Recycling strategies determining agent

Abstract

A method, in particular computer-implemented method, for monitoring and / or controlling processing of a target product, the method comprising: obtaining, in particular receiving, a request for processing of a target product, wherein the request is associated with an indication of the target product, providing a task instruction associated with the request to a data-driven model for selecting one or more operating engine(s) configured to determine a processing step associated with the target product from a plurality of operating engines, wherein the data-driven model is configured to follow task instructions, providing the indication of the target product to the one or more selected operating engine(s) for determining the processing step associated with the target product, providing the determined processing step for monitoring and / or controlling processing of a target product.

Description

241318WO01 - Secondary Filing TextBASF Coatings GmbHRECYCLING STRATEGIES DETERMINING AGENTTECHNICAL FIELDThe invention relates to a method, in particular computer-implemented method, for monitoring and / or controlling processing of a target product, a method, in particular computer-implemented method, for determining a processing step associated with a target product, an apparatus and / or a system, use of a data-driven model for selecting one or more operating engine(s) configured to determine a processing step associated with the target product from a plurality of operating engines.BACKGROUNDCurrently, a large fraction of the global waste is incinerated. To arrive at a more sustainable handling of materials, it is desired to close the loop between recyclers and material production.SUMMARYAny disclosure, embodiments and examples described herein relate to the methods, the systems, apparatuses, chemical products and computer elements lined out above and below. Advantageously, the benefits provided by any of the embodiments and examples equally apply to all other embodiments and examples.In another aspect, it relates to a method, in particular computer-implemented method, for monitoring and / or controlling processing of a target product, the method comprising: obtaining, in particular receiving, a request for processing of a target product, wherein the request is associated with an indication of the target product, providing a task instruction associated with the request to a data-driven model for selecting one or more operating engine(s) configured to determine a processing step associated with the target product from a plurality of operating engines, wherein the data-driven model is configured to follow task instructions,241318WO01 - Secondary Filing Text BASF Coatings GmbH determining the processing step associated with the target product by providing the indication of the target product to the one or more selected operating engine(s) for, providing the determined processing step for monitoring and / or controlling processing of a target product.In another aspect, it relates to a method, in particular computer-implemented method, for determining a processing step associated with a target product, the method comprising: obtaining, in particular receiving, a request for processing of a target product, wherein the request is associated with an indication of the target product, providing a task instruction associated with the request to a data-driven model for selecting one or more operating engine(s) configured to determine the processing step associated with the target product from a plurality of operating engines, wherein the data-driven model is configured to follow task instructions, determining the processing step associated with the target product by providing the indication of the target product to the one or more selected operating engine(s) for, providing the determined processing step for monitoring and / or controlling processing of a target product.In another aspect, it relates to an apparatus and / or system comprising at least one processor and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to carry out the steps of the method as described herein.EMBODIMENTSIn the following, terminology as used herein and / or the technical field of the present disclosure will be outlined by ways of definitions and / or examples. Where examples are given, it is to be understood that the present disclosure is not limited to said examples.241318WO01 - Secondary Filing Text BASF Coatings GmbHTo close the loop, the end-of-life product needs to be separated, i.e. disassembled, accordingly. Typically, an end-of-life product comprise a plurality of materials, sometimes also blended materials. To allow for recycling, the materials of the end-of- life product need to be separated. Depending on the degree of separation different R- strategies may be available such as reuse, recycling, remanufacturing, waste incineration or the like. Tons of different end products with different materials may be received by the recycler 114. To handle this mass of end-of-life products or parts thereof efficiently, fast, scalable and reliable determination of R-strategies are required. Still, R-strategies need to be tailored to the end-of-life product at hand.By providing a request for processing a target product to a data-driven model, a plurality of different requests related to a plurality of different end-products may be processed in a fast and scalable manner. Further, the request can be independent of a predefined structure, i.e. including unstructured data such as natural language from a human user, i.e. a worker associated with a recycling facility. Additionally or alternatively, this allows to specify the target product even if a corresponding ID is unknown orMore than that, by processing the request by the data-driven model, the R-strategies obtained from the request can be tailored to the end-of-life product at hand while up-to- date data can be utilized for deriving said R-strategies. Ultimately, this allows to tailor the processing of end-of-life products to an intended use of the recycled end-of-life product. Therefore, this disclosure contributes to closing the loop in chemical production networks tailored to the available end-of-life products and current processing options.These and other objects, which become apparent upon reading the following description, are solved by the subject matters of the independent claims. The dependent claims refer to embodiments of the invention.In an embodiment, providing input data to the data-driven model may comprise mapping the input data to a numerical representation of the input data. The numerical241318WO01 - Secondary Filing Text BASF Coatings GmbH representation of the input data may comprise a tensor associated with the input data and / or obtained from the input data. In particular, the numerical representation of the input data may be indicative of two or more elements of the input data and a relation between the two or more elements of the input data. Preferably, providing input data to the data-driven model may comprise at least one of identifying two or more elements of the input data, mapping the two or more elements of the input data to a numerical representation of the two or more elements, mapping the numerical representation of the two or more elements to a numerical representation of a predefined size related to the numerical representation of the two or more elements, mapping the numerical representation of the predefined size related to the numerical representation of the two or more elements to a numerical representation of the two or more elements and a relation between the two or more elements or a combination thereof.In an embodiment, processing the input data and / or generating the output data from the input data may comprise processing the numerical representation of the input data, in particular the numerical representation of the two or more elements and the relation between the two or more elements. Processing the numerical representation of the two or more elements and the relation between the two or more elements may comprise mapping the numerical representation of the two or more elements, and optionally the relation between the two or more elements to a numerical representation of the output data. The numerical representation of the output data may be mapped to the output data, in particular based on a relation between the numerical representation of data and the data. In particular, the data may be of a data type according to the input data. Hence, the output data may be of the data type according to the input data, e.g. of the same data type as the input data and / or of the data type specified by the input data. Preferably, processing the numerical representation of the two or more elements and the relation between the two or more elements may comprise at least one of generating two or more numerical representations of the two or more elements and the relation between the two or more elements from the numerical representation of the two or more elements and the relation between the two or more elements, modifying the two or more numerical representation of the two or more elements and the relation between the two or more elements by applying a filter to the two or more241318WO01 - Secondary Filing TextBASF Coatings GmbH numerical representations of the two or more elements and the relation between the two or more elements, concatenating the two or more numerical representations of the two or more elements and the relation between the two or more elements, mapping the concatenated numerical representation of the two or more elements and the relation between the two or more elements to a numerical representation of the output data or a combination thereof.In an embodiment, the one or more data-driven model may be pretrained data-driven model(s). The pretrained data-driven model(s) may be parametrized and / or trained based on data with a plurality of contexts and / or unstructured data, in particular text data and optionally numerical data such as tabular data or image data. The pretrained data-driven model(s) may be configured to perform a plurality of task and / to process data of a plurality of contexts. The pretrained data-driven model(s) may be configured to perform the task according to the provided task instruction. Hence, the pretrained data-driven model may be configured to be provided with a plurality of different task instructions and / or provide a plurality of different types of output data upon receiving different task instructions.In an embodiment, the one or more data-driven model(s) may be finetuned data-driven model(s). The finetuned data-driven model(s) may be obtained by training pretrained data-driven model(s) configured to perform a plurality of tasks according to a plurality of task instructions. The finetuned data-driven model(s) may trained additionally on a training data set comprising a plurality of task instructions of one type and corresponding output data. The fine tuned data-driven model may be trained additionally to provide output data of a predefined type according to the training data set. The finetuned data-driven model may be configured to be provided with a plurality of different task instructions and / or provide a plurality of different types of output data upon receiving different types of task instructions. Further, the finetuned data-driven model may be configured for providing one type of output data upon receiving one type of task instruction with a higher accuracy than providing other types of output data upon receiving other types of task instructions.241318WO01 - Secondary Filing TextBASF Coatings GmbHIn an embodiment, the request may comprise the indication of the target product. The indication may be suitable for identifying and / or may characterize the target product. The indication