A computer-implemented method for data analysis comprises obtaining a plurality of first observations, the plurality of first observations including one or more values of one or more first parameters, the plurality of first observations grouped into a plurality of groups; constructing a first histogram using the values of at least one of the one or more first parameters, included in the plurality of first observations; constructing, for each of the plurality of groups, a second histogram having bins corresponding to bins of the first histogram, wherein each of the bins of the second histogram includes a count of the first observations, among the first observations that belong to the one of the plurality of groups, having one or more values corresponding to the one of the bins; and outputting the second histograms constructed for the plurality of groups.
G06F 17/18 - Complex mathematical operations for evaluating statistical data
G06V 10/34 - Smoothing or thinning of the patternMorphological operationsSkeletonisation
G06V 10/50 - Extraction of image or video features by performing operations within image blocksExtraction of image or video features by using histograms, e.g. histogram of oriented gradients [HoG]Extraction of image or video features by summing image-intensity valuesProjection analysis
G06V 20/69 - Microscopic objects, e.g. biological cells or cellular parts
2.
DATA DRIVEN SAMPLING METHODS FOR BAYESIAN PARAMETER ESTIMATIONS OF PHYSICAL MODELS ON PARTIAL DIFFERENTIAL EQUATIONS FOR MASS TRANSFER
The application relates to a computer-implemented method for configuring and/or controlling a bioprocessing system that is configured to physically perform and/or simulate a bioprocess, a respective computer program product, a method for performing a bioprocess, a control device for controlling and/or configuring a bioprocessing system and a bioprocess. The method uses a probabilistic algorithm to determine a predictive posterior distribution for a target set of model parameter values and configure and/or control at least one state parameter of the bioprocessing system cand/or bioprocess based on the determined predictive posterior distribution. The probabilistic algorithm employs a combination of evaluation methods based on a common physical model, including a numerical simulation method, a search method for searching in a data storage storing a plurality precomputed solution-parameter sets of the physical model and an artificial intelligence based method using a physics informed artificial intelligence model (such as physics informed neural network model), which is trained based on the solutions of the physical model generated by the simulation method and the physical model.
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
C12M 1/36 - Apparatus for enzymology or microbiology including condition or time responsive control, e.g. automatically controlled fermentors
G01N 35/00 - Automatic analysis not limited to methods or materials provided for in any single one of groups Handling materials therefor
G05B 13/04 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
Methods and systems for monitoring a cell population in cell culture, and for controlling a cell culture process are described. The methods include: obtaining one or more images of the cell population acquired using label-free imaging at one or more time points during the cell culture process, predicting one or more metrics indicative of a cell state transition in the cell population using a statistical model that takes the label-free image-derived features as inputs, wherein the cell culture process is associated with a base protocol for obtaining the cell state transition comprising one or more interventions defined by one or more process parameters, and the predicting one or more metrics indicative of the cell state transition process is repeated for a plurality of candidate values of at least one of the one or more process parameters of at least one of said interventions to obtain a plurality of sets of one or more metrics indicative of the cell state transition process; and wherein comparing the predicted plurality of sets of one or more metrics indicative of the cell state transition process provides an indication of the suitability of the candidate values to achieve the cell state transition.
Methods and systems for monitoring a cell population in culture are described. The method includes the steps of: obtaining one or more images of the cell population acquired using label-free imaging during the cell culture process, processing the one or more images to obtain one or more label-free image-derived features, and predicting one or more metrics indicative of a cell state transition in the cell population using a statistical model that takes the label-free image-derived features as inputs and provides the one or more metrics indicative of a cell state transition in the cell population as output. The metrics indicative of a cell state transition in the cell population are metrics that characterise the progress and/or outcome of a cell state transition process occurring in a cell population, and the inputs of the statistical model do not include any feature obtained using an invasive or destructive measurement process.
Methods for monitoring, controlling, optimising and simulating a bioprocess comprising a cell culture in a bioreactor are provided. The methods comprise obtaining the values of one or more state variables of a state space model at one or more maturities, and predicting the value of one or more critical quality attributes of a product of the bioprocess using a machine learning model trained to predict the value of the one or more critical quality attributes based on input variables comprising values of the one or more state variables or variables derived therefrom, at one or more maturities. The state space model comprises a kinetic growth model representing changes in the state of the cell culture and a material balance model representing changes in the bulk concentration of one or more metabolites in the bioreactor. Systems, computer readable media implementing such methods, and methods for providing tools to implement such methods, are also provided.
INSTITUT NATIONAL DES SCIENCES APPLIQUEES DE LYON (France)
ECOLE SUPERIEURE DE CHIMIE, PHYSIQUE, ELECTRONIQUE DE LYON (France)
CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE (France)
Inventor
Sjögren, Rickard
Reif, Oscar-Werner
Trygg, Johan
Petiot, Emma
Marquette, Christophe
Chastagnier, Laura
Abstract
Methods for monitoring a process for producing multicellular structures from a cell population in a 3D cell culture are described The methods comprise obtaining one or more 3D images of the 3D cell culture that are acquired by imaging the cell culture at different depths and/or from different angles, processing the one or more images to obtain a plurality of image-derived features, and determining the value of one or more metrics indicative of the progress or outcome of the maturation process using a statistical model adapted to predict the values of the one or more metrics indicative of the progress or outcome of the maturation process using inputs comprising the plurality of image-derived features. Related methods, systems and products are also described.
A method for predicting a readout of a second assay from a readout of a first assay is described. The method comprises obtaining, for one or more experimental conditions, a readout from the first assay; predicting, using the readout from the first assay, a readout from the second assay using a machine learning model comprising: first and second assay models that have been trained to provide a latent representation of a readout from the first and second assays, respectively, using training data comprising readouts from the first and second assays, respectively, for a plurality of experimental conditions; and a translation model that has been trained to predict a latent representation of the second assay model from a latent representation of the first assay model, using training data comprising readouts from the first and second assays for a plurality of experimental conditions.
Computer-implemented monitoring of monoclonal quality of cell growth is specifically applicable to development of cell lines for the manufacturing of biopharmaceuticals. In one aspect, a computer-implemented method comprises: acquiring a sequence of images of a cell culture taken at different times during cell growth; processing each image in the sequence of images to identify cell locations of cells in the cell culture; determining for at least some of the images in the sequence of images the number of cells from the identified cell locations; determining for at least one image in the sequence of images a spatial distribution of cells from the identified cell locations; evaluating compliance of the determined numbers of cells and the determined spatial distribution of cells with predetermined evaluation conditions being characteristic of monoclonal growth; and assessing and outputting a monoclonal quality indicator based on the evaluated compliance with the predetermined evaluation conditions.
