Generation of pre-qualified software-based models for production process applications

Abstract

The hereby disclosed invention pertains to a method and system for creating and executing customized software-based models for simulating production processes. It involves defining simulation targets, selecting suitable equations from a digital catalog, and using a Model Builder Module to assemble these equations into a model. The digital catalog consists of equations stored in the Functional Mock-up Unit (FMU) format, which can be easily updated and reused. The method allows for rapid model creation and modification by simply adjusting a configuration file, eliminating the need for repeated qualification. This approach enhances the accuracy and efficiency of simulations, enabling real-time adjustments to production parameters based on model predictions. Additionally, the invention includes a workflow for adding new equations to the digital catalog, ensuring it remains current and comprehensive. The system supports various modeling types, including mechanistic, empirical, and machine learning models, and is compatible with GxP environments. This flexible and adaptable solution is particularly useful for optimizing bioprocesses in bioreactors, providing precise control over production conditions and outcomes.

Description

[0001] ForeignFiling text P24-152

[0002] - 1 -

[0003] Generation of pre-qualified software-based models for production process applications

[0004] The hereby described invention discloses a method and a system for

[0005] 5 establishing and training a customized scientific model for simulating a production process.

[0006] Technical Field

[0007] The invention deals with the technological area of model engineering for simulation of experiments in the context of digital twin development for bioprocessing.

[0008] Background and description of the prior art

[0009] 15

[0010] A digital twin is a virtual representation of a physical system or process that replicates its characteristics, behaviors, and interactions. A model is an essential component of a digital twin as it provides the foundation for simulating and representing the real-world system accurately. By simulating

[0011] 20 the model, the digital twin can replicate the system's performance, behavior, and response to different inputs and conditions.

[0012] Bioprocessing modeling involves the development and application of mathematical models to simulate and predict the behavior and performance of bioprocesses. It encompasses various stages of biomanufacturing, including upstream processes, e.g., cell culture and production or fermentation, downstream processes like purification, filtration and process integration.

[0013] 30 Through the application of mathematical modeling and simulation, bioprocessing modeling contributes to the development of efficient, ForeignFiling text P24-152

[0014] - 2 - scalable, and cost-effective processes for the production of biopharmaceutical products.

[0015] 5 Using mathematical models in bioprocessing therefore offers several benefits. These models enhance process understanding by providing insights into complex systems, enabling researchers to optimize process parameters and improve product quality and yield. By simulating different scenarios, models help reduce the need for extensive experimentation, saving time and costs. Furthermore, models facilitate process scale-up and technology transfer by accurately predicting performance at larger scales, ensuring smooth transitions from development to production.

[0016] However, setting up a mathematical model can be a time-consuming

[0017] 15 process, taking several months and requiring a thorough understanding of the underlying process. Additionally, qualifying model-based systems is not a straightforward task as it involves verifying the model's performance in terms of product quality and process robustness.

[0018] 20 Because biological systems naturally demonstrate variability, one model developed for a specific bioprocess needs to be adjusted to accommodate the description of a new cell line or produced molecule. Changing the parameters or the equations of an existing model thus requires a binary change. In a manufacturing GxP environment that will necessitate a new software validation and deployment that can take weeks or months.

[0019] Validation in the context of this invention means, according to the definition of the ICH (International Council for Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use) Q7, that

[0020] 30 a documented program that provides a high degree of assurance that a specific process, method, or system will consistently produce a result meeting pre-determined acceptance criteria. ForeignFiling text P24-152

[0021] - 3 -

[0022] Qualification, according to the definition of the MHRA (Medicines and Healthcare products Regulatory Agency), means furthermore that an action of proving that any instrument or equipment works correctly and actually

[0023] 5 leadsto the expected results. The word “validation” is sometimes widened to incorporate the concept of qualification.

[0024] Another definition according to the ICH Q3A-R2, says that the process of acquiring and evaluating data that establishes the biological safety of an individual impurity or a given impurity profile at the level(s) specified.

[0025] Verification means; according to the definition provided by ISO (International Organization for Standardization) ASTM E 2500 (American Society for Testing and Materials), that a systematic approach to verify that

[0026] 15 manufacturing systems, acting singly or in combination, are fit for intended use, have been properly installed, and are operating correctly. This is an umbrella term that encompasses all types of approaches to assuring systems are fit for use such as qualification, commissioning and qualification, verification, system validation, or other.

