公开(公告)号：US11676373B2

公开(公告)日：2023-06-13

申请号：US16938362

申请日：2020-07-24

Applicant: Apple Inc.

Inventor： Jeff Gonion ,  Duncan Robert Kerr

IPC: G06V40/16 ,  G06F1/3231 ,  G06F21/32 ,  G06F21/62 ,  G06K9/62 ,  G06F3/00 ,  G06F3/0482 ,  G06F3/0485 ,  G06V10/94 ,  G06F18/40

CPC classification number: G06V10/945 ,  G06F1/3231 ,  G06F3/005 ,  G06F3/0482 ,  G06F3/0485 ,  G06F18/40 ,  G06F21/32 ,  G06F21/629 ,  G06V40/16 ,  G06V40/164 ,  G06V40/172 ,  Y02D10/00

Abstract: Systems and methods are provided for control of a personal computing device based on user face detection and recognition techniques.

公开(公告)号：US20230177824A1

公开(公告)日：2023-06-08

申请号：US18161666

申请日：2023-01-30

Applicant: Adobe Inc.

Inventor： Brian Price ,  Scott Cohen ,  Mai Long ,  Jun Hao Liew

IPC: G06V10/82 ,  G06N3/084 ,  G06T7/11 ,  G06V10/26 ,  G06V10/44 ,  G06F18/40 ,  G06N3/045 ,  G06N5/01 ,  G06V10/94 ,  G06V10/20

CPC classification number: G06V10/82 ,  G06N3/084 ,  G06T7/11 ,  G06V10/26 ,  G06V10/454 ,  G06F18/40 ,  G06N3/045 ,  G06N5/01 ,  G06V10/945 ,  G06V10/255 ,  G06N3/044

Abstract: Systems and methods are disclosed for selecting target objects within digital images utilizing a multi-modal object selection neural network trained to accommodate multiple input modalities. In particular, in one or more embodiments, the disclosed systems and methods generate a trained neural network based on training digital images and training indicators corresponding to various input modalities. Moreover, one or more embodiments of the disclosed systems and methods utilize a trained neural network and iterative user inputs corresponding to different input modalities to select target objects in digital images. Specifically, the disclosed systems and methods can transform user inputs into distance maps that can be utilized in conjunction with color channels and a trained neural network to identify pixels that reflect the target object.

公开(公告)号：US11671275B2

公开(公告)日：2023-06-06

申请号：US17941700

申请日：2022-09-09

Applicant: HANWHA TECHWIN CO., LTD.

Inventor： Ho Jung Lee ,  Mi Ran Cho ,  Yeon Woo Kim

IPC: G06F3/04842 ,  H04L12/28 ,  G06F3/04847 ,  G06F3/16 ,  H04L67/12 ,  G06F3/0481 ,  G06F3/0488 ,  G06V10/20 ,  H04L67/75 ,  G06F18/40 ,  G06T17/10 ,  G06F3/04883 ,  H04L67/131

CPC classification number: H04L12/282 ,  G06F3/0481 ,  G06F3/0488 ,  G06F3/04847 ,  G06F3/167 ,  G06F18/40 ,  G06V10/255 ,  H04L67/12 ,  H04L67/75 ,  G06F3/04842 ,  G06F3/04883 ,  G06T17/10 ,  H04L67/131 ,  H04L2012/2841

Abstract: A device and a method for controlling a device using a real-time image are provided. The method includes: receiving an image captured by an image capturing device connected to a network to display the image in real-time; searching for the device that is connected to the network and is controllable; designating, within the image, a setting zone corresponding to the device; receiving a user input; and controlling the device selected according to the user input. A location of the setting zone within the image may be updated according to a change in the image. The user may receive immediate visual feedback on how the devices are being controlled. The user may control a device displayed on the screen on which the real-time indoor image is displayed without having to navigate through different sub-menus for different devices.

公开(公告)号：US20230153396A1

公开(公告)日：2023-05-18

申请号：US18151268

申请日：2023-01-06

Applicant: Snap Inc.

Inventor： Zehao Xue ,  Zhou Ren

IPC: G06F18/40 ,  G06N20/00 ,  G06F18/214

CPC classification number: G06F18/40 ,  G06N20/00 ,  G06F18/214 ,  G06V2201/10 ,  G06V20/68

Abstract: Systems and methods are provided for analyzing, by a computing device, location data associated with a location of the computing device to determine that an image or video captured using a messaging application on the computing device is captured near a food-related venue or event, receiving input related to food associated with the food-related venue or event, sending the image or video and the input related to food associated with the food-related venue or event to a computing system to train a machine learning model for food detection, and updating the messaging application to comprise the trained machine learning model for food detection.

