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61. 发明专利

DK2751725T3 ANVENDELSE AF KERNEMAGNETISK RESONANS OG NÆR-INFRARØD TIL ANALYSE AF BIOLOGISKE PRØVER 未知
公开(公告)号：DK2751725T3
公开(公告)日：2021-01-25
申请号：DK12775862
申请日：2012-08-31
申请人： BIOGEN MA INC
发明人： ALI AMR , LANAN MAUREEN
IPC分类号： G01R33/465 , G01N21/35 , G01N24/08 , G16C20/20 , G16C20/70

62. 发明专利

IL277595D0 Method and system for computational modelling and simulation applied to drug characterization and/or optimization 未知
公开(公告)号：IL277595D0
公开(公告)日：2020-11-30
申请号：IL27759520
申请日：2020-09-24
申请人： INSILICOTRIALS TECH S R L , LUCA EMILI , ALESSIA BARETTA , ALBERTO PALAZZIN , ROBERTA BURSI
发明人： LUCA EMILI , ALESSIA BARETTA , ALBERTO PALAZZIN , ROBERTA BURSI
IPC分类号： G16C20/50 , G16C20/30 , G16C20/70 , G16H50/50
摘要： Finally, the method comprises the step of processing the computational modeling and/or simulation output data Dout, on the basis of the information on selection and setting I6 of one or more modeling and/or simulation output parameters, to report the requested modeling and/or simulation results R in a format selected by the user, and the step of providing such requested simulation results through the user interface 4.

63. 发明专利

AU2019244695A1 Method and system for computational modelling and simulation applied to drug characterization and/or optimization 未知
公开(公告)号：AU2019244695A1
公开(公告)日：2020-11-19
申请号：AU2019244695
申请日：2019-03-26
申请人： INSILICOTRIALS TECH S R L
发明人： EMILI LUCA , BURSI ROBERTA , BARETTA ALESSIA , PALAZZIN ALBERTO
IPC分类号： G16H50/50 , G16C20/70
摘要： A method for computational modeling and simulation applied to drug characterization and/or optimization is described. The method comprises the step of storing on a computational platform 1 digital data for modeling characteristics of an individual D1, digital data for modeling characteristics of an animal D2, digital data for modeling properties of chemical and/or biological compounds and/or drugs D3, digital modeling data of biological targets D4, digital modeling data of disease features and/or therapeutic areas D5 towards which the drug is directed. The method then comprises the step of receiving selection and/or setting information I which can be entered by a user through a user interface 4 which can be connected to the Internet. Such information include information on selection and/or definition and/or setting l1 of a pharmacometric and/or physiological model; information on selection and/or definition and/or setting I2 of a chemical and/or pharmacological and/or biological system model; information on selection and/or definition and/or setting I3 of a screening and/or optimization model; information on selection and setting I4 of a modeling and/or simulation type, and/or information on selection and setting I5 of one or more modeling and/or simulation input parameters and information on selection and setting I5 of one or more modeling and/or simulation output parameters I6. The method then comprises the step of processing, by means of the computational platform 1, the aforementioned types of selection and/or definition and/or setting information to develop a pharmacological and/or physiological model M1 (based on the digital modeling data of an individual D1 and/or digital modeling data of an animal D2), a chemical and/or pharmacological and/or biological and/or therapeutic system model M2 (based on digital modeling data of chemical and/or biological compounds and/or drugs D3 and/or digital modeling data of biological targets D4 and/or digital modeling data of diseases and/or therapeutic areas D5), and a screening and/or optimization model M3 (based on digital modeling data of disease features and/or therapeutic areas D5 and also on the aforementioned digital modeling data of chemical and/or biological compounds and/or drugs D3 and/or digital modeling data of biological targets D4). Then, the selection and/or setting information I entered by the user is processed for preparing input setting data Din for one or more computational modeling and/or simulation software programs included in the computational platform. The method then comprises executing computational modeling and/or simulation, by the one or more computational modeling and/or simulation software programs, on the basis of the abovementioned input setting data Din, and based on the pharmacometric and/or physiological model M1, on the chemical and/or pharmacological and/or biological system model M2, and on the screening and/or optimization model M3, to obtain computational modeling and/or simulation output data Dout. Finally, the method comprises the step of processing the computational modeling and/or simulation output data Dout, on the basis of the information on selection and setting l6of one or more modeling and/or simulation output parameters, to report the requested modeling and/or simulation results R in a format selected by the user, and the step of providing such requested simulation results through the user interface 4.