of the target product may be related to a name of the target product, a composition of the target product, a chemical product related to the target product, a producer associated with the target product or a combination thereof.In an embodiment, the target product may comprise an end product, in particular an end-of-life product. Additionally or alternatively, the target product may be produced from a chemical product. The target product may comprise one or more component(s).In an embodiment, the task instruction may be further associated with functional specification data. The functional specification data may be indicative of and / or may characterize one or more function(s) associated with the plurality of operating engines. The functional specification data may be suitable for identifying one or more function(s) and / or operation(s) to be performed by the one or more operating engine(s).In an embodiment, the target processing data set may be indicative of at least one target processing step, target processing instructions and / or may be related to the at least one target processing step. The target processing data set may characterize a requirement associated with the at least one target processing step and / or the target processing instructions. The requirement may be location-specific and / or productspecific. The requirement may characterize one or more attribute(s) associated with processing the component associated with the requirement and / or the target processing data set. The one or more attribute(s) may be related to and / or may comprise one or more properties of the target processing step.In an embodiment, an indication of a feasibility of the determined one or more processing step(s) associated with the one or more component(s) may be obtained, in particular received, e.g. from a database comprising a plurality of indications of feasibilities of a plurality of processing step(s). Additionally or alternatively, the indication of the feasibility may be obtained, in particular received, via a user interface. Additionally or alternatively, the indication of the feasibility may be obtained, in241318WO01 - Secondary Filing TextBASF Coatings GmbH particular received, from an entity associated with the one or more processing step(s) in particular in response to providing a request for obtaining the indication of the feasibility of the one or more processing step(s). In an embodiment, an indication of a point in time associated with processing the target product may be obtained, in particular received, preferably together with the request for processing the target product. In an embodiment, the request for processing the target product may comprise the indication of the point in time. The indication of the feasibility may be based on the point in time. The request for obtaining the indication of the feasibility may comprise the point in time. By taking a measure for a feasibility of a processing step into account, newly arising processing steps e.g. in the nearer future can be evaluated for determining the processing route. Further, adapting the processing route according to the feasibility allows to manage resources associated with processing the target product,. Thereby, unnecessarily high resource invests for processing can be avoided while in total the efficiency of using the resources for processing the target products can be increased.In an embodiment, the request may comprise unstructured data. Unstructured data may comprise text data and / or natural language data. The request may be obtained, in particular received, via an interface such as a user interface.In an embodiment, providing the indication of the target product to the one or more selected operating engine(s) may trigger the one or more selected operating engine(s) to identify one or more component(s) associated with the target product based on the indication of the target product, obtain, in particular receive, a processing score per identified component of the target product, wherein the processing score is indicative of a processing of the identified component(s) into one or more subcomponent(s), obtain, in particular determine, a processing step per identified component according to the processing score. The processing score may be indicative of a processing of the one or more component(s) associated with the processing score. The processing score may be indicative of a type of processing of the one241318WO01 - Secondary Filing TextBASF Coatings GmbH or more component(s). The type of processing may relate to a processing of the one or more component(s) associated with producing another product, i.e. processing of the one or more component(s) for producing another product, or a processing of the one or more component(s) as waste. Processing of the one or more component(s) associated with producing another product, i.e. processing of the one or more component(s) for producing another product, may include transforming the one or more component(s) via one or more physical and / or chemical treatment(s) or reusing the one or more component(s). In particular, any one of the methods may further comprise: identifying one or more component(s) associated with the target product based on the indication of the target product, obtaining, in particular receiving, a processing score per identified component, obtaining, in particular determining, a processing step per identified component according to the processing score. The processing score may be indicative of one or more subcomponent(s) associated with the one or more component(s). Further, the processing score may be indicative of a processing of the one or more component(s) as a whole or separated into one or more subcomponent(s). By doing so, it can be identified whether the target product can be separated into components and potentially further subcomponents. Thereby, materials can be separated more effectively. As a consequence, recycling, remanufacturing and / or reusing is enabled rather than burning both (sub-)components when combined.In an embodiment, providing the indication of the target product to the one or more selected operating engine(s) may trigger the one or more selected operating engine(s) to identify one or more component(s) associated with the target product based on the indication of the target product, obtain, in particular receive, a processing score per identified component of the target product, wherein the processing score is indicative of a processing of the identified component(s) into one or more subcomponent(s), obtain, in particular determine, a processing step per identified component according to the processing score.241318WO01 - Secondary Filing Text BASF Coatings GmbHThe processing score may comprise two or more subscores. The two or more subscores may be associated with two or more types of processing the one or more component(s). In an embodiment, the processing score may comprise two or more subscores. The two or more subscores may be related to different scoring schemas and / or different processing measures. For example, the two or more subscores may be related to two or more types of processing the one or more component(s). The two or more subscores may be combined according to two or more weighting factor associated with the two or more subscores. For example, at least one of the two or more processing scores may be related to a processing of the one or more component(s) to another product and the at least one other of the two or more processing scores may be related to processing the one or more component(s) as waste. The types of processing may comprise remanufacturing, reusing, recycling and / or burning. By doing so, different types of processing are evaluated separately. This allows to focus on different factors decisive for evaluating the different types of processing. For example, reusing requires judgment of the component as a whole, i.e. the shape and / or the functioning of the component while recycling requires evaluation of the material independent of the shape or the functioning of the component. Consequently, more accurate and reliable R-strategies can be obtained. Ultimately, this contributes to closing the loop between end-of-life product and input materials.In an embodiment, providing the indication of the target product to the one or more selected operating engine(s) may trigger the one or more selected operating engine(s) to identify one or more component(s) associated with the target product based on the indication of the target product, obtain, in particular receive, a processing score per identified component of the target product, wherein the processing score is indicative of a processing of the identified component(s) into one or more subcomponent(s), obtain, in particular determine, a processing step per identified component according to the processing score.241318WO01 - Secondary Filing TextBASF Coatings GmbHThe processing score may be obtained, in particular determined, based on an image of the target product. In an embodiment, the processing score may be obtained, in particular received, per component, in particular according to an image of the target product. By determining R-strategies based on an image, target products can be evaluated individually. Thereby, R-strategies are more targeted towards the target product, i.e. the product at hand. Consequently, potential for different processing purposes can be leveraged more effectively. Ultimately, this contributes to closing the loop between end-of-life product and input materials. Determining the processing model score based on the image may comprise providing the image to a classification model configured to determine processing scores upon receiving images. The classification model may be trained and / or parameterized to provide the processing score associated with the image upon receiving the image. The processing score associated with the image and / or the target product may be received, in particular from the classification model, in response to providing the image to the classification model. The classification model may be for example a neural network, in particular a convolutional neural network, or a tree-based approach. The classification model may be trained based on a training data set comprising historical images and corresponding processing scores. Additionally or alternatively, determining a processing score may comprise providing the image of the target product and receiving the processing score in response, e.g. via a user interface. By utilizing a classification model, the processing score can be determined objectively according to a predefined criteria, e.g. specified during the training of the classification model. Hence, R-strategies determined upon using processing scores determined by the classification model are more consistent while allowing for scale-up. In turn, large amounts of target products can be analyzed individually and reliably. Ultimately, this contributes to closing the loop between end-of-life product and input materials.In an embodiment, providing the indication of the target product to the one or more selected operating engine(s) may trigger the one or more selected operating engine(s) to241318WO01 - Secondary Filing TextBASF Coatings GmbH identify one or more component(s) associated with the target product based on the indication of the target product, obtain, in particular receive, a processing score per identified component of the target product, wherein the processing score is indicative of a processing of the identified component(s) into one or more subcomponent(s), obtain, in particular determine, a processing step per identified component according to the processing score.The processing score may be obtained, in particular received, via a user interface. The processing score may be obtained, in particular received, in response to providing