公开(公告)号：US20240194351A1

公开(公告)日：2024-06-13

申请号：US18065483

申请日：2022-12-13

Applicant: Cerner Innovation, Inc.

Inventor： Praveen Bhat Gurpur ,  Winston Rohan D'souza ,  Ramadevi Kumaresan

IPC: G16H50/30 ,  G06F18/2113 ,  G06F18/2413 ,  G06N3/088 ,  G16H50/70

CPC classification number: G16H50/30 ,  G06F18/2113 ,  G06F18/24147 ,  G06N3/088 ,  G16H50/70

Abstract: A diagnostic and decision support technology is provided for determining the likely prognosis and potential treatment for patients experiencing a condition, such as COVID-19, for example. In particular, a mechanism is provided for receiving a historical patient dataset comprising one or more historical health parameters associated with a plurality of historical patients. Additionally, a patient dataset is received comprising one or more patient health parameters associated with a patient. A cluster is identified based on the similarity of the patient dataset and a plurality of historical patient datasets. From the cluster, a set of treatable features are identified and evaluated for their potential impact on the patient's successful recovery from the condition. A recommendation is generated based on the evaluation as to what feature should be treated first to decrease the mortality of the patient.

公开(公告)号：US12008497B2

公开(公告)日：2024-06-11

申请号：US17110922

申请日：2020-12-03

Applicant: NB Ventures, Inc.

Inventor： Subhash Makhija ,  Vanamali Porethi ,  Kiran Kumar Kandula ,  Vengatakrishnan Narayanasamy ,  Shivendra Singh Malik

IPC: G06Q10/0631 ,  G06F16/215 ,  G06F16/23 ,  G06F16/28 ,  G06N3/044 ,  G06N3/045 ,  G06N3/088 ,  G06Q10/087 ,  G06Q30/0201 ,  G06Q30/0202

CPC classification number: G06Q10/06315 ,  G06F16/215 ,  G06F16/2365 ,  G06F16/285 ,  G06N3/044 ,  G06N3/045 ,  G06N3/088 ,  G06Q10/087 ,  G06Q30/0201 ,  G06Q30/0202

Abstract: The present invention provides a data processing system and method for demand sensing and forecasting. The invention includes generating hierarchical data set from historical data of one or more objects and processing the hierarchical data based on one or more forecasting data models created by an artificial engine to predict data trend. The invention determines required safety stock for each category of the one or more objects.

公开(公告)号：US12008486B2

公开(公告)日：2024-06-11

申请号：US17173380

申请日：2021-02-11

Applicant: Cisco Technology, Inc.

Inventor： Hugo Latapie ,  Ozkan Kilic ,  Ramana Rao V. R. Kompella ,  Myungjin Lee ,  Simon Matthew Young

IPC: G06N5/043 ,  G06N3/042 ,  G06N3/088 ,  G06N5/02

CPC classification number: G06N5/043 ,  G06N3/042 ,  G06N5/02 ,  G06N3/088

Abstract: In one embodiment, a device maintains a metamodel that describes a monitored system. The metamodel comprises a plurality of layers ranging from a sub-symbolic space to a symbolic space. The device tracks updates to the metamodel over time. The device updates the metamodel based in part on sub-symbolic time series data generated by the monitored system. The device receives, from a learning agent, a request for the updates to a particular layer of the metamodel associated with a specified time period. The device provides, to the learning agent, data indicative of one or more updates to the particular layer of the metamodel associated with the specified time period.

公开(公告)号：US12008459B2

公开(公告)日：2024-06-11

申请号：US16443440

申请日：2019-06-17

Applicant: Microsoft Technology Licensing, LLC

Inventor： Weizhu Chen ,  Pengcheng He ,  Xiaodong Liu ,  Jianfeng Gao

IPC: G06N3/088 ,  G06F40/20 ,  G06N3/045 ,  G06N3/047

CPC classification number: G06N3/047 ,  G06F40/20 ,  G06N3/045 ,  G06N3/088

Abstract: This document relates to architectures and training procedures for multi-task machine learning models, such as neural networks. One example method involves providing a multi-task machine learning model having one or more shared layers and two or more task-specific layers. The method can also involve performing a pretraining stage on the one or more shared layers using one or more unsupervised prediction tasks. The method can also involve performing a tuning stage on the one or more shared layers and the two or more task-specific layers using respective task-specific objectives.

