公开(公告)号：WO2022016011A1

公开(公告)日：2022-01-20

申请号：PCT/US2021/041885

申请日：2021-07-15

Applicant: APPLIED MATERIALS, INC.

Inventor： ISKANDAR, Jimmy ,  ARMACOST, Michael D.

IPC: G05B23/02 ,  G05B19/418 ,  H01L21/67 ,  G06K9/627 ,  G06K9/6284 ,  G06K9/6289 ,  G06K9/6298 ,  G06N3/04

Abstract: Implementations disclosed describe a method and a system to perform the method of obtaining a reduced representation of a plurality of sensor statistics representative of data collected by a plurality of sensors associated with a device manufacturing system performing a manufacturing operation. The method further includes generating, using a plurality of outlier detection models, a plurality of outlier scores, each of the plurality of outlier scores generated based on the reduced representation of the plurality of sensor statistics using a respective one of the plurality of outlier detection models. The method further includes processing the plurality of outlier scores using a detector neural network to generate an anomaly score indicative of a likelihood of an anomaly associated with the manufacturing operation.

公开(公告)号：WO2022015310A1

公开(公告)日：2022-01-20

申请号：PCT/US2020/042313

申请日：2020-07-16

Applicant: LANDMARK GRAPHICS CORPORATION

Inventor： TITUS, Zainab Diana ,  CAUSER, Annabel ,  BAINES, Graham ,  YALLUP, Christine ,  ADEYEMI, Olutobi ,  SERVAIS, Marc Paul

IPC: G06N20/00 ,  G01W1/10 ,  G01K17/08 ,  G01N25/18 ,  G06F17/175 ,  G06K9/6256 ,  G06K9/6262 ,  G06K9/6298

Abstract: A heat flow modeler preprocesses geological and heat flow data for an earth formation for inputting into a plurality of supervised learning models. The heat flow modeler trains the plurality of supervised learning models on the preprocessed geological data to estimate heat flow throughout the earth formation. The heat flow modeler interpolates the estimated heat flow values to a set of desired locations in the earth formation and cosimulates the preprocessed heat flow values with the interpolated heat flow values as auxiliary variables to generate a cosimulated heat flow map. A final heat flow map is generated by rasterizing the cosimulated heat flow map.

公开(公告)号：WO2021141921A1

公开(公告)日：2021-07-15

申请号：PCT/US2021/012218

申请日：2021-01-05

Applicant: CLEERLY, INC.

Inventor： MIN, James K. ,  EARLS, James P. ,  RODRIGUES MARQUES, Hugo Miguel

IPC: A61B6/00 ,  A61B6/04 ,  A61B6/10 ,  A61B5/0066 ,  A61B5/0075 ,  A61B5/055 ,  A61B5/7267 ,  A61B5/742 ,  A61B5/7475 ,  A61B6/032 ,  A61B6/037 ,  A61B6/481 ,  A61B6/504 ,  A61B6/5205 ,  A61B8/12 ,  A61B8/14 ,  A61K49/04 ,  G06K2009/363 ,  G06K2209/05 ,  G06K9/3216 ,  G06K9/36 ,  G06K9/6215 ,  G06K9/627 ,  G06K9/6298 ,  G06T2207/10081 ,  G06T2207/10088 ,  G06T2207/10101 ,  G06T2207/10132 ,  G06T2207/20081 ,  G06T2207/30048 ,  G06T2207/30101 ,  G06T7/0012

Abstract: The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and/or dynamically identify one or more features, such as plaque and vessels, and/or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and/or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and/or quantified parameters.

公开(公告)号：WO2022212306A1

公开(公告)日：2022-10-06

申请号：PCT/US2022/022247

申请日：2022-03-29

Applicant: ALLSTATE INSURANCE COMPANY

Inventor： LIU, Dongmin ,  PHAN, Giang ,  WALDMAN, Marcus ,  INOYATOV, Akmal ,  ROETS, Lindes ,  MILLER, Jeremy ,  GOULET, Kevin

IPC: G06N20/00 ,  G06K9/623 ,  G06K9/6257 ,  G06K9/6277 ,  G06K9/6298

Abstract: Aspects of the disclosure relate to using machine learning methods for identity health scoring. A computing platform may train a machine learning model, using historical event information, by: 1) classifying the historical event information using logical regression, and 2) after classifying the historical event information, performing time series calibration on the classified historical event information, wherein training the machine learning model configures the machine learning model to output identity health information. The computing platform may receive new event information. The computing platform may input the new event information into the machine learning model, which may cause the machine learning model to output the identity health information. The computing platform may send, to a client device, the identity health information and one or more commands directing the client device to display an identity health interface, which may cause the client device to display the identity health interface.

公开(公告)号：WO2021203088A1

公开(公告)日：2021-10-07

申请号：PCT/US2021/025747

申请日：2021-04-05

Applicant: INSURANCE SERVICES OFFICE, INC.

Inventor： DARDIA, Kristen ,  WALLER, Tracey ,  SINGH, Ekampreet ,  MATIJOSAITIENE, Irina ,  STOLL, Frederick

IPC: G06F16/00 ,  G06F16/35 ,  G06F16/55 ,  G06N5/02 ,  G06Q30/02 ,  G06F16/2365 ,  G06F16/285 ,  G06K9/6287 ,  G06K9/6298

Abstract: Systems and methods for dynamic computer modeling using incomplete data are provided. The system can yield a total score indicative of an accuracy and reliability of a model for a given application based on incomplete data, wherein the system can receive one or more sets of datasets, and classify. A final component score for each dataset is determined by applying a weighted classification component value to the classification component score for each dataset. If the system determines that there is at least one incomplete or missing dataset among the set of datasets, the system determines a final component score and determines the total score by aggregating the final component scores for each complete dataset and the final component scores for each incomplete or missing dataset.