Automated controller configuration recommendation system

Various aspects of the subject technology relate to systems, methods, and machine-readable media for adjusting controller settings. The method includes receiving, through a controller associated with a user, controller input for software. The method also includes determining, based on the controller input, a user profile for the user comprising at least a skill level and an input tendency of the user. The method also includes providing suggested adjustments to the controller settings intended to improve performance of the user in relation to the software, the controller settings comprising at least one of controller sensitivity or controller assignments. The method also includes receiving approval of the user to implement the suggested adjustments to the controller settings. The method also includes adjusting the controller settings based on the approval of the user.

TECHNICAL FIELD

The present disclosure generally relates to controller configurations, and more particularly to automated controller configuration recommendations.

BACKGROUND

Controllers may be utilized by users to interact with software. The controllers may translate the tactile input from the user into commands for the software. However, users are not all alike, and controller settings may not be optimized for each user. Although controller settings may be adjusted, they often include a myriad of customization options, and are therefore too cumbersome for users to customize on their own. As a result, the user experience in interacting with the software may become limited.

BRIEF SUMMARY

The subject disclosure provides for systems and methods for automated controller configuration recommendations. In an aspect, a machine learning system may account for how users are interfacing with software (e.g., a simulation, a video game, a developer tool, etc.) through a controller (e.g., analog/digital controllers including a gamepad, keyboard and mouse, a control surface, a handicapped accessible controller, steering wheel, flight stick, pedals, etc.). The system may provide recommendations for configuration settings of the controller to aid each user's respective tendencies. Additionally, configuration profiles can be shared on a social media platform and updated over time. The system may also account for configuration settings that are used by the most skilled users, and then makes a comparison to users who have similar tendencies to form recommendations. The system may further query the user for user approval for changes. The system may also be configured for automatic/dynamic adjustments, if desired by the user.

According to one embodiment of the present disclosure, a computer-implemented method for adjusting controller settings is provided. The method includes receiving, through a controller associated with a user, controller input for software. The method also includes determining, based on the controller input, a user profile for the user comprising at least a skill level and an input tendency of the user. The method also includes providing suggested adjustments to the controller settings intended to improve performance of the user in relation to the software, the controller settings comprising at least one of controller sensitivity or controller assignments. The method also includes receiving approval of the user to implement the suggested adjustments to the controller settings. The method also includes adjusting the controller settings based on the approval of the user.

According to one embodiment of the present disclosure, a system is provided including a processor and a memory comprising instructions stored thereon, which when executed by the processor, causes the processor to perform a method for adjusting controller settings. The method includes receiving, through a controller associated with a user, controller input for software. The method also includes determining, based on the controller input, a user profile for the user comprising at least a skill level and an input tendency of the user. The method also includes providing suggested adjustments to the controller settings intended to improve performance of the user in relation to the software, the controller settings comprising at least one of controller sensitivity or controller assignments. The method also includes receiving approval of the user to implement the suggested adjustments to the controller settings. The method also includes adjusting the controller settings based on the approval of the user.

According to one embodiment of the present disclosure, a non-transitory computer-readable storage medium is provided including instructions (e.g., stored sequences of instructions) that, when executed by a processor, cause the processor to perform a method for adjusting controller settings. The method includes receiving, through a controller associated with a user, controller input for software. The method also includes determining, based on the controller input, a user profile for the user comprising at least a skill level and an input tendency of the user. The method also includes providing suggested adjustments to the controller settings intended to improve performance of the user in relation to the software, the controller settings comprising at least one of controller sensitivity or controller assignments. The method also includes receiving approval of the user to implement the suggested adjustments to the controller settings. The method also includes adjusting the controller settings based on the approval of the user.

