NVIDIA Corporation was recently awarded a patent outlining a system for recommending video games to players based on their individual playstyle preferences.
The system analyses player interactions with multiple video games to create aggregate playstyle profiles for each video game, allowing for tailored recommendations that align with a player’s specific gameplay preferences rather than relying on genre or popularity.
By comparing a player’s playstyle patterns to these aggregate profiles, the system can identify and recommend other video games with similar playstyle characteristics.
The system takes into account various attributes associated with the video games, such as genre, console compatibility, aesthetics, ratings, release date, and more, in generating recommendations.
Earlier today, we came across a recently published patent from NVIDIA Corporation titled, “PLAYSTYLE ANALYSIS FOR GAME RECOMMENDATIONS,” filed in December 2021 under the name of NVIDIA Corporation. The patent, published earlier this month, describes a system for recommending games to players based on their playstyle preferences.
“In various examples, data representing user interactions with a plurality of games are analyzed to generate an aggregate playstyle profile for each game. The aggregate playstyle profiles of games participated in a by a user may be used to recommend one or more other games with a similar aggregate playstyle profile,” reads the abstract for the patent.
“In embodiments, an individual user’s playstyle patterns may be used to determine games having similar playstyle profiles to eh user’s playstyle patterns. In this way, game recommendations are more tailored to a particular type of gameplay, and not only to particular genres of games or games that are currently the most popular.”
Conventionally, video game recommendations are often based on factors like genre or popularity, which may not necessarily align with a player’s specific playstyle. This patent introduces a more personalised approach to game recommendations by analysing the way players interact with video games and identifying their unique playstyles.
The system collects data on user interactions with various video games to create an aggregate playstyle profile for each video game. This profile represents the typical playstyle associated with that video game. By comparing a player’s playstyle patterns to these aggregate profiles, the system can recommend other video games that have similar playstyle characteristics.
For example, if a player tends to play video games in a relaxed and methodical manner, with fewer inputs to the video game controller, the system would identify video games that have been found to have a similar playstyle and recommend them to the player. This way, the recommendations are more tailored to the player’s specific gameplay preferences, rather than just considering genres or overall popularity.
Additionally, the system can also analyse the playstyle patterns of a particular video game played by a player and compare them to patterns from other video games. This allows the system to generate recommendations based on the similarities between the playstyles of different video games.
According to NVIDIA Corporation’s claims, the system determines a similarity score between the player’s interactions with the first video game and the other video games. Based on this score and the association of the data with the first video game, the system recommends the first video game to the player.
The system determines the frequency of the player’s inputs to the video game controllers and changes in pixel values across frames as part of the data analysis. The recommendation of the video game is based on the similarity score being higher than a certain threshold. The video game attributes associated with the first video game and the other video games are considered in the recommendation process.
The recommendation of the video game takes into account other attributes associated with the video games, such as genre, console compatibility, game aesthetic, age rating, content rating, release date, theme, setting, or expected time to completion. Alternatively, the system uses a machine learning model to compute the similarity score based on the data.
It generates data based on player interactions with the first video game and the second video game. Hence, if the player has played the first video game, the system recommends the second video game to the player. This operates in a similar manner to collaborative filtering, a widely used recommendation algorithm that considers both user similarities and item similarities to generate recommendations.
It is crucial to acknowledge that the system described is currently just a patent and does not guarantee adoption by NVIDIA Corporation. Although the implementation of this patented system may offer a more immersive and enjoyable gameplay experience, it is not a certainty. If this system does come to fruition, players can anticipate uncovering hidden treasures that cater specifically to their unique playstyle, surpassing the limitations of conventional genre-based recommendations.
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