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Analysis

1 / 3,743
1.WO/2025/234376METHOD FOR PREDICTING AND ANALYZING PHYSICAL PROPERTIES OF RUBBER MATERIAL
WO - 13.11.2025
Int.Class G16C 60/00
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
CCOMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
60Computational materials science, i.e. ICT specially adapted for investigating the physical or chemical properties of materials or phenomena associated with their design, synthesis, processing, characterisation or utilisation
 Appl.No PCT/JP2025/016319 Applicant SUMITOMO RUBBER INDUSTRIES, LTD. Inventor ITO, Wakana
The present invention provides a method for predicting physical properties of a rubber material having high prediction accuracy. The present invention pertains to a method for predicting physical properties of a rubber material that is being continuously produced.
2.20250349394SYSTEM AND METHOD FOR SPECTROSCOPIC DETERMINATION OF CHEMICAL COMPOSITIONS FROM SAMPLE SCANS
US - 13.11.2025
Int.Class G16C 20/20
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
20Identification of molecular entities, parts thereof or of chemical compositions
 Appl.No 19201617 Applicant THERMO SCIENTIFIC PORTABLE ANALYTICAL INSTRUMENTS INC. Inventor Emily Berman

Systems and methods for spectroscopic determination of chemical compositions from sample scans. One method includes receiving a first user selection input representative of a user selection of any one or one or more scan modes and receiving a second user selection input representative of a user selection of any one of the one or more target chemical substances to identify. The method also includes receiving scan data generated by a scan of the sample of the unknown chemical composition, and determining a result based on the received scan data generated by the scan of the sample of the unknown chemical composition. The method further includes comparing the result against an expected result for a sample scan associated with the selected target chemical substance and generating instructions for displaying indicia representative of a primary compound associated with the selected target chemical substance.

3.WO/2025/235571ENHANCED IRON-BASED OLIGOMERIZATION OF ETHYLENE USING MACHINE LEARNING-BASED K-VALUE PREDICTION
WO - 13.11.2025
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 PCT/US2025/028086 Applicant CHEVRON PHILLIPS CHEMICAL COMPANY LP Inventor WEBSTER-GARDINER, Michael S.
A machine learning model predicts a K value for a new iron ethylene oligomerization catalyst structure, where the K value has not yet been experimentally determined. The system utilizes a device comprising memory coupled to at least one processor, the memory having instructions that cause the at least one processor to: input a set of reaction conditions and a new iron ethylene oligomerization catalyst structure comprising a ligand to a random forest machine learning regressor model, wherein the random forest machine learning regressor model is trained on a data set comprising multi-dimensional features for tested iron ethylene oligomerization catalyst structures, wherein the multi-dimensional features comprise experimental K values, physical features, molecular features, and connective steric factors for each of the tested iron ethylene oligomerization catalyst structures
4.WO/2025/235286DEVICES AND METHODS FOR SPECIALTY CHEMICAL DEVELOPMENT UNDER DIFFERENT TEST CONDITIONS WITH ARTIFICIAL INTELLIGENCE MODELS
WO - 13.11.2025
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/US2025/027230 Applicant CHAMPIONX LLC Inventor JIN, Ming-Zhao
Technologies for specialty chemical development and testing include devices and methods for normalizing historical specialty chemical test results and training a chemical composition predictor to predict chemical components of a formulation given a test condition and a normalized performance indicator based on the normalized test results. The specialty chemical may be a corrosion indicator, and the normalized performance indicator may be corrosion rate. The devices and methods may predict a predicted composition with the trained chemical composition predictor. the devices and methods may filter the normalized test results based on the predicted composition and train a formulation optimization predictor to predict a normalized performance indicator based on the filtered test results. The devices and methods may generate multiple candidate chemical formulations based on the predicted composition and predict a normalized performance indicator for each candidate chemical formulation with the trained formulation optimization predictor.
5.20250349393Enhanced Machine Learning for Iron-Based Oligomerization of Ethylene K-Value Prediction
US - 13.11.2025
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 19200415 Applicant Chevron Phillips Chemical Company LP Inventor Michael S. Webster-Gardiner

A machine learning model predicts a K value for a new iron ethylene oligomerization catalyst structure, where the K value has not yet been experimentally determined.

