公开(公告)号：WO2022240439A1

公开(公告)日：2022-11-17

申请号：PCT/US2021/055751

申请日：2021-10-20

Applicant: NOTCO DELAWARE, LLC

Inventor： KAWAS GARCÍA, Nebil ,  BALBONTÍN PUIG, Francisca ,  HYLAND, Karina ,  ABARCA, Cristóbal ,  CLAVERO, Francisco ,  HERRERA, Eugenio ,  LEAL, Daniel ,  MARTÍNEZ, Ariel ,  OLEA, Pablo ,  QUIROZ, Nicolás ,  RICCI, Joaquín

IPC: G06K9/62 ,  G06N3/08 ,  G06F16/2423 ,  G06F16/2457 ,  G06F16/248 ,  G06K9/6256 ,  G06K9/6263 ,  G06N3/0454 ,  G06N3/0472 ,  G06V10/774 ,  G06V10/7784

Abstract: Techniques to mimic a target food item using artificial intelligence are disclosed. A formula generator is trained using ingredients and using recipes and, given a target food item, determines a formula that matches the given target food item. A flavor generator is trained using recipes and their associated flavor information and, given a formula, the flavor generator determines a flavor profile for the given formula. The flavor profile may be used to assist the formula generator in generating a subsequent formula. A recipe generator is trained using recipes and, given a formula, determines a cooking process for the given formula. A food item may be cooked according to a recipe, and feedback, including a flavor profile, may be provided for the cooked food item. The recipe and its feedback may be added to a training set for the flavor generator.

公开(公告)号：WO2022046753A1

公开(公告)日：2022-03-03

申请号：PCT/US2021/047325

申请日：2021-08-24

Applicant: PEPTILOGICS, INC.

Inventor： LEE, Francis ,  STECKBECK, Jonathan D. ,  HOLSTE, Hannes

IPC: G16B40/00 ,  G16B40/10 ,  G16B40/20 ,  G16B40/30 ,  G06F17/14 ,  G06N3/0445 ,  G06N3/0454 ,  G06N3/0472 ,  G06N3/0481 ,  G06N3/08 ,  G06N3/126 ,  G06N7/005 ,  G16C20/30 ,  G16C20/70

Abstract: In one aspect, a method for pre-clinical validation of an effectiveness of a candidate drug compound is disclosed. The method may include receiving, at a processing device, a signal that comprises at least two wavelengths that are each associated with a respective biomarker, wherein the signal is received subsequent to administering the candidate drug compound to a proxy organism, such organism including at least two assays configured to reveal the respective biomarkers. The method also may include analyzing the signal to obtain the at least two wavelengths, and detecting, based on an analysis of the at least two wavelengths, whether each of the respective biomarkers are present.

公开(公告)号：WO2022005626A1

公开(公告)日：2022-01-06

申请号：PCT/US2021/032129

申请日：2021-05-13

Applicant: MICROSOFT TECHNOLOGY LICENSING, LLC

Inventor： ZHANG, Cheng ,  LI, Yingzhen ,  TSCHIATSCHEK, Sebastian ,  YIN, Haiyan ,  KIM, Jooyeon

IPC: G06N3/04 ,  G06N3/08 ,  G06N20/00 ,  G06N3/0454 ,  G06N3/0472 ,  G06N5/022

Abstract: A computer-implemented method of training a model comprising a sequence of stages, each stage in the sequence comprises: a VAE comprising a respective first encoder arranged to encode a respective subset of the real-world features into a respective latent space representation, and a respective first decoder arranged to decode from the respective latent space representation to a respective decoded version of the respective set of real- world features; at least each but the last stage in the sequence comprises: a respective second decoder arranged to decode from the respective latent space representation to predict one or more respective actions; and each successive stage in the sequence following the first stage, each succeeding a respective preceding stage in the sequence, further comprises: a sequential network arranged to transform from the latent representation from the preceding stage to the latent space representation of the successive stage.