an image of the target product and / or an instruction for providing the processing score associated with the one or more component(s) of the target product. The processing score may be obtained, in particular received, from a dismantles By obtaining the processing score via the user interface, human experts associated with dismantling products can provide processing scores associated with the target product, i.e. the product at hand. This allows for an extensive evaluation of the target product as a whole. Furthermore, the processing score can be determined based on additional factors to appearance such as haptic or measurements of properties of the target product. Ultimately, this contributes to closing the loop between end-of-life product and input materials.In an embodiment, providing the indication of the target product to the one or more selected operating engine(s) may trigger the one or more selected operating engine(s) to identify one or more component(s) associated with the target product based on the indication of the target product, obtain, in particular receive, a processing score per identified component of the target product, wherein the processing score is indicative of a processing of the identified component(s) into one or more subcomponent(s), obtain, in particular determine, a processing step per identified component according to the processing score.241318WO01 - Secondary Filing Text BASF Coatings GmbHThe processing score may be obtained, in particular determined according to a property of the target product. The property of the target product may be obtained, in particular received via a user interface. The property may be obtained based on sensor data collected in relation to the target product. By obtaining the processing score based on a property of the target product, the processing score can be determined based on additional factors to appearance. Ultimately, this contributes to closing the loop between end-of-life product and input materials.In an embodiment, providing the indication of the target product to the one or more selected operating engine(s) may trigger the one or more selected operating engine(s) to identify one or more component(s) associated with the target product based on the indication of the target product, obtain, in particular determine, a processing score per identified component of the target product, wherein the processing score is indicative of a processing of the identified component(s) into one or more subcomponent(s), identify one or more subcomponent(s) of associated with the one or more component(s) if the processing score associated with the one or more component(s) is within a dismantling range, obtain, in particular determine, a processing score per identified subcomponent of the target product, obtain, in particular determine, a processing step per identified component and subcomponent according to the processing score. If the processing score associated with the one or more component(s) may be within a dismantling range, the one or more subcomponent(s) of the one or more component(s) may be identified and a processing score may be determined per one or more subcomponent(s). If the processing score associated with the one or more subcomponent(s) may be within the dismantling range, the steps may be repeated until the determined processing scores may be outside of the dismantling range. Thereby, the target product can be broken up into several components according to whether dismantling may be possible and / or required for processing the one or241318WO01 - Secondary Filing TextBASF Coatings GmbH more component(s). Hence, this contributes to tailoring the processing of the target product to available processing methods.In an embodiment, providing the indication of the target product to the one or more selected operating engine(s) may trigger the one or more selected operating engine(s) to identify one or more component(s) associated with the target product based on the indication of the target product, obtain, in particular receive, a processing score per identified component of the target product, wherein the processing score is indicative of a processing of the identified component(s) into one or more subcomponent(s), obtain, in particular determine, a processing step per identified component according to the processing score. Identifying the one or more component(s) may comprise obtaining, in particular receiving, an indication of the one or more component(s), in particular via a user interface and / or from a digital twin associated with the target product. The digital twin of the target product may be a digital representation of a physical entity of the target product. The digital representation of the physical entity may comprise a defined semantic description of said physical entity of the target product. The digital twin of the target product may hence be a digital version of the physical entity of the target product. The digital twin may be a digital representation of the target product in a real-world system. The digital twin may reflect the form and behavior of the target product associated with the digital twin. Additionally or alternatively, the digital twin may mirror the properties of the target product during its lifetime. For example, sensors may capture real-time (or near real-time) data, such as transport data, from the target product to relay it back to a remote digital twin. The digital twin may be updated to maintain its correspondence to the physical entity of the target product. Further, identifying the one or more component(s) may comprise providing the indication of the target product, in particular via a user interface.Providing the indication of the target product may trigger receiving the indication of the one or more component(s). In an embodiment, providing the indication of the target product may comprise providing a query associated with the indication241318WO01 - Secondary Filing TextBASF Coatings GmbH of the target product for retrieving the indication of the one or more component(s) from the digital twin associated with the target product. The query may be provided to a digital twin registry for retrieving the indication of the one or more component(s) from the digital twin associated with the target product. The digital twin registry may comprise a plurality of digital twins including the digital twin associated with the target product. By doing so, R-strategies for target products can be determined in real-time operating on lately retrieved data.In an embodiment, providing the indication of the target product to the one or more selected operating engine(s) may trigger the one or more selected operating engine(s) to identify one or more component(s) associated with the target product based on the indication of the target product, obtain, in particular receive, a processing score per identified component of the target product, wherein the processing score is indicative of a processing of the identified component(s) into one or more subcomponent(s), obtain, in particular determine, a processing step per identified component according to the processing score. Determining the processing step per identified component may comprise determining if the processing score associated with at least one component may be within a dismantling range and may trigger identifying of one or more subcomponent(s) of the at least one component associated with the processing score within the dismantling range. If the processing score associated with the one or more component(s) may be within a dismantling range, the one or more subcomponent(s) of the one or more component(s) may be identified and a processing score may be determined per one or more subcomponent(s). If the processing score associated with the one or more subcomponent(s) may be within the dismantling range, the steps may be repeated until the determined processing scores may be outside of the dismantling range. The processing score may be indicative of a processing of the one or more component(s) associated with the processing score. The processing score may be indicative of a type of processing of the one or more component(s). The type of processing may relate to a processing of the one or more241318WO01 - Secondary Filing Text BASF Coatings GmbH component(s) associated with producing another product, i.e. processing of the one or more component(s) for producing another product, or a processing of the one or more component(s) as waste. Processing of the one or more component(s) associated with producing another product, ie processing of the one or more component(s) for producing another product, may include transforming the one or more component(s) via one or more physical and / or chemical treatment(s) or reusing the one or more component(s). The processing score may comprise a dismantling score indicative of whether the component associated with the dismantling score may comprise one or more subcomponent(s). Thereby, the target product can be broken up into several components according to whether dismantling may be possible and / or required for processing the one or more component(s). Hence, this contributes to tailoring the processing of the target product to available processing methods.In an embodiment, any one of the methods may further comprise obtaining, in particular receiving, an indication of an availability of the determined processing step, in particular via a user interface and / or from a database comprising a plurality of indications of availabilities of a plurality of processing step(s), and wherein the processing step is provided in response to obtaining, in particular receiving, the indication of the availability of the determined processing step. The indication of the availability may comprise an indication that the determined processing step may be available. Additionally or alternatively, the indication of the availability may be obtained, in particular received, from an entity associated with the one or more processing step(s) in particular in response to providing a request for the availability of the one or more processing step(s). By determining if certain process steps are available, the processing routes for processing the target product can be determined reliably and in real-time. In some instances, availability of recycling or remanufacturing services may change over time. In particular, use of end products may change rapidly. As a result, reusing components may be available at selected points in time. Therefore, adapting processing strategies according to the availability of services allows to tailor processing of the target product. Ultimately, this increases efficiency of processing the241318WO01 - Secondary Filing TextBASF Coatings GmbH target product as occasionally available processing steps can be taken into account when determining processing routes.Any one of the methods may further comprise obtaining, in particular receiving, an indication of a point in time associated with processing the target product, preferably together with the request for processing the target product, and wherein the indication of the availability may be obtained, in particular received, based on and / or according to the indication of the point in time. In an embodiment, the request for processing the target product may comprise the indication of the point in time. The indication of the availability may be based on the point in time. The request for obtaining the availability of the feasibility may comprise the point in time.Any one of the methods may further comprise obtaining, in particular receiving, an indication of a location associated with the target product, in particular the one or more component(s), and wherein the indication of the availability may be obtained, in particular received, based on and / or according to the indication of the location. In an embodiment, the