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IC_EX:G16H* AND EN_ALLTXT:(coronavirus OR coronaviruses OR coronaviridae OR coronavirinae OR orthocoronavirus OR orthocoronaviruses OR orthocoronaviridae OR orthocoronavirinae OR betacoronavirus OR betacoronaviruses OR betacoronaviridae OR betacoronavirinae OR sarbecovirus OR sarbecoviruses OR sarbecoviridae OR sarbecovirinae OR "severe acute respiratory syndrome" OR sars OR "2019 ncov" OR covid)

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Analysis

1.WO/2021/237292METHODS OF ASSESSING RISK OF DEVELOPING A SEVERE RESPONSE TO CORONAVIRUS INFECTION
WO 02.12.2021
Int.Class C12Q 1/6883
CCHEMISTRY; METALLURGY
12BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
QMEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
1Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
68involving nucleic acids
6876Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
6883for diseases caused by alterations of genetic material
Appl.No PCT/AU2021/050507 Applicant GENETIC TECHNOLOGIES LIMITED Inventor DITE, Gillian Sue
The present disclosure relates to methods and systems for assessing the risk of a human subject developing a severe response to a Coronavirus infection, such as a severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) virus infection.
2.20210390508System For Controlling The Spread Of Infectious Diseases
US 16.12.2021
Int.Class G06Q 10/10
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
10Administration; Management
10Office automation, e.g. computer aided management of electronic mail or groupware; Time management, e.g. calendars, reminders, meetings or time accounting
Appl.No 17345711 Applicant Bipin KOLLURI Inventor Bipin KOLLURI

This invention relates to systems and methods for controlling the spread of microorganism infections or infectious disease. Present invention is particularly directed to providing systems and methods that allow secure distribution of self-expiring health badges to the subjects free of Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Individuals desirious of reducing the risks of infection and preventing the spread of such diseases participate in the testing program whereby regular periodic screening tests are performed to indicate the infection status of each such individual. The self-expiring badge given to infection-free individuals uses a time indicator and provides a clear indication of expiration of the badge after a predetermined period of time interval. Furthermore, the individuals may be employees of a business entity and testing all employees periodically can provide an infection-free work environment.

