公开(公告)号：US20240330754A1

公开(公告)日：2024-10-03

申请号：US18194501

申请日：2023-03-31

申请人： ADOBE INC.

发明人： Soumya UNNIKRISHNAN ,  Saina LAJEVARDI ,  Michele SAAD

IPC分类号： G06N20/00 ,  G06N3/0455 ,  G06Q30/018 ,  G06Q30/0283 ,  G06Q30/0601

CPC分类号： G06N20/00 ,  G06N3/0455 ,  G06Q30/018 ,  G06Q30/0283 ,  G06Q30/0625 ,  G06Q30/0631

摘要： Methods and systems are provided for using machine learning to efficiently promote eco-friendly products. In embodiments described herein, a product descriptions associated with a product is obtained. The product description includes subject matter indicating an environmental effect of the product. Thereafter, a score for the product correlated to the environmental effect of the product is generated by a machine learning model based on the product description of the product. The score is then provided for presentation to a user to indicate the correlated environmental effect of the product.

公开(公告)号：US20240330602A1

公开(公告)日：2024-10-03

申请号：US18194644

申请日：2023-04-02

申请人： Microsoft Technology Licensing, LLC

发明人： Linsey LAMBA ,  Hongmei LIU ,  Aarushi ARORA ,  Balaji SEETHARAMAN ,  Aakanksha Prithwi RAJ ,  Gokul Prasanth P ,  Jaiprakash SEKAR ,  Prasanna VENKATESAN ,  Rajesh K

IPC分类号： G06F40/47 ,  G06F40/253 ,  G06F40/55 ,  G06N3/0455 ,  G06N3/0895

CPC分类号： G06F40/47 ,  G06F40/253 ,  G06F40/55 ,  G06N3/0455 ,  G06N3/0895

摘要： A method for training a machine learning model using positive and negative synthetic data is implemented via a computing system including a processor. The method includes generating synthetic data using a generative pre-trained transformer bidirectional language model and self-supervising the generated synthetic data based on positive traits including rule-based criteria and/or model-based criteria. The method also includes generating a set of positive synthetic data labels with gradient scale rating based on the self-supervised synthetic data, synthesizing a set of negative synthetic data labels by self-supervising the positive synthetic data labels, and training a machine learning model using the set of positive synthetic data labels and the set of negative synthetic data labels. Another method further includes utilizing the trained machine learning model to generate Objectives and Key Results (OKRs) within the context of an enterprise application.

公开(公告)号：US12106214B2

公开(公告)日：2024-10-01

申请号：US17968085

申请日：2022-10-18

申请人： SAMSUNG ELECTRONICS CO., LTD. ,  UNIVERSITAET ZUERICH

发明人： Stefan Braun ,  Daniel Neil ,  Enea Ceolini ,  Jithendar Anumula ,  Shih-Chii Liu

IPC分类号： G06N3/08 ,  G06F18/2413 ,  G06F18/25 ,  G06N3/04 ,  G06N3/0442 ,  G06N3/0455 ,  G06N3/0464 ,  G06N3/084 ,  G06V10/44 ,  G06V10/46 ,  G06V10/764 ,  G06V10/80 ,  G06V10/82 ,  G06V20/10 ,  G10L15/16 ,  G10L15/20 ,  G10L15/24

CPC分类号： G06N3/08 ,  G06F18/2413 ,  G06F18/256 ,  G06N3/04 ,  G06N3/0442 ,  G06N3/0455 ,  G06N3/0464 ,  G06N3/084 ,  G06V10/454 ,  G06V10/462 ,  G06V10/764 ,  G06V10/806 ,  G06V10/811 ,  G06V20/10 ,  G10L15/16 ,  G06V10/82 ,  G10L15/20 ,  G10L15/24

摘要： A sensor transformation attention network (STAN) model including sensors configured to collect input signals, attention modules configured to calculate attention scores of feature vectors corresponding to the input signals, a merge module configured to calculate attention values of the attention scores, and generate a merged transformation vector based on the attention values and the feature vectors, and a task-specific module configured to classify the merged transformation vector is provided.

公开(公告)号：US20240312193A1

公开(公告)日：2024-09-19

申请号：US18339075

申请日：2023-06-21

申请人： Samsung Display Co., Ltd.

