公开(公告)号：US12118063B2

公开(公告)日：2024-10-15

申请号：US17209051

申请日：2021-03-22

申请人： BEIJING BAIDU NETCOM SCIENCE AND TECHNOLOGY CO., LTD.

发明人： Zhen Li ,  Yukun Li ,  Yu Sun

IPC分类号： G06F40/30 ,  G06F18/214 ,  G06F18/2413

CPC分类号： G06F18/2148 ,  G06F18/24147 ,  G06F40/30

摘要： The present disclosure provides a method, apparatus, electronic device and storage medium for training a semantic similarity model, which relates to the field of artificial intelligence. A specific implementation solution is as follows: obtaining a target field to be used by a semantic similarity model to be trained; calculating respective correlations between the target field and application fields corresponding to each of training datasets in known multiple training datasets; training the semantic similarity model with the training datasets in turn, according to the respective correlations between the target field and the application fields corresponding to each of the training datasets. According to the technical solution of the present disclosure, it is possible to, in the fine-tuning phase, more purposefully train the semantic similarity model with the training datasets with reference to the correlations between the target field and the application fields corresponding to the training datasets, thereby effectively improving the learning capability of the sematic similarity model and effectively improving the accuracy of the trained semantic similarity model.

公开(公告)号：US12118046B2

公开(公告)日：2024-10-15

申请号：US17653151

申请日：2022-03-02

申请人： Coupang Corp.

发明人： Nuri Mehmet Gokhan ,  Varun A Samtani

IPC分类号： G06V10/74 ,  G06F16/951 ,  G06F18/22 ,  G06F18/23 ,  G06F18/2413 ,  G06N3/02 ,  G06N5/01 ,  G06N20/00 ,  G06Q10/0875 ,  G06Q30/02 ,  G06Q30/0201 ,  G06Q30/0283 ,  G06T7/194 ,  G06T11/60 ,  G06V20/40 ,  G10L17/10

CPC分类号： G06F16/951 ,  G06F18/22 ,  G06F18/23 ,  G06F18/24147 ,  G06N3/02 ,  G06N5/01 ,  G06N20/00 ,  G06Q10/0875 ,  G06Q30/02 ,  G06Q30/0201 ,  G06Q30/0283 ,  G06T7/194 ,  G06T11/60 ,  G06V10/761 ,  G06V20/41 ,  G10L17/10

摘要： Systems and methods for correlating item data are disclosed. A system for correlating item data may include a memory storing instructions and at least one processor configured to execute instructions to perform operations including: receiving text and image data associated with a reference item from a remote device; converting, using a computer-modeled embedding layer, at least one image to an image embedding; comparing the image embedding to reference embeddings stored in a database; selecting a subset of the candidate item text as candidate text data based on the comparison; selecting a subset of the candidate item images as candidate image data based on the comparison; selecting a text correlation model; determining a first similarity score; selecting an image correlation model; determining a second similarity score; calculating a confidence score based on the first and second similarity scores; and performing a responsive action based on the calculated confidence score.

公开(公告)号：US12056941B2

公开(公告)日：2024-08-06

申请号：US18100998

申请日：2023-01-24

申请人： Insurance Services Office, Inc.

发明人： Khoi Nguyen ,  Maneesh Kumar Singh

IPC分类号： G06V20/62 ,  G06F18/20 ,  G06F18/2413 ,  G06V30/10 ,  G06V30/18 ,  G06V30/19 ,  G06V30/413 ,  G06V30/414

CPC分类号： G06V20/62 ,  G06F18/24147 ,  G06F18/295 ,  G06V30/18057 ,  G06V30/19173 ,  G06V30/413 ,  G06V30/414 ,  G06V30/10

摘要： Computer vision systems and methods for text classification are provided. The system detects a plurality of text regions in an image and generates a bounding box for each detected text region. The system utilizes a neural network to recognize text present within each bounding box and classifies the recognized text, based on at least one extracted feature of each bounding box and the recognized text present within each bounding box, according to a plurality of predefined tags. The system can associate a key with a value and return a key-value pair for each predefined tag.