公开(公告)号：WO2022005616A1

公开(公告)日：2022-01-06

申请号：PCT/US2021/031472

申请日：2021-05-10

Applicant: MICROSOFT TECHNOLOGY LICENSING, LLC

Inventor： LIU, Yunxin ,  HOU, Jiahui

IPC: G06F21/62 ,  G06F21/74 ,  G06F21/53 ,  G06N3/02 ,  G06F21/6218 ,  G06N3/0454 ,  G06N3/0472 ,  G06N3/063

Abstract: According to implementations of the subject matter described herein, there is provided a solution for secure execution of a machine learning network. An operation of a first network layer of a machine learning network is executed in an uTEE of a computing device based on an input of the first network layer and a first set of modified parameter values, to obtain a first error intermediate output. The modified parameter values are determined by modifying at least one subset of parameter values of the first network layer with first secret data. A first corrected intermediate output is determined in a TEE of the computing device by modifying the first error intermediate output at least based on the input and first secret data. A network output is determined based on the first corrected intermediate output. In this way, it is possible to protect the confidentiality of the machine learning network.

公开(公告)号：WO2021262530A1

公开(公告)日：2021-12-30

申请号：PCT/US2021/037939

申请日：2021-06-17

Applicant: PRICE TECHNOLOGIES INC.

Inventor： JAIN, Rahul ,  MOFFITT, Steven Douglas

IPC: G06F16/50 ,  G06F16/53 ,  G06F16/58 ,  G06N3/02 ,  G06N3/08 ,  G06K9/6215 ,  G06K9/627 ,  G06K9/6284 ,  G06K9/629 ,  G06N3/0454 ,  G06N3/0472 ,  G06Q30/0627 ,  G06Q30/0629 ,  G06Q30/0631 ,  G06V10/82

Abstract: Methods and systems for generating a plurality of matching items that match a reference item are disclosed. The method includes first determining reference attribute data for the reference item, where the reference attribute data is multimodal. Next, selecting a deep learning multimodal matching model from a plurality of candidate multimodal matching models. The selected deep learning multimodal matching model has a first deep learning neural network (DLNN) for processing data having a first data mode and a second DLNN analyzer for processing data having a second data mode. Then, matching a potential matching item to the reference item using the selected deep learning multimodal matching model to generate a match score, where the match score is computed based on the reference attribute data for the reference item and attribute data for the potential matching item. Finally, adding the potential matching item to the plurality of matching items based on the match score.

公开(公告)号：WO2021253686A1

公开(公告)日：2021-12-23

申请号：PCT/CN2020/119545

申请日：2020-09-30

Applicant: 北京迈格威科技有限公司 ,  成都旷视金智科技有限公司

Inventor： 罗孙锋 ,  王光甫 ,  陈远鹏 ,  刘帅成

IPC: G06K9/62 ,  G06K9/6256 ,  G06N3/0472 ,  G06N3/08 ,  G06V10/751

Abstract: 本公开提供了一种特征点跟踪训练及跟踪方法、装置、电子设备及存储介质，该跟踪训练方法包括：获取样本视频中的相邻两帧，将一帧作为初始帧，将另一帧作为目标帧；对初始帧进行特征点检测，得到特征点坐标；通过孪生的特征提取神经网络得到初始帧对应的特征张量和目标帧对应的特征张量；从初始帧对应的特征张量中确定特征点坐标对应的特征向量，并将特征向量与目标帧对应的特征张量进行局部匹配，得到匹配得分图；将匹配得分图输入特征点跟踪神经网络，得到特征点坐标对应的预测坐标；确定预测坐标与匹配得分图中最高得分对应坐标的损失值；根据损失值，对网络参数进行调整，循环执行上述步骤，直至损失值收敛。本公开减少了对数据标注的依赖。

公开(公告)号：WO2021250218A1

公开(公告)日：2021-12-16

申请号：PCT/EP2021/065725

申请日：2021-06-10

Applicant: IMPLICITY

Inventor： CRESPIN, Eliot ,  ROSIER, Arnaud ,  PERLMUTTER, David

IPC: A61B5/352 ,  A61B5/364 ,  A61B5/366 ,  A61B5/00 ,  G16H50/20 ,  A61B5/361 ,  G16H50/30 ,  G16H50/70 ,  G06N3/00 ,  G06N3/04 ,  A61B5/0006 ,  A61B5/0022 ,  A61B5/347 ,  A61B5/363 ,  A61B5/7221 ,  A61B5/726 ,  A61B5/7264 ,  A61B5/7267 ,  A61B5/7282 ,  G06N20/20 ,  G06N3/0445 ,  G06N3/0454 ,  G06N3/0472 ,  G06N3/08 ,  G06N5/003 ,  G16H40/63 ,  G16H40/67

Abstract: The present invention relates to a method (100) and a system for analyzing electrocardiographic segments previously derived from a cardiac device.