According to one embodiment of the present disclosure, a system is provided that includes means for storing instructions, and means for executing the stored instructions that, when executed by the means, cause the means to perform a method for adjusting controller settings. The method includes receiving, through a controller associated with a user, controller input for software. The method also includes determining, based on the controller input, a user profile for the user comprising at least a skill level and an input tendency of the user. The method also includes providing suggested adjustments to the controller settings intended to improve performance of the user in relation to the software, the controller settings comprising at least one of controller sensitivity or controller assignments. The method also includes receiving approval of the user to implement the suggested adjustments to the controller settings. The method also includes adjusting the controller settings based on the approval of the user.

DETAILED DESCRIPTION

Controllers may be utilized by users to interact with software. The controllers may translate the tactile input from the user into commands for the software. Typically, the default controller configuration for software focuses on ease of learning, and generally is not optimized for skilled performance. However, users are not all alike, and the default controller settings may not be optimized for each user. Although controller settings may be adjusted, they often include myriad customization options, and are therefore too cumbersome for users to customize on their own. As a result, the user experience in interacting with the software may become limited, and the user may eventually quit interactions with the software altogether.

Furthermore, controller assignments are no longer just straight forward button assignments and preferred layouts. With multiple analog inputs (e.g., triggers and thumb sticks) as well as multiple game/player states that exist (e.g., running, jumping, flying, driving, aiming, etc.), input mapping has become highly customizable and highly personalized. A user's controller layout (including analog sensitivities) and game settings (turning on/off certain features) can be a competitive advantage. In most competitive/e-sports style games, the default controls are almost never used by high level players. The defaults are designed to be simple and easy to pick up but usually have major disadvantages. For example, in a shooter with the jump button assigned to the face of the controller, a player cannot aim while jumping because the user's right thumb is needed for both. Additionally, joystick/thumb stick sensitivity almost always defaults quite low for ease of control, but a high sensitivity would allow players to turn around faster when approached from behind, for example.

Aspects of the present disclosure address these issues by providing for systems and methods for automated controller configuration recommendations. In an aspect, a machine learning system may account for how users are interfacing with software (e.g., a simulation, a video game, a developer tool, etc.) through a controller (e.g., analog/digital controllers including a gamepad, keyboard and mouse, a control surface, a handicapped accessible controller, steering wheel, flight stick, pedals, etc.). The system may provide recommendations for configuration settings of the controller to aid each user's respective tendencies. Additionally, configuration profiles (e.g., user profiles) can be shared on a social media platform and updated over time. This allows for the users to follow each other through their profiles. For example, a user may follow their friends and/or favorite streamers, etc., and may receive notifications of controller configuration changes to those profiles. The user may then also incorporate and merge down the controller configuration changes to their own profile. The system may also account for configuration settings that are used by the most skilled users, and then makes a comparison to users who have similar tendencies to form recommendations. The system may further query the user for user approval for changes. The system may also be configured for automatic/dynamic adjustments, if desired by the user.

The disclosed system addresses a problem in traditional controllers tied to computer technology, namely, the technical problem of adjusting controller settings to fit each user's tendencies. The disclosed system solves this technical problem by providing a solution also rooted in computer technology, namely, by providing for automated controller configuration recommendations.

FIG. 1illustrates an exemplary controller100, according to certain aspects of the present disclosure. The controller100may include a directional pad102, buttons104, a left joystick114, a right joystick116, a left bumper106, a right bumper108, a left trigger110, and a right trigger112. The controller100may also include a trackpad118and additional buttons (e.g., additional button120). In an implementation, the left joystick114and/or the right joystick116may be configured to be depressed downward to provide additional avenues of input from the joysticks114,116.

According to aspects, the directional pad102may include buttons corresponding to up, down, left, and right. According to aspects, the buttons104may include at least one button or more. It is understood that although four buttons104are illustrated, more or less buttons104may be included without departing from the scope of the disclosure.

According to aspects, the directional pad102, left joystick114, left bumper106, and left trigger110may be controlled by a user's left hand. For example, the user's left thumb may be used to control the left joystick114or the directional pad102, and the user's left index finger may be used to control the left bumper106and/or the left trigger110. According to aspects, the right joystick116, the buttons104, the right bumper108, and the right trigger112may be controlled by a user's right hand. For example, the user's right thumb may be used to control the right joystick116or the buttons104, and the user's right index finger may be used to control the right bumper108and/or the right trigger112.

