公开(公告)号：US12182332B2

公开(公告)日：2024-12-31

申请号：US17830990

申请日：2022-06-02

Applicant: Sony Interactive Entertainment Inc.

Inventor： Jorge Arroyo Palacios ,  Chockalingam Ravi Sundaram ,  Mark Anthony ,  Michael Hardisty ,  Sandeep Bansal

IPC: A63F13/215 ,  A63F13/211 ,  A63F13/213 ,  A63F13/2145 ,  A63F13/218 ,  A63F13/235 ,  G06F3/01 ,  G06F3/03 ,  G06F3/0346 ,  G06N20/20

Abstract: Methods and systems are provided for verifying an input provided at a controller including detecting a finger gesture on a surface of the controller. Responsive to detecting the finger gesture, multi-modal data is collected from a plurality of sensors and components tracking the finger gesture. The multi-modal data is used to generate an ensemble model using machine learning algorithm. The ensemble model is trained in accordance to training rules defined for different finger gestures. An output is identified from the ensemble model for the finger gesture. The output is interpreted to define an input for an interactive application selected for interaction.

公开(公告)号：US20230393662A1

公开(公告)日：2023-12-07

申请号：US17830990

申请日：2022-06-02

Applicant: Sony Interactive Entertainment Inc.

Inventor： Jorge Arroyo Palacios ,  Chockalingam Ravi Sundaram ,  Mark Anthony ,  Michael Hardisty ,  Sandeep Bansal

IPC: G06F3/01 ,  G06N20/20 ,  G06F3/03 ,  G06F3/0346 ,  A63F13/2145 ,  A63F13/211 ,  A63F13/218 ,  A63F13/235 ,  A63F13/213 ,  A63F13/215

CPC classification number: G06F3/017 ,  G06N20/20 ,  G06F3/0304 ,  G06F3/0346 ,  A63F13/2145 ,  A63F13/211 ,  A63F13/218 ,  A63F13/235 ,  A63F13/213 ,  A63F13/215 ,  A63F2300/1031 ,  A63F2300/105 ,  A63F2300/1056 ,  A63F2300/1075 ,  A63F2300/1081 ,  A63F2300/1087

Abstract: Methods and systems are provided for verifying an input provided at a controller including detecting a finger gesture on a surface of the controller. Responsive to detecting the finger gesture, multi-modal data is collected from a plurality of sensors and components tracking the finger gesture. The multi-modal data is used to generate an ensemble model using machine learning algorithm. The ensemble model is trained in accordance to training rules defined for different finger gestures. An output is identified from the ensemble model for the finger gesture. The output is interpreted to define an input for an interactive application selected for interaction.