[0027] 20

[0028] GxP-based software systems should undergo thorough validation and qualification processes to ensure their suitability for use in regulated environments. This involves rigorous testing, documentation, and adherence to industry guidelines and standards. Validation ensures that the software functions as intended and that data integrity is maintained throughout its lifecycle.

[0029] Any modifications to the GxP-based software, including system upgrades, patches, or configuration changes, are properly evaluated, documented,

[0030] 30 and tested. ForeignFiling text P24-152

[0031] - 4 -

[0032] In a GxP scenario, validation is the process of demonstrating that any "entity" used in the process of drug development is suitable for its intended use and performs the functions that are expected of the entity. Computer system validation provides documented evidence substantiating the

[0033] 5 performance of the entity in question.

[0034] A very general approach of how to build a scientific model engineering workflow has been disclosed by the published work of Daume et al. 2020. A final developed technical solution to solve the task of model building or engineering has also been provided in the yet not published European patent application EP 24305186. However - programming or updating a model simulating bioprocessing, partially or end to end, is still a binary change. In a manufacturing GxP environment that will necessitate a new software validation and deployment that can take weeks of months.

[0035] 15 Therefore existing models are less often changed and opportunities to optimize the process performance, e.g. an increase in titer, or stability, e.g. losing the production, are missed.

[0036] The task of this patent application is therefore to find a way which

[0037] 20 addresses this problem of allowing any end-users without modeling skills to create models or adapt models for different bioprocesses without the need of completely repeating the model creation / adapting and qualification and GxP validation process.

[0038] Summary of the invention

[0039] This task has been solved by a method for creating a customized softwarebased model for simulating a production process via a software-based Model Builder Module on a computer, wherein the Model Builder Module is

[0040] 30 connected to a digital catalogue consisting of at least one prequalified equations for calculating production process parameters, comprising the following steps of Defining, by a user, a simulation target for the software ForeignFiling text P24-152

[0041] - 5 - based model, including relevant production process parameters, in particular process steps, cell line, produced molecule, variables the model predicts and threshold values of required model quality indicators;

[0042] Selecting, by a user, at least one prequalified equation from the digital

[0043] 5 catalogue to calculate the required production process parameters for the simulation target in the Model Builder Module; Creating, by the Model Builder Module, a new software-based model which is specific to the respective production process parameters the model describes via assembling the selected suitable equations according to rules specified in a configuration file accessable to the computer; and Preparing, by the Model Builder Module, the created software-based model for an application by the user to simulate a production process. The method involves defining a simulation target with specific production parameters, selecting suitable equations from a digital catalog, and using a Model Builder Module to

[0044] 15 assemble these equations into a model. The preparation step ensures the model is ready for application in simulating the production process. The main advantage of this approach is that it enables to create new or adapt existing models describing the or a specific behavior of a production process without the need of validating and qualifying the newly created or

[0045] 20 adapted model, by using only already prequalified equations as model components. Prequalified means in this context that they are already qualified according to the previously described qualification process, e.g. following the MHRA or ICH definition. This includes the ability to create precise, application-specific models that can simulate complex production processes accurately, thereby optimizing production efficiency and reducing the need for physical trial-and-error methods. To avoid misunderstandings: While creating a new model is done via the disclosed steps of selecting existing equations, the process is actually the same for adapting existing models. In this scenario just some or all of the already used prequalified

[0046] 30 equations are re-selected and new prequalified equations are added. ForeignFiling text P24-152

[0047] - 6 -

[0048] Advantageous and therefore preferred further developments of this invention emerge from the associated subclaims and from the description and the associated drawings.

[0049] 5 One of those preferred further developments of the disclosed method comprise that the software-based model is used as a Digital Twin to simulate biological and / or chemical reactions. This include enhanced predictive capabilities and real-time monitoring specifically for biological and / or chemical related production processes, leading to better control and optimization of reaction conditions and outcomes.

[0050] Another one of those preferred further developments of the disclosed method comprise that the biological and / or chemical reactions are production bioprocesses in a bioreactor and the simulation is describing the

[0051] 15 biochemical mechanisms underlying the production bioprocesses occurring in the bioreactor. That allows detailed insights into bioprocesses, enabling more precise control and optimization of bioreactor operations, which can lead to higher yields and more consistent product quality.

