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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

1.2020102631The 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.
2.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.
3.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.
4.2020102382AN ARTIFICIAL INTELLIGENCE BASED SYSTEM FOR COVID PREVENTION
AU 08.10.2020
Int.Class G16H 50/20
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
20for computer-aided diagnosis, e.g. based on medical expert systems
Appl.No 2020102382 Applicant Bhasin, Narinder Kumar DR Inventor
The present invention relates to an artificial intelligence based system for COVID prevention. The proposed invention incorporates a definite number of subsystems each enabling checks and/or preventive actions against the most obvious symptoms of coronavirus. The invention has to be deployed at the entry point of the place of human assembly restricts the virus-infected people from entering the premise
5.2021102333AN UTILIZATION OF ARTIFICIAL INTELLIGENCE BASED SYSTEM FOR PREVENTING COVID EFFECT ON HUMAN BODY
AU 20.05.2021
Int.Class A61B 5/08
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
5Measuring for diagnostic purposes; Identification of persons
08Measuring devices for evaluating the respiratory organs
Appl.No 2021102333 Applicant G., Pavithra DR Inventor
AN UTILIZATION OF ARTIFICIAL INTELLIGENCE BASED SYSTEM FOR PREVENTING COVID EFFECT ON HUMAN BODY The present invention relates to a utilization of artificial intelligence based system for preventing covid effect on human body. The proposed invention incorporates a definite number of subsystems each enabling checks and/or preventive actions against the most obvious symptoms of coronavirus. The invention has to be deployed at the entry point of the place of human assembly restricts the virus-infected people from entering the premise
6.202131015870HAMAMELITANNIN AND ROSMARINIC ACID AS COVID-19 INHIBITORS
IN 07.05.2021
Int.Class C07D /
CCHEMISTRY; METALLURGY
07ORGANIC CHEMISTRY
DHETEROCYCLIC COMPOUNDS
Appl.No 202131015870 Applicant Rajesh Kumar Das Inventor Rajesh Kumar Das
The present invention relates to identifying and screening best phytochemicals as potent inhibitors against COVID-19as it was recently, caused the outbreak situation of global public health. For this research we choose two standard drugs namely hamamelitannin and rosmarinic acid as a probable inhibitor of pandemic COVID-19 receptor as compared to antimalarial drug hydroxychloroquine, anti-viral drug remdesivir and also baricitinib. This study was done by taking into consideration of molecular docking study, performed with Auto Dock 4.0 (AD4.0). The results shown protein-ligand interaction of hamamelitannin and rosmarinic acid showing comparable binding energies than that of clinically applying probable COVID-19 inhibitors hydroxychloroquine (an anti-malarial drug) and remdesivir (an anti-viral drug).
7.2021105080Machine Learning Model for Predicting Severity Prognosis in Patients infected with COVID-19
AU 26.08.2021
Int.Class G16H 50/20
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
20for computer-aided diagnosis, e.g. based on medical expert systems
Appl.No 2021105080 Applicant ., Sharmila Assistant Prof. Inventor
Our invention is a machine education sample in envisaging strictness projection within COVID 19-infected diseased persons. The new coronavirus, dubbed is a fiber ribonucleic acid beta coronavirus that was first discovered in city, is now spreading across 6.00 continents, producing significant damage towards diseased persons having no particular methods to provide predictive results. The goal assesses achievable consequences upon chest CT regarding diseased persons having symbols, indications about metastatic medicine variables COVID-19 disturbance, correlate concerning illness. In this regard, it's also expected to construct a specific machine learning algorithmic programmed for this aim, based on pulmonic segmentation, that might identify possible prognostic factors based on further accurate data. Several hypotheses advocate machine education sample model seems effective for clinical, imaging, and medical knowledge, and capable of predicting hard prognosis of COVID-19-sufferers. Each center collects a convenience sample (at least twenty cases for each outcome) based on the inclusion and exclusion criteria. From March to May 2020, we'll judge patients who arrive at the hospital with clinical indications and symptoms of acute metastatic syndrome. TOTAL NO OF SHEET: 03 NO OF FIG: 03 Figure.1: Machine Learning Model for Predicting Severity Prognosis in Patients infected with COVID-19, Flow Chart.
8.10888283COVID-19 symptoms alert machine (CSAM) scanners
US 12.01.2021
Int.Class A61B 5/00
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
5Measuring for diagnostic purposes; Identification of persons
Appl.No 16917896 Applicant Boonsieng Benjauthrit Inventor Boonsieng Benjauthrit