As described above, there are limitations to a user's ability to interact with the controller100. For example, the user's left thumb may be used to control either the left joystick114or the directional pad102, but not both at the same time. Similarly, the user's right thumb may be used to control either the right joystick116or the buttons104, but not both at the same time. Therefore, in certain scenarios, it would be extremely difficult for the user to direct their intent through the controller100because of these shortcomings in the controller design and/or button mapping. Additionally, the sensitivities of the joysticks114,116may not be optimized for the user, which may cause unintended errors by the user.

According to aspects, the controller100may be utilized to interact with software. For example, the software may include video games, a flight simulator, a driving simulator, etc. It is understood that the software includes real world devices as well, including, but not limited to, car software, audio recording software, production software, etc.

It is understood that the illustrated controller100is exemplary only, and other controllers may be included without departing from the scope of the disclosure. For example, the controller may be an analog/digital controller, and may include a gamepad, a footpad, a control surface, a navigation controller (e.g., for navigating a car, airplane, space ship, etc.), a handicapped accessible controller, steering wheel, flight stick, pedals, etc. It is further understood that each controller type may include similar shortcomings to those described above for the controller100.

It is understood that controller inputs may be assigned to single and/or multiple functions based on contexts of the software that is running (e.g., a user that gets into a vehicle or opens a menu via object interactions would have different inputs/functions). It is further understood that the controllers may be digital devices (e.g., controller outputs to may be digital outputs), but some of the inputs may be analog in nature (e.g., input may be an analog input from a human user).

FIG. 2illustrates an exemplary graphical user interface (GUI)200for automatically adjusting controller settings (e.g., settings of a controller), according to certain aspects of the present disclosure. The GUI200may include a listing of controller settings202and a log204of implemented adjustments206(e.g., recommendations). In an implementation, the GUI200may include an option for a user to enable or disable automated adjustments to the settings of the controller.

According to aspects, a machine learning system may account for how users are interfacing with software (e.g., a simulation, a video game, a developer tool, etc.) through a controller (e.g., an analog/digital controller including a gamepad, keyboard and mouse, a control surface, a handicapped accessible controller, steering wheel, flight stick, pedals, etc.). The system may provide recommendations206for configuration settings of the controller to aid each user's respective tendencies. In an implementation, machine learning may be utilized to build a model that maps player skill level/play style built from all gathered player telemetry and settings to recommend changes to their control setup to improve certain aspects of how they play. For example, user performance data may be gathered through telemetry of the software. Additionally, configuration profiles (e.g., user profiles) can be shared on a social media platform and updated over time. The system may also account for configuration settings that are used by the most skilled users, and then makes a comparison to users who have similar tendencies to form recommendations.

For example, in a soccer video game, a skilled user may be associated with a particular team composition, and the skilled user may also have remapped controller assignments (e.g., reassigned buttons) that make it easier to play with that team more aggressively. As such, any player who has a similar team composition that also plays aggressively may receive a recommendation to similarly remap their controller assignments according to the skilled user's profile in order to improve their playstyle.

As another example, in a first-person shooter game, it may be determined that a player is often shot from behind. As a result, the system may recommend increasing turning sensitivity. Furthermore, if the player's aim with recoil removed is often sporadic and/or off target, the system may recommend reduced sensitivity while aiming down iron sites. The system may also recommend, for a current ranking level, that most users changed their button mappings for a jumping action from a face button (e.g., button104) to a bumper (e.g., right bumper108), and so the player should also consider a similar change to button mappings.

It is understood that the described techniques apply equally to configuring analog inputs (e.g., based on sensitivity curves, etc.) as well as the above-described layout configuration, which may also include digital inputs (e.g., remapping buttons/inputs).

The system may also be utilized to account for in-game changes that impact configuration settings. For example, in a first-person shooter game, a sniper rifle may have higher recoil in a subsequent patch to the game. In order to compensate for this change, the system may recommend a change in input sensitivity.