公开(公告)号：US20240233923A1

公开(公告)日：2024-07-11

申请号：US18614524

申请日：2024-03-22

申请人： Align Technology, Inc.

发明人： Ya XUE ,  Jeeyoung CHOI ,  Justin B. MOORE ,  Anton SPIRIDONOV

IPC分类号： G16H30/40 ,  A61C7/00 ,  A61C7/08 ,  A61C9/00 ,  G06F18/21 ,  G06F18/2413 ,  G06F18/243 ,  G06N3/08 ,  G06T7/10 ,  G06T7/66 ,  G16H50/20 ,  G16H50/50

CPC分类号： G16H30/40 ,  A61C7/002 ,  A61C7/08 ,  A61C9/0053 ,  G06F18/21 ,  G06F18/24147 ,  G06F18/24323 ,  G06T7/10 ,  G06T7/66 ,  G06N3/08 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/20164 ,  G06T2207/30036 ,  G16H50/20 ,  G16H50/50

摘要： Methods and systems for automatically determining an eruption status and/or primary or permanent tooth type of a target tooth. Methods may include determining tooth shape features of the target tooth from a 3D model of the patient's teeth. The methods may also include normalizing at least some of the tooth shape features using the tooth shape features of one or more reference teeth. The normalized tooth shape features may be applied to a classifier. Applying the normalized tooth shape features to the classifier may include applying either a first level binary classifier or a first level binary classifier and a second level binary classifier to the normalized tooth shape features.

公开(公告)号：US20240223825A1

公开(公告)日：2024-07-04

申请号：US18610920

申请日：2024-03-20

申请人： Google LLC

发明人： Bartlomiej Wolowiec ,  Andreea-Alexandra Ganciulescu

IPC分类号： H04N21/234 ,  G06F18/213 ,  G06F18/2413 ,  G06N3/04 ,  H04N21/258 ,  H04N21/2743 ,  H04N21/454

CPC分类号： H04N21/23418 ,  G06F18/213 ,  G06F18/24147 ,  G06N3/04 ,  H04N21/25875 ,  H04N21/2743 ,  H04N21/454

摘要： Techniques are disclosed for identifying videos containing objectionable content. An example method comprises identifying, by a computing system, a video uploaded to a video sharing service, generating an embedding for the video using a neural network, wherein the embedding specifies a location of the video in a multi-dimensional space where a plurality of videos are located based on content of the videos, identifying from the videos a plurality of associated videos that each have an associated embedding that is located within a predetermined distance of the embedding, determining whether the video is likely to include a particular type of objectionable content by at least determining at least a predetermined amount of the associated videos that contain the particular type of objectionable content, and responsive to determining that the video is likely to include the particular type of objectionable content, causing the video to be blocked from the video sharing service.

公开(公告)号：US12020786B2

公开(公告)日：2024-06-25

申请号：US16869560

申请日：2020-05-07

申请人： Apixio, LLC

发明人： John Zhu ,  Noah Lieberman ,  Ha Pham ,  Vishnuvyas Sethumadhavan

IPC分类号： G16H10/60 ,  G06F18/21 ,  G06F18/231 ,  G06F18/2413 ,  G06V30/413 ,  G16H15/00 ,  G16H40/20 ,  G16H50/20 ,  G16H50/70

CPC分类号： G16H10/60 ,  G06F18/2178 ,  G06F18/231 ,  G06F18/24147 ,  G06V30/413 ,  G16H15/00 ,  G16H40/20 ,  G16H50/20 ,  G16H50/70

摘要： An electronic medical record (EMR) analysis machine automatically clusters electronic medical records to produce an initial EMR analysis model and to identify high-value EMR documents such that human analysts can focus effort on labeling only high-value EMR documents to iteratively and extremely efficiently train an EMR analysis model. High-value sample EMR documents are identified as those whose membership in one or more clusters is most ambiguous, i.e., nearest the cluster boundary.

公开(公告)号：US12019990B2

公开(公告)日：2024-06-25

申请号：US17124030

申请日：2020-12-16

申请人： BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD.