A COVID-19 Symptoms Alert Machine (CSAM) scanner, or apparatus, is described herein. This apparatus employs Artificial Intelligent (AI) technology in combination with the latest mobile device technology (viz. smart phone/smart watch) to quickly help track down people who have COVID-19 symptoms anywhere and anytime, isolate them, and professionally handle them, not allowing SARS-CoV-2 virus to spread. CSAM automatically measures body temperature and assesses lung conditions such as pulmonary fibrosis and B-lines (for asymptomatic people), and other current health vital information (CHVI), furnished by the participant, such as fever, sore throat, headache, and body ache to generate an alert signal when COVID-19 symptoms are found significant and to send it out to a COVID-19 control center. The alerted participant is then immediately required to go to the COVID-19 control center or be picked up by a special COVID-19 emergency vehicle for isolation and further evaluation and testing. If the testing turns out to be COVID-19 positive, the participant will be quarantined and treated appropriately according to COVID-19 protocol until he/she is tested COVID-19 negative. In the meantime, people who have been in close physical contact with this participant will be alerted and requested to be immediately checked for COVID-19 symptoms. If anyone is found to have COVID-19 symptoms, then he/she must go through the same protocol. The process is repeated until all people in the cluster are tested COVID-19 negative. This will ensure that SARS-CoV-2 virus for this cluster has been completely eliminated. A rapid deployment of this type of apparatus throughout communities where people tend to congregate such as superstores, supermarkets, and any other establishments, small or large, can help to contain the rapid spread of the disease, as well as to give more confidence to the general public. People, who pass through this apparatus without an alert signal, should feel more confident in carrying out their activities, though social distancing and other COVID-19 precautionary requirements should still be maintained. The concept can be further expanded to cover shopping malls, concert halls, sports arenas, and any other large events including highways and freeways with the help of mobile phone technologies, transponders, and other mobile devices. By working on the 0.6% (around 2 million infected people in the US as of June 2020) quickly and effectively, instead of on the 99.4% (330 million, the remaining population) by locking people at home and closing down all businesses and activities; we can save a significant amount of money and hassles. (A long lockdown can also lead to a collapse of our economy and can consequently lead to a worldwide calamity.) In this way the 99.4% will not be burdened with the virus problem and can live normally without having to take any test. It is probably the only effective approach in solving the COVID-19 problem at the moment because vaccines and known COVID-19 cures are not yet available. Even if SARS-CoV-2 vaccines are available presently, they may not be practical to implement economically and operationally in time to contain the virus worldwide due to the massive amount of people (viz. over 7 billion).

9.2020102250AN ARTIFICIAL INTELLIGENCE AND IOT BASED COVID-19 EARLY WARNING SYSTEM FOR SENIOR CITIZENS
AU 01.10.2020
Int.Class A61B 5/00
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
5Measuring for diagnostic purposes; Identification of persons
Appl.No 2020102250 Applicant Babu, G. Venkata Suresh DR Inventor
The present invention relates to an artificial intelligence based covid-19 early warning system for senior citizens. In the proposed system, various types of health sensors collect body temperature, heartbeat, respiratory rate, resting pulse oxygen saturation, the partial pressure of oxygen, multiple pulmonary lobes from various body parts of the patients. Then an early warning score (EWS) is derived by comparing the deviation of values with standard benchmark values collected from normal people. If the EWS is high, then an early warning message will be sent as SMS to the respective health care unit along with the details of location, using Wi-Fi module. This Wi-Fi module sends the signals through the IoT technology. Following invention described in detail with the help of figure 1 of sheet 1 which shows block diagram of the proposed system, figure 2 of sheet 2 shows the architecture of the developed system with sensors and communication channels. Figure 1
10.2021102530VOLUNTARY DATA COLLECTION MECHANISM FROM THE GENERAL PUBLIC AND DETECTION FOR COVID SYMPTOMS
AU 03.06.2021
Int.Class G16H 10/20
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
10ICT specially adapted for the handling or processing of patient-related medical or healthcare data
20for electronic clinical trials or questionnaires
Appl.No 2021102530 Applicant B, Madhu MRS Inventor
The COVID-19 global epidemic has shown the critical factor that diagnostic tests serve in controlling transmissible infections. Whenever the danger is unpredictable and persistent, as in the ongoing coronavirus disease pandemic, anxiety could become persistent and cumbersome. Recently released SARS-CoV-2 diagnostic approaches have differing throughput, batch processing capability, equipment requirements, analytical results, and processing times ranged from a few minutes to several hours. These considerations should be taken into account when choosing a safe and swift diagnostic approach to aid in making an effective assessment and prompting preventive health strategies. This invention proposes the voluntary data collection approach like online survey, questionnaires and based on the information the covid symptoms are detected. As a standardized solution, a 15-20-minute online survey in a serial cross-sectional style with many information collections is proposed. Computer-assisted telephone interviews (CATI) polling can be used as an optional or alternate data collection tool where exposure to computers or smartphones are restricted in many countries. This is an empirical analysis of voluntary inclusion in the normal community, with individuals considered as minimal risk. Possible threats found comprise only the hassle of spending time to answer to the questionnaire, because with the new constraint's individuals endure, several users now have more free time. The factors and details demanded need not qualify for the identification of particular ethnicity or vulnerable populations. The Borg scoring system was used to allocate scores for typical characteristics which were then integrated into a recently created Hashmi-Asif Covid-evaluation Map. Pearson correlation and Spearman Correlation coefficients were used to determine correlations among signs and symptoms and the progression of Covid-19 (rho). Linear regression analysis is used to determine the function with greatest correlation. The occurrence of signs and syndromes in the enhancement of Covid-19 is determined using a two-tailed Chi-square test with Cramer's V power. Variations in indications and syndromes are integrated into four-tiered map which depicts the levels of diseases. Figure 2: Detection of covid symptoms using linear regression Figure 3: Microsoft Azure cloud Storage Platform