64. 发明专利

CA3132214A1 EMBEDDED SENSOR DEVICES AND METHODS 未知
公开(公告)号：CA3132214A1
公开(公告)日：2020-10-08
申请号：CA3132214
申请日：2020-04-03
申请人： GHODS POURIA , ALIZADEH ROUHOLLAH , FAHIM ANDREW , DE CARUFEL SARAH , SALEHI MUSTAFA
发明人： GHODS POURIA , ALIZADEH ROUHOLLAH , FAHIM ANDREW , DE CARUFEL SARAH , SALEHI MUSTAFA
IPC分类号： G01D11/00 , B28B23/02 , C23F13/04 , G01K3/00 , G01N25/48 , G16C20/70 , G16C20/90 , G16C60/00 , H01Q1/42 , H04W4/38
摘要： Many construction materials are chemically active materials whose structural properties parameters, physical-mechanical properties, etc. need to be determined. By exploiting embedded wireless sensors within these materials from initial wet manufactured state to final solid capillary-porous material assessment of initial and subsequent properties can be established allowing determination of current and future performance of the construction material. Embedded sensors can also monitor lifetime properties to identify performance degradations in the construction material as well as other construction elements embedded within or around the construction material. Further, the data accumulated from initial manufacturing to extended lifetime allows for additional assessments and improvements with respect to selection of construction material mix for a particular project at a particular location and time, improving the assessment of proactive repair and/or remedial work, quality control monitoring, cost reduction etc.

65. 发明专利

CA3129069A1 SYSTEMS AND METHODS FOR PREDICTING THE OLFACTORY PROPERTIES OF MOLECULES USING MACHINE LEARNING 未知
公开(公告)号：CA3129069A1
公开(公告)日：2020-08-13
申请号：CA3129069
申请日：2020-02-10
申请人： GOOGLE LLC
发明人： WILTSCHKO ALEXANDER , SANCHEZ-LENGELING BENJAMIN
IPC分类号： G16C20/30 , G06N3/04 , G06N3/08 , G06N3/12 , G06N5/00 , G16C20/70
摘要： The present disclosure provides systems and methods for predicting olfactory properties of a molecule. One example method includes obtaining a machine-learned graph neural network trained to predict olfactory properties of molecules based at least in part on chemical structure data associated with the molecules. The method includes obtaining a graph that graphically describes a chemical structure of a selected molecule. The method includes providing the graph as input to the machine-learned graph neural network. The method includes receiving prediction data descriptive of one or more predicted olfactory properties of the selected molecule as an output of the machine-learned graph neural network. The method includes providing the prediction data descriptive of the one or more predicted olfactory properties of the selected molecule as an output.

66. 发明专利

LU501574B1 SYSTEM AND METHOD FOR CONSTRUCTING MOLECULAR REACTION FORCE FIELD BASED ON REINFORCEMENT LEARNING 未知
公开(公告)号：LU501574B1
公开(公告)日：2022-09-01
申请号：LU501574
申请日：2022-03-02
申请人： UNIV NORTHEASTERN
发明人： MENG XIANGYING
IPC分类号： G16C10/00 , G16C20/70
摘要： The present invention provides a system and method for constructing a molecular reaction force field based on reinforcement learning, and relates to the technical field of material informatics. The system includes an input and output module, a parameter and configuration module, a molecular dynamics interface module, an environment setting module, and an optimization module; the input and output module transfers an initial reaction force field description file to the parameter and configuration module; the environment setting module is configured to save data and output initial force field parameters to the molecular dynamics interface module; the molecular dynamics module is configured to output material property errors obtained to the optimization module; and the optimization module is configured to obtain optimized force field parameters, form an optimized force field file force field file and output the same to the input and output module.