DEUTSCHES FORSCHUNGSZENTRUM FÜR KÜNSTLICHE INTELLIGENZ (DFKI) GMBH (Germany)
Inventor
Asim, Muhammad Nabeel
Ahmed, Sheraz
Zehe, Christoph
Trygg, Johan
Cloarec, Olivier
Abstract
A method is provided for optimizing protein expression. The method comprises: obtaining, by a processor (102), a plurality of amino acid sequences and corresponding known efficiency values, each known efficiency value indicating efficiency of expressing a protein having a corresponding one of the plurality of amino acid sequences; for each one of a plurality of prediction algorithms, obtaining, by the processor (102), a prediction function according to the one of the plurality of prediction algorithms, wherein the prediction function outputs a predicted efficiency value for expressing a protein having an amino acid sequence corresponding to an input numerical vector; evaluating, by the processor (102), the obtained prediction function by comparing predicted efficiency values output by the obtained prediction function with the known efficiency values; selecting, by the processor (102), at least one prediction algorithm from among the plurality of prediction algorithms based on said evaluating; predicting, by the processor (102), using the at least one prediction algorithm and the prediction function obtained with the at least one prediction algorithm, one or more efficiency values for expressing one or more proteins respectively having one or more specified amino acid sequences; and outputting, by the processor (102), the one or more specified amino acid sequences and the one or more efficiency values predicted for the one or more specified amino acid sequences.
Methods for controlling a perfusion bioprocess are described, the method comprising: controlling the value of one or more controlled variables selected from the number or density of viable cells in the bioreactor, the volume of culture in the bioreactor, and the concentration of one or more metabolites in the bioreactor using one or more control loops each configured to control a non-overlapping subset of the controlled variables by setting the value of one or more manipulated variables selected from the feed flow rate, the bleed flow rate and the permeate flow rate, wherein at least one of the control loops uses a controller that identifies a value of the one or more manipulated variables that optimises a control objective using a prediction of the value of the subset of controlled variables from a dynamic model of the bioprocess describing the rate of change of the one or more controlled variables. Systems, computer readable media implementing such methods, and methods for providing tools to implement such methods, are also provided.
C12M 1/34 - Measuring or testing with condition measuring or sensing means, e.g. colony counters
C12M 1/36 - Apparatus for enzymology or microbiology including condition or time responsive control, e.g. automatically controlled fermentors
G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
11.
COMPUTER-IMPLEMENTED METHOD, COMPUTER PROGRAM PRODUCT, CONTROL DEVICE AND SYSTEM FOR CONTROLLING A CHROMATOGRAPHY SYSTEM
A computer implemented method for detecting foam on the surface of a liquid medium contained in a vessel is described. The method including the steps of receiving a sample image of at least a portion of the vessel comprising the liquid-gas interface and classifying the sample image between a first class and at least one second class, associated with different amounts of foam on the surface of the liquid. The classifying is performed by a deep neural network classifier that has been trained using a plurality of training images of at least a portion of a vessel comprising a liquid-gas interface. The plurality of training images may comprise at least some images that differ from each other by one or more of: the location of the liquid-gas interface on the image, the polar and/or azimuthal angle at which the liquid-gas interface is viewed on the image, and the light intensity or colour temperature of the one or more light sources that illuminated the imaged portion of the vessel when the image was acquired. Related methods for controlling a bioprocess, for providing a tool, and related systems and computer software products are also described.
A computer implemented method for monitoring a bioprocess comprising a cell culture in a bioreactor is provided. The method including the steps of: obtaining measurements of the amount of biomass and the amount of one or more metabolites in the bioreactor as a function of bioprocess maturity, using the measurements to determining one or more metabolic condition variables; using a pre-trained multivariate model to determine the value of one or more latent variables as a function of bioprocess maturity, wherein the multivariate model is a linear model that uses process variables including the metabolic condition variables as predictor variables and maturity as a response variable; comparing the value(s) of the one or more latent variables to one or more predetermined values as a function of maturity; and determining on the basis of the comparison whether the bioprocess is operating normally.
A computer-implemented method is provided for processing images. The method can include down-sampling a plurality of first images having a first resolution for obtaining a plurality of second images having a second resolution and training an artificial neural network model to process an input image and output an output image having a higher resolution than the input image.
A computer-implemented method is provided for analyzing videos of a living system captured with microscopic imaging. The method can include obtaining a base dataset including one or more videos captured with microscopic imaging with at least one of the one or more videos including a cellular event, and cropping out, from the base dataset, sub-videos including one or more objects of interest that may be involved in the cellular event. An artificial neural network (ANN) model can be trained using the plurality of selected sub-videos as training data, to perform unsupervised video alignment, a query sub-video can be aligned using the trained ANN model, and a determination can be made whether or not the query sub-video includes the cellular event.
A computer-implemented method for data analysis comprises obtaining a plurality of first observations, each one of the plurality of first observations including one or more values of one or more first parameters, the plurality of first observations grouped into a plurality of groups; constructing a first histogram using the values of at least one of the one or more first parameters, included in the plurality of first observations; constructing, for each one of the plurality of groups, a second histogram having bins corresponding to bins of the first histogram, wherein each one of the bins of the second histogram includes a count of the first observations, among the first observations that belong to the one of the plurality of groups, having one or more values corresponding to the one of the bins for the at least one of the one or more first parameters; and outputting the second histograms.
G06F 17/18 - Complex mathematical operations for evaluating statistical data
G06V 10/34 - Smoothing or thinning of the patternMorphological operationsSkeletonisation
G06V 10/50 - Extraction of image or video features by performing operations within image blocksExtraction of image or video features by using histograms, e.g. histogram of oriented gradients [HoG]Extraction of image or video features by summing image-intensity valuesProjection analysis
G06V 20/69 - Microscopic objects, e.g. biological cells or cellular parts
17.
Computer-implemented method, computer program product and hybrid system for cell metabolism state observer
Techniques for predicting an amount of at least one biomaterial produced or consumed by a biological system in a bioreactor are provided. Process conditions and metabolite concentrations are measured for the biological system as a function of time. Metabolic rates for the biological system, including specific consumption rates of metabolites and specific production rates of metabolites are determined. The process conditions and the metabolic rates are provided to a hybrid system model configured to predict production of the biomaterial. The hybrid system model includes a kinetic growth model configured to estimate cell growth as a function of time and a metabolic condition model based on metabolite specific consumption or secretion rates and select process conditions, wherein the metabolic condition model is configured to classify the biological system into a metabolic state. An amount of the biomaterial based on the hybrid system model is predicted.
Methods for monitoring, controlling and simulating a bioprocess comprising a cell culture in a bioreactor are provided. The methods comprise obtaining values of one or more process conditions for the bioprocess at one or more maturities, and determining the specific transport rates of one or more metabolites in the cell culture using the values obtained as input to a machine learning model trained to predict the specific transport rates of the one or more metabolites at a latest maturity of the one or more maturities or a later maturity based at least in part on the values of one or more process conditions for the bioprocess at the one or more preceding maturities. The methods further comprise predicting one or more features of the bioprocess based at least in part on the determined specific transport rates. Systems, computer readable media and methods for providing tools to implement such methods are also provided.
G16B 5/00 - ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
C12M 1/36 - Apparatus for enzymology or microbiology including condition or time responsive control, e.g. automatically controlled fermentors
C12M 1/34 - Measuring or testing with condition measuring or sensing means, e.g. colony counters
Methods and systems for monitoring a cell population in culture are described. The method includes the steps of: obtaining one or more images of the cell population acquired using label- free imaging during the cell culture process, processing the one or more images to obtain one or more label-free image-derived features, and predicting one or more metrics indicative of a cell state transition in the cell population using a statistical model that takes the label-free image-derived features as inputs and provides the one or more metrics indicative of a cell state transition in the cell population as output. The metrics indicative of a cell state transition in the cell population are metrics that characterise the progress and/or outcome of a cell state transition process occurring in a cell population, and the inputs of the statistical model do not include any feature obtained using an invasive or destructive measurement process.