[0052] 20 Another one of those preferred further developments of the disclosed method comprise that several mechanistic links are provided for selection, which describe the production process parameters of the software-based model and are maintained in an equation library in a generic form using the functional mock-up unit (FMU) standard. Users then can select these links to describe production process parameters. The allows standardized and reusable equation modules, facilitating the rapid assembly and reconfiguration of models, thereby saving time and resources.

[0053] Another one of those preferred further developments of the disclosed

[0054] 30 method comprise that the creation of the software-based model additionally uses existing production process data to improve the production process performance or stability. By using existing production process data in the ForeignFiling text P24-152

[0055] - 7 - model creation it leads to leveraging historical data to enhance model accuracy and predictive power, leading to improved process performance and stability by anticipating and mitigating potential issues.

[0056] 5 Another one of those preferred further developments of the disclosed method comprise that the existing production process data is used by the Model Builder Module to compute an equation orchestration and fitting product process parameters to ensure the best prediction of the process target. This allows optimized model parameters and better-targeted predictions, resulting in more accurate and reliable simulations.

[0057] Another solution of the given task is a method for creating and / or adapting a digital catalogue stored in a database on a computer, comprising of at least one prequalified equation for calculating process parameters of a

[0058] 15 production process, via the computer, wherein the at least one prequalified equation are stored in a format following the functional mock-up unit (FMU) standard und new entries to create or amend the digital catalogue are added by the following method steps of Selecting a new equation describing a production process parameter by the user; Converting the

[0059] 20 equation to the FMU format thus creating an Equation FMU entry by the computer; Qualifying the Equation FMU entry by the user; and Importing of the qualified equation FMU to the digital catalog via the computer. That leads to maintaining an up-to-date and comprehensive repository of equations that can be easily accessed and utilized for model creation, enhancing the flexibility and applicability of the modeling system.

[0060] Another one of those preferred further developments of the disclosed methods comprise that the production processes are production bioprocesses in a bioreactor and the new Equation FMU entries which are

[0061] 30 added in the catalog represent a new chemical biological behavior mixing mechanistic and / or statistical computation and different programming languages and / or frameworks. Therefore the ability to model complex ForeignFiling text P24-152

[0062] - 8 - biochemical behaviors using diverse computational approaches and tools is provided, improving the accuracy and versatility of the models.

[0063] Another solution of the given task is a method for enhancing a production

[0064] 5 process by creating a suitable software-based model for the production process according to the previously described methods, simulating the production process on a computer by predicting at least one value for production process parameters with the created software-based model, and enhancing the production process by adapting the process parameters according to the results of the predicted at least one value. This outlines a method for enhancing a production process by creating, simulating, and using a software-based model to predict and adjust production parameters. It therefore provides the ability to continuously optimize production processes based on model predictions, leading to improved efficiency, reduced waste,

[0065] 15 and better product quality.

[0066] Another one of those preferred further developments of the disclosed methods comprise that the created and / or adapted software-based model is validated by a software run on the computer, which could either be the

[0067] 20 Model Builder Module or a separate software, by evaluating the model performance with regarding to its prediction accuracy. This adds a validation step for the created / adapted model, evaluating its performance and prediction accuracy. While validation of the created and / or adapted is not required due to using only prequalified Equation entries, applying it further increases the reliability and accuracy of the model before it is used to inform production decisions, thereby reducing risks and improving confidence in the model’s outputs.

[0068] Another one of those preferred further developments of the disclosed

[0069] 30 methods comprise that as software-based model either a physical model, in particular mechanistic models, empirical models, phenomenological models ForeignFiling text P24-152

[0070] - 9 - and / or equation based-models, or a machine learning model is used. This specifies the scope of the software-based model to include various types of models, such as mechanistic, empirical, phenomenological, equation-based, and machine learning models. That allows a flexibility to choose the most

[0071] 5 appropriate modeling approach for different production scenarios, enhancing the adaptability and effectiveness of the modeling system.

[0072] Another one of those preferred further developments of the disclosed methods comprise that as software-based Model Builder Module a GxP- based software system is used which is adapted to a manufacturing GxP environment for the production process. This ensures compliance with known regulatory standards, ensuring that the modeling process adheres to approved manufacturing practices, which is critical for industries such as pharmaceuticals and biotechnology.

