公开(公告)号：US20240424390A1

公开(公告)日：2024-12-26

申请号：US18340208

申请日：2023-06-23

Applicant: Sony Interactive Entertainment Inc.

Inventor： Michael Taylor ,  Yasunori Matsui ,  Yurika Kondo ,  Mihee Kang

IPC: A63F13/428 ,  G06F3/01

Abstract: A machine learning-based model is configured to make inferences about computer game actions to execute based on dynamic, varying player gestures and to translate those game actions into input sequence macros. In some instances, the button sequence mapping for the macros can even dynamically change based on game state so that different macros for the same computer game action might be inferred by the model depending on game state.

公开(公告)号：US20250010199A1

公开(公告)日：2025-01-09

申请号：US18348298

申请日：2023-07-06

Applicant: Sony Interactive Entertainment Inc.

Inventor： Michael Taylor ,  Yasunori Matsui ,  Yurika Kondo ,  Mihee Kang

IPC: A63F13/67 ,  A63F13/213

Abstract: A machine learning-based model or other system is configured to release user commands to a computer game at a time when the command correctly aligns with what is going on in the computer game. As such, the command might be predicted and released before the user input action is completed, or might be delayed after completion and processing until a later time when the command is most appropriate for release.

公开(公告)号：US20250010181A1

公开(公告)日：2025-01-09

申请号：US18348301

申请日：2023-07-06

Applicant: Sony Interactive Entertainment Inc.

Inventor： Michael Taylor ,  Yasunori Matsui ,  Yurika Kondo ,  Mihee Kang

IPC: A63F13/44 ,  A63F13/424 ,  A63F13/428 ,  A63F13/67

Abstract: A system is configured to determine a window of time during which to execute a particular gesture or voice command to a computer game. The system otherwise does not execute the command outside the window of time, helping to reduce false positives from unintended input.

公开(公告)号：US20240424406A1

公开(公告)日：2024-12-26

申请号：US18340222

申请日：2023-06-23

Applicant: Sony Interactive Entertainment Inc.

Inventor： Michael Taylor ,  Yasunori Matsui ,  Yurika Kondo ,  Mihee Kang

IPC: A63F13/67 ,  A63F13/40

Abstract: A machine learning-based model is configured to make inferences about computer game actions to execute based on dynamic, varying player gestures and to translate those game actions into input sequence macros. In some instances, the button sequence mapping for the macros can even dynamically change based on game state so that different macros for the same computer game action might be inferred by the model depending on game state.