the set of clusters. For example, if the user interaction data indicates that the user plays the video game 112 for several hours at a time when successfully completing objectives and ceases to play the video game 112 within about 10 minutes on average when failing to complete an objective, then the user clustering system 134 may determine that the user is a player who spends significant time playing video games and does not enjoy significant challenge. Continuing this example, the user clustering system 134 may identify the user cluster from the set of clusters that is associated with players who play video games for a significant amount of time and tend to enjoy games with less than a threshold level of difficulty.

In some cases, the determination of the cluster from the set of clusters may include identifying how difficult the user finds the video game 112 based on the user interaction data obtained at the block 504. Further, the determination of the cluster of a set of clusters may include identifying the user's actions or reactions to events within the video game including events relating to the success or failure of the user in overcoming challenges within the video game 112. Engagement characteristics or user interaction characteristics for the user with respect to the video game may be determined based on the user's behavior with respect to the success or failure of satisfying objectives within the video game 112. These engagement characteristics and/or other characteristics related to the user that are derived from the user's interaction with the video game may be compared to characteristics associated with the set of clusters to identify a corresponding cluster to associate with the user.

In some embodiments, the engagement characteristics may be presented to the user and, in response, the user clustering system 134 may receive input from the user regarding the engagement characteristics. For example, the user may indicate whether the user agrees with the analysis. As another example, the user may indicate that he or she was experimenting with a new play style that the user does or does not plan to continue using. The user clustering system 134 may use the user input to adjust or confirm its selection of a particular user cluster. In some cases, the user input may be weighted based on the amount of data the user clustering system 134 has obtained at block 504. For example, the user input may be weighted more heavily for users with a little history (such as two or three play sessions) and weighted less heavily for users with a significant amount of history (such as fifty or a hundred play sessions).

In some cases, the interactive computing system 130 may cause sliders, or some other user interface element, to be displayed to the user to indicate the user's engagement characteristics on a spectrum. For example, the slider may indicate that the user tends to attempt challenges more often than average or that the user on average tends to be more successful at certain challenges than other users. Although the analysis of the user interaction data may be presented to the user, the user may not be informed that the information is being used to adjust the difficulty level of at least portions of the video game 112.

After the user has played a portion of the video game 112, the user clustering system 134 may question the user to help determine user preferences or to obtain information regarding how the user viewed the difficulty of the portion of the video game 112. The user may be questioned after the user indicates that the user is ending a play session. Thus, the user can be questioned regarding his or her experience without interrupting the user's play experience. Further, the user clustering system 134 may parse chat message data of the user to determine the user's engagement level and/or how difficult the user finds the video game 112.

At block 510, the user clustering system 134 associates the user with the identified cluster. Associating the user with the identified cluster may include storing an association between the user and the identified cluster at the user data repository 138.

In some embodiments, the process 500 may be used to determine how likely a user is to stop playing the video game 112. This determination can sometimes be referred to as a “churn rate” or “churn” and can be associated with how often a user switches video games or stops playing certain video games. For example, a user who tends to play video games for one or two play sessions and then move on to another video game may have a high churn rate. By identifying such users, it may be possible to modify the difficulty settings of the video game to reduce the rate of churn. For example, if it is determined that simple games, or games that do not provide an adequate challenge for the user results in the user ceasing to play such games, the user may be associated with a cluster of users who prefer difficult video games. Further, as is described in more detail below, based on the identity of the cluster to associate with the user, the difficulty of the video game 112 may be refined in an effort to reduce the probability that the user ceases to play the video game 112.

Example Difficulty Setting Process

FIG. 6 presents a flowchart of an embodiment of a difficulty setting process 600 for an application, such as the video game 112. The process 600 can be implemented by any system that can dynamically set or adjust the difficulty of a video game based at least in part on monitor activity of the user. For example, the process 600, in whole or in part, can be implemented by an interactive computing system 130, a difficulty configuration system 132, a user clustering system 134, or a user computing system 110, among others. Although any number of systems, in whole or in part, can implement the process 600, to simplify discussion, the process 600 will be described with respect to particular systems.

The process 600 may be performed when the video game 112 is executed or when a play session of the video game 112 is started. In some cases, the process 600 may be performed when a user begins a new game or starts a new account with respect to video game 112. Further, the process 600 may be performed each time the user loads a previously saved game with respect to the video game 112. In some cases, the process 600 may be performed, or repeated, at a particular time or in response to a trigger event within the video game 112. For example, the process 600 may occur each time the user fails to complete or succeeds in completing particular objectives within the video game 112. In some cases, the process 600 may occur after a threshold number of objectives are completed or after a threshold number of attempts to complete an objective are unsuccessful.