According to aspects, a configuration profile (e.g., user profile) may be generated for each user that includes the customized settings each user has for their controller. The user profile may include at least a skill level and an input tendency of the user. Each configuration profile may be shared to social media so that other users may search for and use profiles that are popular in their game/software communities. For example, these configuration profiles may be game setting specific and may naturally iterate over time so those changes can be pushed to other users' games/software automatically. Additionally, the integration with a social platform would aid in reinforcing the machine learning system so that it may stay updated regarding which configurations are most utilized and whether users are enjoying them.

According to aspects, the user may be presented with a short questionnaire to aid the machine learning model in determining recommendations206to the user. For example, the user may specify a playstyle (e.g., offensive, defensive), preferred weapons, competitiveness, etc. From there, the user may begin interacting with the software (e.g., video game, simulator, etc.) and the machine learning system will make recommendations206accordingly. In an implementation, the user may be presented with each recommendation206and given the option whether to accept or deny the recommendation206. The log204may be updated to reflect each change that was made. The log204may also include a history of which recommendations were accepted or denied as well. With each choice by the user, the machine learning system may understand better how to adjust the controller settings to fit the tendencies of that specific user.

According to aspects, the recommendations206may be incremental, so that the user is able to adjust to the new controller settings. For example, if the user is accustomed to a joystick sensitivity setting of 5, and the sensitivity is suddenly increased to 10, then the user will likely not be able to interact as efficiently with the software because the change is too large. Instead, the system may increase the sensitivity incrementally to allow the user time to become accustomed to the new settings. Eventually, the user may be able to have a sensitivity of 10. In an implementation, the adjustments may be implemented automatically through continuous monitoring so that the user slowly becomes more adept with each new adjustment over time.

According to aspects, the recommendations206may be based on user performance. For example, player statistics, hit percentages, defensive tendencies, offensive tendencies, player ratios, etc., may be utilized for recommendations206. In an implementation, these factors that influence the reconfiguration of the controller settings may be displayed to the user through the GUI200. In an implementation, the system may be configured by the user to query the user for user approval for recommendations206. The system may also be configured to automatically/dynamically implement the recommendations206, if desired by the user. For example, the user may toggle an ON/OFF switch through the GUI200. The ON/OFF switch may control whether to query the user for approval (e.g., switch is OFF), or to automatically/dynamically implement the recommendations206(e.g., switch is ON).

According to aspects, the recommendations206generated by the machine learning system may be utilized to improve accessibility to users with disabilities as well. For example, it may help users discover more suitable accessibility settings. Furthermore, it is understood that the recommendations206may be applied to any type of analog/digital controller, including, but not limited to, a gamepad, a footpad, a control surface, a navigation controller (e.g., for navigating a car, airplane, space ship, etc.), a handicapped accessible controller, steering wheel, flight stick, pedals, etc. In this way, the recommendations206may be applied in various contexts, including, but not limited to, video games, simulators, etc., for both disabled and non-disabled users.

According to aspects, the machine learning system may include algorithms, including but not limited to, machine learning algorithms, if/then telemetry engines, etc. As described herein, some non-limiting examples of machine learning algorithms that can be used to generate the recommendations206may 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.

FIG. 3illustrates a system300configured for adjusting controller settings (e.g., settings of a controller), in accordance with one or more implementations. In some implementations, system300may include one or more computing platforms302. Computing platform(s)302may be configured to communicate with one or more remote platforms304according to a client/server architecture, a peer-to-peer architecture, and/or other architectures. Remote platform(s)304may be configured to communicate with other remote platforms via computing platform(s)302and/or according to a client/server architecture, a peer-to-peer architecture, and/or other architectures. Users may access system300via remote platform(s)304.

Computing platform(s)302may be configured by machine-readable instructions306. Machine-readable instructions306may include one or more instruction modules. The instruction modules may include computer program modules. The instruction modules may include one or more of controller receiving module308, user profile determination module310, adjustment providing module312, approval receiving module314, setting adjusting module316, user performance data gathering module318, user profile comparing module320, setting sharing module322, and/or other instruction modules.