6.WO/2025/235574ENHANCED MACHINE LEARNING FOR IRON-BASED OLIGOMERIZATION OF ETHYLENE K-VALUE PREDICTION
WO - 13.11.2025
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 PCT/US2025/028092 Applicant CHEVRON PHILLIPS CHEMICAL COMPANY LP Inventor WEBSTER-GARDINER, Michael S.
A machine learning model predicts a K value for a new iron ethylene oligomerization catalyst structure, where the K value has not yet been experimentally determined. A system comprising: a device comprising memory coupled to at least one processor, the memory having instructions that cause the at least one processor to: convert a tested iron ethylene oligomerization catalyst structure having an experimental K value to a first computer-readable string; generate, based on the first computer-readable string, chemical features of the tested iron ethylene oligomerization catalyst structure.
7.WO/2025/235349SELF-CALIBRATING THREE-PHASE FLOW WATER-CUT LASER SENSING USING AN UNSUPERVISED MACHINE LEARNING MODEL
WO - 13.11.2025
Int.Class G01N 21/39
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 sub-millimetre waves, infrared, visible or ultraviolet light
17Systems in which incident light is modified in accordance with the properties of the material investigated
25Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
31Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
39using tunable lasers
 Appl.No PCT/US2025/027698 Applicant SAUDI ARABIAN OIL COMPANY Inventor AL IBRAHIM, Emad
Systems and methods for a self-calibrating three-phase flow water-cut laser sensing using an unsupervised machine learning model are disclosed. The methods include creating (500) a training data set, wherein the training data set comprises training mixture spectra; training (502), using the training data set, an unsupervised machine learning model to estimate an estimated water-cut and an estimated path-length fraction value, wherein, via the training, the unsupervised machine learning model calibrates itself to determine the estimated water-cut and the estimated path-length fraction value; obtaining (504) an observed mixture spectrum from a water-cut laser sensor; estimating (504), using the trained unsupervised machine learning model, the estimated water-cut and the estimated path-length fraction value from the observed mixture spectrum; determining (506), from the estimated path-length fraction value, an estimated gas fraction value; and determining (508) a composition of fluids in a separator using the estimated water-cut and the estimated gas fraction value.
8.WO/2025/233223PHARMACODYNAMICS MODELLING
WO - 13.11.2025
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/EP2025/061977 Applicant F. HOFFMANN-LA ROCHE AG Inventor CHERKAOUI RBATI, Mohammed Hussain
Computer-implemented methods of predicting the in vivo dynamics of one or more pharmacodynamics biomarkers for a drug, and methods of designing in vivo pharmacodynamics experiments for a drug using such methods are described. The methods comprise obtaining annullin vivonullpharmacokinetics model that predicts the concentration of the drug in a body compartment as a function of time after administering the drug; obtaining an in vitro pharmacodynamics model that predicts the level of the one or more biomarkers in an in vitro assay comprising cells cultured in the presence of the drug, as a function of time and concentration of the drug in the culture medium in which the cells are cultured; and simulating an in vivo level profile for each of said one or more pharmacodynamics biomarkers using the in vivo pharmacokinetics model and the in vitro pharmacodynamics model.
9.20250349392Enhanced Iron-Based Oligomerization of Ethylene Using Machine Learning-Based K-Value Prediction
US - 13.11.2025
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 19200394 Applicant Chevron Phillips Chemical Company LP Inventor Michael S. Webster-Gardiner

A machine learning model predicts a K value for a new iron ethylene oligomerization catalyst structure, where the α value has not yet been experimentally determined.

10.WO/2025/231559PLATFORM FOR MICROBIALLY PRODUCING COLORANTS
WO - 13.11.2025
Int.Class G16C 20/00
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
 Appl.No PCT/CA2025/050663 Applicant LITE-1: MICROBIAL COLOUR LTD. Inventor GRAHAM, Sarah Elizabeth
A method, system, computer program, and computer platform for AI-guided design, production, and application of biosynthesized colorants. The invention enables a computer platform system to receive user-defined input data such as a target colorant, functional attributes, and environmental impact constraints, and to generate a customized recipe for biosynthesized production and/or use of the colorant molecules. The recipe may include selection of microbial strains, pigment molecules or precursors, fermentation parameters, extraction and post- processing techniques, and application instructions. The system can monitor process parameters during deployment, either in a simulation or physically, determine real-time changes, and generate updated recipes accordingly. The invention supports precursor-based workflows (e.g., Tryptone conversion to violacein), chemical or enzymatic post-processing, and predictive modeling of color, biosynthetic feasibility, and compliance. This disclosed system provides a scalable, sustainable, and programmable production of colorants across textile, cosmetic, plastic, food, and biomedical industries.
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