request for processing the target product may comprise the indication of the location. The indication of the availability may be based on the location. The request for obtaining the availability of the feasibility may comprise the location.In an embodiment, providing the indication of the target product to the one or more selected operating engine(s) may trigger the one or more selected operating engine(s) to identify one or more component(s) associated with the target product based on the indication of the target product, obtain, in particular receive, a processing score per identified component of the target product, wherein the processing score is indicative of a processing of the identified component(s) into one or more subcomponent(s), obtain, in particular determine, a processing step per identified component according to the processing score and an indication of a location associated with the target product, in particular the one or more component(s), and / or an indication of a point in time associated with processing the target product. In an241318WO01 - Secondary Filing TextBASF Coatings GmbH embodiment, the request for processing the target product may comprise the indication of the location. The indication of the availability may be based on the location. The request for obtaining the availability of the feasibility may comprise the location. In an embodiment, the request for processing the target product may comprise the indication of the point in time. The indication of the availability may be based on the point in time. The request for obtaining the availability of the feasibility may comprise the point in time. By determining the processing step according to the location of the target product and / or the point in time associated with processing of the target product, the processing routes for processing the target product can be determined reliably and based on locally and temporally available processing steps. In some instances, availability of recycling or remanufacturing services may change over time. In particular, use of end products may change rapidly. As a result, reusing components may be available at selected points in time. Therefore, adapting processing strategies according to the availability of services allows to tailor processing of the target product. Ultimately, this increases efficiency of processing the target product as occasionally available processing steps can be taken into account when determining processing routes.In an embodiment, providing the indication of the target product to the one or more selected operating engine(s) may trigger the one or more selected operating engine(s) to identify one or more component(s) associated with the target product based on the indication of the target product, obtain, in particular receive, a processing score per identified component of the target product, wherein the processing score is indicative of a processing of the identified component(s) into one or more subcomponent(s), obtain, in particular determine, a processing step per identified component according to the processing score, determine a processing route by combining the one or more processing step(s), and wherein providing the processing step comprises providing the determined processing route.241318WO01 - Secondary Filing Text BASF Coatings GmbHIn an embodiment, providing the indication of the target product to the one or more selected operating engine(s) may trigger the one or more selected operating engine(s) to identify one or more component(s) associated with the target product based on the indication of the target product, obtain, in particular receive, a processing score per identified component of the target product, wherein the processing score is indicative of a processing of the identified component(s) into one or more subcomponent(s), obtain, in particular determine, a processing step per identified component according to the processing score, determine a processing route score per processing route by combining the at least one processing score associated with the at least one processing route by the one or more operating engine(s), selecting a processing route based on the processing route score by the one or more operating engine(s), and wherein providing the processing step comprises providing the selected processing route.The processing route score may be obtained by mathematically relating the processing route scores, e.g. by multiplying, adding or the like. The processing route score may be indicative of a processing of the target product via the one or more processing route(s). The processing route score may be related to an availability of the one or more processing step(s) associated with the one or more processing route(s). For example, where a processing step of route A may be unavailable, the processing route score may be low in contrast to a processing step of route B where all steps may be available. Further, the processing route score may be indicative of resources associated with processing the target product according to the one or more processing route(s).By evaluating the processing route as a whole, dependencies between processing steps become transparent. Hence, the processing route can be selected upon the to be241318WO01 - Secondary Filing TextBASF Coatings GmbH conducted processing steps. Ultimately, this improves the reliability of monitoring and / or controlling processing of target products.The processing route score may be obtained may forming a linear combination of processing scores associated with the one or more processing route(s). The processing scores may be weighted according to an availability score and / or a feasibility score. The availability score may be a measure for an availability of the one or more processing step(s) associated with the one or more processing route(s). The feasibility score may be indicative of resources associated with processing the target product according to the one or more processing route(s). A high processing route score may indicate an availability of the processing route and / or a low resource invest associated with processing the target product.In an example embodiment, determining the processing step by the one or more selected operating engine(s) may refer to triggering the at least one selected operating engine to determine the at least one selected operating engine.BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGSTo easily identify the discussion of any particular element or act, the most significant digit or digits in a reference number refer to the figure number in which that element is first introduced.In the following, the present disclosure is further described with reference to the enclosed figures. The same reference numbers in the drawings and this disclosure are intended to refer to the same or like elements, components, and / or parts.FIG. 1 illustrates an example embodiment of a product life cycle associated with an end product.FIG. 2 illustrates an embodiment of separating an end-of-life product into one or more component(s) and / or associated a processing score to the one or more component(s)241318WO01 - Secondary Filing TextBASF Coatings GmbHFIG. 3 illustrates an embodiment of a method for monitoring and / or controlling processing of target product.FIG. 4 illustrates a user interface for obtaining a processing route.FIG. 5 illustrates an embodiment of a method for monitoring and / or controlling processing of target product.FIG. 6 illustrates a user interface for verifying a processing route.FIG. 7 illustrates an embodiment of a classification model configured to determine processing scores.FIG. 8 illustrates an embodiment of training an embedding layer.FIG. 9A illustrates an embodiment of a transformer encoder architecture.FIG. 9B illustrates an embodiment of a transformer decoder architecture.FIG. 9C illustrates an embodiment of a transformer encoder-decoder architecture.FIG. 10 illustrates an embodiment of a Mamba architecture.FIG. 11 illustrates an embodiment of training and / or deploying the transformer encoder, the transformer decoder and / or the transformer encoder-decoder.FIG. 12 illustrates an embodiment of input embedding.FIG. 13 illustrates an embodiment of input embedding.241318WO01 - Secondary Filing Text BASF Coatings GmbHFIG. 14 illustrates an embodiment of providing input data for generating and / or providing output data.DETAILED DESCRIPTIONThe following embodiments are mere examples for implementing the method, the system or application device disclosed herein and shall not be considered limiting.FIG. 1 illustrates an example embodiment of a product life cycle associated with an end product.The end product may be a car for example. This disclosure applies equally to any other end product, in particular produced from chemical products. The production of the end product may be initiated by providing raw materials from an input material supplier 102 to a chemical product producer 104. The input material supplier may be responsible for providing the necessary raw materials to the chemical product producer. These raw materials can vary depending on the specific product being manufactured. The chemical product producer may process the raw materials supplied by the input material supplier 102 and transform them into the desired chemical product. This can involve various processes such as mixing, refining, and packaging. In chemical industry, chemical products are produced on a ton-scale and one chemical product can serve for different purposes. Furthermore, chemical products are obtained from chains of several chemical production step typically including a plurality of chemical reactions. For example, the production system may be a Verbund system. In such a system, the production of chemical products may start with converting naphtha to smaller molecules and may be continued with further reaction steps to end at more complex molecular structures and / or chemical compositions. Hence, chemical production networks such as a Verbund system can receive a plurality of input materials for producing the chemical products. The chemical product is provided to a chemical product consumer 106. The chemical product consumer is the entity that may received and process the chemical product for obtaining supply products. These supply products can be processed e.g. by an end product producer such as an OEM 108 to241318WO01 - Secondary Filing Text BASF Coatings GmbH obtain the end product. Hence, the supply products such as wheels, chassis or the like are typically parts of the end product to be assembled by the OEM 108. The end product producer, i.e. the OEM 108 may provide the end product to an end product user 110. The use product user may be the end consumer who may utilize the product that may comprise the chemical product. This can be an individual or a company that may use the product for its intended purpose. At an end-of-life stage, the end product may be provided to and / or may be collected by an EOL product collector 112. The EOL product collector may be responsible for collecting and managing products that have reached the end of their useful life. This can involve recycling centers, waste management companies, or even specialized collection programs. The collected end- of-life products can be provided to a recycler 114. The recycler may take the collected EOL products and processes them to extract valuable materials or components. In this case, the recycler may focus on extracting and reusing the input materials needed by the chemical product producer. This enables the closed-loop system where the recycler can provide parts of the end-of-life product, ie the materials, back to the chemical product producer, reducing the need for new raw materials and hence, saving resources.To close the loop, the end-of-life product needs to be separated, i.e. disassembled, accordingly. Typically, an