Methods and systems for monitoring a cell population in cell culture, and for controlling a cell culture process are described. The methods include: obtaining one or more images of the cell population acquired using label-free imaging at one or more time points during the cell culture process, predicting one or more metrics indicative of a cell state transition in the cell population using a statistical model that takes the label-free image-derived features as inputs, wherein the cell culture process is associated with a base protocol for obtaining the cell state transition comprising one or more interventions defined by one or more process parameters, and the predicting one or more metrics indicative of the cell state transition process is repeated for a plurality of candidate values of at least one of the one or more process parameters of at least one of said interventions to obtain a plurality of sets of one or more metrics indicative of the cell state transition process; and wherein comparing the predicted plurality of sets of one or more metrics indicative of the cell state transition process provides an indication of the suitability of the candidate values to achieve the cell state transition.
A computer-implemented method for simulating a cell culture process is provided. The method includes: obtaining measurable parameter values that are measured with respect to at least one operation of the cell culture process, the measurable parameter values being values of measurable parameters in a model of the cell culture process, wherein one or more of the measurable parameters relate to one or more operating conditions of the cell culture process; estimating, using Bayesian inference with the obtained measurable parameter values, values of unmeasurable parameters in the model, wherein the model describes the cell culture process with coupled ordinary differential equations including the measurable parameters and the unmeasurable parameters, wherein one or more of the unmeasurable parameters relate to lysed cells in the cell culture process; receiving one or more new measurable parameter values relating to said one or more operating conditions of the cell culture process; simulating the cell culture process using: the model of the cell culture process; the estimated values of the unmeasurable parameters in the model; and the received one or more new measurable parameter values.
G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
22.
MONITORING, SIMULATION AND CONTROL OF BIOPROCESSES
Methods for monitoring, controlling, optimising and simulating a bioprocess comprising a cell culture in a bioreactor are provided. The methods comprise obtaining the values of one or more state variables of a state space model at one or more maturities, and predicting the value of one or more critical quality attributes of a product of the bioprocess using a machine learning model trained to predict the value of the one or more critical quality attributes based on input variables comprising values of the one or more state variables or variables derived therefrom, at one or more maturities. The state space model comprises a kinetic growth model representing changes in the state of the cell culture and a material balance model representing changes in the bulk concentration of one or more metabolites in the bioreactor. Systems, computer readable media implementing such methods, and methods for providing tools to implement such methods, are also provided.
C12M 1/36 - Apparatus for enzymology or microbiology including condition or time responsive control, e.g. automatically controlled fermentors
G05B 13/04 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
23.
ADAPTING CONTROL OF A CELL CULTURE IN A PRODUCTION SCALE VESSEL WITH REGARD TO A STARTING MEDIUM
A computer implemented and a system for adapting control of a cell culture in a production-scale vessel with regard to a starting medium are provided. The method comprises providing multiple production-scale process trajectories, receiving a media lot for the cell culture, and sampling first media from the media lot for possible use in the production-scale vessel. The method also comprises starting a seed train using the first media to achieve inoculation of the production-scale vessel, providing a plurality of micro-scale vessels in a process control device, and sampling second media from the media lot for the micro-scale vessels. Cells from the seed train can be introduced into the micro-scale vessels to start cell cultures in each of the micro-scale vessels.
Method and device assembly for predicting a parameter in a bioprocess based on Raman spectroscopy and method and device assembly for controlling a bioprocess
A method of predicting a parameter of a medium to be observed in a bioprocess based on Raman spectroscopy including the steps of acquiring a first series of preparatory Raman spectra of an aqueous medium using a first measuring assembly; normalizing the first series of preparatory Raman spectra based on a characteristic band of water from at least one Raman spectrum acquired with the first measuring assembly; building a multivariate model for the parameter based on the normalized preparatory Raman spectra; acquiring predictive Raman spectra of the medium to be observed during the bioprocess with another measuring assembly; normalizing the predictive Raman spectra based on a characteristic band of water from at least one Raman spectrum acquired with the other measuring assembly; and applying the built model to the predictive Raman spectra for predicting the parameter.
09 - Scientific and electric apparatus and instruments
41 - Education, entertainment, sporting and cultural services
42 - Scientific, technological and industrial services, research and design
Goods & Services
Downloadable computer software for facilitating data processing, secure data exchange, data analysis, data modeling, and computational analysis for the bio-pharma, pharmaceutical, chemical, food and beverage, petrochemical, paper, steel and mining, flat panel or fragrance industry; downloadable application programming interface (API) software; downloadable computer software for use as an application programming interface (API); downloadable computer operating software and downloadable computer data processing software for the bio-pharma, pharmaceutical, chemical, food and beverage, petrochemical, paper, steel and mining, flat panel or fragrance industry; all of the aforementioned goods specifically intended for use in research, process development, and manufacturing Education services in the nature of instruction, coaching, tutoring, classes, seminars, conferences, conventions, and workshops in the field of software for data analytics, data modeling, and computational analysis; training in the field of software for data analytics, data modeling, and computational analysis; arranging and conducting of training seminars in the field of software for data analytics, data modeling, and computational analysis Software design and development; computer software installation; computer software consultancy services; computer software maintenance; technical support services, namely, troubleshooting of computer software problems; all of the aforementioned services specifically intended for use in research, process development, and manufacturing
09 - Scientific and electric apparatus and instruments
41 - Education, entertainment, sporting and cultural services
42 - Scientific, technological and industrial services, research and design
Goods & Services
Downloadable computer software for data processing, secure data exchange, data analysis, data modeling, data visualization, and data exploration; downloadable computer software for scientific and research use, namely, downloadable computer software for process monitoring, forecasting, and optimization of experiment conditions and production processes; downloadable computer software, namely, software development tools for the creation and customization of data analytics applications; downloadable computer software for flowsheet modeling for product or process development; downloadable computer operating software and downloadable computer data processing software for the bio-pharma, pharmaceutical, chemical, food and beverage, petrochemical, paper, steel and mining, flat panel or fragrance industry; all of the aforementioned goods specifically intended for use in research, process development, and manufacturing Education services in the nature of instruction, coaching, tutoring, classes, seminars, conferences, conventions, and workshops in the field of software for data processing, secure data exchange, data analysis, data modeling, data visualization, and data exploration, software for scientific and research use, and software for product or process development in the bio-pharma, pharmaceutical, chemical, food and beverage, petrochemical, paper, steel and mining, flat panel or fragrance industry; training in the field of software for data processing, secure data exchange, data analysis, data modeling, data visualization, and data exploration, software for scientific and research use, and software for product or process development in the bio-pharma, pharmaceutical, chemical, food and beverage, petrochemical, paper, steel and mining, flat panel or fragrance industry; arranging and conducting of training seminars in the field of software for data processing, secure data exchange, data analysis, data modeling, data visualization, and data exploration, software for scientific and research use, and software for product or process development in the bio-pharma, pharmaceutical, chemical, food and beverage, petrochemical, paper, steel and mining, flat panel or fragrance industry Software design and development; computer software installation; computer software consultancy services; computer software maintenance; technical support services, namely, troubleshooting of computer software problems; all of the aforementioned services specifically intended for use in research, process development, and manufacturing
29.