发明人： Haifeng Wang ,  Wenbin Jiang ,  Yajuan Lv ,  Yong Zhu ,  Hua Wu

IPC分类号： G06N20/00 ,  G06F18/214 ,  G06F18/2413 ,  G06F40/279 ,  G06F40/30 ,  G06N5/022

CPC分类号： G06F40/30 ,  G06F18/214 ,  G06F18/24147 ,  G06F40/279 ,  G06N5/022

摘要： The present application discloses a text processing method and device based on natural language processing and a knowledge graph, and relates to the in-depth field of artificial intelligence technology. A specific implementation is: an electronic device uses a joint learning model to obtain a semantic representation, which is obtained by the joint learning model by combining knowledge graph representation learning and natural language representation learning, it combines a knowledge graph representation learning and a natural language representation learning, compared to using only the knowledge graph representation learning or the natural language representation learning to learn semantic representation of a prediction object, factors considered by the joint learning model are more in quantity and comprehensiveness, so accuracy of semantic representation can be improved, and thus accuracy of text processing can be improved.

公开(公告)号：US12014282B2

公开(公告)日：2024-06-18

申请号：US17338404

申请日：2021-06-03

申请人： TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED

发明人： Shimin Chen ,  Songjie Niu ,  Dongyan Zhou ,  Donghai Yu ,  Shijie Sun

IPC分类号： G06K9/00 ,  G06F18/214 ,  G06F18/2323 ,  G06F18/2413 ,  G06N3/045 ,  G06N3/08 ,  G06N5/022

CPC分类号： G06N5/022 ,  G06F18/214 ,  G06F18/2323 ,  G06F18/24147 ,  G06N3/045 ,  G06N3/08

摘要： Embodiments of this disclosure include a data processing method. In the method, a training sample set that includes a plurality of graph computing task training samples is obtained. At least one performance indicator feature of each of the graph computing task training samples is extracted. The at least one performance indicator feature includes one or more of a graph data feature, a graph processing platform feature, a graph algorithm feature, and a machine hardware feature. A target performance prediction model is generated based on a mapping relationship between actual execution times of the graph computing task training samples and the performance indicator features. According to at least one performance indicator feature of an inputted graph computing task test sample, a predicted execution time of the graph computing task test sample is output based on the target performance prediction model.

公开(公告)号：US20240193367A1

公开(公告)日：2024-06-13

申请号：US18062962

申请日：2022-12-07

申请人： Optum, Inc.

发明人： Puneet Juneja ,  Hari Sankar Jayasankar Sasilekha ,  Pawan Sharma

IPC分类号： G06F40/295 ,  G06F16/31 ,  G06F16/33 ,  G06F18/22 ,  G06F18/2413

CPC分类号： G06F40/295 ,  G06F16/316 ,  G06F16/3334 ,  G06F18/22 ,  G06F18/24147

摘要： A method comprises receiving an incident report comprising a textual description of an incident; generating a regularized incident report in which out-of-vocabulary terms in the received incident report are replaced with in-vocabulary terms; determining importance measures for a plurality of incident report terms, wherein each of the incident report terms is in the regularized incident report; generating an incident matrix in which similarity values are defined for combinations of terms in the incident report and terms in a predetermined term set; generating an incident vector based on the incident matrix and the importance measures for the terms in the incident report; applying one or more machine learning (ML) models that identify, based on the incident vector, relevant software support records and/or software modules, wherein the relevant software support records and the software modules are potentially relevant to the incident; and outputting data identifying relevant software support records and/or software modules.

公开(公告)号：US20240184778A1

公开(公告)日：2024-06-06

申请号：US18062005

申请日：2022-12-05

申请人： Samsung Electronics Co., Ltd.

发明人： Matthew BRYSON ,  Vikas SINHA ,  Manali SHARMA ,  Ehsan NAJAFABADI

IPC分类号： G06F16/2453 ,  G06F18/2413

CPC分类号： G06F16/2453 ,  G06F18/24147

摘要： Systems and methods for finding nearest neighbors. In some embodiments, the system includes a processing circuit. The processing circuit may be configured to perform a method, the method including: selecting, based on a first query vector, a selected method, the selected method being a nearest neighbor selection method; and performing the selected method to select a first nearest neighbor from a data set, based on the first query vector.