[0073] 15

[0074] A further solution to the given task is a system for establishing, training and using a customized software-based model and / or enhancing a production process, comprising a computer providing a software based Model Builder Module, a digital catalogue database connected to the computer, input and

[0075] 20 output means connected to the computer for entering and outputting data by the computer, wherein the system is configured to perform the steps of the previously explained method. This integrated system supports the full lifecycle of model creation and use, from data entry and model building to simulation and process enhancement, thereby streamlining operations and improving overall efficiency.

[0076] A further solution to the given task is a computer program product and a computer-readable storage medium and / or data carrier signal having stored thereon the computer program product, which comprises instructions which

[0077] 30 cause the involved computers to perform the method steps of the previously described methods. Therefore the provided software solution can be easily deployed on multiple computers, various platforms, ensuring ForeignFiling text P24-152

[0078] - 10 - consistent and automated execution of the modeling process and furthermore ensuring accessibility and ease of use for different users and systems.

[0079] 5 Detailed description of the invention

[0080] The method and system according to the invention and functionally advantageous developments of those are described in more detail below with reference to the associated drawings using at least one preferred exemplary embodiment. In the drawings, elements that correspond to one another are provided with the same reference numerals.

[0081] The drawings show:

[0082] Figure 1 : a working example of the process of adding new pre-qualified equations to the digital equation catalog

[0083] Figure 2: a schematic organigramm showing the Model Builder Module in a context of a preferred use case

[0084] Figure 3: the model composition page of the Model Builder Module Figure 4: a schematic workflow showing the invented creation process of a new composite model

[0085] Figure 1 shows an overview about the necessary method steps performed by an application which represents a preferred embodiment. The steps itself are performed divergent in every exemplary embodiment dependent on the different conditions. The system which performs the method including its components in for a specific use case is shown in Figure 2. The shown structure is exemplary for this preferred embodiment. It can change for a other embodiments. Especially the kind of involved computers

[0086] 30 can differ greatly, depending on how much of the steps is performed by human users with the help of computers and application software or done automatically by specific computers using for instance Al based software. ForeignFiling text P24-152

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[0088] In the preferred application, a software in form of a model builder module called Merlin is dedicated to the model building task. The kind of models that can be created are varied and depend on the process the model is

[0089] 5 supposed to simulate. In the peculiar preferred embodiment of the invention described in the following, mechanistic models are used, meaning models made of equations describing the biochemical mechanisms underlying the process occurring in a bioreactor (bioreactions) thus giving a better understanding to the user on this process and enabling him to adapt and enhance the process by applying the gained understanding and insight. These models can be considered as Digital Twins model of the real bioreactor, since they calculate and describe the behavior of the respective bioreactor and its bioreactions.

[0090] 15 In this module, a workflow including several steps for building a model was programmed and digitalized. The workflow includes the definition of critical reactions and their kinetics, translation into a set of equations of the mathematical model, calibration of the model on available experimental data and checking the validity and quality of the model. This workflow has

[0091] 20 been adapted from the mentioned published work of Daume et al. 2020. Figure 3 shows the model composition page of the Merlin module. The indicated squares represent elements from the scientific workflow that were adapted to be programmed in the Merlin module. They consist of three main features:

[0092] 1 ) The translation of the reaction schemes into mass balance equations, as shown in Figure 4, where an algorithm has been implemented into the app to automatically generate the mass balance equations from the reactions scheme. This provides the advantage of allowing to create the model in a

[0093] 30 more efficient manner. The user can directly visualize the correspondence between reactions and model equations ForeignFiling text P24-152

[0094] - 12 -

[0095] 2) An equation library, as seen in Figure 4 which provides several mechanistic links, which are formula for describing parameters of the model, being available to the user selection. The equation library can be both stored in a cloud storage or in any local storage. They are maintained

[0096] 5 in an equation library in a generic form using the FMU (functional unit mock-up) standard. Their main advantage is that, depending on the cell line, the clone and other factors, the reaction mechanisms are not the same and that’s why the Merlin module provides the user with a wide list of mechanistic links. The user can even add himself mechanistic links via the equation catalog. The Merlin module is very flexible to accommodate customers specific needs.

[0097] 3) The third feature is a rule-engine disclosed in Figure 4 which provides expertise to an unexperienced user.