ACCELERATED DOCUMENT CATEGORIZATION USING MACHINE LEARNING
A computer-implemented method is provided. The method may comprise: obtaining at least one document to be classified; classifying, using a machine learning model including an artificial neural network (ANN) and an attention mechanism, the at least one document into at least two classes; determining, for each of the at least one document, a confidence value of the classifying, based on one or more outputs of one or more nodes comprised in the ANN; assigning, to each of the at least one document, based at least in part on the confidence value, one of at least two categories that are associated with different degrees of credibility of the classifying; and providing for display one or more of the at least one document with: the assigned category and attention information that indicates significance of one or more parts of each document provided for display in the classifying of said document.
A computer-implemented method is provided. The method may comprise: obtaining at least one document to be classified; classifying, using a machine learning model including an artificial neural network (ANN) and an attention mechanism, the at least one document into at least two classes; determining, for each of the at least one document, a confidence value of the classifying, based on one or more outputs of one or more nodes comprised in the ANN; assigning, to each of the at least one document, based at least in part on the confidence value, one of at least two categories that are associated with different degrees of credibility of the classifying; and providing for display one or more of the at least one document with: the assigned category and attention information that indicates significance of one or more parts of each document provided for display in the classifying of said document.
Aspects relate to a computer-implemented method, a computer program and a system for storing a heterogeneous sequence of discrete-time data determined from a process to produce a chemical, pharmaceutical, biopharmaceutical and/or biological product. The method comprises receiving the discrete-time data, the discrete-time data comprising data from one or more first scientific instruments and including data comprising one or more timestamps corresponding to one or more digital signals.
G16H 10/40 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
The present description relates to computer-implemented monitoring of monoclonal quality of cell growth, which is specifically applicable for the development of cell lines for the manufacturing of biopharmaceuticals. In one aspect a computer-implemented method is provided comprising: acquiring (ST10) a sequence of images (10) of a cell culture taken at different times during cell growth; processing (ST20) each image in the sequence of images to identify cell locations of cells in the cell culture; determining (ST40) for at least some of the images in the sequence of images the number of cells from the identified cell locations; determining (ST30) for at least one image in the sequence of images a spatial distribution of cells from the identified cell locations; evaluating compliance of the determined numbers of cells and the determined spatial distribution of cells with predetermined evaluation conditions being characteristic of monoclonal growth; and assessing and outputting a monoclonal quality indicator based on the evaluated compliance with the predetermined evaluation conditions.
A computer-implemented method for analyzing data obtained for a chemical and/or biological process comprises: obtaining a result of statistical data analysis on the data obtained with respect to the chemical and/or biological process; calculating, for values of process parameters obtained at groups of time points during batch processes of the chemical and/or biological process, a ratio of a correlation value to a confidence value of the correlation value, the correlation value indicating a correlation between the values of the process parameter and at a process output value; calculating, for process parameters, an average of absolute values of the ratios calculated for the values of the process parameter obtained at different groups of time points during the batch processes; excluding the values of one of the process parameters having a smallest average; and iterating, until at least one specified condition is met.
Aspects of the application relate to methods, a computer program and a process control device. According to one aspect, a computer-implemented method for determining a multivariate process chart is provided. The multivariate process chart is to be used to control a process to produce a chemical, pharmaceutical, biopharmaceutical and/or biological product. The multivariate process chart includes a first trajectory, an upper limit for the first trajectory and a lower limit for the first trajectory.
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
G01N 35/00 - Automatic analysis not limited to methods or materials provided for in any single one of groups Handling materials therefor
C12M 1/36 - Apparatus for enzymology or microbiology including condition or time responsive control, e.g. automatically controlled fermentors
Aspects of the application relate to computer-implemented methods, process control devices, and a computer program. According to one aspect, a computer-implemented method for controlling a process in a plurality of first scale vessels via a first process control device is provided. Each of the first scale vessels contains fluid and the process is for producing a chemical, pharmaceutical, biopharmaceutical and/or biological product. The method comprises controlling, by the first process control device and at least partially in parallel, the process in each of the first scale vessels. The method can include periodically determining, prior to an assigning decision and at a first frequency, first sets of process parameter values for each of the process parameters from each of the first scale vessels.
A computer implemented method for monitoring a bioprocess comprising a cell culture in a bioreactor is provided. The method including the steps of: obtaining measurements of the amount of biomass and the amount of one or more metabolites in the bioreactor as a function of bioprocess maturity, using the measurements to determining one or more metabolic condition variables; using a pre-trained multivariate model to determine the value of one or more latent variables as a function of bioprocess maturity, wherein the multivariate model is a linear model that uses process variables including the metabolic condition variables as predictor variables and maturity as a response variable; comparing the value(s) of the one or more latent variables to one or more predetermined values as a function of maturity; and determining on the basis of the comparison whether the bioprocess is operating normally.
G05B 13/04 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
C12M 1/36 - Apparatus for enzymology or microbiology including condition or time responsive control, e.g. automatically controlled fermentors
A computer implemented method for detecting foam on the surface of a liquid medium contained in a vessel is described. The method including the steps of receiving a sample image of at least a portion of the vessel comprising the liquid-gas interface and classifying the sample image between a first class and at least one second class, associated with different amounts of foam on the surface of the liquid. The classifying is performed by a deep neural network classifier that has been trained using a plurality of training images of at least a portion of a vessel comprising a liquid-gas interface. The plurality of training images may comprise at least some images that differ from each other by one or more of: the location of the liquid-gas interface on the image, the polar and/or azimuthal angle at which the liquid- gas interface is viewed on the image, and the light intensity or colour temperature of the one or more light sources that illuminated the imaged portion of the vessel when the image was acquired. Related methods for controlling a bioprocess, for providing a tool, and related systems and computer software products are also described.
A computer-implemented method is provided for processing images. The method comprises: down-sampling a plurality of first images having a first resolution for obtaining a plurality of second images having a second resolution, the first resolution being higher than the second resolution, each one of the plurality of second images being a down-sampled version of one of the plurality of first images; training an artificial neural network, ANN, model (40) to process an input image and output an output image having a higher resolution than the input image, wherein training data for the training comprises pairs of images, each pair of images including: one of the plurality of second images as an input to the ANN model (40); and one of the plurality of first images, corresponding to the one of the plurality of second images, as a desired output from the ANN model (40) in case the one of the plurality of second images is input to the ANN model (40); inputting at least one of the plurality of first images to the trained ANN model (40); and obtaining at least one output image from the trained ANN model (40), the at least one output image having a third resolution that is higher than the first resolution.
A computer-implemented method is provided for data analysis. The method comprises: obtaining (S10) a plurality of first observations, each one of the plurality of first observations including one or more values of one or more first parameters, the plurality of first observations being grouped into a plurality of groups; constructing (S50) a first histogram using the values of at least one of the one or more first parameters, included in the plurality of first observations; constructing (S60), for each one of the plurality of groups, a second histogram having bins corresponding to bins of the first histogram, wherein each one of the bins of the second histogram includes a count of the first observations, among the first observations that belong to the one of the plurality of groups, having one or more values corresponding to the one of the bins for the at least one of the one or more first parameters; and outputting (S70) the second histograms constructed for the plurality of groups.
According to some aspects of the disclosure, a computer-implemented method, a computer program and a process control device for selecting at least one set of target cells from multiple sets of candidate cells are provided. The method can include receiving data collected from a plurality of processes, wherein each of the processes produces a distinct set of candidate cells. The method further comprises the received data including values of process outputs being a product quality attribute or a key performance indicator for selecting the target cells.