公开(公告)号：US20240346817A1

公开(公告)日：2024-10-17

申请号：US18756740

申请日：2024-06-27

Applicant: Apple Inc.

Inventor： Jeff GONION ,  Duncan Robert KERR

IPC: G06V10/94 ,  G06F1/3231 ,  G06F3/00 ,  G06F3/0482 ,  G06F3/0485 ,  G06F18/40 ,  G06F21/32 ,  G06F21/62 ,  G06V40/16

CPC classification number: G06V10/945 ,  G06F1/3231 ,  G06F3/005 ,  G06F3/0482 ,  G06F3/0485 ,  G06F18/40 ,  G06F21/32 ,  G06F21/629 ,  G06V40/16 ,  G06V40/164 ,  G06V40/172 ,  Y02D10/00

Abstract: Systems and methods are provided for control of a personal computing device based on user face detection and recognition techniques.

公开(公告)号：US12099909B2

公开(公告)日：2024-09-24

申请号：US16125743

申请日：2018-09-09

Applicant: Tazi Al Systems, Inc.

Inventor： Tanju Cataltepe

IPC: G06N20/20 ,  G05B13/02 ,  G05B23/02 ,  G06F3/16 ,  G06F16/2455 ,  G06F18/10 ,  G06F18/15 ,  G06F18/21 ,  G06F18/2115 ,  G06F18/23 ,  G06F18/40 ,  G06N3/04 ,  G06N3/045 ,  G06N3/08 ,  G06N5/043 ,  G06N5/045 ,  G06N7/00 ,  G06N20/00 ,  G06V10/28 ,  G06V10/70 ,  G06V10/72 ,  G06V10/77 ,  G06V10/778 ,  G06V10/80

CPC classification number: G06N20/20 ,  G05B13/028 ,  G05B23/0221 ,  G05B23/0229 ,  G06F3/165 ,  G06F16/24568 ,  G06F18/10 ,  G06F18/15 ,  G06F18/2115 ,  G06F18/2178 ,  G06F18/23 ,  G06F18/40 ,  G06N3/04 ,  G06N3/045 ,  G06N3/08 ,  G06N5/043 ,  G06N5/045 ,  G06N7/00 ,  G06N20/00 ,  G06V10/28 ,  G06V10/70 ,  G06V10/72 ,  G06V10/77 ,  G06V10/778 ,  G06V10/7784 ,  G06V10/80 ,  G06V10/803 ,  G06T2207/20081

Abstract: An Online Machine Learning System (OMLS) including an Online Explanation System (OES), updated continuously, configured to provide instance level explanations and model level explanations to a user; an Online Human Expert Feedback System (OEFS), updated continuously, configured to obtain expert instance level feedback and expert model level feedback, for optimization of operation of the OMLS; an Online Machine Learning Engine (OMLE) for incorporating and utilizing one or more machine learning algorithms or models utilizing features to generate a result, and capable of incorporating and utilizing multiple different machine learning algorithms or models, wherein the OMLS is configured to perform continuous online machine learning.

公开(公告)号：US12096131B2

公开(公告)日：2024-09-17

申请号：US17512338

申请日：2021-10-27

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Quockhanh Dinh ,  Kyonghwan Jin ,  Youngo Park ,  Kwangpyo Choi

IPC: G06T7/90 ,  G06F18/214 ,  G06F18/40 ,  G06N20/00 ,  G06T3/4015 ,  G06T3/4038 ,  G06T5/20 ,  G06T5/70 ,  H04N23/81 ,  H04N23/90

CPC classification number: H04N23/81 ,  G06F18/2148 ,  G06F18/40 ,  G06N20/00 ,  G06T3/4015 ,  G06T3/4038 ,  G06T5/20 ,  G06T5/70 ,  G06T7/90 ,  H04N23/90 ,  G06T2207/10016 ,  G06T2207/10024 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/20182

Abstract: A method includes obtaining, by a second processor of the electronic device, from a first processor of the electronic device, a control signal for obtaining image data, loading any one learning model of at least one learning model into a memory, obtaining, by using the camera module, raw image data of the object, from light reflected from the object, the raw image data being configured to have a specified color array consisting of a plurality of colors with respect to a plurality of pixels, obtaining, by using the loaded any one learning model, a color data set with respect to the plurality of pixels from the obtained raw image data, the color data set including a plurality of pieces of color data classified according to the plurality of colors, and obtaining the noise-reduced image data of the object by using the obtained color data set.

公开(公告)号：US12094456B2

公开(公告)日：2024-09-17

申请号：US17119093

申请日：2020-12-11

Applicant: Shanghai Cambricon Information Technology Co., Ltd.