[0098] 15

[0099] Within this module, a workflow including several steps for building a model according to the new prequalified model creation approach was programmed and digitalized. The workflow consists of two main actions, each consisting of several method steps. Figure 1 illustrates the workflow

[0100] 20 for adding a new qualified equation to the equation catalog. This workflow begins with the selection and building of an equation in the Functional Mock-up Unit (FMU) format. The equation, represented by the differential equation \(\frac{dy}{dt} = \frac{Q}{V}(C_{in} - C_{out})\), is initially constructed in the FMU format, which includes both the equation and a solver. This FMU format ensures that the equations are standardized and can be easily integrated into the Model Builder Module. Following the construction of the equation, the next step involves the qualification of the equation FMU. This qualification process may include running a series of predefined tests to verify the robustness, accuracy, and performance of the

[0101] 30 equation against historical production data. Once the equation FMU is qualified and deemed suitable for use, it is then imported into the digital catalog. The digital catalog, as depicted in the lower part of Figure 1 , ForeignFiling text P24-152

[0102] - 13 - consists of multiple FMU entries, each representing different equations, such as \(y = ax + b\), \(y = (1 -x) + a\), and \(y = x \times z\). These entries are organized in a manner that allows for easy access and selection by users when creating or modifying models. The digital catalog serves as a

[0103] 5 repository of pre-qualified equations that can be reused in various modeling scenarios, thereby enhancing the flexibility and efficiency of the model creation process. This allows to generate infinite qualified composite models which are adaptable to every production process which is covered by the added FMU entries.

[0104] Figure 4 on the other hand provides a detailed depiction of the secaon action which is about the configuration and execution of a composite model. In this working example, the production rate \(\phi_1\) is calculated by assembling three different FMUs. These FMUs, labeled as 1 , 2, and 3,

[0105] 15 correspond to the equations \(y = x \times z\), \(y = (a \times x) \div (x + b)\), and \(y = (1 - x) \div a\), respectively. The FMUs are combined according to the rules specified in a configuration file, which is accessible to the computer. The configuration file, as shown in part (b) of Figure 4, contains the parameters and values required for the assembly of the FMUs, such as

[0106] 20 \(fmu1 (x) = fmu2\), \(fmu(z) = fmu3\), \(a = 10\), \(b = 20\), and \(a = 0.1\). This configuration file allows for the orchestration of the equations in a manner that ensures the accurate calculation of the production rate \(\phi_1\). Once the configuration is set, the model is executed as depicted in part (c) of Figure 4. The execution of the model involves running the assembled FMUs to simulate the production process and calculate the desired parameters. The flexibility of this approach is highlighted by the ability to create new models or modify existing ones by simply changing the configuration file without the need for re-qualifying the model.

[0107] 30 In a preferred practical scenario, a tailored model is initially used to simulate a bioprocess for producing a therapeutic protein in a bioreactor. During the production run, an unanticipated reaction is observed: the media ForeignFiling text P24-152

[0108] - 14 - contains a different metabolite that influences cell growth. This necessitates an update to the model by incorporating a new equation to account for the cell behavior in the presence of this metabolite. To address this, the user selects a new equation describing the influence of the different metabolite

[0109] 5 on cell growth. This equation is then converted to the FMU format, creating an Equation FMU entry. The new Equation FMU entry is qualified by running it against benchmark datasets to ensure accuracy and performance. Once qualified, the Equation FMU is imported into the digital catalog. The configuration file is updated to include the new Equation FMU, specifying how it interacts with the existing equations to account for the metabolite's influence on cell growth. The updated model is recreated by the Model Builder Module using the updated configuration file. The new model is validated against historical and current process data to ensure it accurately predicts the influence of the metabolite on cell growth. The

[0110] 15 validated model is then used to simulate the production process again, now accurately accounting for the presence of the metabolite. The simulation predicts the new cell growth rate and protein yield with improved accuracy, leading to an enhanced production process by adapting the process parameters according to the simulation results. This approach

[0111] 20 demonstrates the flexibility and adaptability of the invention, allowing for quick responses to unanticipated changes in the production process, ensuring accurate simulations, and optimized production outcomes.

[0112] So in summary: When a production process’ performance or stability need to be improved a model can be created or completely changed based on process existing data and the goals defined. The model is composed of many equations that are orchestrated and parametrized to create a complex model. It is a complete different model based on new equations mechanistic link and their parameters. Known equations are saved in a

[0113] 30 catalog to be re-used in this orchestration. New equation can be added in the catalog to represent new chemical biological behavior mixing mechanistic or statistical computation and different programming language ForeignFiling text P24-152

[0114] - 15 - or framework. The bioprocessing modelling tool computes the equation orchestration and fitting parameters from exsiting data for the best prediction of process target (eg titer). This configuration is now a new model, tailored to the target production process.

