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Analysis

1.WO/2023/002804MODEL GENERATION DEVICE, PREDICTION DEVICE, MODEL GENERATION METHOD, PREDICTION METHOD, AND RESIN COMPOSITION MANUFACTURING SYSTEM
WO 26.01.2023
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/JP2022/025344 Applicant TOKUYAMA CORPORATION Inventor TAI, Minami
According to the present invention, a condition under which required characteristics of a resin composition are satisfied is identified more efficiently than in the past. In this model generation device (100), a first machine learning unit (21) generates, on the basis of first input data (110) and second input data (120) forming a pair with the first input data (110), a first prediction model (MODEL1) for predicting unknown resin composition characteristic data from one or more of (i) arbitrary inorganic filler characteristic data, (ii) arbitrary resin characteristic data, (iii) arbitrary inorganic filler compounding data, and (iv) arbitrary resin compounding data. A second machine learning unit (22) generates, on the basis of the first prediction model (MODEL1), a second prediction model (MODEL2) for predicting one or more of (i) predicted inorganic filler characteristic data, (ii) predicted resin characteristic data, (iii) predicted inorganic filler compounding data, and (iv) predicted resin compounding data satisfying arbitrary resin composition characteristic data.
2.WO/2023/004149METHODS AND MODEL SYSTEMS FOR ASSESSING THERAPEUTIC PROPERTIES OF CANDIDATE AGENTS AND RELATED COMPUTER READABLE MEDIA AND SYSTEMS
WO 26.01.2023
Int.Class G01N 33/50
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
Appl.No PCT/US2022/038069 Applicant THE REGENTS OF THE UNIVERSITY OF CALIFORNIA Inventor GOODARZI, Hani
Provided herein are in-vitro, in-vivo, and ex-vivo models systems, methods of creating such model systems, and methods of using such model systems for assessing one or more therapeutic properties of a candidate agent or identifying a new therapeutic target. Also provided are computer-readable media and systems that find use, e.g., in practicing the methods of the present disclosure.
3.20230020166EFFICIENT QUANTUM CHEMISTRY SIMULATION USING GATE-BASED QUBIT QUANTUM DEVICES
US 19.01.2023
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 17777730 Applicant QU & CO CHEMISTRY B.V. Inventor Vincent Elfving

A method for simulating a quantum chemistry system comprises determining a hard-core bosonic Hamiltonian describing the quantum chemistry system, the Hamiltonian model restricting the electronic states to electron singlet state configurations; determining a “paired-electron unitary coupled cluster with double excitations” (pUCCD) ansatz, the ansatz being restricted to paired-electron configurations; mapping the pUCCD ansatz to qubit operations of a quantum circuit that comprises a set of qubits and gates for enabling pairs of qubits to interact with each other: and, determining a trial state on the quantum circuit by applying the qubit operations defined by the mapped pUCCD ansatz to the qubits; and, determining an energy of the quantum chemistry system based on the trial state and the restricted Hamiltonian, grouping the Hamiltonian terms into three sets of operators which can be measured simultaneously; and, an error-mitigation technique, based on post-selection of the quantum measurements with the known particle number.

4.WO/2023/285622PREDICTION OF PHARMACOKINETIC CURVES
WO 19.01.2023
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/EP2022/069799 Applicant F. HOFFMANN-LA ROCHE AG Inventor BRÄM, Dominic
A computer-implemented method of predicting at least one future point on a pharmacokinetic curve for a given species comprises: receiving an input comprising data representing a sequence of concentration-time points of a pharmacokinetic curve, each concentration-time point indicative of an amount of the given species in a subject's body at a respective time; applying a machine learning model to the input data, the machine learning model configured to generate an output comprising at least one subsequent concentration-time point in the pharmacokinetic curve. Computer-implemented methods of training machine learning models are also provided.
5.20230019202METHOD AND ELECTRONIC DEVICE FOR GENERATING MOLECULE SET, AND STORAGE MEDIUM THEREOF
US 19.01.2023
Int.Class G16C 20/50
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
50Molecular design, e.g. of drugs
Appl.No 17936422 Applicant BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD. Inventor Zhiyuan Chen

Embodiments of the present disclosure provide a method and electronic device for generating a molecule set and a storage medium thereof. The method obtains the first initialization molecule subset from the initialization molecule set with the pre-screening model; acquires the physical information of at least one initialization molecule in the first initialization molecule subset, and screens at least one initialization molecule based on the physical information to obtain the screened molecule set; acquires the biochemical experimental evaluation value of at least one molecule in the screened molecule set; and obtains the target molecule set based on the biochemical experimental evaluation value of at least one molecule.