Controller receiving module308may be configured to receive, through a controller associated with a user, controller input for software. By way of non-limiting example, the software may include at least one of a flight simulator, a driving simulator, or a video game.

User profile determination module310may be configured to determine, based on the controller input, a user profile for the user including at least a skill level and an input tendency of the user.

Adjustment providing module312may be configured to provide suggested adjustments to settings of the controller intended to improve performance of the user in relation to the software. The suggested adjustments may be incremental. The suggested adjustments may be based at least or in part on a machine learning model. By way of non-limiting example, the machine learning model may include at least one of a regression algorithm, an instance-based algorithm, a decision tree algorithm, a Bayesian algorithm, a clustering algorithm, an association rule learning algorithm, an artificial neural network algorithm, a deep learning algorithm, a dimensionality reduction algorithm, or an ensemble algorithm. The settings of the controller may include at least one of controller sensitivity or controller assignments.

Adjustment providing module312may be configured to provide the suggested adjustments based on the comparing.

Approval receiving module314may be configured to receive approval of the user to implement the suggested adjustments to the settings of the controller.

Setting adjusting module316may be configured to adjust the settings of the controller based on the approval of the user.

User performance data gathering module318may be configured to gather user performance data through telemetry of the software.

User profile comparing module320may be configured to compare the user profile of the user with other user profiles of other users.

Setting sharing module322may be configured to share the settings of the controller for the user with other users. According to aspects, the described systems may not be linked or connected for the system to function. For example, a car may have a trained model installed that does not communicate with any external network in order to function.

In some implementations, by way of non-limiting example, the controller may be an analog controller and/or digital controller, and may include at least one of a gamepad, a footpad, a control surface, a navigation controller, a handicapped accessible controller, steering wheel, flight stick, or pedals. In some implementations, by way of non-limiting example, the controller may include at least one of a light sensor, an audio sensor, or a tactile sensor. The controller may also include embedded computing systems (e.g., such as for a car).

A given remote platform304may include one or more processors configured to execute computer program modules. The computer program modules may be configured to enable an expert or user associated with the given remote platform304to interface with system300and/or external resources324, and/or provide other functionality attributed herein to remote platform(s)304. By way of non-limiting example, a given remote platform304and/or a given computing platform302may include one or more of a server, a desktop computer, a laptop computer, a handheld computer, a tablet computing platform, a NetBook, a Smartphone, a gaming console, and/or other computing platforms.

External resources324may include sources of information outside of system300, external entities participating with system300, and/or other resources. In some implementations, some or all of the functionality attributed herein to external resources324may be provided by resources included in system300.

Computing platform(s)302may include electronic storage326, one or more processors328, and/or other components. Computing platform(s)302may include communication lines, or ports to enable the exchange of information with a network and/or other computing platforms. Illustration of computing platform(s)302inFIG. 3is not intended to be limiting. Computing platform(s)302may include a plurality of hardware, software, and/or firmware components operating together to provide the functionality attributed herein to computing platform(s)302. For example, computing platform(s)302may be implemented by a cloud of computing platforms operating together as computing platform(s)302.

Electronic storage326may comprise non-transitory storage media that electronically stores information. The electronic storage media of electronic storage326may include one or both of system storage that is provided integrally (i.e., substantially non-removable) with computing platform(s)302and/or removable storage that is removably connectable to computing platform(s)302via, for example, a port (e.g., a USB port, a firewire port, etc.) or a drive (e.g., a disk drive, etc.). Electronic storage326may include one or more of optically readable storage media (e.g., optical disks, etc.), magnetically readable storage media (e.g., magnetic tape, magnetic hard drive, floppy drive, etc.), electrical charge-based storage media (e.g., EEPROM, RAM, etc.), solid-state storage media (e.g., flash drive, etc.), and/or other electronically readable storage media. Electronic storage326may include one or more virtual storage resources (e.g., cloud storage, a virtual private network, and/or other virtual storage resources). Electronic storage326may store software algorithms, information determined by processor(s)328, information received from computing platform(s)302, information received from remote platform(s)304, and/or other information that enables computing platform(s)302to function as described herein.