end-of-life product comprise a plurality of materials, sometimes also blended materials. To allow for recycling, the materials of the end-of- life product need to be separated. Depending on the degree of separation different recycling strategies may be available such as reuse, recycling, remanufacturing, waste incineration or the like. Tons of different end products with different materials may be received by the recycler 114. To handle this mass of end-of-life products or parts thereof efficiently, fast, scalable and reliable determination of recycling strategies are required. Still, recycling strategies need to be tailored to the end-of-life product at hand.By providing a request for processing a target product to a data-driven model, a plurality of different requests related to a plurality of different end-products may be processed in a fast and scalable manner. Further, the request can be independent of a241318WO01 - Secondary Filing TextBASF Coatings GmbH predefined structure, i.e. including unstructured data such as natural language from a human user, i.e. a worker associated with a recycling facility. More than that, by processing the request by the data-driven model, the recycling strategies obtained from the request can be tailored to the end-of-life product at hand while up-to-date data can be utilized for deriving said recycling strategies. Thus, real-time development and / or evaluation of recycling strategies is enabled. Ultimately, this allows to tailor the recycling of end-of-life products to an intended use of the recycled end-of-life product. Therefore, this disclosure contributes to closing the loop in chemical production networks tailored to the available end-of-life products and current recycling options.FIG. 2 illustrates an embodiment of separating an end-of-life product into one or more component(s) and / or associated a processing score to the one or more component(s)The end-of-life product may be a car for example. This disclosure applies equally to any other end product, in particular produced from chemical products. The end-of-life product may comprise one or more component(s). For example, the one or more component(s) may be tied together. The end-of-life product may be disassembled into the one or more component(s). The one or more component(s) may be recycled, reused, remanufactured, incinerated or the like. Even though, the end-of-life product in total may be at the end-of-life stage, the one or more component(s) may still be usable.Per component, a processing score may be determined. The processing score may be indicative of one or more subcomponent(s) associated with the one or more component(s). Further, the processing score may be indicative of a processing of the one or more component(s) as a whole or separated into one or more subcomponent(s). For example, the processing score may be higher with respect to a wheel including a felly can be separated into the wheel and the felly. The wheel may be made from rubber, while the felly may be made from aluminium. Hence, the felly may be recycled and / or reused differently from the wheel. The processing score being within a dismantling range may indicate that the wheel and the felly should be separated from each other to improve recycling and / or reuse. Similarly, the chassis may be separated from the whole vehicle and / or a shell of the vehicle. The processing of the one or more241318WO01 - Secondary Filing TextBASF Coatings GmbH component(s) may depend on an availability of recycling services at the location of the end-of-life product. Hence, the chassis may be broken up into further parts and / or processed together depending on the recycling services presented at the location of the end-of-life product, in particular the chassis.FIG. 3 illustrates an embodiment of a method for monitoring and / or controlling processing of target product.A request for processing a target product may be obtained, in particular received 302. The request may be related to, in particular may comprise, an indication of the target product. The indication may be suitable for identifying and / or may characterize the target product. The request may be obtained via an interface, e.g. a user interface. An example of said user interface may be depictured in FIG. 6.The request for processing the target product may be provided to a data-driven model for selecting one or more operating engine(s) 304. Providing the request to the data- driven model may comprise providing a selection task instruction to the data-driven model for selecting the one or more operating engine(s). The selection task instruction may be related to, in particular comprise, the request and an indication of a plurality of operating engines comprising the to be selected one or more operating engine(s). The data-driven model may be configured to follow task instructions. An example of said data-driven model can be seen in FIG. 9A to FIG. 14.The one or more selected operating engine(s) may be configured to provide an indication of a processing of at least a part of the target product, i.e. the one or more selected operating engine(s) may be configured to provide a processing route associated with the target product. The processing route may comprise at least one processing step associated with the target product. The indication of the processing of at least the part of the target product may be obtained by a combination of any one of 306 to 318.241318WO01 - Secondary Filing Text BASF Coatings GmbHAn indication of the one or more component(s) associated with the target product and optionally a location associated with the target product may be obtained 306, in particular received, based on the indication of the target product, in particular by the one or more selected operating engine(s). For example, the one or more selected operating engine(s) may comprise a database. The database may comprise a plurality of indications of a plurality of components associated with a plurality of products. Obtaining the indication of the one or more component(s) and / or the location may comprise generating a query for obtaining the indication of the one or more component(s) and / or the location, providing the query and receiving in response to providing the query the indication of the one or more component(s) and / or the location. Preferably, the indication of the one or more component(s) may be and / or may comprise data related digital twin of the target product. The digital twin of the target product may be a digital representation of a physical entity of the target product. The digital representation of the physical entity may comprise a defined semantic description of said physical entity of the target product. The digital twin of the target product may hence be a digital version of the physical entity of the target product. The digital twin may be a digital representation of the target product in a real-world system. The digital twin may reflect the form and behavior of the target product associated with the digital twin. Additionally or alternatively, the digital twin may mirror the properties of the target product during its lifetime. For example, sensors may capture real-time (or near real-time) data, such as transport data, from the target product to relay it back to a remote digital twin. The digital twin may be updated to maintain its correspondence to the physical entity of the target product. By doing so, processing routes of target products can be determined in real-time operating on lately retrieved data.One or more component(s) associated with at least a part of the target product may be identified 308, in particular according to the indication of the target product. In an embodiment, an indication of a plurality of components associated with one or more product(s) comprising the target product may be obtained. The one or more component(s) may be identified based on the indication of the plurality of components and the indication of the target product. The indication of the target product may comprise an image associated with the target product. Identifying the one or more241318WO01 - Secondary Filing TextBASF Coatings GmbH component(s) may comprise verifying a presence of the one or more component(s). Hence, identifying the one or more component(s) may comprise obtaining, in particular receiving, an indication of a presence of the one or more component(s).A processing score may be determined per component 310, in particular according to an image of the target product. The image of the target product may be provided to a classification model configured to determine processing scores upon receiving images. The classification model may be for example a neural network, in particular a convolutional neural network, or a tree-based approach. The classification model may be trained based on a training data set comprising historical images and corresponding processing scores. Additionally or alternatively, determining a processing score may comprise providing the image of the target product and receiving the processing score in response, e.g. via a user interface.Additionally or alternatively, the processing score may be obtained, in particular received, from a data storage such as a digital twin associated with the target product. The data storage associated with the target product, e.g. the digital twin, may comprise the processing score associated with the one or more component(s) of the target product and / or processing data suitable for obtaining the processing score. Additionally or alternatively, the processing score may be obtained, in particular received, via an interface such as a user interface. The processing score may be determined by a dismantler via visual inspection and provided via the user interface.In an embodiment, the processing score may comprise two or more subscores. The two or more subscores may be related to different scoring schemas and / or different processing measures. For example, the two or more subscores may be related to two or more types of processing the one or more component(s). The two or more subscores may be combined according to two or more weighting factor associated with the two or more subscores. For example, at least one of the two or more processing scores may be related to a processing of the one or more component(s) to another product and the at least one other of the two or more processing scores may be related to processing the one or more component(s) as waste.241318WO01 - Secondary Filing Text BASF Coatings GmbHIf the processing score associated with the one or more component(s) may be within a dismantling range, the one or more subcomponent(s) of the one or more component(s) may be identified and a processing score may be determined per one or more subcomponent(s). If the processing score associated with the one or more subcomponent(s) may be within the dismantling range, the steps may be repeated until the determined processing scores may be outside of the dismantling range. Thereby, the target product can be broken up into several components according to whether dismantling may be possible and / or required for processing the one or more component(s). Hence, this contributes to tailoring the processing of the target product to available processing methods.A processing step may be determined per component, in particular by the one or more operating engine(s) based on the processing score 312. The processing score may be indicative of a processing of the one or more component(s) associated with the processing score. The processing score may be indicative of a type of processing of the one or more component(s). The type of processing may relate to a processing of the one or more component(s) associated with producing another product, i.e. processing of the one or more component(s) for producing another product, or a processing of the one or more