公开(公告)号：US20240363098A1

公开(公告)日：2024-10-31

申请号：US18770736

申请日：2024-07-12

Applicant: NEOSAPIENCE, INC.

Inventor： Taesu KIM ,  Younggun LEE

IPC: G10L13/10 ,  G06F40/40 ,  G06N3/04 ,  G06N3/044 ,  G06N3/045 ,  G06N3/08 ,  G10L13/033 ,  G10L13/047 ,  G10L13/08 ,  G10L25/30

CPC classification number: G10L13/10 ,  G06F40/40 ,  G06N3/04 ,  G06N3/044 ,  G06N3/045 ,  G06N3/08 ,  G10L13/033 ,  G10L13/047 ,  G10L13/086 ,  G10L25/30

Abstract: A speech translation method using a multilingual text-to-speech synthesis model includes receiving input speech data of the first language and an articulatory feature of a speaker regarding the first language, converting the input speech data of the first language into a text of the first language, converting the text of the first language into a text of the second language, and generating output speech data for the text of the second language that simulates the speaker's speech by inputting the text of the second language and the articulatory feature of the speaker to a single artificial neural network text-to-speech synthesis model.

公开(公告)号：US20240362481A1

公开(公告)日：2024-10-31

申请号：US18662481

申请日：2024-05-13

Applicant: DeepMind Technologies Limited

Inventor： Volodymyr Mnih ,  Adrià Puigdomènech Badia ,  Alexander Benjamin Graves ,  Timothy James Alexander Harley ,  David Silver ,  Koray Kavukcuoglu

IPC: G06N3/08 ,  G06N3/04 ,  G06N3/045

CPC classification number: G06N3/08 ,  G06N3/04 ,  G06N3/045

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for asynchronous deep reinforcement learning. One of the systems includes a plurality of workers, wherein each worker is configured to operate independently of each other worker, and wherein each worker is associated with a respective actor that interacts with a respective replica of the environment during the training of the deep neural network.

公开(公告)号：US20240362456A1

公开(公告)日：2024-10-31

申请号：US18770518

申请日：2024-07-11

Applicant: UNTETHER AI CORPORATION

Inventor： William Martin SNELGROVE ,  Darrick WIEBE

IPC: G06N3/045 ,  G06F9/38 ,  G06F13/40 ,  G06N3/063

CPC classification number: G06N3/045 ,  G06F9/3887 ,  G06F13/4022 ,  G06N3/063 ,  Y02D10/00

Abstract: A system and method for enhancing C*RAM, improving its performance for known applications such as video processing but also making it well suited to low-power implementation of neural nets. The required computing engine is decomposed into banks of enhanced C*RAM each having a SIMD controller, thus allowing operations at several scales simultaneously. Several configurations of suitable controllers are discussed, along with communication structures and enhanced processing elements.

公开(公告)号：US12133084B2

公开(公告)日：2024-10-29

申请号：US18654756

申请日：2024-05-03

Applicant: Digital Global Systems, Inc.

Inventor： Armando Montalvo ,  Bryce Simmons

IPC: H04L12/28 ,  G06F30/27 ,  G06N3/02 ,  G06N5/022 ,  G06N5/04 ,  G06N20/00 ,  G06N20/10 ,  G06N20/20 ,  H04L41/0893 ,  H04W16/10 ,  H04W24/02 ,  H04W72/0453 ,  G06N3/042 ,  G06N3/045 ,  H04L41/0894 ,  H04W16/14 ,  H04W24/08

CPC classification number: H04W16/10 ,  G06F30/27 ,  G06N3/02 ,  G06N5/022 ,  G06N5/04 ,  G06N20/00 ,  G06N20/10 ,  G06N20/20 ,  H04L41/0893 ,  H04W24/02 ,  H04W72/0453 ,  G06N3/042 ,  G06N3/045 ,  H04L41/0894 ,  H04W16/14 ,  H04W24/08

Abstract: Systems, methods, and apparatuses for providing dynamic, prioritized spectrum utilization management. The system includes at least one monitoring sensor, at least one data analysis engine, at least one application, a semantic engine, a programmable rules and policy editor, a tip and cue server, and/or a control panel. The tip and cue server is operable utilize the environmental awareness from the data processed by the at least one data analysis engine in combination with additional information to create actionable data.

公开(公告)号：US12131528B2

公开(公告)日：2024-10-29

申请号：US17454029

申请日：2021-11-08

Applicant: SK hynix Inc. ,  Korea Advanced Institute of Science and Technology

Inventor： Jun Seo ,  Younghyun Park ,  Jaekyun Moon

IPC: G06V20/00 ,  G06F18/214 ,  G06N3/045 ,  G06N3/048

CPC classification number: G06V20/00 ,  G06F18/214 ,  G06N3/045 ,  G06N3/048

Abstract: An object detecting device includes a feature extracting circuit configured to extract first feature data from an input image; a feature transforming circuit configured to transform the first feature data into transformed feature data according to a transformation function; and a decoder circuit configured to decode the transformed feature data into a region map indicating a detected object.