[0115] 5

[0116] 15

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[0118] 30

Claims

ForeignFiling text P24-152- 16 -Patent claims1. Method for creating and / or adapting a customized software-based model for simulating a production process via a software-based Model5 Builder Module on a computer, wherein the Model Builder Module is connected to a digital catalogue consisting of at least one prequalified equation for calculating production process parameters, the following steps comprising:• Defining, by a user, a simulation target for the software-based model, including relevant production process parameters, in particular process steps, cell line, produced molecule, variables the model predicts and threshold values of required model quality indicators;• Selecting, by a user, at least one prequalified equations from the15 digital catalogue to calculate the required production process parameters for the simulation target in the Model Builder Module;• Creating, by the Model Builder Module, a new software-based model which is specific to the respective production process parameters the model describes via assembling the selected20 suitable equations according to rules specified in a configuration file accessable to the computer; and• Preparing, by the Model Builder Module, the created softwarebased model for an application by the user to simulate a production process.

2. Method according to claim 1 , wherein the software-based model is used as a Digital Twin to simulate biological and / or chemical reactions.ForeignFiling text P24-152- 17 -3. Method according to claim 1 or claim 2, wherein the biological and / or chemical reactions are production bioprocesses in a bioreactor and the simulation is describing the biochemical mechanisms underlying the production bioprocesses occurring in the5 bioreactor.

4. Method according to any of the previous claims, wherein several mechanistic links are provided for selection, which describe the production process parameters of the software-based model and are maintained in an equation library in a generic form using the functional mock-up unit (FMU) standard.

5. Method according to any of the previous claims, wherein the creation of the software-based model additionally uses existing15 production process data to improve the production process performance or stability.

6. Method according to claim 5, wherein the existing production process data is used by the Model Builder20 Module to compute an equation orchestration and fitting product process parameters to ensure the best prediction of the process target.

7. Method for creating and / or amending a digital catalogue stored in a database on a computer, comprising of at least one prequalified equation for calculating process parameters of a production process, via the computer, wherein the equations are stored in a format following the functional mock-up unit (FMU) standard und new entries to create or amend the digital catalogue are added by the following30 method steps:• Selecting a new prequalified equation describing a production process parameter by the user;ForeignFiling text P24-152- 18 -• Converting the equation to the FMU format thus creating an Equation FMU entry by the computer;• Qualifying the Equation FMU entry by the user;• Importing of the qualified equation FMU to the digital catalog via5 the computer.

8. Method according to claim 7, wherein the production processes are production bioprocesses in a bioreactor and the new Equation FMU entries which are added in the catalog represent a new chemical biological behavior mixing mechanistic and / or statistical computation and different programming languages and / or frameworks.

9. Method for enhancing a production process by creating and / or adapting15 a suitable software-based model for the production process according to the method of claims 1 to 8, simulating the production process on a computer by predicting at least one value for production process parameters with the created software-based model, and enhancing the production process by adapting the process parameters according to the20 results of the predicted at least one value.

10. Method according to claim 9, wherein the created software-based model is validated by a software run on the computer, which could either be the Model Builder Module or a separate software, by evaluating the model performance with regarding to its prediction accuracy.

11. Method according to any of the previous claims, wherein as softwarebased model either a physical model, in particular mechanistic models,30 empirical models, phenomenological models and / or equation based- models, or a machine learning model is used.ForeignFiling text P24-152- 19 -12. Method according to any of the previous claims, wherein as softwarebased Model Builder Module a GxP-based software system is used which is adapted to a manufacturing GxP environment for the production process.

513. System for establishing, training and using a customized software-based model and / or enhancing a production process, comprising a computer providing a software based Model Builder Module, an digital catalogue database connected to the computer, input and output means connected to the computer for entering and outputting data by the computer, wherein the system is configured to perform the method steps of claims 1 to 12.

14. Computer program product comprising instructions which cause the15 involved computers to perform the method steps of claims 1 to 12.

15. Computer-readable storage medium and / or data carrier signal having stored thereon the computer program product of claim 14 which cause the involved computers to carry out the method steps of claims 1 to 12.20

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