公开(公告)号：US20240362489A1

公开(公告)日：2024-10-31

申请号：US18769814

申请日：2024-07-11

Applicant: KEISUUGIKEN CORPORATION

Inventor： Naohiro HAYAISHI ,  Kazuma TAKAHARA

IPC: G06N3/084 ,  G06F18/21 ,  G06F18/214 ,  G06N20/00 ,  G06V10/20 ,  G06V10/26 ,  G06V10/44 ,  G06V10/56 ,  G06V10/82 ,  G06V20/66

CPC classification number: G06N3/084 ,  G06F18/214 ,  G06F18/217 ,  G06N20/00 ,  G06V10/255 ,  G06V10/26 ,  G06V10/454 ,  G06V10/56 ,  G06V10/82 ,  G06V20/66

Abstract: A counting apparatus includes: a storage unit storing a learning model, the learning model being trained using multiple pairs of training input images each obtained by capturing an image of multiple count target objects with the same shape, and training output images each containing teaching figures that are arranged at respective positions of the multiple count target objects. A captured image acquiring unit acquiring a captured image of multiple count target objects; an output image acquiring unit acquiring an output image in which the count target objects contained in the captured image are converted into count target figures, by applying the captured image to the learning model. A counting unit counting the number of count target objects, using the multiple count target figures contained in the output image; and an output unit outputting the number of count target objects counted by the counting unit.

公开(公告)号：US20240362488A1

公开(公告)日：2024-10-31

申请号：US18641446

申请日：2024-04-22

Applicant: GUANGXI UNIVERSITY

Inventor： Ying YANG ,  Kai YANG ,  Shuaihu YANG ,  Min FENG

IPC: G06N3/084

CPC classification number: G06N3/084

Abstract: The invention discloses a big data analysis system for engine quality detection and prediction, comprising an oil acquisition module for collecting oil in an engine; an oil analysis module for obtaining spectral data, ferrographic data, and physicochemical data of the oil; a data fusion module for fusing the spectral data, ferrographic data and physicochemical data based on a fuzzy logic and a D-S evidence theory to obtain oil fusion data; an oil prediction module for constructing an oil prediction model, training the oil prediction model based on the oil fusion data, and predicting the oil in the engine based on a trained oil prediction model to obtain oil prediction data; a quality detection module connected with the oil prediction module for obtaining a wear degree of the engine and completing a quality prediction of the engine based on the oil prediction data.

公开(公告)号：US12131259B2

公开(公告)日：2024-10-29

申请号：US17109034

申请日：2020-12-01

Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor： Clement Decrop ,  Tushar Agrawal ,  Jeremy R. Fox ,  Sarbajit K. Rakshit

IPC: G06N3/08 ,  G06F40/30 ,  G06N3/044 ,  G06N3/084

CPC classification number: G06N3/084 ,  G06F40/30 ,  G06N3/044

Abstract: A processor-implemented method for predicting alternative communications based on textual analysis. The method includes building, by machine learning, a model to predict an optimal communication method, whereby the building includes training the model on a knowledge corpus of historic data and user data, and results of previous predictions in similar circumstances. The method further includes intercepting textual communication within communication channels, wherein the intercepting comprises a keyboard capture, a screen capture, or both a keyboard capture and a screen capture. The method further includes identifying, by pattern analysis, sentiment analysis, and textual analysis, topics, sentiments, and participants within the intercepted textual communication. The method further includes predicting, by the model, the optimal communication method, whereby the optimal communication method comprises continuing the textual communication, a video conference or a telephone conference.

公开(公告)号：US20240355091A1

公开(公告)日：2024-10-24

申请号：US18600349

申请日：2024-03-08

Applicant: Capital One Services, LLC

Inventor： Mark IBRAHIM ,  John PAISLEY ,  Ceena MODARRES ,  Melissa LOUIE

IPC: G06V10/762 ,  G06F18/23213 ,  G06N3/04 ,  G06N3/084

CPC classification number: G06V10/763 ,  G06F18/23213 ,  G06N3/04 ,  G06N3/084

Abstract: Embodiments include techniques to determine a set of credit risk assessment data samples, generate local credit risk assessment attributions for the set of credit risk assessment samples, and normalize each local credit risk assessment attribution of the local credit risk assessment attributions. Further, embodiments techniques to compare each pair of normalized local credit risk assessment attributions and assign a rank distance thereto proportional to a degree of ranking differences between the pair of normalized local credit risk assessment attributions. The techniques also include applying a K-medoids clustering algorithm to generate clusters of the local risk assessment attributions, generating global attributions, and determining insights for the neural network based on the global attributions.

公开(公告)号：US20240346608A1

公开(公告)日：2024-10-17

申请号：US18294476

申请日：2022-08-02

Applicant: Schlumberger Technology Corporation

Inventor： Kassem Ghorayeb ,  Hussein Hayek ,  Ahmad Harb ,  Tarek Naous

IPC: G06Q50/06 ,  E21B41/00 ,  G06N3/0499 ,  G06N3/084 ,  G06Q10/0631

CPC classification number: G06Q50/06 ,  G06N3/0499 ,  G06N3/084 ,  G06Q10/06313 ,  E21B41/00

Abstract: A method may include receiving input data of one or more reservoir well section locations and a facility location and initializing the machine learning algorithm based on the input data. Moreover, the machine learning model may be trained to determine one or more well trajectories that adhere to a set of constraints based on a training dataset of predefined well trajectory solutions. The method may also include determining, via the machine learning algorithm, a well trajectory design between the facility location and at least one of the reservoir well section locations based on the facility location and the reservoir well section location.