Inventor： Tianshi Chen ,  Shaoli Liu ,  Zai Wang ,  Shuai Hu

IPC: G06F16/9535 ,  G06F18/20 ,  G06F18/21 ,  G06F18/2431 ,  G06F18/40 ,  G06N3/04 ,  G06N3/08 ,  G06Q30/0601 ,  G06T1/20 ,  G06T3/4046 ,  G10L15/16 ,  G10L17/00 ,  G10L17/18

CPC classification number: G10L15/16 ,  G06F16/9535 ,  G06F18/21 ,  G06F18/2431 ,  G06F18/285 ,  G06F18/40 ,  G06N3/04 ,  G06N3/08 ,  G06Q30/0631 ,  G06T1/20 ,  G06T3/4046 ,  G10L17/00 ,  G10L17/18 ,  G06T2200/28

Abstract: Disclosed are an information processing method and a terminal device. The method comprises: acquiring first information, wherein the first information is information to be processed by a terminal device, calling an operation instruction in a calculation apparatus to calculate the first information so as to obtain second information, and outputting the second information. By means of the examples in the present disclosure, a calculation apparatus of a terminal device can be used to call an operation instruction to process first information, so as to output second information of a target desired by a user, thereby improving the information processing efficiency. The present technical solution has advantages of a fast computation speed and high efficiency.

公开(公告)号：US20240289625A1

公开(公告)日：2024-08-29

申请号：US18428455

申请日：2024-01-31

Applicant: Neurala, Inc.

Inventor： Matthew Luciw ,  Santiago OLIVERA ,  Anatoly Gorshechnikov ,  Jeremy Wurbs ,  Heather Marie Ames ,  Massimiliano Versace

IPC: G06N3/084 ,  G06F18/23211 ,  G06F18/40 ,  G06N3/04 ,  G06N3/044 ,  G06N3/045 ,  G06N3/08 ,  G06V10/20 ,  G06V10/44 ,  G06V10/764 ,  G06V10/82 ,  G06V10/94 ,  G06V20/13 ,  G06V20/17

CPC classification number: G06N3/084 ,  G06F18/23211 ,  G06F18/40 ,  G06N3/0409 ,  G06N3/044 ,  G06N3/045 ,  G06N3/08 ,  G06V10/255 ,  G06V10/454 ,  G06V10/764 ,  G06V10/82 ,  G06V10/95 ,  G06V20/17 ,  G06V20/13

Abstract: Lifelong Deep Neural Network (L-DNN) technology revolutionizes Deep Learning by enabling fast, post-deployment learning without extensive training, heavy computing resources, or massive data storage. It uses a representation-rich, DNN-based subsystem (Module A) with a fast-learning subsystem (Module B) to learn new features quickly without forgetting previously learned features. Compared to a conventional DNN, L-DNN uses much less data to build robust networks, dramatically shorter training time, and learning on-device instead of on servers. It can add new knowledge without re-training or storing data. As a result, an edge device with L-DNN can learn continuously after deployment, eliminating massive costs in data collection and annotation, memory and data storage, and compute power. This fast, local, on-device learning can be used for security, supply chain monitoring, disaster and emergency response, and drone-based inspection of infrastructure and properties, among other applications.

公开(公告)号：US12057110B2

公开(公告)日：2024-08-06

申请号：US17119213

申请日：2020-12-11

Applicant: Shanghai Cambricon Information Technology Co., Ltd.

Inventor： Tianshi Chen ,  Shaoli Liu ,  Zai Wang ,  Shuai Hu

IPC: G10L15/16 ,  G06F16/9535 ,  G06F18/20 ,  G06F18/21 ,  G06F18/2431 ,  G06F18/40 ,  G06N3/04 ,  G06N3/08 ,  G06Q30/0601 ,  G06T1/20 ,  G06T3/4046 ,  G10L17/00 ,  G10L17/18

CPC classification number: G10L15/16 ,  G06F16/9535 ,  G06F18/21 ,  G06F18/2431 ,  G06F18/285 ,  G06F18/40 ,  G06N3/04 ,  G06N3/08 ,  G06Q30/0631 ,  G06T1/20 ,  G06T3/4046 ,  G10L17/00 ,  G10L17/18 ,  G06T2200/28

Abstract: An information processing method applied to a computation circuit is disclosed. The computation circuit includes a communication circuit and an operation circuit. The method includes controlling, by the computation circuit, the communication circuit to obtain a voice to be identified input by a user; controlling, by the computation circuit, the operation circuit to obtain and call an operation instruction to perform voice identification processing on the voice to be identified to obtain target text information corresponding to the voice to be identified. The operation instruction is a preset instruction for voice identification.