发明人： Qisen Cheng ,  Shuhui Qu ,  Kaushik Balakrishnan ,  Janghwan Lee

IPC分类号： G06V10/80 ,  G06N3/0455 ,  G06T7/00

CPC分类号： G06V10/803 ,  G06N3/0455 ,  G06T7/0004 ,  G06T2207/20081

摘要： A method may include providing a data set including rows of data. The rows of data may include at least one row of unpaired modality including a first modality, and at least one row of paired modality may include both the first modality and a second modality. The method may further include imputing, by a modality-specific encoder, the at least one row of unpaired modality by interpolating embeddings from the second modality of the paired modality; training, in a latent space, the modality-specific encoder based on the imputation for unimodal prediction and bimodal prediction; and generating a confidence value for the unimodal prediction and the bimodal prediction.

公开(公告)号：US20240311987A1

公开(公告)日：2024-09-19

申请号：US18184911

申请日：2023-03-16

申请人： International Business Machines Corporation

发明人： Eliyahu SCHWARTZ ,  Leonid KARLINSKY ,  Sivan HARARY ,  Assaf ARBELLE

IPC分类号： G06T7/00 ,  G06N3/0455

CPC分类号： G06T7/0002 ,  G06N3/0455 ,  G06T2207/10024 ,  G06T2207/20021 ,  G06T2207/20081

摘要： An example system includes a processor that can randomly mask tokens using different masks to generate different subsets of masked tokens. The processor can process the different sets of masked tokens via a pretrained masked auto-encoder (MAE) encoder to output intermediate representations. The processor can process the intermediate representations via a pretrained MAE decoder to output reconstructed images. The processor can further compare input image with the output reconstructed images to generate an anomaly score.

公开(公告)号：US20240311619A1

公开(公告)日：2024-09-19

申请号：US18602497

申请日：2024-03-12

申请人： University of South Florida

发明人： John Licato

IPC分类号： G06N3/0455 ,  G06F40/40

CPC分类号： G06N3/0455 ,  G06F40/40

摘要： An example method includes receiving a text string; generating, by a first machine learning model, a feature values for the text string, where the feature values correspond to attributes of the text string; inputting the text string and the plurality of feature values into a second machine learning model; and generating, by the second machine learning model, a formal representation of the text string.

公开(公告)号：US20240303470A1

公开(公告)日：2024-09-12

申请号：US18666930

申请日：2024-05-17

申请人： HUAWEI TECHNOLOGIES CO., LTD.

发明人： Zhongwei WANG ,  Rongchen ZHU ,  Han GAO

IPC分类号： G06N3/0464 ,  G06N3/0455 ,  G06N3/08 ,  G06T1/20

CPC分类号： G06N3/0464 ,  G06N3/0455 ,  G06N3/08 ,  G06T1/20

摘要： This application discloses a construction method and apparatus for a bipartite graph, and a display method and apparatus for a bipartite graph. The construction method includes: searching a computational graph for at least one cross-communication edge corresponding to a first communication node, where the first communication node is one of M communication nodes included in the computational graph, the first communication node corresponds to P predecessor nodes and Q successor nodes, each of the at least one cross-communication edge indicates a communication path between one of the P predecessor nodes and one of the Q successor nodes, and no cross-communication edge passes through the M communication nodes; and cutting cross-communication edges respectively corresponding to the M communication nodes, and performing an aggregation operation to obtain the bipartite graph, where any two of the M communication nodes are connected without an edge in the bipartite graph.

公开(公告)号：US20240281618A1

公开(公告)日：2024-08-22

申请号：US18649304

申请日：2024-04-29

申请人： UNLIKELY ARTIFICIAL INTELLIGENCE LIMITED

发明人： William TUNSTALL-PEDOE ,  Finlay CURRAN ,  Harry ROSCOE ,  Robert HEYWOOD

IPC分类号： G06F40/35 ,  G06F16/242 ,  G06F16/31 ,  G06F16/332 ,  G06F16/951 ,  G06F40/123 ,  G06F40/126 ,  G06F40/20 ,  G06F40/205 ,  G06F40/211 ,  G06F40/226 ,  G06F40/242 ,  G06F40/279 ,  G06F40/30 ,  G06F40/45 ,  G06F40/47 ,  G06F40/58 ,  G06N3/0442 ,  G06N3/0455 ,  G06N3/0499 ,  G06N3/08 ,  G06N3/091 ,  G06N5/02 ,  G06Q10/1053 ,  G06Q30/0251 ,  G06Q30/0601 ,  G10L15/08 ,  G10L15/16 ,  G10L15/18 ,  G10L15/22 ,  G10L15/26 ,  G10L25/63 ,  G16H10/60 ,  H04L51/02