The process 600 begins at block 602 where the difficulty configuration system 132 identifies a user of the video game 112. In some cases, the block 602 is performed in response to the user accessing the video game 112. However, as described above, one or more portions of the process 600 may be performed in response to other events, such as in game trigger events. Identifying the user of the video game 112 may be based on a user identifier or avatar. In some cases, the block 602 may include one or more of the embodiments previously described with respect to the block 502.

At block 604, the difficulty configuration system 132 determines a user cluster associated with the user. Determining a user cluster associated with the user may include accessing a user data repository 138 to identify one or more clusters associated with the user. In some cases, the user cluster may be associated with both the user and the video game 112. In some such cases, the user may be associated with multiple user clusters. For example, the user may be associated with a different user cluster for each video game played by the user. In other cases, the user may be associated with a single user cluster for a set of video games. In some cases, the set of video games may be all video games played by the user or all video games published by an entity associated with the interactive computing system 130. In other cases, the set of video games may be video games of a specific genre or theme.

Based on the identified user cluster, the difficulty configuration system 132 determines configuration values for a set of one or more knobs associated with the video game 112. These configuration values may be identified by the user cluster. Alternatively, the configuration values may be determined based on a difficulty level associated with the user cluster. In some cases, at least some of the configuration values are specific or fixed values associated with the user cluster or determined based on characteristics associated with the user cluster. Alternatively, or in addition, at least some of the configuration values may be selected using an algorithm or randomly from a particular pool or set of values associated with the user cluster.

At block 608, the difficulty configuration system 132 accesses recent user interaction data for the user with respect to the video game 112. This recent user interaction data may be provided to the difficulty configuration system 132 by the video game 112. Alternatively, or in addition, the difficulty configuration system 132 may access the recent user interaction data from the user interaction history repository 116. In yet other cases, the difficulty configuration system 132 may access the recent user interaction data from the user data repository 138.

The recent user interaction data may include user interaction data collected during a current play session, within a threshold time period (for example, within the last week), or within a threshold number of play sessions (for example, the most recent three play sessions). Generally, although not necessarily, the time period or the number of play sessions from which the recent user interaction data is collected is less and/or more recent than the historical data used with respect to the process 500 to determine a user cluster associated with the user.

Advantageously, in certain embodiments, by accessing the user cluster associated with the user and by accessing the recent user interaction data, the difficulty configuration system 132 can make a prediction as to the user's success at playing the video game 112 with a particular level of difficulty. Further, the difficulty configuration system 132 can make a prediction regarding the user's behavior with respect to the video game 112. Based at least in part on these predictions, the difficulty configuration system 132 can select configuration values that can be sent to the video game 112 to adjust the difficulty of the video game 112 accordingly. In some embodiments, the block 606 or the block 608 may be optional or omitted. For example, in cases where the user has not previously played the video game 112, the block 608 may be omitted. As another example, in cases where the user has not accumulated enough historical user interaction data to associate the user with a user cluster, the block 606 may be omitted. Alternatively, the user may be associated with a default user cluster so as to determine an initial set of configuration values for the set of knobs associated with the video game 112. As another alternative, the user may be associated with a user cluster based at least in part on information provided by the user.

At block 610, the difficulty configuration system 132 may adjust the configuration values for the set of knobs based at least in part on the recent user interaction data for the user. Adjusting the configuration values may make the video game 112 easier or more challenging for the user. Further, adjusting the configuration values may modify the state of the video game 112 and/or number of features associated with the video game 112. For example, adjusting the configuration values may modify the layout of a level within the video game 112. As another example, adjusting the configuration values may modify the timing of item drops within the video game 112 and/or the type of item drops within the video game 112. In some cases, the configuration values may be seed values that are used by the video game 112 in generating one or more aspects of the video game 112, such as a level layout or a start position of a user controlled character within the video game 112. It should be understood that the present disclosure does not limit the type of modifications that may be made to the video game 112. However, typically, the modifications made to the video game 112 will result in an adjustment of the level of difficulty of the video game 112. Advantageously, in certain embodiments, by adjusting particular knobs within the video game 112 based on historical and/or recent user interaction data, the difficulty of the video game 112 may be adjusted in a more granular basis compared to static difficulty level settings associated with some video games.

In some cases, adjusting the difficulty settings for the video game may include changing configuration values that adjust a defined set of features in the video game 112. Alternatively, the difficulty configuration system 132 may determine configuration settings based at least in part on an evaluation of specific skills of the user as determined from the recent user interaction data. For example, it may be determined that the user is failing a number of objectives in the video game 112 because the user has trouble timing jumps. If the user is a type of user who will stop playing a video game 112 when the user has trouble completing objectives, as determined by the user cluster associated with the user, the difficult configuration system 132 may generate configuration values for the capabilities of the playable character in the video game 112 to make it easier to time the jumps (for examp