公开(公告)号：US20240365064A1

公开(公告)日：2024-10-31

申请号：US18605470

申请日：2024-03-14

Applicant: Apple Inc.

Inventor： Andrew E. Greenwood ,  Esge B. Andersen ,  Jamil Dhanani

IPC: H04R5/04 ,  H04R5/033

CPC classification number: H04R5/04 ,  H04R5/033 ,  H04R2430/01

Abstract: A method that includes receiving audio content; playing back the audio content through a speaker at a first volume setting of a volume control of the electronic device, wherein the volume control comprises a plurality of sequential volume settings; determining a noise level within an ambient environment captured by a microphone; receiving a single adjustment to the volume control to change the first volume setting by one volume setting; responsive to receiving the single adjustment, determining a second volume setting based on at least one of the noise level, a content level of the audio content, and historical data indicating past adjustments to the volume control by a user, wherein the second volume setting is either greater than or less than the first volume setting by more than one volume setting; and changing the first volume setting of the electronic device to the second volume setting.

公开(公告)号：US11175898B2

公开(公告)日：2021-11-16

申请号：US16583191

申请日：2019-09-25

Applicant: Apple Inc.

Inventor： Timothy S. Paek ,  Francesco Rossi ,  Jamil Dhanani ,  Keith P. Avery ,  Minwoo Jeong ,  Xiaojin Shi ,  Harveen Kaur ,  Brandt M. Westing

IPC: G06F9/44 ,  G06F8/41 ,  G06F8/60 ,  G06N3/04 ,  G06N3/08

Abstract: The subject technology receives a neural network model in a model format, the model format including information for a set of layers of the neural network model, each layer of the set of layers including a set of respective operations. The subject technology generates neural network (NN) code from the neural network model, the NN code being in a programming language distinct from the model format, and the NN code comprising a respective memory allocation for each respective layer of the set of layers of the neural network model, where the generating comprises determining the respective memory allocation for each respective layer based at least in part on a resource constraint of a target device. The subject technology compiles the NN code into a binary format. The subject technology generates a package for deploying the compiled NN code on the target device.

公开(公告)号：US11416136B2

公开(公告)日：2022-08-16

申请号：US17167896

申请日：2021-02-04

Applicant: Apple Inc.

Inventor： John M. Nefulda ,  Keith P. Avery ,  Madhu Chinthakunta ,  Christopher B. Fleizach ,  Varun Maudgalya ,  Sommer E. Panage ,  Xinyi Yan ,  Garrett L. Weinberg ,  Michal K. Wegrzynski ,  William Caruso ,  Kenneth S. Friedman ,  Jamil Dhanani ,  Muhammad Amir Shafiq ,  Minwoo Jeong ,  Timothy S. Paek ,  Viet Huy Le ,  Heriberto Nieto ,  Brandt M. Westing ,  Rishabh Yadav

IPC: G06F3/048 ,  G06F3/04847 ,  H04M1/72466 ,  G06F3/0482 ,  G06F3/01

Abstract: The present disclosure generally relates to assigning tasks to various user inputs, and detecting and responding to user inputs. In some embodiments, the present disclosure relates to assigning tasks to various user inputs received on a back surface of a device, and detecting and responding to user inputs on the back surface of the device.

公开(公告)号：US11704592B2

公开(公告)日：2023-07-18

申请号：US16937479

申请日：2020-07-23

Applicant: Apple Inc.

Inventor： Keith P. Avery ,  Jamil Dhanani ,  Harveen Kaur ,  Varun Maudgalya ,  Timothy S. Paek ,  Dmytro Rudchenko ,  Brandt M. Westing ,  Minwoo Jeong

IPC: G06F3/0488 ,  G06F3/01 ,  G06F3/0481 ,  G06N20/00 ,  G06F3/04883 ,  H04R3/04 ,  G06N3/08

CPC classification number: G06N20/00 ,  G06F3/011 ,  G06F3/017 ,  G06F3/04883 ,  H04R3/04 ,  G06N3/08 ,  H04R2430/01

Abstract: The subject technology receives, from a first sensor of a device, first sensor output of a first type. The subject technology receives, from a second sensor of the device, second sensor output of a second type, the first and second sensors being non-touch sensors. The subject technology provides the first sensor output and the second sensor output as inputs to a machine learning model, the machine learning model having been trained to output a predicted touch-based gesture based on sensor output of the first type and sensor output of the second type. The subject technology provides a predicted touch-based gesture based on output from the machine learning model. Further, the subject technology adjusts an audio output level of the device based on the predicted gesture, and where the device is an audio output device.