6.4120138SYSTEM AND METHOD FOR MOLECULAR PROPERTY PREDICTION USING HYPERGRAPH MESSAGE PASSING NEURAL NETWORK (HMPNN)
EP 18.01.2023
Int.Class G06N 3/04
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
04Architecture, e.g. interconnection topology
Appl.No 21201603 Applicant TATA CONSULTANCY SERVICES LTD Inventor SAKHINANA SAGAR SRINIVAS
7.202231076944MACHINE LEARNING APPROACH FOR ESTIMATION OF GENETIC DIVERSITY AND PHYTOCHEMICAL CONSTITUENT ACTIVITIES IN PIPER BETLE L. LEAVE VARIETIES BASED ON MORPHOLOGICAL AND MOLECULAR MARKERS
IN 13.01.2023
Int.Class A61K /
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
KPREPARATIONS FOR MEDICAL, DENTAL, OR TOILET PURPOSES
Appl.No 202231076944 Applicant Dr Biswajit Patra Inventor Dr Biswajit Patra
Machine learning approach for estimation of genetic diversity and phytochemical constituent activities in Piper betle L. leave varieties based on morphological and molecular markers is the proposed invention. The proposed invention used morphological markers like leaf length, leaf width, and petiole size. By using the morphological markers, hierarchical cluster analysis was carried out, which grouped these four cultivars into two major clusters. In molecular marker analysis, a total of ten RAPD primers used, generating 43 number of amplified bands. Among them, 15 number of polymorphic bands and seven unique bands were found. The genetic diversity and relatedness among the four cultivars were computed using Jaccard’s similarity coefficient. The dendrogram grouped all the four cultivars into two main clusters. This RAPD banding patterns can be useful for genetic diversity studies, for cultivar selection, and to marker assist breeding programs. Phytochemical similarities and differences among the species were characterised through multivariate analyses approaches. Principal component analysis based on the relative abundance of phytochemicals, indicated that the betel cultivars could be grouped into three groups. Our results of FTIR, GC-MS and NMR based profiling combined with multivariate analyses suggest that untargeted metabolomics can play a crucial role in documenting metabolic signatures of endemic betel leaf varieties
8.202241075430MOBILE DEVICE MARKETING AND ADVERTISING PLATFORMS, METHODS AND SYSTEMS USING ARTIFICIAL INTELLIGENCE
IN 13.01.2023
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 202241075430 Applicant Prathibha Vikram Inventor Prathibha Vikram
“MOBILE DEVICE MARKETING AND ADVERTISING PLATFORMS, METHODS AND SYSTEMS USING ARTIFICIAL INTELLIGENCE”Accordingly, embodiments herein disclose mobile device marketing and advertising platforms, methods and systems using artificial intelligence (AI). The method comprising the steps of: receiving premium digital content from an artificial intelligence (AI) in response to one or more users participating in one or more of the advertising promotion activities; displaying a digital display system configured to plan an advertising promotion targeted to one or more users of mobile devices, wherein the advertising promotion includes one or more locations in which premium digital content is accessible; setting one or more objectives for the advertising promotion in response to one or more inputs, said each objective having one or more performance metrics; and generating one or more reports relating to performance of the advertising promotion in response to the received data and at least one of the performance metrics.Figure to be published with Abstract: Figure 1Dated this 20th day of December, 2022 PoojaIN/PA/1838Agent for the Applicant
9.202341001662MACHINE LEARNING BASED STATE OF HEALTH ESTIMATION OF LITHIUM ION BATTERIES IN ELECTRIC VEHICLES
IN 13.01.2023
Int.Class G06N /
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
Appl.No 202341001662 Applicant Cambridge Institute of Technology Bengaluru Inventor Dr. Sujatha B G
Data preprocessing approach that is needed to have the advanced algorithms does not carry any data aware approach in implementing the machine learning algorithm. An approach that carry out the data preprocessing algorithm by implementing the data aware approach is not carried out in the previous literatures. Algorithms that automatically detect whether the data is balanced or imbalanced and apply the data preprocessing according to the data available is developed in this research. Generalization in independent machine learning methods gets a hit due to different capability issues inherent to each method. The algorithm that could combine the advantages of different machine learning algorithms solves the generalization issue to a greater extent. Although isolation forest algorithms have proved to be unsupervised learning algorithms with good anomaly detection in the input variables a better orthogonal input can be derived from the input data. SOH estimation being the most crucial electrical vehicle indication applications the foolproof nature of the estimation algorithm is a primary indicator for the research thus carried out. A method that can handle a large amount of data (in millions) needs a method with higher generality and highly orthogonal input data. Possibility of higher correlation among the sample data insists on better feature engineering techniques for a better prediction performance.
10.20230010373METHOD FOR OPTIMIZING A MEASUREMENT RATE OF A FIELD DEVICE
US 12.01.2023
Int.Class G01N 35/00
GPHYSICS
01MEASURING; TESTING
NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
35Automatic analysis not limited to methods or materials provided for in any single one of groups G01N1/-G01N33/148; Handling materials therefor
Appl.No 17783957 Applicant Endress+Hauser SE+Co. KG Inventor Dietmar Frühauf

The present disclosure relates to a method for optimizing a measurement rate of a field device in a measurement system. The measurement system includes at least one second field device in which a measurement variable of the field device is correlated with the measurement variable of the second field device. The method determines a respective specific correlation pattern between the first measurement variable and the second measurement variable based on a learning phase. This makes it possible to check the measured values from the second field device for the correlation pattern during normal measurement operation and to change the measurement rate of the field device during the corresponding time window. This makes it possible to increase the service life and/or availability in the process installation.