公开(公告)号：US20240408483A1

公开(公告)日：2024-12-12

申请号：US18208159

申请日：2023-06-09

Applicant: Sony Interactive Entertainment Inc.

Inventor： Rathish Krishnan ,  Chockalingam Ravi Sundaram ,  Charlie Denison ,  Ryder McMinn ,  Orlando Cardoso ,  Warren Benedetto ,  Vinit Acharya

IPC: A63F13/52 ,  A63F13/5378 ,  A63F13/60 ,  G06F3/04817 ,  G06V20/70

Abstract: A system for generating gameplay context information for a game may include a game screen classification module trained to classify contextually relevant data from gameplay data, one or more game object recognition modules trained to detect game icons from gameplay data, and a multimodal context generation neural network module trained to generate structured gameplay context information from the contextually relevant data and icons within the gameplay data. The multimodal context generation neural network module at least partially generates structured gameplay context information. The modules may include neural networks trained by suitable machine learning algorithms using suitable masked data and labeled data.

公开(公告)号：US20240412036A1

公开(公告)日：2024-12-12

申请号：US18208166

申请日：2023-06-09

Applicant: Sony Interactive Entertainment Inc.

Inventor： Rathish Krishnan ,  Chockalingam Ravi Sundaram ,  Charlie Denison ,  Ryder McMinn ,  Orlando Cardoso ,  Warren Benedetto ,  Vinit Acharya

IPC: G06N3/045 ,  G06N3/08

Abstract: Application context may be interpolated between application state updates from structured and unstructured application state data. Irrelevant unimodal modules may be deactivated based on the structured application state data while relevant unimodal modules remain active. Unimodal features are generated from the unstructured application using the relevant modules. A neural module selection network module may be trained with a machine learning algorithm. Each unimodal modules may generate unimodal feature vectors from unstructured application data. A context state update module may determine which unimodal modules are irrelevant from structured application state data and deactivate the irrelevant modules but not the relevant ones. A multimodal neural network may take the active unimodal feature vectors and predict structured context data and send it to a uniform data system.