EA Patents An Automated Coaching System For Online Gaming
EA's system will measure the performance of players using its own parameters and teach them how to improve.
The gaming industry has been evolving at an unprecedented rate in recent years. Game developers are constantly trying to innovate game design and create crafty mechanics and unforgettable gameplay experiences in order to amaze players and win them over with their games.
For this reason, developers are constantly trying to create innovative mechanics such as the Nemesis system by Monolith for Middle-Earth games. So it is no surprise to know that such ambitious systems and mechanics are patented so that others may not use them without the developer/publisher/owner’s permission.
Recently, EA filed a patent for a revolutionary coaching system that will measure players’ performances and teach them how to improve.
- EA has patented a system that will coach online players.
- The system will measure different criteria to measure the performance of players and generate a behaviour model.
- The model will be used by the system to predict what changes can the player make to improve their skills.
The patent is for “AUTOMATED COACHING FOR ONLINE GAMING”, and as the name states, it is supposedly an automatic system. The patent was first filed on April 2, 2020.
The basic description reads:
A game coaching system identifies gameplay data associated with online game players of an online game and determines, based at least in part on the gameplay data, a performance model to predict performance metric(s) of individual players. The performance model may be used to conduct a sensitivity analysis to determine which perturbations to individual parameters of gameplay data result in improvements to the performance metric(s) of a player. This sensitivity analysis may be used to identify action(s) to recommend to the player to improve his or her gameplay performance. The action(s) that are recommended to the player may be decided based at least in part on the predicted impact of those action(s) on player performance and/or the ease of implementing the action(s). The game coaching system may provide updated action recommendations to players to allow the player to improve his or her gameplay performance over time.
The patent is for a very useful system that EA may add to its future online games. Basically, it is a system that aims to improve players’ skills by actively informing them of corrections they can make to their general playstyle so that they can improve their skills.
The patent aims to essentially ‘coach’ players and tell them how they can improve their skills and what exactly is it that they must focus on in order to play much better.
EA’s coaching system does so by measuring different parameters from a player’s online matches and creating a performance based on the data.
The system then uses the performance model to predict different criteria of a player’s performance. This includes performing calculations and predicting what alterations in certain behaviour during an online match or a player’s playstyle will produce a specific kind of positive effect that would lead to the player completing a match’s objectives better.
As the description states, the system will recommend different actions to increase player performance, and the recommendations will be based on what the system predicted effect of those recommendations on player performance, while also on how easy they will be to implement for the player, as not everybody is skilled enough for that, or for monumental changes to their playstyle.
Naturally, there are many who are filled with envy as they see others kill their opponents in-game with ease in a mere blink of an eye, and wish to become better themselves. With this system, it may very well be possible.
Moreover, improving a player’s skills and performance is not the only effect this would have on them.
An online game player may become disengaged from an online game if he or she is not able to improve his or her performance in the online game over time. With the complexity of modern online games, it may be difficult for players to know how to improve their online gaming performance. According to example embodiments of the disclosure, players may be provided with recommendations to improve their online gaming performance. These recommendations may be specific actions that one can take when playing the online game.
As the description states, naturally, people lose interest in a game if they are unable to improve. Thus, they leave the game when there is no sense of enjoyment that can be derived from experiencing it, and with the growing complexity of online games these days, not everybody may not know how to improve themselves.
This is where EA’s coaching system would step in. It would judge the data it obtained from a player’s performance and would make recommendations on how to improve based on that, and could be as simple as performing specific actions when playing a game.
For this purpose, there are a wide variety of metrics that the system will consider and measure in order to create a performance model of the player.
The player gaming data may include one or more performance metrics (e.g., damage per match score, matchmaking rating, etc.), as well as one or more gameplay data associated with individual ones of the players. The gameplay data may include various characteristics of a player’s gameplay, such as, in the context of a battle royale, first person shooter-type game, the amount of use of certain types of weapons, level of walking/running, levels of climbing, levels of communications with other players, accuracy of weapons use, etc. The gameplay data, as described herein, may include numerical and/or descriptive characteristics of a player’s gameplay in one or many sessions of an online game. In other words, the gameplay data may include descriptive statistics and/or summaries of the aspects of a player’s gameplay over a period of time and/or number of sessions.
This clearly indicates that there will be different metrics measured such as how much damage a player does in a match or even how accurate the player’s shots are. Moreover, it will also take into account the game mode, as there are many different ones such as Deathmatch and Battle Royale, to name two.
The metrics it measures will be different and catered to the main objective of the game mode and the player’s behavior in that, in addition to statistical data from not only one, but multiple sessions in the game.
The patent description further states that it will employ different methods to measure the data and create performance models, such as artificial intelligence and machine learning to name a few.
After it has created a performance model, it will perform different simulations in order to predict what changes in behavior or playstyle will create what type of outcomes, and choose the changes that lead to the best outcomes in a player’s performance based on how feasible it may be for the player as well.
As described before, the system will measure different criteria. These may include:
For example, if a player throws 75 grenades during gameplay of a certain online game, the game coaching system(s) may determine what the effect of the player throwing 72 grenades would be on his or her damage per match score, as well as the effect of the player throwing 78 grenades on his or her damage per match score. Although a +/−3 grenades thrown perturbation is used in this example sensitivity analysis of the number of grenades thrown, it should be understood that any suitable magnitude of perturbation may be used with any quantifiable gameplay data to determine its impact on one or more performance metric.
Measuring similar statistics and different statistics depending on the game mode as well, the coaching system will generate different types of recommendations that it believes will improve the performance and skill of the player, and ultimately lead to the player having more fun and satisfaction when playing.
What’s exciting is that it will not stop here. The coaching system will also measure your performance after it has provided its recommendations, to see which recommendations you followed and what their result was. After that, it may revise its model to give the player more recommendations to improve.
This is all certainly very exciting. How effective it is, we can only judge when the system is actually applied to a game, but this system is certainly enticing, as it not only adds another layer of competitiveness to a game but also may save a game from certain death due to a dwindling fanbase.
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