3.WO/2021/247682METHODS FOR MITIGATING OR DIMINISHING SPREAD OF PATHOGENIC INFECTIONS
WO 09.12.2021
Int.Class G16H 50/80
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
50ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
80for detecting, monitoring or modelling epidemics or pandemics, e.g. flu
Appl.No PCT/US2021/035421 Applicant ROSS, Peter M. Inventor ROSS, Peter M.
The present methods comprise initial administration of a first rapid test, such as rapid antigen test, that has a low likelihood of false positive results and a known likelihood of false negative results, alone or in conjunction with other rapid tests, to identify a first subpopulation of positives, to which an infection mitigation protocol (such as quarantine or the administration of a therapeutic treatment) is administered. Negatives are recorded and provisionally released from further study. A diagnostic test is then administered to this subpopulation, to identify the portion of this first subpopulation that were false positives in the first test. Ideally, a positive result for the diagnostic test is pathognomonic for the infection. Any infection mitigation protocol (such as quarantine) may then be discontinued for those testing negative in the first population and intensified for those testing positive, such as with contact tracing or other protocols, to identify all true positives and those they may have infected within the population.
4.2020102631The Severity Level and Early Prediction of Covid-19 Using CEDCNN Classifier
AU 29.10.2020
Int.Class G16H 50/80
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
50ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
80for detecting, monitoring or modelling epidemics or pandemics, e.g. flu
Appl.No 2020102631 Applicant A, Anbuchezian DR Inventor
The Severity Level and Early Prediction of Covid-19 Using CEDCNN Classifier The rapid spread of Coronavirus disease 2019 (COVID-19) has brought doctors, researchers, and data scientists together to find a solution. Scientists are using sophisticated technologies, such as big data analytics, machine learning, and natural language processing for tracking the virus and learning more about it. On the one hand, the excess of stored data has considerably increased the opportunities to interrelate and analyze them. This proposed invention is to predict the severity level and early prediction of COVID-19 using Cross Entropy-based Deep Convolutional Neural Network (CEDCNN) for big data. This invented work is composed of '3' steps, namely, disease prediction, severity level analysis, and early prediction. In the first phase, initially, the dataset is preprocessed, then the important features are extracted from the dataset, and finally, the disease is clustered into positive and predictive using Taxicab Norm K Means (TNKM). In phase 2, the proposed system utilizes CEDCNN for severity analysis, which classifies a high, low, and moderate level. In phase 3, the non-coronavirus data undergoes preprocessing, and then important features are extracted from the dataset. Finally, the potential level of the patient against the coronavirus is predicted by the Mahalanobis Distance Ranking (MDR) method.
5.202141037260MACHINE LEARNING MODEL ON PREDICTIVE DIAGNOSIS AND PREVENTION OF COVID-19 OUTBREAK
IN 27.08.2021
Int.Class G06N /
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
Appl.No 202141037260 Applicant DR. M. PUSHPA RANI (Director and Professor) Inventor DR. M. PUSHPA RANI (Director and Professor)
The present invention relates to a machine learning model on predictive diagnosis and prevention of covid-19 outbreak. The invention is to gauge potential findings on chest CT images of patients with signs and symptoms of metabolic process syndromes and positive epidemiologic factors for covid-19 infection and correlate them with the course of the sickness. Subsequently, a specific ML-machine learning formula is utilized to predict potential prognostic factors, through pneumonic segmentation and lot of correct results. Our various hypothesis is that the machine learning model supports clinical, tomography and epidemiologic information that is able to predict the severity prognosis of patients infected with covid-19 and prevent the outbreak effectively in real-time.
6.WO/2021/236288INTELLIGENT WORKFLOW ANALYSIS FOR TREATING COVID-19 USING EXPOSABLE CLOUD-BASED REGISTRIES
WO 25.11.2021
Int.Class G16H 50/80
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
50ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
80for detecting, monitoring or modelling epidemics or pandemics, e.g. flu
Appl.No PCT/US2021/028647 Applicant HOFFMANN-LA ROCHE INC. Inventor MOLERO LEON, Silvia Elena
Disclosed herein are systems, methods, and techniques for building and using a data platform to facilitate intelligent identification of coronavirus disease 2019 (COVID-19) related diagnoses, treatment selection, and interaction tracing. The present disclosure relates to a cloud-based application that generates outputs predictive of a subject's COVID-19 diagnoses and/or suitability for COVID-19 treatments.
7.WO/2021/204902SARS-COV-2 INFECTION RISK ASSESSMENT METHOD
WO 14.10.2021
Int.Class G01N 33/68
GPHYSICS
01MEASURING; TESTING
NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
33Investigating or analysing materials by specific methods not covered by groups G01N1/-G01N31/131
48Biological material, e.g. blood, urine; Haemocytometers
50Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
68involving proteins, peptides or amino acids
Appl.No PCT/EP2021/059107 Applicant VIROGATES A/S Inventor EUGEN-OLSEN, Jesper
Increased levels of soluble urokinase-type plasminogen activator receptor (suPAR), particularly a plasma level of over 4.75 ng/ml or 6 ng/nl, have been found to be a predictor of whether a subject with COVID-19 symptoms and/or SARS-CoV-2 infection will require oxygen supplementation.
8.202121036722DEEP LEARNING MODEL FOR PREDICTING SEVERITY PROGNOSIS IN PATIENTS INFECTED WITH COVID-19
IN 31.12.2021
Int.Class G06N /
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
Appl.No 202121036722 Applicant Dr. Jambi Ratna Raja Kumar (Associate Professor Inventor Dr. Jambi Ratna Raja Kumar (Associate Professor
ABSTRACTOur Invention Machine Learning Model for Predicting Severity Prognosis in Patients Infected with COVID-19.cis to the very new coronavirus, that began to be known as SARS-CoV-2, could be a advanced fiber polymer beta coronavirus, at first known in metropolis (Hubei province, China) and presently spreading across six continents inflicting a substantial hurt to patients, with no specific tools as yet to produce prognostic outcomes. The Invention is to gauge potential findings on chest CT of patients with signs and symptoms of metabolic process syndromes and positive epidemiologic factors for COVID-19 infection and to correlate them with the course of the sickness. during this sense, it's conjointly expected to develop specific ML- machine learning formula for this purpose, through pneumonic segmentation, which may predict potential prognostic factors, through a lot of correct results. Our various hypothesis is that the machine learning model supported clinical, tomography and epidemiologic information are going to be able to predict the severity prognosis of patients infected with COVID-19. we'll perform a multicenter retrospective longitudinal study to get an outsized variety of cases in a very short amount of your time, for higher study validation.
9.202141052476METHOD FOR UTILIZING MATHEMATICAL MODELS FOR ANALYSIS OF DATA RELATED TO NOVEL CORONAVIRUS (COVID-19)
IN 03.12.2021
Int.Class G06Q /
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
Appl.No 202141052476 Applicant Dr. B. HARI PRASAD (Professor) Inventor Dr. B. HARI PRASAD (Professor)
Title: METHOD FOR UTILIZING MATHEMATICAL MODELS FOR ANALYSIS OF DATA RELATED TO NOVEL CORONAVIRUS (COVID-19) The present invention provides a novel approach in analyzing data related to novel coronavirus. The present invention provides a method for utilizing one or more mathematical models for analyzing biological data related to novel coronavirus. The mathematical model aggregates data related to novel coronavirus (Covid-19) to provide a population informed assessment of infection. The method receives a communication comprising user data and identifies a sub-set of users from a plurality of other users having a connectivity score above a threshold connectivity score and based in part on comparing user data with aggregated data from the plurality of other users. The method then identifies common attributes from the user data from the sub-set of other users and implements the mathematical model to analyze the aggregated data related to novel coronavirus.
10.WO/2021/202620METABOLOMICS APPROACH COMBINED WITH MACHINE LEARNING TO RECOGNIZE A MEDICAL CONDITION
WO 07.10.2021
Int.Class G16H 50/70
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
50ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
70for mining of medical data, e.g. analysing previous cases of other patients
Appl.No PCT/US2021/025015 Applicant THE BOARD OF TRUSTEES OF THE LELAND STANFORD JUNIOR UNIVERSITY Inventor RAJPURKAR, Pranav
Provided are methods, compositions, systems and devices comprising applying a metabolite biomarker signature determined by machine learning to a biological sample from a patient to recognize a medical condition.