公开(公告)号：US20250036940A1

公开(公告)日：2025-01-30

申请号：US18637161

申请日：2024-04-16

Applicant: Apple Inc.

Inventor： Ali MOIN ,  Ran LIU ,  Ellen L. ZIPPI ,  Mohammad Hadi POUR ANSARI ,  Christopher M. SANDINO ,  Erdrin AZEMI

IPC: G06N3/08

Abstract: The subject technology provides frequency-aware masked autoencoders for multimodal pretraining on frequency-based signals. An apparatus receives input data comprising frequency-based signal information associated with one or more modalities. The apparatus transforms the input data from a time domain to a frequency domain. The apparatus generates a frequency-embedded latent representation of the input data comprising time-domain and frequency-domain information. The apparatus also generates a masked frequency-embedded latent representation by masking one or more frequency components in the frequency-embedded latent representation. The apparatus produces a trained machine learning model by training a neural network to predict one or more masked frequency components of the frequency-embedded latent representation.

公开(公告)号：US20250004566A1

公开(公告)日：2025-01-02

申请号：US18751182

申请日：2024-06-21

Applicant: Apple Inc.

Inventor： Charles MAALOUF ,  Kaan E. DOGRUSOZ ,  Giovanni M. AGNOLI ,  Louis W. BOKMA ,  Adam J. LEONARD ,  Behrooz SHAHSAVARI ,  Yiqiang NIE ,  Hojjat Seyed MOUSAVI ,  Heriberto NIETO ,  Christopher M. SANDINO

IPC: G06F3/01 ,  G06N20/20

Abstract: Aspects of the subject technology provide improved gesture detection including collection of data from multiple sensors into a data structure that may be analyzed to estimate a plurality of gesture inferences, and then the plurality of gesture inferences may be integrated into a detected gesture for the period of time. Analysis of the data package may be performed by separate machine learning models, each model producing a corresponding gesture inference.

公开(公告)号：US20240103632A1

公开(公告)日：2024-03-28

申请号：US18369833

申请日：2023-09-18

Applicant: Apple Inc.

Inventor： Matthias R. HOHMANN ,  Anna SEDLACKOVA ,  Bradley W. GRIFFIN ,  Christopher M. SANDINO ,  Darius A. SATONGAR ,  Erdrin AZEMI ,  Kaan E. DOGRUSOZ ,  Paul G. PUSKARICH ,  Gergo PALKOVICS

IPC: G06F3/01 ,  G06F3/0346

CPC classification number: G06F3/017 ,  G06F3/0346 ,  G06F3/011 ,  G06F3/016

Abstract: Aspects of the subject technology relate to providing gesture-based control of electronic devices. Providing gesture-based control may include determining, with a machine learning system that includes multiple machine learning models, a prediction of one or more gestures and their corresponding probabilities of being performed. A likelihood of the user's intent to actually perform that gesture may then be generated, based on the prediction and a gesture detection factor. The likelihood may be dynamically updated over time, and a visual, auditory, and/or haptic indicator of the likelihood may be provided as user feedback. The visual, auditory, and/or haptic indicator may be helpful to guide the user to the correct gesture if the gesture is intended, or to stop performing an action similar to the gesture if the gesture is not intended.