G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
G05B 13/04 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
43.
COMPUTER-IMPLEMENTED METHOD, COMPUTER PROGRAM PRODUCT AND SYSTEM FOR ANALYZING VIDEOS CAPTURED WITH MICROSCOPIC IMAGING
A computer-implemented method is provided for analyzing videos of a living system captured with microscopic imaging. The method comprises: obtaining (S10) a base dataset including one or more videos captured with microscopic imaging, at least one of the one or more videos including a cellular event; cropping out (S30), from the base dataset, sub-videos including one or more objects of interest that may be involved in the cellular event; receiving (S40) information indicating a plurality of sub-videos selected from among the sub-videos that are cropped out from the base dataset, the plurality of selected sub-videos including the cellular event; training (S50) an artificial neural network, ANN, model, using the plurality of selected sub-videos as training data, to perform unsupervised video alignment; obtaining (S602) a query sub-video, the query sub-video being: one of the sub-videos that are cropped out from the base dataset, or a sub-video cropped out from a video that is captured with microscopic imaging and that is not included in the base dataset; aligning (S604), using the trained ANN model, the query sub-video with a reference sub-video that is one of the plurality of selected sub-videos; and determining (S606), according to a result of the aligning, whether or not the query sub-video includes the cellular event.
A computer-implemented method for analysis of cell images comprises obtaining a deep neural network and a training dataset, the deep neural network comprising a plurality of hidden layers; obtaining first sets of intermediate output values that are output from at least one of the plurality of hidden layers; constructing a latent variable model using the first sets of intermediate output values, the latent variable model mapping the first sets of intermediate output values to first sets of projected values in a sub-space that has a dimension lower than the sets of the intermediate outputs; obtaining a second set of intermediate output values by inputting a received new cell image to the deep neural network; mapping, using the latent variable model, the second set of intermediate output values to a second set of projected values; and determining whether the received new cell image is an outlier.
An example method comprises receiving a new observation characterizing at least one parameter of an entity; inputting the new observation to a deep neural network having hidden layers; obtaining a second set of intermediate output values that are output from at least one of the hidden layers by inputting the received new observation to the deep neural network; mapping the second set of intermediate output values to a second set of projected values; determining whether or not the received new observation is an outlier with respect to the training dataset based on the latent variable model and the second set of projected values, calculating a prediction for the new observation; and determining a result indicative of the occurrence of at least one anomaly in the entity based on the prediction and the determination whether or not the new observation is an outlier.
Techniques for predicting an amount of at least one biomaterial produced or consumed by a biological system in a bioreactor are provided. Process conditions and metabolite concentrations are measured for the biological system as a function of time. Metabolic rates for the biological system, including specific consumption rates of metabolites and specific production rates of metabolites are determined. The process conditions and the metabolic rates are provided to a hybrid system model configured to predict production of the biomaterial. The hybrid system model includes a kinetic growth model configured to estimate cell growth as a function of time and a metabolic condition model based on metabolite specific consumption or secretion rates and select process conditions, wherein the metabolic condition model is configured to classify the biological system into a metabolic state. An amount of the biomaterial based on the hybrid system model is predicted.
Methods for monitoring, controlling and simulating a bioprocess comprising a cell culture in a bioreactor are provided. The methods comprise obtaining values of one or more process conditions for the bioprocess at one or more maturities, and determining the specific transport rates of one or more metabolites in the cell culture using the values obtained as input to a machine learning model trained to predict the specific transport rates of the one or more metabolites at a latest maturity of the one or more maturities or a later maturity based at least in part on the values of one or more process conditions for the bioprocess at the one or more preceding maturities. The methods further comprise predicting one or more features of the bioprocess based at least in part on the determined specific transport rates. Systems, computer readable media and methods for providing tools to implement such methods are also provided.
G05B 13/04 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
C12M 1/36 - Apparatus for enzymology or microbiology including condition or time responsive control, e.g. automatically controlled fermentors
48.
COMPUTER-IMPLEMENTED METHOD, COMPUTER PROGRAM PRODUCT AND SYSTEM FOR SIMULATING A CELL CULTURE PROCESS
A computer-implemented method for simulating a cell culture process is provided. The method includes: obtaining (S10) measurable parameter values that are measured with respect to at least one operation of the cell culture process, the measurable parameter values being values of measurable parameters in a model of the cell culture process, wherein one or more of the measurable parameters relate to one or more operating conditions of the cell culture process; estimating (S20), using Bayesian inference with the obtained measurable parameter values, values of unmeasurable parameters in the model, wherein the model describes the cell culture process with coupled ordinary differential equations including the measurable parameters and the unmeasurable parameters, wherein one or more of the unmeasurable parameters relate to lysed cells in the cell culture process; receiving (S30) one or more new measurable parameter values relating to said one or more operating conditions of the cell culture process; simulating (S40) the cell culture process using: the model of the cell culture process; the estimated values of the unmeasurable parameters in the model; and the received one or more new measurable parameter values.
Techniques for predicting an amount of at least one biomaterial produced or consumed by a biological system in a bioreactor are provided. Process conditions and metabolite concentrations are measured for the biological system as a function of time. Metabolic rates for the biological system, including specific consumption rates of metabolites and specific production rates of metabolites are determined. The process conditions and the metabolic rates are provided to a hybrid system model configured to predict production of the biomaterial. The hybrid system model includes a kinetic growth model configured to estimate cell growth as a function of time and a metabolic condition model based on metabolite specific consumption or secretion rates and select process conditions, wherein the metabolic condition model is configured to classify the biological system into a metabolic state. An amount of the biomaterial based on the hybrid system model is predicted.
G05B 13/04 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
C12M 1/36 - Apparatus for enzymology or microbiology including condition or time responsive control, e.g. automatically controlled fermentors
50.
ADAPTING CONTROL OF A CELL CULTURE IN A PRODUCTION SCALE VESSEL WITH REGARD TO A STARTING MEDIUM
A computer implemented and a system for adapting control of a cell culture in a production-scale vessel with regard to a starting medium are provided. The method comprises providing multiple production- scale process trajectories, each derived from a successfully controlled cell culture. The method further comprises receiving a media lot for the cell culture. The method further comprises sampling first media from the media lot for possible use in the production-scale vessel. Moreover, the method comprises starting a seed train using the first media to achieve inoculation of the production-scale vessel. The method further comprises providing a plurality of micro-scale vessels in a process control device, wherein the production-scale is greater than the micro-scale. The method further comprises sampling second media from the media lot for the micro-scale vessels, wherein each of the micro-scale vessels receives a representative portion of the media lot. In addition, the method comprises introducing cells from the seed train into the micro-scale vessels to start cell cultures in each of the micro-scale vessels.