67. 发明专利

GB2557113B Producing chemical formulations with cognitive computing 未知
公开(公告)号：GB2557113B
公开(公告)日：2022-07-20
申请号：GB201803138
申请日：2015-10-30
申请人： HALLIBURTON ENERGY SERVICES INC
发明人： DALE E JAMISON , PAUL H MORAN , ROBERT LYNN WILLIAMS , AMIR BAR
IPC分类号： G16C20/70
摘要： A cognitive computing system for producing chemical formulations, in some embodiments, comprises: neurosynaptic processing logic; and one or more information repositories accessible to the neurosynaptic processing logic, said one or more repositories storing resources, wherein the neurosynaptic processing logic determines a first chemical formulation to achieve a target and to satisfy one or more constraints, produces and tests said first chemical formulation, and analyzes the results of the test using said resources to determine a second chemical formulation, wherein the second chemical formulation more closely achieves the target and satisfies the one or more constraints than the first chemical formulation.

68. 发明专利

AU2020351837A1 Systems and methods for synergistic pesticide screening 未知
公开(公告)号：AU2020351837A1
公开(公告)日：2022-04-21
申请号：AU2020351837
申请日：2020-09-25
申请人： TERRAMERA INC
发明人： LAMBRINOUDIS COSTANTINOS , SHOKATIAN SADEGH , BUI LE LINH , SNOW OLIVER
IPC分类号： G16C20/00 , A01N61/00 , G01N33/00 , G16C20/30 , G16C20/64 , G16C20/70
摘要： A computer system that predicts synergistic interactions between pesticidal and synergistic compounds of a pesticidal composition is described. The system provides a trained classifier that provides probabilistic predictions of the synergy between two or more compounds on a pest. The system may select features for transformation, encode them, generate one or more predictions, and combine the predictions. The predictions may be evaluated by experimental testing, e.g. in vitro or in planta, and/or used to formulate and/or apply a pesticidal composition.

69. 发明专利

HRP20220055T1 METABOLOMIČKO PROFILIRANJE POVREDE CENTRALNOG ŽIVČANOG SUSTAVA 未知
公开(公告)号：HRP20220055T1
公开(公告)日：2022-04-15
申请号：HRP20220055
申请日：2016-03-18
申请人： LONDON HEALTH SCI CT RES INC
发明人： FRASER DOUGLAS DALE , BARTHA ROBERT , BROWN ARTHUR , STEWART TANYA CHARYK , DALEY MARK , DEKABAN GREG A , DOHERTY TIMOTHY , FISCHER LISA , HOLMES JEFF , MENON RAVI , RUPAR TONY , SHOEMAKER J KEVIN
IPC分类号： G01N33/48 , A61B5/00 , G01N24/08 , G01N33/483 , G01N33/487 , G01N33/68 , G16B40/00 , G16C20/70

70. 发明专利

ES2905320T3 Perfil metabolómico de la lesión del sistema nervioso central 未知
公开(公告)号：ES2905320T3
公开(公告)日：2022-04-07
申请号：ES16767575
申请日：2016-03-18
申请人： LONDON HEALTH SCI CT RES INC
发明人： FRASER DOUGLAS DALE , BARTHA ROBERT , BROWN ARTHUR , STEWART TANYA CHARYK , DALEY MARK , DEKABAN GREG A , DOHERTY TIMOTHY , FISCHER LISA , HOLMES JEFF , MENON RAVI , RUPAR TONY , SHOEMAKER J KEVIN
IPC分类号： G01N33/48 , A61B5/00 , G01N24/08 , G01N33/483 , G01N33/487 , G01N33/68 , G16B40/00 , G16C20/70
摘要： Un método para diagnosticar una lesión cerebral traumática leve (mTBI) en un sujeto que comprende: (a) obtener un perfil de metabolitos a partir de una muestra de prueba biológica del sujeto; y (b) usar análisis estadístico multivariado y aprendizaje automático para comparar el perfil del sujeto con un conjunto predeterminado de perfiles de metabolitos de mTBI y un conjunto predeterminado de perfiles de metabolitos sin mTBI para determinar si el sujeto tiene mTBI, en el que el conjunto predeterminado de perfiles de metabolitos comprende metabolitos seleccionados de los metabolitos incluidos en las Tablas 2 y 3.

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