公开(公告)号：US12131501B2

公开(公告)日：2024-10-29

申请号：US17394175

申请日：2021-08-04

Applicant: Infosys Limited

Inventor： Ujwal Bhate ,  Ninad Jayant Kulkarni ,  Mohammedshadab Mohammedaslam Shaikh ,  Arnab Chakravarty ,  Pratyush Choubey ,  Neeraj Kumar Gulia ,  Jigar Sanjay Shah

IPC: G06T7/73 ,  G06N3/045 ,  G06T7/11 ,  G06T7/20 ,  G06T17/20

CPC classification number: G06T7/75 ,  G06N3/045 ,  G06T7/11 ,  G06T7/20 ,  G06T17/20 ,  G06T2207/20084 ,  G06T2207/30184

Abstract: A method and/or system for automated estimation of 3D orientation of a physical asset using deep learning models and computer vision algorithms, according to one or more embodiments. The system may be configured to receive images of the physical asset and camera orientation data as input, use deep learning neural network models to isolate the physical assets across the images, track each physical asset instance throughout the images and derive a 3D point cloud model of each asset by projecting binary masks of the asset contours from different view-points. The 3D point cloud model is further processed and supplemented with camera orientation data to estimate the 3D orientation of one or more assets present in the images.

公开(公告)号：US12130616B2

公开(公告)日：2024-10-29

申请号：US17358260

申请日：2021-06-25

Applicant: NEC Laboratories America, Inc.

Inventor： Masanao Natsumeda ,  Haifeng Chen

IPC: G05B23/02 ,  G06N3/045 ,  G06N3/08

CPC classification number: G05B23/0283 ,  G05B23/024 ,  G05B23/0272 ,  G06N3/045 ,  G06N3/08

Abstract: Systems and methods for determining a remaining useful life of a system. The system and method include one or more processors; a memory coupled to the one or more processors; a data acquisition unit configured to receive run-to-failure time series data; a neural network training unit configured to train a neural network model to determine a point in time that a health index changes from a healthy stage to a degradation stage; a remaining useful life estimation unit configured to estimate a first remaining useful life of the system based on the point in time; estimate a second remaining useful life of the system by converting a feature representation output by the second neural network; minimize the difference between the first remaining useful life and the second remaining useful life; classify the health stage based on a probability; and an output unit configured to send a warning to a user.

公开(公告)号：US20240355109A1

公开(公告)日：2024-10-24

申请号：US18746977

申请日：2024-06-18

Applicant: Google LLC

Inventor： Michael Sahngwon Ryoo ,  Anthony Jacob Piergiovanni ,  Mingxing Tan ,  Anelia Angelova

IPC: G06V10/82 ,  G06N3/045 ,  G06T1/20 ,  G06T3/4046 ,  G06T7/207 ,  G06V10/776

CPC classification number: G06V10/82 ,  G06N3/045 ,  G06T1/20 ,  G06T3/4046 ,  G06T7/207 ,  G06V10/776 ,  G06T2207/10016 ,  G06T2207/20081 ,  G06T2207/20084

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining one or more neural network architectures of a neural network for performing a video processing neural network task. In one aspect, a method comprises: at each of a plurality of iterations: selecting a parent neural network architecture from a set of neural network architectures; training a neural network having the parent neural network architecture to perform the video processing neural network task, comprising determining trained values of connection weight parameters of the parent neural network architecture; generating a new neural network architecture based at least in part on the trained values of the connection weight parameters of the parent neural network architecture; and adding the new neural network architecture to the set of neural network architectures.

公开(公告)号：US20240354767A1

公开(公告)日：2024-10-24

申请号：US18760398

申请日：2024-07-01

Applicant: American Express Travel Related Services Company, Inc.

Inventor： Manish Gupta ,  Avinash Tripathy ,  Adit Agrawal ,  Madhan Rajasekkharan ,  Abhinav Jain

IPC: G06Q20/40 ,  G06N3/045 ,  G06N3/10

CPC classification number: G06Q20/4016 ,  G06N3/045 ,  G06N3/10

Abstract: Disclosed are various embodiments for leveraging deep learning-based recurrent neural networks (RNNs) using time-series data to evaluate fraud risk for an incoming transaction associated with a user account. Time-series attributes can be extracted from historical transaction data and the incoming transaction data. The time-series attributes can be defined as an array of sequential events that are inputted into an RNN-based machine-learning framework to predict whether an incoming or otherwise pending transaction is fraudulent given the spending sequence. An RNN-based time-series prediction model can be trained to understand and predict patterns associated with a user's spending history according to the inputted time-series data in order to predict whether the transaction is fraudulent.

公开(公告)号：US20240354588A1

公开(公告)日：2024-10-24

申请号：US18303525

申请日：2023-04-19

Applicant: Microsoft Technology Licensing, LLC

Inventor： Jorge Alexandre SILVA TAVARES

IPC: G06N3/096 ,  G06N3/045

CPC classification number: G06N3/096 ,  G06N3/045

Abstract: A system for generating one or more task-specific machine learning models for use in conjunction with one or more accelerated machine learning models is configurable to (i) identify a selected search space from a plurality of pre-defined search spaces; (ii) determine a set of candidate model architectures from the selected search space utilizing model architecture search; (iii) train a set of task-specific machine learning models based upon the set of candidate model architectures using a set of training data comprising input data comprising at least a set of embeddings generated by one or more accelerated machine learning models and task-specific ground truth output; and (iv) output one or more task-specific machine learning models from the set of task-specific machine learning models based upon an evaluation of performance of each task-specific machine learning model.