Processor(s)328may be configured to provide information processing capabilities in computing platform(s)302. As such, processor(s)328may include one or more of a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a state machine, and/or other mechanisms for electronically processing information. Although processor(s)328is shown inFIG. 3as a single entity, this is for illustrative purposes only. In some implementations, processor(s)328may include a plurality of processing units. These processing units may be physically located within the same device, or processor(s)328may represent processing functionality of a plurality of devices operating in coordination. Processor(s)328may be configured to execute modules308,310,312,314,316,318,320, and/or322, and/or other modules. Processor(s)328may be configured to execute modules308,310,312,314,316,318,320, and/or322, and/or other modules by software; hardware; firmware; some combination of software, hardware, and/or firmware; and/or other mechanisms for configuring processing capabilities on processor(s)328. As used herein, the term “module” may refer to any component or set of components that perform the functionality attributed to the module. This may include one or more physical processors during execution of processor readable instructions, the processor readable instructions, circuitry, hardware, storage media, or any other components.

It should be appreciated that although modules308,310,312,314,316,318,320, and/or322are illustrated inFIG. 3as being implemented within a single processing unit, in implementations in which processor(s)328includes multiple processing units, one or more of modules308,310,312,314,316,318,320, and/or322may be implemented remotely from the other modules. The description of the functionality provided by the different modules308,310,312,314,316,318,320, and/or322described below is for illustrative purposes, and is not intended to be limiting, as any of modules308,310,312,314,316,318,320, and/or322may provide more or less functionality than is described. For example, one or more of modules308,310,312,314,316,318,320, and/or322may be eliminated, and some or all of its functionality may be provided by other ones of modules308,310,312,314,316,318,320, and/or322. As another example, processor(s)328may be configured to execute one or more additional modules that may perform some or all of the functionality attributed below to one of modules308,310,312,314,316,318,320, and/or322.

FIG. 4illustrates an example flow diagram (e.g., process400) for adjusting controller settings, according to certain aspects of the disclosure. For explanatory purposes, the example process400is described herein with reference toFIGS. 1-3. Further for explanatory purposes, the steps of the example process400are described herein as occurring in serial, or linearly. However, multiple instances of the example process400may occur in parallel. For purposes of explanation of the subject technology, the process400will be discussed in reference toFIGS. 1-3.

At step402, controller input for software is received through a controller associated with a user. At step404a user profile for the user is determined based on the controller input from the user. The user profile may include at least a skill level and an input tendency of the user.

At step406, suggested adjustments to the controller settings are provided, which are intended to improve performance of the user in relation to the software. The controller settings may include at least one of controller sensitivity or controller button assignments.

At step408, approval is received of the user to implement the suggested adjustments to the controller settings. At step410, the controller settings are adjusted based on the approval of the user.

For example, as described above in relation toFIGS. 1-3, at step402, controller input is received (e.g., by controller receiving module308) for software through a controller100associated with a user. At step404a user profile for the user is determined (e.g., by user profile determination module310) based on the controller input from the user. The user profile may include at least a skill level and an input tendency of the user. At step406, suggested adjustments206to settings of the controller are provided (e.g., through GUI200), which are intended to improve performance of the user in relation to the software. The settings202of the controller100may include at least one of controller sensitivity or controller button assignments (e.g., controller settings202). At step408, approval is received of the user to implement the suggested adjustments206to the settings202of the controller100. At step410, the settings202of the controller100are adjusted based on the approval of the user.

According to an aspect, the controller may be an analog controller and/or digital controller (e.g., analog/digital controller) that includes at least one of a gamepad, a footpad, a control surface, a navigation controller, a handicapped accessible controller, steering wheel, flight stick, or pedals. According to an aspect, the software includes at least one of a flight simulator, a driving simulator, or a video game.