component(s) as waste. Processing of the one or more component(s) associated with producing another product, i.e. processing of the one or more component(s) for producing another product, may include transforming the one or more component(s) via one or more physical and / or chemical treatment(s) or reusing the one or more component(s).Determining the processing step may include providing a processing task instruction related to indication of the target product and / or the one or more processing score(s) and an indication of a plurality of processing steps to a data-driven model for determining the one or more processing step(s) associated with the one or more component(s). Further the processing task instruction may be related to and / or may include a historical indication of the target product and / or the one or more processing score(s) and a historical processing step associated with the historical indication of the241318WO01 - Secondary Filing Text BASF Coatings GmbH target product and / or the one or more processing score(s). The data-driven model may be configured to follow the task instruction. The indication of the plurality of processing steps may relate the plurality of processing steps to a plurality of processing scores. An example of said data-driven model can be seen in FIG. 9A to FIG. 14.Additionally or alternatively, determining the processing step may include determining if the one or more processing score(s) may be within a dismantling range, a reuse range, a remanufacture range, a recycling range, a waste range or a combination thereof.Additionally or alternatively, the one or more component(s) may be grouped according to a material associated with the one or more component(s) into at least one group of components. Grouping the one or more component(s) into at least one group of components may include obtaining an indication of the material associated with the target product from a database comprising indications of the material associated with a plurality of products and / or from a digital twin associated with the target product. The digital twin associated with the target product may be the digital twin of the target product and / or an input product for obtaining the target product. Hence, at least one group of components associated with at least one material may be obtained. The at least one group of components may be suitable for being processed together and / or by the same entity. Processing the group of components together allows to streamline resources for processing of the respective components to the individual groups of material. Thereby, non-related components such as components not connected in a product, can be processed together. This is advantageous since the material of the components is decisive for recycling and / or remanufacturing the components. Ultimately, the required resources for processing the group of components can be lower than processing each component by itself. Thereby, the efficiency of processing the target product can be increased and less resources are used in total.In an embodiment, an indication of an availability of the determined one or more processing step(s) associated with the one or more component(s) may be obtained, in particular received, e.g. from a database comprising a plurality of indications of241318WO01 - Secondary Filing Text BASF Coatings GmbH availabilities of a plurality of processing step(s). Additionally or alternatively, the indication of the availability may be obtained, in particular received, via a user interface. Additionally or alternatively, the indication of the availability may be obtained, in particular received, from an entity associated with the one or more processing step(s) in particular in response to providing a request for the availability of the one or more processing step(s). In an embodiment, an indication of a point in time associated with processing the target product may be obtained, in particular received, preferably together with the request for processing the target product. In an embodiment, the request for processing the target product may comprise the indication of the point in time. The indication of the availability may be based on the point in time. The request for obtaining the availability of the feasibility may comprise the point in time.By determining if certain process steps are available, the processing routes for processing the target product can be determined reliably and in real-time. In some instances, availability of recycling or remanufacturing services may change over time. In particular, use of end products may change rapidly. As a result, reusing components may be available at selected points in time. Therefore, adapting processing strategies according to the availability of services allows to tailor processing of the target product. Ultimately, this increases efficiency of processing the target product as occasionally available processing steps can be taken into account when determining processing routes.In an embodiment, an indication of a feasibility of the determined one or more processing step(s) associated with the one or more component(s) may be obtained, in particular received, e.g. from a database comprising a plurality of indications of feasibilities of a plurality of processing step(s). Additionally or alternatively, the indication of the feasibility may be obtained, in particular received, via a user interface. Additionally or alternatively, the indication of the feasibility may be obtained, in particular received, from an entity associated with the one or more processing step(s) in particular in response to providing a request for obtaining the indication of the feasibility of the one or more processing step(s). In an embodiment, an indication of a point in time associated with processing the target product may be obtained, in241318WO01 - Secondary Filing TextBASF Coatings GmbH particular received, preferably together with the request for processing the target product. In an embodiment, the request for processing the target product may comprise the indication of the point in time. The indication of the feasibility may be based on the point in time. The request for obtaining the indication of the feasibility may comprise the point in time.By taking a measure for a feasibility of a processing step into account, newly arising processing steps e.g. in the nearer future can be evaluated for determining the processing route. Further, adapting the processing route according to the feasibility allows to manage resources associated with processing the target product,. Thereby, unnecessarily high resource invests for processing can be avoided while in total the efficiency of using the resources for processing the target products can be increased.A processing route score may be determined per processing route by combining the one or more processing score(s) associated with the one or more processing step(s) and / or the group of components per processing route314. The processing route score may be obtained by mathematically relating the processing route scores, e.g. by multiplying, adding or the like. The processing route score may be indicative of a processing of the target product via the one or more processing route(s). The processing route score may be related to an availability of the one or more processing step(s) associated with the one or more processing route(s). For example, where a processing step of route A may be unavailable, the processing route score may be low in contrast to a processing step of route B where all steps may be available. Further, the processing route score may be indicative of resources associated with processing the target product according to the one or more processing route(s).By evaluating the processing route as a whole, dependencies between processing steps become transparent. Hence, the processing route can be selected upon the to be conducted processing steps. Ultimately, this improves the reliability of monitoring and / or controlling processing of target products.241318WO01 - Secondary Filing TextBASF Coatings GmbHThe processing route score may be obtained may forming a linear combination of processing scores associated with the one or more processing route(s). The processing scores may be weighted according to an availability score and / or a feasibility score. The availability score may be a measure for an availability of the one or more processing step(s) associated with the one or more processing route(s). The feasibility score may be indicative of resources associated with processing the target product according to the one or more processing route(s). A high processing route score may indicate an availability of the processing route and / or a low resource invest associated with processing the target product.A processing route may be selected according to the processing route score, in particular by the one or more operating engine(s) 316. This may include determining if the one or more processing route score(s) associated with the one or more processing route score(s) may be within a processing route range. The processing route range may be provided via a user interface.The selected processing route for processing the target product according to the selected processing route may be provided 318. The selected processing route may be provided via a user interface. For example, the selected processing route may be displayed to a worker associated with a processing facility for processing at least a part of the target product. Providing the selected processing route, e.g. via a display, may instruct the workers of processing facilities to perform the processing steps associated with the processing route tailored to the target product at hand.FIG. 4 illustrates a user interface for obtaining a processing route.FIG. 5 illustrates an embodiment of a method for monitoring and / or controlling processing of target product.An indication of a processing route for processing a target product may be obtained, in particular received 502. The processing route may be indicative of and / or may comprise one or more processing step(s) per component of the target product. The241318WO01 - Secondary Filing TextBASF Coatings GmbH processing route may be obtained via a user interface and / or as described in the context of FIG. 3. Hence, obtaining the processing route may comprise determining a processing route as described in the context of FIG. 3. The indication of the processing route may comprise the processing route. The indication of the processing route may comprise one or more processing step(s) and optionally a relation between the one or more processing step(s). The relation between the one or more processing step(s) may indicate a sequence associated with the one or more processing step(s).A plurality of target processing data sets associated with a plurality of components may be obtained, in particular received 504. The plurality of target processing data sets may be indicative of a target processing of the plurality of components associated with the target processing data set.The plurality of the target processing data sets may be received from a database. The database may comprise a plurality of target processing data sets associated with a plurality of components. The plurality of target processing data sets obtained may be associated with the target product. The plurality of target processing data sets comprised by the database may be associated with a plurality of products.The target product may comprise the one or more component(s) associated with the target product. The indication of the processing route may be further indicative of and / or may further comprise the one or more component(s) associated with the target product. Preferably, the indication of the processing route may be further indicative of and / or may further comprise a relation between the one