公开(公告)号：US12001950B2

公开(公告)日：2024-06-04

申请号：US16299828

申请日：2019-03-12

Applicant: International Business Machines Corporation

Inventor： Yang Zhang ,  Chuang Gan

IPC: G06N3/08 ,  G06N3/088 ,  G10L21/0208

CPC classification number: G06N3/08 ,  G06N3/088 ,  G10L21/0208

Abstract: Mechanisms are provided for implementing a generative adversarial network (GAN) based restoration system. A first neural network of a generator of the GAN based restoration system is trained to generate an artificial audio spectrogram having a target damage characteristic based on an input audio spectrogram and a target damage vector. An original audio recording spectrogram is input to the trained generator, where the original audio recording spectrogram corresponds to an original audio recording and an input target damage vector. The trained generator processes the original audio recording spectrogram to generate an artificial audio recording spectrogram having a level of damage corresponding to the input target damage vector. A spectrogram inversion module converts the artificial audio recording spectrogram to an artificial audio recording waveform output.

公开(公告)号：US11996900B2

公开(公告)日：2024-05-28

申请号：US16221235

申请日：2018-12-14

Applicant: Strong Force IoT Portfolio 2016, LLC

Inventor： Charles Howard Cella ,  Gerald William Duffy, Jr. ,  Jeffrey P. McGuckin ,  Mehul Desai

IPC: G06N3/02 ,  B62D15/02 ,  G01M13/028 ,  G01M13/04 ,  G01M13/045 ,  G05B13/02 ,  G05B19/418 ,  G05B23/02 ,  G06F18/21 ,  G06N3/006 ,  G06N3/044 ,  G06N3/045 ,  G06N3/047 ,  G06N3/084 ,  G06N3/088 ,  G06N5/046 ,  G06N7/01 ,  G06N20/00 ,  G06Q10/04 ,  G06Q10/0639 ,  G06Q30/02 ,  G06Q30/06 ,  G06Q50/00 ,  G06V10/778 ,  G06V10/82 ,  G16Z99/00 ,  H02M1/12 ,  H03M1/12 ,  H04B17/23 ,  H04B17/29 ,  H04B17/309 ,  H04B17/318 ,  H04B17/345 ,  H04L1/00 ,  H04L1/18 ,  H04L1/1867 ,  H04L67/1097 ,  H04L67/12 ,  H04W4/38 ,  H04W4/70 ,  B62D5/04 ,  G05B19/042 ,  G06F17/18 ,  G06F18/25 ,  G06N3/126 ,  H04B17/40 ,  H04L5/00 ,  H04L67/306

CPC classification number: H04B17/29 ,  B62D15/0215 ,  G01M13/028 ,  G01M13/04 ,  G01M13/045 ,  G05B13/028 ,  G05B19/4183 ,  G05B19/4184 ,  G05B19/41845 ,  G05B19/4185 ,  G05B19/41865 ,  G05B19/41875 ,  G05B23/0221 ,  G05B23/0229 ,  G05B23/024 ,  G05B23/0264 ,  G05B23/0283 ,  G05B23/0286 ,  G05B23/0289 ,  G05B23/0291 ,  G05B23/0294 ,  G05B23/0297 ,  G06F18/2178 ,  G06N3/006 ,  G06N3/02 ,  G06N3/044 ,  G06N3/045 ,  G06N3/047 ,  G06N3/084 ,  G06N3/088 ,  G06N5/046 ,  G06N7/01 ,  G06N20/00 ,  G06Q10/04 ,  G06Q10/0639 ,  G06Q30/02 ,  G06Q30/0278 ,  G06Q30/06 ,  G06Q50/00 ,  G06V10/7784 ,  G06V10/82 ,  G16Z99/00 ,  H02M1/12 ,  H03M1/12 ,  H04B17/23 ,  H04B17/309 ,  H04B17/318 ,  H04B17/345 ,  H04L1/0002 ,  H04L1/0041 ,  H04L1/18 ,  H04L1/1874 ,  H04L67/1097 ,  H04L67/12 ,  H04W4/38 ,  H04W4/70 ,  B62D5/0463 ,  G05B19/042 ,  G05B23/02 ,  G05B23/0208 ,  G05B2219/32287 ,  G05B2219/35001 ,  G05B2219/37337 ,  G05B2219/37351 ,  G05B2219/37434 ,  G05B2219/37537 ,  G05B2219/40115 ,  G05B2219/45004 ,  G05B2219/45129 ,  G06F17/18 ,  G06F18/21 ,  G06F18/217 ,  G06F18/25 ,  G06N3/126 ,  H04B17/40 ,  H04L1/0009 ,  H04L5/0064 ,  H04L67/306 ,  Y02P80/10 ,  Y02P90/02 ,  Y02P90/80 ,  Y04S50/00 ,  Y04S50/12 ,  Y10S707/99939

Abstract: Methods and an expert system for processing a plurality of inputs collected from sensors in an industrial environment are disclosed. A modular neural network, where the expert system uses one type of neural network for recognizing a pattern relating to at least one of: the sensors, components of the industrial environment and a different neural network for self-organizing a data collection activity in the industrial environment is disclosed. A data communication network configured to communicate at least a portion of the plurality of inputs collected from the sensors to storage device is also disclosed.

