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Analysis

1.WO/2022/204728METHODS AND SYSTEMS FOR DEVELOPING MIXING PROTOCOLS
WO 29.09.2022
Int.Class G16C 20/10
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
CCOMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
20Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
10Analysis or design of chemical reactions, syntheses or processes
Appl.No PCT/US2022/071363 Applicant REGENERON PHARMACEUTICALS, INC. Inventor KENYON, Ross
A method of developing a predictive model may include identifying mixing protocol parameters for the predictive model, identifying an evaluation criterion for the predictive model, selecting test values for the mixing protocol parameters, identifying a computational fluid dynamics (CFD) simulation required to be performed in order to generate the evaluation criteria, conducting the CFD simulation for each combination of test values, thereby generating evaluation criteria corresponding to each combination of test values, generating a domain of potential predictive models relating the mixing protocol parameters to the evaluation criterion, identifying a pool of candidate predictive models from the domain of potential predictive models, and ranking the pool of candidate predictive models.
2.WO/2022/203734MACHINE LEARNING FOR PREDICTING THE PROPERTIES OF CHEMICAL FORMULATIONS
WO 29.09.2022
Int.Class G16C 20/30
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
CCOMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
20Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
30Prediction of properties of chemical compounds, compositions or mixtures
Appl.No PCT/US2021/063436 Applicant GOOGLE LLC Inventor LEE, Brian Kihoon
Chemical formulation property prediction can involve understanding each molecule individually and the mixture as a whole. Machine-learned models can be utilized to extract individual and holistic data to generate accurate predictions of the properties of mixtures. Properties that can include, but are not limited to, olfactory properties, taste properties, color properties, viscosity properties, and other commercially, industrially, or pharmaceutically beneficial properties.
3.WO/2022/203421SHEARING PROCESS SIMULATION METHOD
WO 29.09.2022
Int.Class G16C 20/60
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
CCOMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
20Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
60In silico combinatorial chemistry
Appl.No PCT/KR2022/004134 Applicant INDUSTRY ACADEMIC COOPERATION FOUNDATION GYEONGSANG NATIONAL UNIVERSITY Inventor JOUN, Man Soo
A shearing process simulation method of the present invention may comprise: a first step in which finite elements and nodes are generated in a raw material; a second step of calculating the fracture surface of the sheared material obtained by applying shearing force to the raw material; a third step of classifying the elements of the sheared material into a first group and a second group using the fracture surface as a boundary; a fourth step of averaging the information of the second group elements or second group nodes belonging to the second group to obtain an average; and a fifth step of reflecting the average on the fracture surface to generate the final fracture surface.
4.WO/2022/203306METHOD AND DIAGNOSTIC DEVICE FOR DETERMINING HYPERGLYCEMIA BY USING MACHINE LEARNING MODEL
WO 29.09.2022
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 PCT/KR2022/003896 Applicant HEM PHARMA INC. Inventor JI, Yo Sep
A method for determining the presence or absence of hyperglycemia by using a machine learning model may comprise the steps of: analyzing a mixture in which intestine-derived material collected from an individual is mixed with an intestinal environment-like composition; extracting a plurality of microbial data on the basis of results of analysis of the mixture; selecting a microorganism-related variable to be used in a machine learning model, from among the plurality of microbial data, on the basis of a preset variable selection algorithm; training the machine learning model by using the microorganism-related variable; and inputting microbial data extracted from a subject being examined into the trained machine learning model to determine the presence or absence of hyperglycemia. The microorganism-related variable may include the amount of at least one microorganism selected from families belonging to the order Oscillospirales, the order Lachnospirales, the order Lactobacillales, and the order Peptostreptococcales-Tissierellales.
5.20220310213METHOD FOR DESIGNING TERNARY CATALYST USING MACHINE LEARNING
US 29.09.2022
Int.Class G16C 20/70
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
CCOMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
20Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
70Machine learning, data mining or chemometrics
Appl.No 17523372 Applicant Hyundai Motor Company Inventor Seunghyo NOH

Disclosed is a method of manufacturing a ternary catalyst for an oxygen reduction reaction. The method may include constructing a database including catalytic activity of oxygen reduction reaction (ORR) of PtFeCu nanoparticles using machine-learning-based neural network potential (NNP), determining thermodynamically stable PtFeCu nanoparticles through Monte Carlo calculation, and selecting a type of the PtFeCu nanoparticles by analyzing a structure of PtFeCu nanoparticles.