METHOD AND DEVICE ASSEMBLY FOR PREDICTING A PARAMETER IN A BIOPROCESS BASED ON RAMAN SPECTROSCOPY AND METHOD AND DEVICE ASSEMBLY FOR CONTROLLING A BIOPROCESS
A method of predicting a parameter of a medium to be observed in a bioprocess based on Raman spectroscopy comprises the steps of: acquiring a first series of preparatory Raman spectra of an aqueous medium using a first measuring assembly; normalizing the first series of preparatory Raman spectra based on a characteristic band of water from at least one Raman spectrum acquired with the first measuring assembly; building a multivariate model for the parameter based on the normalized preparatory Raman spectra; acquiring predictive Raman spectra of the medium to be observed during the bioprocess with another measuring assembly; normalizing the predictive Raman spectra based on a characteristic band of water from at least one Raman spectrum acquired with the other measuring assembly; and applying the built model to the predictive Raman spectra for predicting the parameter. A device assembly for predicting a parameter of a medium to be observed in a bioprocess is adapted to carry out this method.
G05B 17/02 - Systems involving the use of models or simulators of said systems electric
G05B 13/04 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
52.
STORING DATA FROM A PROCESS TO PRODUCE A CHEMICAL, PHARMACEUTICAL, BIOPHARMACEUTICAL AND/OR BIOLOGICAL PRODUCT
Aspects relate to a computer-implemented method, a computer program and a system for storing a heterogeneous sequence of discrete-time data determined from a process to produce a chemical, pharmaceutical, biopharmaceutical and/or biological product. The method comprises receiving the discrete-time data, the discrete-time data comprising first data from a first scientific instrument, the first data including a first timestamp corresponding to a first digital signal. The method further comprises receiving second data from a second scientific instrument, the second data including a second timestamp corresponding to a second digital signal. The first scientific instrument differs from the second scientific instrument. The method further comprises storing the first data and first metadata in a first record of a database. The first record comprises a first intensities field having a first data type, and a first descriptors field having a second data type. The method further comprises storing the second data and second metadata in a second record of the database. The second record comprises a second intensities field having the first data type, and a second descriptors field having the second data type. The first metadata includes a first identifier and the second metadata includes a second identifier. When the first data includes first intensities and first descriptors of the first digital signal, storing the first data further comprises storing the first intensities in the first intensities field, and storing the first descriptors in the first descriptors field. When the second data includes second intensities and second descriptors of the second digital signal, storing the second data further comprises storing the second intensities in the second intensities field, and storing the second descriptors in the second descriptors field.
G16H 10/40 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
G06F 16/00 - Information retrievalDatabase structures thereforFile system structures therefor
53.
MULTIVARIATE PROCESS CHART TO CONTROL A PROCESS TO PRODUCE A CHEMICAL, PHARMACEUTICAL, BIOPHARMACEUTICAL AND/OR BIOLOGICAL PRODUCT
Aspects of the application related to methods, a computer program and a process control device. According to one aspect, a computer-implemented method for determining a multivariate process chart is provided. The multivariate process chart is to be used to control a process to produce a chemical, pharmaceutical, biopharmaceutical and/or biological product. The multivariate process chart includes a first trajectory, an upper limit for the first trajectory and a lower limit for the first trajectory.
C12M 1/36 - Apparatus for enzymology or microbiology including condition or time responsive control, e.g. automatically controlled fermentors
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
Aspects of the application relate to computer-implemented methods, process control devices and a computer program. According to one aspect, a computer-implemented method for controlling a process in a plurality of first scale vessels via a first process control device is provided. Each of the first scale vessels contains fluid and the process is for producing a chemical, pharmaceutical, biopharmaceutical and/or biological product. The method comprises receiving, by the first process control device, process parameters, the process parameters including process parameters to be controlled and process parameters to be measured. The method further comprises controlling, by the first process control device and at least partly in parallel, the process in each of the first scale vessels. The method further comprises periodically determining, prior to an assigning decision and at a first frequency, first sets of process parameter values for each of the process parameters from each of the first scale vessels. The method further comprises carrying out the assigning decision by assigning corresponding ones of the first scale vessels to an analysis subset and other ones of the first scale vessels to an excluded subset. The method further comprises periodically determining, after the assigning decision and at a second frequency, second sets of process parameter values for each of the process parameters from the analysis subset of the first scale vessels. The first frequency is different from the second frequency. The method further comprises controlling, by the first process control device and at least partly in parallel, the process in the first scale vessels of the analysis subset according to the second sets of process parameter values.
According to an aspect, a computer-implemented method, a computer program and a process control device for selecting at least one set of target cells from multiple sets of candidate cells are provided. The method comprises receiving data collected from a plurality of processes, wherein each of the processes produces a distinct set of candidate cells. The method further comprises the received data including values of process parameters and process outputs of the processes, each of the process outputs being a product quality attribute or a key performance indicator for selecting the target cells. The method further comprises correlating the received data. The method further comprises receiving a selection of the process parameters and a selection of the process outputs. The method further comprises receiving multivariate evaluation criteria for the selected process parameters and/or the selected process outputs, the multivariate evaluation criteria including one or more of the following: weights for prioritization, prioritization ranges and/or targets. Each target is an extremum and/or a target value. The method further comprises calculating, via a multivariate selection function, scores for each one of the sets of candidate cells from the correlated data according to the multivariate evaluation criteria. The method further comprises ranking the sets of candidate cells according to the scores, and selecting at least one of the sets of candidate cells as the target cells using the ranking.
G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
56.
COMPUTER-IMPLEMENTED METHOD, COMPUTER PROGRAM PRODUCT AND SYSTEM FOR ANOMALY DETECTION AND/OR PREDICTIVE MAINTENANCE
A computer-implemented method and a respective system for anomaly detection and/or predictive maintenance is provided. The method comprises: receiving a new observation characterizing at least one parameter of the entity; inputting the new observation to a deep neural network (100) having a plurality of hidden layers and being trained using a training data set that includes possible observations; obtaining a second set of intermediate output values that are output from at least one of the plurality of hidden layers of the deep neural network by inputting the received new observation to the deep neural network; mapping, using a latent variable model stored in a storage medium, the second set of intermediate output values to a second set of projected values; determining whether or not the received new observation is an outlier with respect to the training dataset based on the latent variable model and the second set of projected values, calculating, by the deep neural network, a prediction for the new observation; and determining a result indicative of the occurrence of at least one anomaly in the entity based on the prediction and the determination whether or not the new observation is an outlier. The latent variable model stored in the storage medium is constructed by obtaining first sets of intermediate output values that are output from said one of the plurality of hidden layers of the deep neural network, each of the first sets of intermediate output values obtained by inputting a different one of the possible observations included in at least a part of the training dataset; and constructing the latent variable model using the first sets of intermediate output values, the latent variable model providing a mapping of the first sets of intermediate output values to first sets of projected values in a sub-space of the latent variable model that has a dimension lower than a dimension of the sets of the intermediate outputs.
A computer-implemented method for data analysis is provided. The method comprises: obtaining a deep neural network (100) for processing images and at least a part of a training dataset used for training the deep neural network, the deep neural network comprising a plurality of hidden layers, the training dataset including possible observations that can be input to the deep neural network; obtaining first sets of intermediate output values that are output from at least one of the plurality of hidden layers, each of the first sets of intermediate output values obtained by inputting a different one of the possible input images included in said at least the part of the training dataset; constructing a latent variable model using the first sets of intermediate output values, the latent variable model providing a mapping of the first sets of intermediate output values to first sets of projected values in a sub-space that has a dimension lower than a dimension of the sets of the intermediate outputs; receiving an observation to be input to the deep neural network; obtaining a second set of intermediate output values that are output from said at least one of the plurality of hidden layers by inputting the received observation to the deep neural network; mapping, using the latent variable model, the second set of intermediate output values to a second set of projected values; and determining whether or not the received observation is an outlier with respect to the training dataset based on the latent variable model and the second set of projected values.
