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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.
2.2020102382AN 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
3.10888283COVID-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).

4.3098079APPARATUS AND METHOD FOR POINT-OF-CARE, RAPID, FIELD-DEPLOYABLE DIAGNOSTIC TESTING OF COVID-19, VIRUSES, ANTIBODIES AND MARKERS - AUTOLAB 20
CA 19.03.2021
Int.Class G01N 21/64
GPHYSICS
01MEASURING; TESTING
NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
21Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
62Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
63optically excited
64Fluorescence; Phosphorescence
Appl.No 3098079 Applicant AUTONOMOUS MEDICAL DEVICES, INC. Inventor

An automated system communicated to a remote server for diagnostically field
testing a
sample taken from a patient using an automated portable handheld instrument to

determine the presence of Covid-19 and/or antibodies thereto includes
microfluidic
circuits defined in a rotatable disk for performing a bioassay using a
microarray to
generate an electrical signal indicative of a bioassay measurement; the
microarray
operationally positioned in the microfluidic circuit; one or more lasers; one
or more
positionable valves in the microfluidic circuit; and a backbone unit for
rotating the disk
according to a protocol to perform the bioassay, for controlling the lasers to
selectively
open the positionable valves in the microfluidic disk, for operating the
microarray to
generate a digital image as a bioassay measurement; for communicating the
bioassay
measurement to the remote server, and for associating the performed bioassay
and its
corresponding bioassay measurement to the patient.

5.2020102250AN 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
6.2020103841A SYSTEM AND A METHOD FOR QUARANTINE EPIDEMIC MODELS
AU 24.12.2020
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 2020103841 Applicant Kumar Keshri, Ajit Inventor
The present disclosure relates to a system and a method for quarantine epidemic models. The disclosed system facilitates three quarantine models of pandemic which accounts the compartments including susceptible population, immigrant population, home isolation population, infectious population, hospital quarantine population, and recovered population. Local and global asymptotic stability is proved for all the three models. Extensive numerical simulations are performed to establish the analytical results with suitable examples. The system discloses that home isolation and quarantine to hospitals are the two-pivot force-control policies under the present situation when no treatment is available for this pandemic.
7.20200291490Risk Stratification for Contagious Disease
US 17.09.2020
Int.Class C12Q 1/70
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
70involving virus or bacteriophage
Appl.No 16855046 Applicant SENSIVA HEALTH LLC Inventor Tarun Jolly

A method of risk stratification for contagious disease is provided. The method performs a reverse transcriptase polymerase chain reaction (RT-PCR) test on a patient to determine the presence of a viral infection. The method next performs tests for the presence of antibody Immunoglobulin M (IgM) and Immunoglobulin G (IgG) assays. The method assigns the patient a level of readiness to return to society corresponding to the combination of the results of the qualitative RT-PCR, IgM, and IgG tests and quantitative IgG+ antibody testing. The levels of readiness to return to society may be verified by a QR code on a mobile device.

8.2020102448EARLY COVID PREDICTION: NEURO FUZZY MULTI-LAYERED DATA CLASSIFIER
AU 15.10.2020
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 2020102448 Applicant Hari Krishna, T. DR Inventor
Coronavirus disease (COVID-19) is a harmful disease caused from new SARS-CoV-2 virus. COVID-19 disease includes the symptoms such as cold, cough, fever and difficulty in breathing. COVID-19 has affected many countries and their spread in world has put humanity at risk. Due to increasing number of cases and their stress on administration as well as health professionals, different prediction techniques were introduced to predict the corona virus disease existence in patients. However, the accuracy was not improved and time consumption was not minimized during the disease prediction. In order to address these problems, Least Square Regressive Gaussian Neuro Fuzzy Multi-Layered Data Classification (LSRGNFM-LDC) Technique is introduced in this article. LSRGNFM-LDC Technique performs efficient COVID prediction with better accuracy and lesser time consumption through feature selection and classification. Deming Least Square Regressive Feature Selection process is carried out for selecting the most relevant features through identifying the line of best fit. After the feature selection process, the data classification is carried out using neuro-fuzzy classifier with help of fuzzy if-then rules for performing prediction process. Finally, the patient data is predicted as the higher risk patient data, medium risk patient data or higher risk patient data in more accurate manner with higher accuracy and lesser time consumption. Experimental evaluation is performed by Novel Corona Virus 2019 Dataset using different metrics like prediction accuracy, prediction time and error rate. The result shows that LSRGNFM-LDC Technique improves the accuracy and minimizes the time consumption as well as error rate than existing works during COVID prediction. EARLY COVID PREDICTION: NEURO FUZZY MULTI-LAYERED DATA CLASSIFIER Diagram Diagram Fuzzy Pedni he efficient d6da prediction Fig 1: EARLY COVID PREDICTION II P a g e
9.2021101034USING CLINICAL ONTOLOGIES TO BUILD KNOWLEDGE BASED CLINICAL DECISION SUPPORT SYSTEM FOR NOVEL CORONAVIRUS (COVID-19) WITH THE ADOPTION OF TELECONFERENCING FOR THE PRIMARY HEALTH CENTRES/SATELLITE CLINICS OF ROYAL OMAN POLICE IN SULTANATE OF OMAN
AU 18.03.2021
Int.Class G16H 50/30
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
30for calculating health indices; for individual health risk assessment
Appl.No 2021101034 Applicant AL GHAFRI, HILAL KHALID Inventor
A clinical decision support system (CDSS) expert system that incorporates artificial intelligence. Initially, the CDSS conducts the diagnosis to detect the symptoms of corona virus. We have used ontologies to represent the clinical protocol available to combat COVID-19. Ontologies include concepts (related to medical knowledge), attributes and semantic relationships between these concepts. Ontologies helps to represent the standard medical terms accurately, allows efficient knowledge sharing and reuse, and supports automatic reasoning. The CDSS considers and evaluates the medical history and other medical issues/complications of the suspected patient. So, once the symptoms of corona virus are confirmed in a patient, the CDSS will provide the necessary recommendations/suggestions/medications based on the patient's current medical condition. Further follow-up and guidance can be provided by the tele-services integrated in the system. Through tele services, the expert doctors can examine the patients and prescribe further tests/ medications in the follow-up session. e-Covid Architecture Patient/User Figure: E-Covid Architecture
10.2020101336AN EFFICIENT MACHINE LEARNING TECHNIQUE TO TRACK THE COVID PATIENTS ALONG WITH SECONDARY CONTACTS
AU 30.07.2020
Int.Class G16H 10/60
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
60for patient-specific data, e.g. for electronic patient records
Appl.No 2020101336 Applicant D, SAISANTHIYA MRS Inventor
An efficient machine learning technique to track the COVID patients along with Primary and secondary contact lists is the proposed invention, which plays a vital role to avoid the spread of this highly contagious disease. Pandemics are bane to the world which will affect the economy and life style of the citizens of any country that is affected. Thus there is a need to automate the process of tracking down the contact list of the COVID patient and monitor the quarantined person 24*7 to check whether the person is violating the isolation rules or not. The invention aims at implementing machine learning and artificial intelligence techniques to statistically analyze the case studies and arriving at conclusions in fraction of time.