6.WO/2022/203307METHOD FOR DETERMINING WHETHER OBESITY IS PRESENT, BY USING MACHINE LEARNING MODEL, AND DIAGNOSTIC DEVICE
WO 29.09.2022
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 PCT/KR2022/003898 Applicant HEM PHARMA INC. Inventor JI, Yo Sep
A method for determining whether obesity is present, by using a machine learning model, comprises the steps of: analyzing a compound in which an intestine-derived substance collected from an individual and an intestinal environment-like composition are mixed; extracting a plurality of microorganism data on the basis of the analysis result of the compound; selecting, from the plurality of microorganism data, on the basis of a preset variable selection algorithm, a microorganism-related variable to be used for a machine learning model; learning the machine learning model by using the microorganism-related variable; and determining whether obesity is present, by inputting, to the learned machine learning model, the microorganism data collected from the individual to be examined. The microorganism-related variable may include an amount of at least one microorganism selected from families belonging to the order Lachnospirales, the order Lactobacillales, and the order Erysipelotrichales.
7.WO/2022/204103MULTIPARAMETER MATERIALS, METHODS AND SYSTEMS FOR ENHANCED BIOREACTOR MANUFACTURE
WO 29.09.2022
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/US2022/021289 Applicant JANSSEN BIOTECH, INC. Inventor O'MAHONY-HARTNETT, Caitlin
Methods for determining glycation on a molecule and/or a glycan structure on a glycosylated molecule through the use of a combination of spectroscopic analysis and chemometric modeling are described. In addition, methods and systems for producing a molecule with a desired level of glycation, including a non-glycated molecule, and/or a desired level of a glycan structure on a glycosylated molecule are described.
8.WO/2022/203353METHOD AND DIAGNOSTIC DEVICE FOR DETERMINING PRESENCE OR ABSENCE OF CONSTIPATION USING MACHINE LEARNING MODEL
WO 29.09.2022
Int.Class G16B 40/20
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
40ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
20Supervised data analysis
Appl.No PCT/KR2022/003982 Applicant HEM PHARMA INC. Inventor JI, Yo Sep
A method for determining the presence or absence of constipation using a machine learning model may comprise the steps of: analyzing a mixture obtained by mixing an intestine-derived material collected from an individual with an intestinal environmental-like composition; extracting a plurality of microorganism data on the basis the analysis result of the mixture; selecting a microorganism-related variable to be used in a machine learning model from among the plurality of microorganism data on the basis of a preset variable selection algorithm; training the machine learning model by using the microorganism-related variable; and determine the presence or absence of constipation by inputting the microorganism data collected from an object to be tested into the trained machine learning model. The microorganism-related variable may include the content the content of one or more microorganisms selected from the genus belonging to the families Lachnospiraceae, Erysipelatoclostridiaceae, Pseudomondaeae, Prevotellaceae, Desulfovibrionaceae, Clostridiaceae, Gemellaceae, Bacteroidaceae, Streptococcusae, Anaerofustaceae, Monoglobaceae, and RF39.
9.20220310212CALCULATION METHOD, CALCULATOR SYSTEM, AND CALCULATOR
US 29.09.2022
Int.Class G16C 10/00
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
CCOMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
10Computational theoretical chemistry, i.e. ICT specially adapted for theoretical aspects of quantum chemistry, molecular mechanics, molecular dynamics or the like
Appl.No 17807079 Applicant JSR CORPORATION Inventor Junta Fuchiwaki

A method is implemented to select a calculator for performing given processing using a quantum algorithm or a combined algorithm of a classical algorithm and the quantum algorithm. The method comprises a calculation operation, a selection operation, and a control operation. The calculation operation calculates a quantum bit or a quantum volume for performing the given processing using the quantum algorithm, or for a portion of the quantum algorithm when performing the given processing using the combined algorithm. The selection operation selects a calculator for performing the given processing based on the quantum bit or the quantum volume. The control operation generates a control signal to be transmitted to the quantum calculator when the selected calculator includes a quantum calculator. The control signal may correspond to an instruction that initiates the quantum calculator to start the quantum algorithm.

10.20220310211NON-TRANSITORY COMPUTER-READABLE STORAGE MEDIUM, INFORMATION PROCESSING APPARATUS, AND INFORMATION PROCESSING METHOD
US 29.09.2022
Int.Class G16C 10/00
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
CCOMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
10Computational theoretical chemistry, i.e. ICT specially adapted for theoretical aspects of quantum chemistry, molecular mechanics, molecular dynamics or the like
Appl.No 17551238 Applicant FUJITSU LIMITED Inventor Hideyuki Jippo

A non-transitory computer-readable storage medium storing an information processing program that causes a processor included in an information processing apparatus that analyzes a first molecule different from all of a plurality of molecules based on characteristic data of each of the plurality of molecules to execute a process, the process includes specifying a structure descriptor that is an index based on each of structures of the plurality of molecules; and generating a model used to analyze the first molecule based on the structure descriptor and a similarity between each of the structures of the plurality of molecules.