or more processing step(s) and the one or more component(s).Said target processing data sets may specify best practices for more efficient processing of the target product. By obtaining the target processing data sets, e.g. from a database, applicable target processing data sets may be retrieved and processed in real-time.241318WO01 - Secondary Filing Text BASF Coatings GmbHAt least one applicable target processing data set associated with the one or more component(s) from the plurality of target processing data sets may be selected 506. Selecting the at least one applicable target processing data set may be based on at least one numerical representation associated with the applicable target processing data set and one or more numerical representation(s) associated with the one or more processing step(s). The numerical representation(s) may be obtained by providing the applicable target processing data set and / or the one or more processing step(s) to one or more embedding layer(s). The one or more embedding layer(s) may be configured to transform data into numerical representations associated with the data. Preferably, selecting the at least one applicable target processing data set may comprise determining a distance between the at least one numerical representation associated with the applicable target processing data set and the one or more numerical representation(s) associated with the one or more processing step(s).Additionally or alternatively, selecting at least one applicable target processing data set associated with the one or more component(s) from the plurality of target processing data sets may comprise providing a processing step selection task instruction to a data-driven model such as the data-driven model described in the context of FIG. 9A to FIG. 14 for selecting the at least one applicable target processing data set. The data- driven model may be configured to follow task instructions. The processing step selection task instruction may be related to, in particular comprise, the obtained plurality of target processing data sets and the obtained processing route.A matching task instruction may be provided to a data-driven model for determining if the at least one applicable target processing data set matches at least one of the one or more processing step(s) associated with the obtained processing route 508. The matching task instruction may be related to and / or may comprise the at least one applicable target processing data set and the indication of the processing route. The data-driven model may be configured to follow task instructions provided to the data- driven model. An example of said data-driven model can be seen in FIG. 9A to FIG. 14.241318WO01 - Secondary Filing TextBASF Coatings GmbHUpon determining a match between the at least one applicable target processing data set and at least one of the processing steps associated with the processing route, providing an indication of a verification of the processing route for processing the target product according to the processing route 520. The indication of the verification may be provided via an interface such as a user interface and / or may be displayed to a worker associated with a processing facility. Providing the indication of the verification, e.g. via a display, may instruct the workers of processing facilities to perform the processing steps associated with the processing route tailored to the target product at hand. In an embodiment, the indication of the verification and the verified processing route may be provided together.Upon determining a mismatch between the at least one applicable target processing data set and at least one processing step associated with the processing route, determining if an alternative processing step to the at least one processing step may be available 510. Determining if an alternative processing step may be available may comprise providing an alternative task instruction related to, in particular comprising, the applicable target processing data set, an indication of a plurality of processing steps and the indication of the at least one processing step mismatching with the target processing data set, i.e. at least a part of the processing route associated with the at least one processing step mismatching with the target processing data set, to the data- driven model. The data-driven model may be triggered by the alternative task instruction to provide an alternative processing step.Additionally or alternatively, the alternative processing step may be identified by retrieving the alternative step from a database comprising a plurality of processing steps based on providing the at least one processing step associated with the mismatch. Retrieving the alternative processing step may include providing a query related to, in particular comprising, the at least one processing step associated with the mismatch to the database and receiving the alternative step from the database in response to providing the query. The query may be obtained according to pre-defined rules for generating the query from the at least one processing step associated with the mismatch and / or by providing a query task instruction related to the at least one241318WO01 - Secondary Filing TextBASF Coatings GmbH processing step associated with the mismatch and an indication of the database comprising the plurality of processing steps to the data-driven model(s). The data- driven model(s) may be configured to follow task instructions provided. The query task instruction provided to the data-driven model(s) may trigger the data-driven model(s) to generate the query for obtaining the alternative processing step based on the at least one processing step associated with the mismatch.If an alternative step can be determined, the alternative step can be included into the processing route, in particular instead of the at least one processing step 516. If no alternative step can be determined, the at least one processing step mismatching with the target processing data set may be removed from the processing route 516. The so- obtained processing route may be provided 518 and / or 514. The processing route may be provided via an interface such as a user interface and / or may be displayed to a worker associated with a processing facility. Providing the processing route, e.g. via a display, may instruct the workers of processing facilities to perform the processing steps associated with the processing route tailored to the target product at hand.In an embodiment, an indication of two or more points in time may be obtained, in particular received eg via a user interface. The indication of the processing route may be obtained as described in the context of 502. 504 to 508 and depending on whether the applicable target processing data match at least one of the processing steps associated with the processing route 510, 512 and / or 516 may be performed per point in time. The target processing data sets may obtained per point in time and / or the applicable target processing data set may be selected per point in time. The obtained target processing data sets may comprise at least one first target processing data set associated with a first point in time and at least one second target processing data set associated with a second point in time. The two or more points in time may relate to and / or may comprise the first point in time and / or the second point in time. The so- obtained processing route(s) may be provided. By obtaining the target processing data sets according to different points in time, the processing route at different points in time can be evaluated. For example, availability or feasibility of processing steps may change over time. By comparing processing routes at different points in time, it can be241318WO01 - Secondary Filing Text BASF Coatings GmbH determined whether and when certain processing routes may be available. Ultimately, this improves tailoring processing of target products to available and / or feasible processing services.In an embodiment, an indication of two or more points in time may be obtained, in particular received e.g. via a user interface. The indication of the processing route may be obtained as described in the context of 502. 504 to 508 and depending on whether the applicable target processing data match at least one of the processing steps associated with the processing route 510, 512 and / or 516 may be performed per point in time. The target processing data sets may obtained per point in time and / or the applicable target processing data set may be selected per point in time. The obtained target processing data sets may comprise at least one first target processing data set associated with a first point in time and at least one second target processing data set associated with a second point in time. The two or more points in time may relate to and / or may comprise the first point in time and / or the second point in time. A processing route score associated with the obtained processing route may be obtained, in particular received and / or determined as described in the context of FIG. 3. At least one of the so-obtained processing routes may be selected according to the processing route scores associated with the processing routes. By comparing processing routes at different points in time, it can be determined whether and when certain processing routes may be available. Ultimately, this improves tailoring processing of target products to available and / or feasible processing services.In an embodiment, upon determining that no alternative step may be available, the at least one processing step associated with the mismatch and at least one further processing step depending on, in particular proceeding, the at least one processing step associated with the mismatch may be excluded from the processing route. By doing so, unfeasible parts of processing routes may be eliminated. Further, the so- obtained processing routes provides a complete sequence of steps for processing the target product via the one or more identified component(s).241318WO01 - Secondary Filing TextBASF Coatings GmbHIn an embodiment, an indication of at least one point in time may be obtained, in particular received e.g. via a user interface. The indication of the processing route may be obtained as described in the context of 502. 