A computer-implemented method for analysis of cell images is provided. The method comprises: obtaining a deep neural network (100) for processing images and at least a part of a training dataset used for training the deep neural network, the deep neural network comprising a plurality of hidden layers and being trained using the training dataset, the training dataset including possible cell images that can be input to the deep neural network; obtaining first sets of intermediate output values that are output from at least one of the plurality of hidden layers, each of the first sets of intermediate output values obtained by inputting a different one of the possible input images included in said at least the part of the training dataset; constructing a latent variable model using the first sets of intermediate output values, the latent variable model providing a mapping of the first sets of intermediate output values to first sets of projected values in a sub-space that has a dimension lower than a dimension of the sets of the intermediate outputs; receiving a new cell image to be input to the deep neural network; obtaining a second set of intermediate output values that are output from said at least one of the plurality of hidden layers by inputting the received new cell image to the deep neural network; mapping, using the latent variable model, the second set of intermediate output values to a second set of projected values; and determining whether or not the received new cell image is an outlier with respect to the training dataset based on the latent variable model and the second set of projected values.
A computer-implemented method for data analysis is provided. A deep neural network (100) is provided for processing images and at least a part of a training dataset used for training the deep neural network, the deep neural network comprising a plurality of hidden layers, the training dataset including possible observations that can be input to the deep neural network; obtaining first sets of intermediate output values that are output from at least one of the plurality of hidden layers, each of the first sets of intermediate output values obtained by inputting a different one of the possible input images included in said at least the part of the training dataset; constructing a latent variable model using the first sets of intermediate output values, the latent variable model providing a mapping of the first sets of intermediate output values to first sets of projected values in a sub-space that has a dimension lower than a dimension of the sets of the intermediate outputs; receiving an observation to be input to the deep neural network; obtaining a second set of intermediate output values that are output from said at least one of the plurality of hidden layers by inputting the received observation to the deep neural network; mapping, using the latent variable model, the second set of intermediate output values to a second set of projected values; and determining whether or not the received observation is an outlier with respect to the training dataset based on the latent variable model and the second set of projected values.
G06V 10/74 - Image or video pattern matchingProximity measures in feature spaces
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
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computer software for designing, monitoring and analysing experiments in product or process development in industry and scientific research educational services, namely, conducting seminars, classes or workshops in the field of computer software, educational training in the field of computer software; arranging of educational seminars in the field of computer software computer software technical support services, namely, troubleshooting of computer software problems, troubleshooting in the nature of diagnostic computer software problems
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computer software for designing, monitoring and analysing experiments in product or process development in industry and scientific research *, excluding software especially and only destined for simulation and model-based control of diesel aftertreatment systems, particularly catalyst systems * educational services, namely, conducting seminars, classes or workshops in the field of computer software, educational training in the field of computer software; arranging of educational seminars in the field of computer software computer software technical support services, namely, troubleshooting of computer software problems, troubleshooting in the nature of diagnostic computer software problems * ; all of the aforesaid services not in relation to software especially and only destined for simulation and model-based control of diesel aftertreatment systems, particularly catalyst systems *
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(1) Computer software for designing, monitoring, and analysing experiments in product or process development for scientific and research purposes, for management purposes and for industrial engineering purposes (1) Education services, namely, conducting seminars, classes or workshops in the field of computer software, educational training in the field of computer software; arranging of educational training in the field of computer software
(2) Software development; software installation; software consultancy services; computer software maintenance; computer software technical support services, namely, troubleshooting of computer software problems, troubleshooting in the nature of diagnostic computer software problems
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(1) Computer software for designing, monitoring, and analysing experiments in product or process development for scientific and research purposes, for management purposes and for industrial engineering purposes (1) Education services, namely, conducting seminars, classes or workshops in the field of computer software, educational training in the field of computer software; arranging of educational training in the field of computer software
(2) Software development; software installation; software consultancy services; computer software maintenance; computer software technical support services, namely, troubleshooting of computer software problems, troubleshooting in the nature of diagnostic computer software problems
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Computer software, excluding software especially and only destined for simulation and model-based control of diesel aftertreatment systems, particularly catalyst systems. Education, training; arranging of seminars. Software development; software installation; software consultancy services; computer software maintenance; computer software technical support services; all of the aforesaid services not in relation to software especially and only destined for simulation and model-based control of diesel aftertreatment systems, particularly catalyst systems.
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(1) Computer software for analyzing, collecting, storing, documenting and visualizing data relating to manufacturing processes for modeling and optimizing development of manufacturing processes for the biopharma, pharmaceutical, chemical, food and beverage, petrochemical, paper, steel and mining, flat panel and fragrance industries; Computer software for analyzing, collecting, storing, documenting and visualizing data related to setting up scientific experiments and analysis of the results of scientific experiments for use in biochemical research, chemical research, cosmetic research, bacteriological research, pharmaceutical research, biopharmaceutical research, biotechnological research in universities, scientific institutions and research and development laboratories in the biopharmaceutical, pharmaceutical, chemical, food and beverage, petrochemical, paper, steel and mining, flat panel and fragrance industries (1) Education in the nature of instruction, coaching, tutoring, classes, seminars, conferences, conventions, workshops in the field of software for data analytics; training in the field of software for data analytics; arranging of seminars in the field of software for data analytics
(2) Software development; software installation; software consultancy services; computer software maintenance; computer software technical support services
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computer operating software and computer data processing software for the bio-pharma, pharmaceutical, chemical, food and beverage [, petrochemical,] paper, [steel and mining, flat panel or fragrance industry] education in the nature of instruction, [coaching, tutoring,] classes, seminars, conferences, [conventions,] workshops in the field of software for data analytics; training in the field of software for data analytics; arranging of seminars
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Scientific, nautical, photographic, cinematographic, optical apparatus and instruments; weighing, measuring, signalling, checking (supervision), life-saving and teaching apparatus and instruments; apparatus and instruments for surveying; apparatus and instrument for conducting, switching, transforming, accumulating, regulating or controlling electricity; apparatus for recording, transmission or reproduction of sounds or images; magnetic data carriers; recording discs; automatic vending machines and mechanisms for coin-operated apparatus; cash registers; calculating machines; data processing equipment; computers; fire extinguishing apparatus. Teaching and education; arranging of guidance/instruction; entertainment; sporting activities; cultural activities. Scientific and technological services and research and design relating thereto; industrial analysis and research services; design and development of computer hardware and software.
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Scientific, nautical, photographic, cinematographic, optical apparatus and instruments; weighing, measuring, signalling, checking (supervision), life-saving and teaching apparatus and instruments; apparatus and instruments for surveying; apparatus and instrument for conducting, switching, transforming, accumulating, regulating or controlling electricity; apparatus for recording, transmission or reproduction of sounds or images; magnetic data carriers; recording discs; automatic vending machines and mechanisms for coin-operated apparatus; cash registers; calculating machines; data processing equipment; computers; fire extinguishing apparatus. Teaching and education; arranging of guidance/instruction; entertainment; sporting activities; cultural activities. Scientific and technological services and research and design relating thereto; industrial analysis and research services; design and development of computer hardware and software.