公开(公告)号：WO2021246943A1

公开(公告)日：2021-12-09

申请号：PCT/SE2021/050515

申请日：2021-06-02

Applicant: NANOSC AB

Inventor： ZAHEDINEJAD, Mohammad ,  MURALIDHAR, Shreyas ,  ÅKERMAN, Johan ,  HOUSHANG, Afshin ,  AWAD, Ahmad

IPC: G06N10/00 ,  G06N3/0445 ,  G06N3/0472 ,  G06N3/049 ,  G06N3/0635 ,  G06N5/003

Abstract: The present invention relates to an Ising Machine utilizing a network of spin Hall nano-oscillators (SHNOs) suitable for computational tasks such as optimization problems. The spin Hall nano- oscillator based Ising machine is provided with a tuning unit arranged to effect the characteristics of at least one individual spin Hall nano-oscillators of the array; and a SHNO read-out unit arranged to detect and transfer a state of at least a one individual spin Hall nano-oscillators of the array.

公开(公告)号：WO2021198704A1

公开(公告)日：2021-10-07

申请号：PCT/GB2021/050828

申请日：2021-04-01

Applicant: HAZY LIMITED

Inventor： ARTHUR, James Reid Desmond ,  ROBINSON, Luke Anthony William ,  KEEN, Harry Richard

IPC: G06F21/62 ,  H04L29/08 ,  G06N20/00 ,  H04L29/06 ,  G06F21/6218 ,  G06F21/6254 ,  G06N3/0454 ,  G06N3/0472 ,  G06N3/084 ,  G06N5/04 ,  H04L63/02

Abstract: A computing system for enabling the analysis of multiple raw data sets whilst protecting the privacy of information within the raw data sets, the system comprising a plurality of synthetic data generators and a data hub. Each synthetic data generator is configured to: access a corresponding raw data set stored in a corresponding one of a plurality of raw data stores; produce, based on the corresponding raw data set, a synthetic data generator model configured to generate a synthetic data set representative of the corresponding raw data set; and push synthetic information including at least one of the corresponding synthetic data set and the synthetic data generator model to the data hub. The data hub is configured to store the synthetic information received from the synthetic data generators for access by one or more clients for analysis. The system is configured such that the data hub cannot directly access the raw data sets and such that the synthetic data information can only be pushed from the synthetic data generators to the data hub.

公开(公告)号：WO2021197783A1

公开(公告)日：2021-10-07

申请号：PCT/EP2021/056099

申请日：2021-03-10

Applicant: ABB SCHWEIZ AG

Inventor： SCHMIDT, Benedikt ,  AMIHAI, Ido ,  KOTRIWALA, Arzam ,  CHIOUA, Moncef ,  LENDERS, Felix ,  JANKA, Dennis ,  HOLLENDER, Martin ,  SCHLAKE, Jan Christoph ,  ABUKWAIK, Hadil ,  KLOEPPER, Benjamin

IPC: G06N20/00 ,  G06N3/08 ,  G06N3/04 ,  G05B23/02 ,  G05B19/418 ,  G05B23/024 ,  G06N3/0454 ,  G06N3/0472

Abstract: A computer-implemented method of generating a training data set for training an artificial intelligence module (10), AI module, is provided. The method comprises providing, on a data storage (102), a first data set (12) and a second data set (14), wherein the first data set 5 includes one or more first data elements (13) indicative of a first operational condition of an industrial system, wherein the second data set includes one or more second data elements (15) indicative of a second operational condition of the industrial system, wherein the first operational condition substantially matches the second operational condition. The method further comprises determining a data transformation for transforming the one or more first 10 data elements (13) of the first data set (12) into the one or more second data elements (15) of the second data set (14), applying the determined data transformation to the one or more first data elements (13) of the first data set and/or to one or more further data elements of one or more further data sets, thereby generating a transformed data set, and generating a training data set for training the AI module (10) based on at least a part of the transformed 15 data set.