CPC分类号： G06F40/35 ,  G06F16/243 ,  G06F16/322 ,  G06F16/3329 ,  G06F16/951 ,  G06F40/123 ,  G06F40/126 ,  G06F40/20 ,  G06F40/205 ,  G06F40/211 ,  G06F40/226 ,  G06F40/242 ,  G06F40/279 ,  G06F40/30 ,  G06F40/45 ,  G06F40/47 ,  G06F40/58 ,  G06N3/0442 ,  G06N3/0455 ,  G06N3/0499 ,  G06N3/08 ,  G06N5/02 ,  G06Q10/1053 ,  G06Q30/0255 ,  G06Q30/0257 ,  G06Q30/0631 ,  G10L15/16 ,  G10L15/1815 ,  G10L15/22 ,  G10L15/26 ,  G10L25/63 ,  G16H10/60 ,  H04L51/02 ,  G06N3/091 ,  G10L2015/088

摘要： A computer implemented method for the automated analysis or use of data is implemented by a voice assistant. The method comprises the steps of: (a) storing in a memory a structured, machine-readable representation of data that conforms to a machine-readable language (‘machine representation’); the machine representation including representations of user speech or text input to a human/machine interface; and (b) automatically processing the machine representations to analyse the user speech or text input.

公开(公告)号：US20240259131A1

公开(公告)日：2024-08-01

申请号：US18290531

申请日：2021-05-21

申请人： LG ELECTRONICS INC.

发明人： Bonghoe KIM ,  Jongwoong SHIN

IPC分类号： H04L1/00 ,  G06N3/0455

CPC分类号： H04L1/0033 ,  G06N3/0455 ,  H04L1/0036

摘要： The present specification provides a method for transmitting/receiving a signal in a wireless communication system by using an auto encoder. More specifically, the method performed by means of a transmission end comprises the steps of: encoding at least one input data block on the basis of a pre-trained transmission end encoder neural network; and transmitting a signal to a reception end on the basis of the encoded at least one input data block, wherein each of activation functions included in the transmission end encoder neural network receives only some of all input values that can be input into each of the activation functions.

公开(公告)号：US12045577B2

公开(公告)日：2024-07-23

申请号：US18088577

申请日：2022-12-25

申请人： UNLIKELY ARTIFICIAL INTELLIGENCE LIMITED

发明人： William Tunstall-Pedoe ,  Finlay Curran ,  Harry Roscoe ,  Robert Heywood

IPC分类号： G06F40/30 ,  G06F16/242 ,  G06F16/31 ,  G06F16/332 ,  G06F16/951 ,  G06F40/123 ,  G06F40/126 ,  G06F40/20 ,  G06F40/205 ,  G06F40/211 ,  G06F40/226 ,  G06F40/242 ,  G06F40/279 ,  G06F40/35 ,  G06F40/45 ,  G06F40/47 ,  G06F40/58 ,  G06N3/0442 ,  G06N3/0455 ,  G06N3/0499 ,  G06N3/08 ,  G06N5/02 ,  G06Q10/1053 ,  G06Q30/0251 ,  G06Q30/0601 ,  G10L15/16 ,  G10L15/18 ,  G10L15/22 ,  G10L15/26 ,  G10L25/63 ,  G16H10/60 ,  H04L51/02 ,  G06N3/091 ,  G10L15/08

CPC分类号： G06F40/35 ,  G06F16/243 ,  G06F16/322 ,  G06F16/3329 ,  G06F16/951 ,  G06F40/123 ,  G06F40/126 ,  G06F40/20 ,  G06F40/205 ,  G06F40/211 ,  G06F40/226 ,  G06F40/242 ,  G06F40/279 ,  G06F40/30 ,  G06F40/45 ,  G06F40/47 ,  G06F40/58 ,  G06N3/0442 ,  G06N3/0455 ,  G06N3/0499 ,  G06N3/08 ,  G06N5/02 ,  G06Q10/1053 ,  G06Q30/0255 ,  G06Q30/0257 ,  G06Q30/0631 ,  G10L15/16 ,  G10L15/1815 ,  G10L15/22 ,  G10L15/26 ,  G10L25/63 ,  G16H10/60 ,  H04L51/02 ,  G06N3/091 ,  G10L2015/088

摘要： A computer implemented method for the automated analysis or use of data is implemented by a voice assistant. The method comprises the steps of: (a) storing in a memory a structured, machine-readable representation of data that conforms to a machine-readable language (‘machine representation’); the machine representation including representations of user speech or text input to a human/machine interface; and (b) automatically processing the machine representations to analyse the user speech or text input.