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Scientific, nautical, photographic, cinematographic, optical apparatus and instruments; weighing, measuring, signalling, checking (supervision), life-saving and teaching apparatus and instruments; apparatus and instruments for surveying; apparatus and instrument for conducting, switching, transforming, accumulating, regulating or controlling electricity; apparatus for recording, transmission or reproduction of sounds or images; magnetic data carriers; recording discs; automatic vending machines and mechanisms for coin-operated apparatus; cash registers; calculating machines; data processing equipment; computers; fire extinguishing apparatus. Teaching and education; arranging of guidance/instruction; entertainment; sporting activities; cultural activities. Scientific and technological services and research and design relating thereto; industrial analysis and research services; design and development of computer hardware and software.
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Nautical, photographic, cinematographic, optical apparatus and instruments; weighing, measuring, life-saving and teaching apparatus and instruments; apparatus and instruments for surveying; apparatus for recording, transmission or reproduction of sounds or images; magnetic data carriers; recording discs; automatic vending machines and mechanisms for coin-operated apparatus; cash registers; calculating machines; data processing equipment; computers; fire extinguishing apparatus. Teaching and education; arranging of guidance/instruction; entertainment; sporting activities; cultural activities. Scientific and technological services and research and design relating thereto; industrial analysis and research services; design and development of computer hardware and software.
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Scientific, nautical, photographic, cinematographic and
optical apparatus and instruments; weighing, measuring,
signalling, checking (supervision), life-saving and teaching
apparatus and instruments; apparatus and instruments for
surveying; apparatus and instruments for conducting,
switching, transforming, accumulating, regulating or
controlling electricity; apparatus for recording,
transmission or reproduction of sounds or images; magnetic
data carriers, recording discs; automatic vending machines
and mechanisms for coin-operated apparatus; cash registers;
calculating machines; data processing equipment; computers;
fire-extinguishing apparatus. Teaching and education; arranging of guidance/instruction;
entertainment; sporting activities; cultural activities. Scientific and technological services and research and
design relating thereto; industrial analysis and research
services; design and development of computer hardware and
software; litigation services.
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Scientific, nautical, photographic, cinematographic and
optical apparatus and instruments; weighing, measuring,
signalling, checking (supervision), life-saving and teaching
apparatus and instruments; apparatus and instruments for
surveying; apparatus and instruments for conducting,
switching, transforming, accumulating, regulating or
controlling electricity; apparatus for recording,
transmission or reproduction of sounds or images; magnetic
data carriers, recording discs; automatic vending machines
and mechanisms for coin-operated apparatus; cash registers;
calculating machines; data processing equipment; computers;
fire-extinguishing apparatus. Teaching and education; arranging of guidance/instruction;
entertainment; sporting activities; cultural activities. Scientific and technological services and research and
design relating thereto; industrial analysis and research
services; design and development of computer hardware and
software; litigation services.
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[ Apparatus for recording, transmission or reproduction of sounds or images; ] prerecorded magnetic data carriers having computer software stored hereon for use in multivariate data analysis, modeling and experimental design; prerecorded recording discs having computer software stored thereon for use in multivariate data analysis, modeling and experimental design; [ automatic vending machines and mechanisms for coin-operated apparatus; cash registers; calculating machines; data processing equipment, namely, data processors; fire-extinguishing apparatus, namely, fire extinguishers ] [ Teaching and education, namely, classes, seminars, conferences, colloquiums and workshops, in the field of design of experiments and multivariate data analysis and modeling; arranging guidance and instruction, namely, vocational guidance and instruction in the field of multivariate data analysis and modeling; organizing community cultural activities and events ] [ Scientific and technological services in the field of analysis of multivariate data, modeling and experimental design, and research and design relating thereto; industrial analysis and research services in the field of analysis of multivariate data, modeling and experimental design; design and development of computer hardware and software; litigation services ]
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42 - Scientific, technological and industrial services, research and design
Goods & Services
Scientific, nautical, photographic, cinematographic, optical apparatus and instruments; weighing, measuring, signalling, checking (supervision), life-saving and teaching apparatus and instruments; apparatus and instruments for surveying; apparatus and instrument for conducting, switching, transforming, accumulating, regulating or controlling electricity; apparatus for recording, transmission or reproduction of sounds or images; magnetic data carriers; recording discs; automatic vending machines and mechanisms for coin-operated apparatus; cash registers; calculating machines; data processing equipment; computers; fire extinguishing apparatus. Teaching and education; arranging of guidance/instruction; entertainment; sporting activities; cultural activities. Scientific and technological services and research and design relating thereto; industrial analysis and research services; design and development of computer hardware and software; litigation services.
09 - Scientific and electric apparatus and instruments
41 - Education, entertainment, sporting and cultural services
42 - Scientific, technological and industrial services, research and design
Goods & Services
Apparatus for recording, transmission or reproduction of sounds or images; prerecorded magnetic data carriers having computer software stored hereon for use in multivariate data analysis, modeling and experimental design; prerecorded recording discs having computer software stored thereon for use in multivariate data analysis, modeling and experimental design; automatic vending machines and mechanisms for coin-operated apparatus; cash registers; calculating machines; data processing equipment, namely, data processors; fire-extinguishing apparatus, namely, fire extinguishers [ Teaching and education, namely, classes, seminars, conferences, colloquiums and workshops, in the field of design of experiments and multivariate data analysis and modeling; arranging guidance and instruction, namely, vocational guidance and instruction in the field of multivariate data analysis and modeling; organizing community cultural activities and events ] [ Scientific and technological services in the field of analysis of multivariate data, modeling and experimental design, and research and design relating thereto; industrial analysis and research services in the field of analysis of multivariate data, modeling and experimental design; design and development of computer hardware and software; litigation services ]
09 - Scientific and electric apparatus and instruments
41 - Education, entertainment, sporting and cultural services
42 - Scientific, technological and industrial services, research and design
Goods & Services
Computer programs for analysis of multivariate data containing information only extractable by applying noise filtration, comprising a module, comprising a mathematical algorithm [Educational sevices, namely, arranging and conducting classes, seminars, conferences and workshops in the field of computer software and the distribution of course materials in connection therewith; Publication of books; Computer education training in connection with installation, maintenance and use of computer software] [Research and development of a computer software for analysis of multivariate data containing information only extractable by applying noise filtration, comprising a mathematical algorithm]
09 - Scientific and electric apparatus and instruments
41 - Education, entertainment, sporting and cultural services
42 - Scientific, technological and industrial services, research and design
Goods & Services
A computer program for analysis of multivariate data, which contains information that is not possible to extract without applying noise filtration, comprising a module, comprising a mathematical algorithm. Education; providing of training; entertainment; sporting and cultural activities. Research and development of a computer software for analysis of multivariate data, which contains information that is not possible to extract without applying noise filtration, comprising a module, comprising a mathematical algorithm.
09 - Scientific and electric apparatus and instruments
Goods & Services
Computer software, namely, a real-time semiconductor fabric-wide wafer tool software application for advanced process fault detection used to monitor information from tooling on the manufacturing line
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Computer software, not intended for use in relation to accounting systems for bank transactions. Computer consultancy services, not intended for use in relation to accounting systems for bank transactions.
09 - Scientific and electric apparatus and instruments
Goods & Services
computer hardware; computer peripherals; computer software for multivariate data analysis and process modeling, and instruction manuals sold as a unit therewith
