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    Using data from a game metadata system to create actionable in-game decisions
    3.
    发明授权
    Using data from a game metadata system to create actionable in-game decisions 有权

    公开(公告)号：US12145064B2

    公开(公告)日：2024-11-19

    申请号：US17472650

    申请日：2021-09-12

    Applicant: Sony Interactive Entertainment Inc.

    Inventor： Charles Denison

    IPC: A63F13/533 ,  A63F13/67 ,  A63F13/79 ,  A63F13/822 ,  G06N20/00

    Abstract: A machine learning (ML) model is used to identify successful outcomes in computer games based on aggregated game metadata including activity, mechanics, actors, statistics, and zones or locations. A strategy includes how a player over time used a character (actor) to employ one or more mechanics (weapons, vehicles) to execute various activities in various zones or locations in a computer game, with strategies being graded for success. Good strategies are then surfaced to subsequent players by, e.g., advising a player to seek a better location in a game, employ a different mechanic based on the game zone the player's character is in such as employ a different car or car configurations, employ a plane, a particular weapon, etc.

    USING DATA FROM A GAME METADATA SYSTEM TO CREATE ACTIONABLE IN-GAME DECISIONS
    6.
    发明申请
    USING DATA FROM A GAME METADATA SYSTEM TO CREATE ACTIONABLE IN-GAME DECISIONS 有权

    公开(公告)号：US20250161810A1

    公开(公告)日：2025-05-22

    申请号：US18923508

    申请日：2024-10-22

    Applicant: Sony Interactive Entertainment Inc.

    Inventor： Charles Denison

    IPC: A63F13/533 ,  A63F13/67 ,  A63F13/79 ,  A63F13/822 ,  G06N20/00

    Abstract: A machine learning (ML) model is used to identify successful outcomes in computer games based on aggregated game metadata including activity, mechanics, actors, statistics, and zones or locations. A strategy includes how a player over time used a character (actor) to employ one or more mechanics (weapons, vehicles) to execute various activities in various zones or locations in a computer game, with strategies being graded for success. Good strategies are then surfaced to subsequent players by, e.g., advising a player to seek a better location in a game, employ a different mechanic based on the game zone the player's character is in such as employ a different car or car configurations, employ a plane, a particular weapon, etc.

    USING DATA FROM A GAME METADATA SYSTEM TO CREATE ACTIONABLE IN-GAME DECISIONS
    8.
    发明申请
    USING DATA FROM A GAME METADATA SYSTEM TO CREATE ACTIONABLE IN-GAME DECISIONS 有权

    公开(公告)号：US20230082732A1

    公开(公告)日：2023-03-16

    申请号：US17472650

    申请日：2021-09-12

    Applicant: Sony Interactive Entertainment Inc.

    Inventor： Charles Denison

    IPC: A63F13/533 ,  A63F13/822 ,  A63F13/79 ,  A63F13/67 ,  G06N20/00

    Abstract: A machine learning (ML) model is used to identify successful outcomes in computer games based on aggregated game metadata including activity, mechanics, actors, statistics, and zones or locations. A strategy includes how a player over time used a character (actor) to employ one or more mechanics (weapons, vehicles) to execute various activities in various zones or locations in a computer game, with strategies being graded for success. Good strategies are then surfaced to subsequent players by, e.g., advising a player to seek a better location in a game, employ a different mechanic based on the game zone the player's character is in such as employ a different car or car configurations, employ a plane, a particular weapon, etc.

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