公开(公告)号：US20240346533A1

公开(公告)日：2024-10-17

申请号：US18740485

申请日：2024-06-11

Applicant: Microsoft Technology Licensing, LLC

Inventor： Mayank SHRIVASTAVA ,  Sagar GOYAL ,  Sahil BHATNAGAR ,  Pin-Jung CHEN ,  Pushpraj SHUKLA ,  Arko P. MUKHERJEE

IPC: G06Q30/0202 ,  G06N3/04 ,  G06N3/084 ,  G06N20/00

CPC classification number: G06Q30/0202 ,  G06N3/04 ,  G06N3/084 ,  G06N20/00

Abstract: The disclosure herein describes a system for generating embeddings representing sequential human activity by self-supervised, deep learning models capable of being utilized by a variety of machine learning prediction models to create predictions and recommendations. An encoder-decoder is provided to create user-specific journeys, including sequenced events, based on human activity data from a plurality of tables, a customer data platform, or other sources. Events are represented by sequential feature vectors. A user-specific embedding representing user activities in relationship to activities of one or more other users is created for each user in a plurality of users. The embeddings are updated in real-time as new activity data is received. The embeddings can be fine-tuned using labeled data to customize the embeddings for a specific predictive model. The embeddings are utilized by predictive models to create product recommendations and predictions, such as customer churn, next steps in a customer journey, etc.

公开(公告)号：US20240346321A1

公开(公告)日：2024-10-17

申请号：US18632708

申请日：2024-04-11

Applicant: Micron Technology, Inc.

Inventor： Febin Sunny ,  Poorna Kale ,  Saideep Tiku

IPC: G06N3/084

CPC classification number: G06N3/084

Abstract: Training an artificial neural network (ANN) can include receiving device design parameters corresponding to a device and operation parameters corresponding to the device. Device throughput characteristics can also be received from a physics solver. Device throughput predictions can be generated utilizing the device design parameters, the operation parameters, and an artificial neural network. A loss gradient can be generated utilizing the device throughput characteristics and the device throughput predictions. The ANN can be trained, utilizing the loss gradient, to generate different device throughput predictions.

公开(公告)号：US20240346320A1

公开(公告)日：2024-10-17

申请号：US18616068

申请日：2024-03-25

Applicant: Rain Neuromorphics Inc.

Inventor： Cagri Eryilmaz

IPC: G06N3/084

CPC classification number: G06N3/084

Abstract: A learning system is described. The learning system includes multiple cores and at least one processor. The cores may perform operations. The processor(s) implement a core selection scheme whereby a subset of the plurality of cores is selected on which at least one operation is to be performed. The processor(s) also implement an operation selection scheme whereby a subset of the operations is selected for each core in the subset of the plurality of cores. Each core in the subset of the plurality cores performs the subset of the operations selected.

公开(公告)号：US12118989B2

公开(公告)日：2024-10-15

申请号：US17507437

申请日：2021-10-21

Applicant: BEIJING BAIDU NETCOM SCIENCE AND TECHNOLOGY CO., LTD.

Inventor： Xu Chen ,  Jinfeng Bai ,  Runqiang Han ,  Lei Jia

IPC: G10L15/20 ,  G06N3/084 ,  G10L15/06 ,  G10L15/22 ,  G10L21/0208 ,  G10L21/0232 ,  G10L21/038 ,  G10L25/30

CPC classification number: G10L15/20 ,  G06N3/084 ,  G10L15/063 ,  G10L15/22 ,  G10L21/0232 ,  G10L21/038 ,  G10L25/30 ,  G10L2021/02082

Abstract: The present disclosure provides a speech processing method, and a method for generating a speech processing model, related to a field of signal processing technologies. The speech processing method includes: obtaining M speech signals to be processed and N reference signals; performing sub-band decomposition on each of the M speech signals and each of the N reference signals to obtain frequency-band components in each speech signal and each reference signal; processing the frequency-band components in each speech signal and each reference signal by using an echo cancellation model, to obtain an ideal ratio mask corresponding to the N reference signals in each frequency band of each speech signal; and performing echo cancellation on each frequency-band component of each speech signal based on the ideal ratio mask corresponding to the N reference signals in each frequency band of each speech signal, to obtain M echo-cancelled speech signals.

公开(公告)号：US12118469B2

公开(公告)日：2024-10-15

申请号：US17667462

申请日：2022-02-08

Applicant: INDUSTRY-ACADEMIC COOPERATION FOUNDATION, YONSEI UNIVERSITY

Inventor： Bum Sub Ham ,  Hyun Jong Park

IPC: G06T7/00 ,  G06N3/084 ,  G06V10/44 ,  G06V10/50 ,  G06V10/82 ,  G06V20/52 ,  G06V40/10

CPC classification number: G06N3/084 ,  G06T7/97 ,  G06V10/454 ,  G06V10/50 ,  G06V10/82 ,  G06V20/52 ,  G06V40/10 ,  G06V40/103 ,  G06V10/457

Abstract: A person re-identification device comprises: a feature extracting and dividing unit, that receives images including a person to be re-identified and extracts a feature of each image according to a pre-learned pattern estimation method to acquire a 3-dimensional feature vector, and divides the 3-dimensional feature vector into a pre-designated size unit to acquire local feature vectors; a one-to-many relational reasoning unit, that estimates the relationship between each of the local feature vectors and remaining local feature vectors, and reflects the estimated relationship to acquire local relational features; a global contrastive pooling unit, that acquires a global contrastive feature by performing global contrastive pooling; and a person re-identification unit, that receives the local relational features and the global contrastive feature as a final descriptor of a corresponding image, and compares the final descriptor with a reference descriptor acquired in advance from an image including a person to be searched.