公开(公告)号：US11996173B1

公开(公告)日：2024-05-28

申请号：US16813213

申请日：2020-03-09

Applicant: IQVIA Inc.

Inventor： Yong Cai ,  Bob Doyle ,  Dong Dai ,  Wenzhe Lu ,  Emily Zhao ,  Steven Rosztoczy

IPC: G16H10/60 ,  G06N3/088 ,  G16H50/70

CPC classification number: G16H10/60 ,  G06N3/088 ,  G16H50/70

Abstract: A computer-assisted method to timely provide notifications of treatments, the method including receiving de-identified longitudinal medical records, each de-identified longitudinal medical record representing a record of a different anonymized patient and encoding information identifying a treatment received by the anonymized patient and receiving notification data including notification records, each notification record encoding information identifying a channel through which the notification was provided. The method includes determining a first channel impact model representing an impact of a notification provided through a first channel on a treatment being received, a second channel impact model representing an impact of a notification provided through a second channel on a treatment being received, and determining a multi-channel impact model representing an impact of notifications being provided through both the first channel and the second channel on a treatment being received.

公开(公告)号：US11996116B2

公开(公告)日：2024-05-28

申请号：US17000583

申请日：2020-08-24

Applicant: Google LLC

Inventor： Joel Shor ,  Ronnie Maor ,  Oran Lang ,  Omry Tuval ,  Marco Tagliasacchi ,  Ira Shavitt ,  Felix de Chaumont Quitry ,  Dotan Emanuel ,  Aren Jansen

IPC: G10L25/30 ,  G06F18/21 ,  G06N3/084 ,  G06N3/088 ,  G06N5/046 ,  G10L25/48

CPC classification number: G10L25/30 ,  G06F18/217 ,  G06N3/084 ,  G06N3/088 ,  G06N5/046 ,  G10L25/48

Abstract: Examples relate to on-device non-semantic representation fine-tuning for speech classification. A computing system may obtain audio data having a speech portion and train a neural network to learn a non-semantic speech representation based on the speech portion of the audio data. The computing system may evaluate performance of the non-semantic speech representation based on a set of benchmark tasks corresponding to a speech domain and perform a fine-tuning process on the non-semantic speech representation based on one or more downstream tasks. The computing system may further generate a model based on the non-semantic representation and provide the model to a mobile computing device. The model is configured to operate locally on the mobile computing device.

公开(公告)号：US11989873B2

公开(公告)日：2024-05-21

申请号：US17495863

申请日：2021-10-07

Applicant: Samsung Electronics Co., Ltd.

Inventor： Wooyong Cho ,  Gun Huh

IPC: G06T7/00 ,  G03F7/00 ,  G06F18/2415 ,  G06N3/047 ,  G06N3/088

CPC classification number: G06T7/0006 ,  G03F7/7065 ,  G06F18/2415 ,  G06N3/047 ,  G06N3/088 ,  G06T2207/10061 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30148

Abstract: The inventive concepts provide a method of providing a stochastic prediction system. The method includes extracting contours of patterns corresponding to a first design layout from a plurality of scanning electron microscope (SEM) images, respectively, generating a first contour histogram image based on the contours, and training a stochastic prediction model by using the first contour histogram image as an output, and by using the first design layout and a first resist image, a first aerial image, a first slope map, a first density map, and/or a first photo map corresponding to the first design layout as inputs, in which the stochastic prediction model comprises a cycle generative adversarial network (GAN).

公开(公告)号：US11989657B2

公开(公告)日：2024-05-21

申请号：US17071285

申请日：2020-10-15

Applicant: Oracle International Corporation

Inventor： Nikan Chavoshi ,  Anatoly Yakovlev ,  Hesam Fathi Moghadam ,  Venkatanathan Varadarajan ,  Sandeep Agrawal ,  Ali Moharrer ,  Jingxiao Cai ,  Sanjay Jinturkar ,  Nipun Agarwal

IPC: G06N20/00 ,  G06N3/088

CPC classification number: G06N3/088 ,  G06N20/00

Abstract: Herein, a computer generates and evaluates many preprocessor configurations for a window preprocessor that transforms a training timeseries dataset for an ML model. With each preprocessor configuration, the window preprocessor is configured. The window preprocessor then converts the training timeseries dataset into a configuration-specific point-based dataset that is based on the preprocessor configuration. The ML model is trained based on the configuration-specific point-based dataset to calculate a score for the preprocessor configuration. Based on the scores of the many preprocessor configurations, an optimal preprocessor configuration is selected for finally configuring the window preprocessor, after which, the window preprocessor can optimally transform a new timeseries dataset such as in an offline or online production environment such as for real-time processing of a live streaming timeseries.