According to an aspect, the suggested adjustments are incremental. According to an aspect, the controller comprises at least one of a light sensor, an audio sensor, or a tactile sensor.

According to an aspect, the suggested adjustments are based at least in part on an algorithm. For example, the algorithm may include a machine learning model, an if/then telemetry engine, etc. According to an aspect, the machine learning model may include at least one of a regression algorithm, an instance-based algorithm, a decision tree algorithm, a Bayesian algorithm, a clustering algorithm, an association rule learning algorithm, an artificial neural network algorithm, a deep learning algorithm, a dimensionality reduction algorithm, or an ensemble algorithm.

According to an aspect the process400may further include gathering user performance data through telemetry of the software. According to an aspect the process400may further include sharing the settings of the controller for the user with other users.

According to an aspect the process400may further include comparing the user profile of the user with other user profiles of other users. According to an aspect the process400may further include providing the suggested adjustments based on the comparing.

FIG. 5is a block diagram illustrating an exemplary computer system500with which aspects of the subject technology can be implemented. In certain aspects, the computer system500may be implemented using hardware or a combination of software and hardware, either in a dedicated server, integrated into another entity, or distributed across multiple entities.

Computer system500(e.g., server and/or client) includes a bus508or other communication mechanism for communicating information, and a processor502coupled with bus508for processing information. By way of example, the computer system500may be implemented with one or more processors502. Processor502may be a general-purpose microprocessor, a microcontroller, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), a controller, a state machine, gated logic, discrete hardware components, or any other suitable entity that can perform calculations or other manipulations of information.

Computer system500can include, in addition to hardware, code that creates an execution environment for the computer program in question, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of them stored in an included memory504, such as a Random Access Memory (RAM), a flash memory, a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable PROM (EPROM), registers, a hard disk, a removable disk, a CD-ROM, a DVD, or any other suitable storage device, coupled to bus508for storing information and instructions to be executed by processor502. The processor502and the memory504can be supplemented by, or incorporated in, special purpose logic circuitry.

Computer system500further includes a data storage device506such as a magnetic disk or optical disk, coupled to bus508for storing information and instructions. Computer system500may be coupled via input/output module510to various devices. The input/output module510can be any input/output module. Exemplary input/output modules510include data ports such as USB ports. The input/output module510is configured to connect to a communications module512. Exemplary communications modules512include networking interface cards, such as Ethernet cards and modems. In certain aspects, the input/output module510is configured to connect to a plurality of devices, such as an input device514and/or an output device516. Exemplary input devices514include a keyboard and a pointing device, e.g., a mouse or a trackball, by which a user can provide input to the computer system500. Other kinds of input devices514can be used to provide for interaction with a user as well, such as a tactile input device, visual input device, audio input device, or brain-computer interface device. For example, feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback, and input from the user can be received in any form, including acoustic, speech, tactile, or brain wave input. Exemplary output devices516include display devices such as an LCD (liquid crystal display) monitor, for displaying information to the user.

According to one aspect of the present disclosure, the above-described gaming systems can be implemented using a computer system500in response to processor502executing one or more sequences of one or more instructions contained in memory504. Such instructions may be read into memory504from another machine-readable medium, such as data storage device506. Execution of the sequences of instructions contained in the main memory504causes processor502to perform the process steps described herein. One or more processors in a multi-processing arrangement may also be employed to execute the sequences of instructions contained in memory504. In alternative aspects, hard-wired circuitry may be used in place of or in combination with software instructions to implement various aspects of the present disclosure. Thus, aspects of the present disclosure are not limited to any specific combination of hardware circuitry and software.

As the user computing system500reads game data and provides a game, information may be read from the game data and stored in a memory device, such as the memory504. Additionally, data from the memory504servers accessed via a network the bus508, or the data storage506may be read and loaded into the memory504. Although data is described as being found in the memory504, it will be understood that data does not have to be stored in the memory504and may be stored in other memory accessible to the processor502or distributed among several media, such as the data storage506.