504 to 508 and depending on whether the applicable target processing data match at least one of the processing steps associated with the processing route 510, 512 and / or 516 may be performed with respect to the obtained point in time. In particular, from the plurality of processing steps associated with a plurality of components at least two processing steps associated with the obtained point in time may be selected. By doing so, available processing steps may be determined in a time related manner. Thereby, frequently changing availabilities of processing steps due to technical limitations such as maximum capacities, changing weather or the like can be taken into account. Ultimately, this improves tailoring processing of target products to available and / or feasible processing services.The target processing data sets may obtained per point in time and / or the applicable target processing data set may be selected per point in time. The obtained target processing data sets may comprise at least one first target processing data set associated with a first point in time and at least one second target processing data set associated with a second point in time. The two or more points in time may relate to and / or may comprise the first point in time and / or the second point in time. A processing route score associated with the obtained processing route may be obtained, in particular received and / or determined as described in the context of FIG.3. At least one of the so-obtained processing routes may be selected according to the processing route scores associated with the processing routes. By comparing processing routes at different points in time, it can be determined whether and when certain processing routes may be available. Ultimately, this improves tailoring processing of target products to available and / or feasible processing services.In an embodiment, identifying an alternative step may trigger determining one or more processing step(s) dependent on the alternative step, preferably a partial processing route proceeding the alternative step and comprising one or more processing step(s), as described in the context of FIG. 3. By doing so, further separations based on the241318WO01 - Secondary Filing TextBASF Coatings GmbH alternative processing steps can be identified. Thereby, materials can be separated more effectively for example because the alternative step allows to separate two types of polymers. In turn, this comes with the benefit of enabling reusing or remanufacturing of the respective components rather than burning both components when combined.FIG. 6 illustrates a user interface for verifying a processing route:FIG. 7 illustrates an embodiment of a classification model configured to determine processing scores.A processing score may be determined per component, in particular according to an image of the target product. The image of the target product may be provided to the classification model 702 configured to determine processing scores upon receiving images. The classification model 702 may be for example a neural network, in particular a convolutional neural network, a recurrent neural network or a LSTM. Additionally or alternatively, the classification model 702 may be and / or comprise a tree-based approach. The classification model 702 may be trained based on a training data set comprising historical images and corresponding processing scores.Additionally or alternatively, determining a processing score may comprise providing the image of the target product and receiving the processing score in response, e.g. via a user interface.FIG. 8 illustrates an embodiment of input embedding.An input embedding may be obtained by training for example a continuous bag of words model (CBOW) or a skip-gram model. The embedding layer may be suitable for generating embedded input data based on input data. Generating embedded input data may refer to embedding input data. Embedding input data may result in a representation associated with the input data. Thus, the embedded input 814 may be the representation associated with the input data. The input data may comprise one or more elements. The one or more elements may be represented by the input vector241318WO01 - Secondary Filing Text BASF Coatings GmbH806. In particular, the embedded input 814 and / or the input vector 806 may be machine- readable and / or processable by a processor. For this purpose, the embedded input 814 and / or the input vector 806 may be a tensor, in particular a first-rank tensor. Specifically, the input vector 806 may be a one-hot vector or a summation of a plurality of one-hot vectors. A one-hot vector may be a vector with one entry unequal to zero. Examples for one-hot vectors may be 808, 810 and 812. The entries unequal to zero in the one-hot vector and / or in the input vector 806 may indicate the element. For example, a lookup table may define the relation between the position of the entries unequal to zero and the element indicated by the one-hot vector. The lookup table may specify a plurality of different elements. The number of different elements may be equal to the number of entries in the one-hot vector. The number of different elements may be referred to as vocabulary size. In an example, the elements may be represented by tokens and a sequence of elements may refer to at least a part of a sentence. The at least a part of the sentence may be represented by a plurality of tokens. A token may represent at least a part of the element and / or word. For example, where one element would be associated with only one word, words such as “embeddings", “embedding” or “embed” would constitute different elements. A first token may represent the stem “embed” and the endings, typically appearing in a plurality of word, may be represented by a second token, a third token and a fourth token. The second token, the third token and the fourth token may be used for representing other words such as “look”, “looking” or the like, preferably together with a fifth token representing the stem “look”. Ultimately, this tokenization of elements associated with a plurality of stems and a plurality of endings results in less tokens to be used for representing a plurality of elements and thus, uses less computational resources.A lookup table specifying a subset of the vocabulary size e.g. of the English language may comprise 10,000 words or more. The embedded input 814 may be a lowerdimensional representation than the input vector 806. For example, typical embedded inputs 814 may comprise some hundreds of different entries. Followingly, the embedded inputs 814 constitute a densified representation of one or more elements using less computational resources. More than that, the embedded input 814 may241318WO01 - Secondary Filing Text BASF Coatings GmbH represent a relation between two or more elements. For example, the words “Italy” and “Germany” may be similar or may be more closely related since they both define European countries, whereas the word “embodiment” may be very different from the two respective words. The smaller the dot product between two embedded inputs 814 may be the more similar the two elements associated with the embedded inputs 814 may be. Hence, the embedded inputs 814 may represent one or more elements accurately and lead to accurate results based on processing the embedded inputs 814.For transforming the input vector 806 into the embedded input 814, the embedding layer may comprise a number of neurons equal to the number of entries in the embedded input 814. Based on the embedded inputs 814, the output layer may generate the output vector 816. The output vector may be a vector and / or may indicate one or more elements. The output vector 816 may indicate one or more elements different from the input vector 806 and / or the one-hot vectors associated with the input vector 806. For this purpose, the output layer may comprise a number of neurons equal to the number of entries of the input vector 806 and / or the output vector 816. The output layer may apply a softmax function to the embedded inputs 814. By doing so, the output vector may comprise the probabilities associated with the elements associated with the entries of the output vector 816 unequal to zero. Hence, from the output vector 816 one or more elements may be obtained with a corresponding probability. Where the input vector 806 may specify one or more sequence(s) of elements, the output vector 816 may specify one or more elements corresponding to the sequence(s) of elements specified by the input vector 806. In the example of FIG. 8, the element associated with vector 818 may correspond to the input vector with a probability of 71 %. Additional or alternative elements may correspond to the input vector as indicated by the output vector with lower probability. By defining a threshold to which the probability may be compared, the selection of the corresponding elements may be tailored to the needs of the user. The elements generated by the model comprising the embedding layer 802 and the output layer 804 may refer to the most probable elements indicated by the output vector 816. Hence, the model depicted in FIG. 8 may generate the element associated with the vector 818 with a confidence score of 71 %.241318WO01 - Secondary Filing Text BASF Coatings GmbHThe model of FIG. 8 may be continuous bag of words (CBOW) model. The CBOW model may be trained based on a training data set comprising a plurality of input vectors and corresponding output vectors. As the training data set may not be labeled, the training of the CBOW model may be referred to as self-supervised. Before training of the CBOW model, the CBOW model may be initialized with random values assigned to the weights of the neurons. During the training of the CBOW model, the input vectors may be passed through the initialized embedding layer and the output layer and a loss may be determined by comparing the output vector obtained by passing the input vector 806 through the model to the output vector corresponding to the input vector 806 as specified by the training data set. Based on the determined loss, backpropagation may be applied to determine the gradients associated with the neurons of the embedding layer 802 and the output layer 804 to lower the loss.According to the determined gradients, the weights of the neurons may be updated by using a gradient descent algorithm. If a predetermined loss may be achieved by the CBOW model, the training may be terminated and a trained CBOW model may be obtained. From the trained CBOW model, the embedding layer 802 may be suitable for embedding input data comprising one or more elements. This embedding layer 802 may be used in other machine-learning architectures requiring an embedding layer 802 such as a transformer encoder, transformer decoder or transformer encoder decoder architecture as described within the context of FIG. 9A, FIG. 9B and FIG. 9C. For training these architectures, a trained embedding layer 802 may be required. Hence, a model such as a CBOW model may be trained prior to training the transformer encoder, transformer decoder or transformer encoder decoder architecture.Further, applying input embedding may include determining a numerical representation of the input data by determining the number of elements and / or parts of the input data. Hence, the numerical representation of the two or more elements, in particular of a predefined size, may be indicative of a number of occurrences of the elements and / or parts of the input data. In an example, the numerical representation of the two or more elements indicative of a number of occurrences of the elements of the input data may241318WO01 - Secondary Filing Text BASF Coatings GmbH be a vector with a plurality of entries where one entry may be indicative of the occurrence of one element of the input data.FIG. 9A illustrates an embodiment of a transformer encoder architecture.The transformer encoder comprises an encoder input 978, one or more encoder blocks 974, 914 and an encoder output. The transformer encoder architecture may be derived from the transformer encoder-decoder architecture as known in the art and shown in FIG. 9C. In particular, the transformer encoder may be referred to as X-former. The transformer encoder architecture may correspond to the encoder architecture associated with the transformer encoder-decoder architecture with an additional encoder output instead of connecting the encoder block directly to the decoder of the transformer encoder-decoder architecture. A plurality of transformer encoder architectures are available in the art such as the bi-directional encoder representations from transformers (BERT).The input data may be received at the encoder input 978. The input data may comprise at least one of text data, numerical data, tabular data, image data or the like. Where the input data may comprise one of text data, numerical data, tabular data, image data or the like, input embedding of a type corresponding to the type of input data may be applied. The type of the input data may be text data, numerical data, tabular data, image data or the like. In an embodiment, the input data may be associated with two or more types of input data. The input embedding may be associated with the two or more types of input embedding, in particular according to the input data. Hence, the input embedding may be configured to map text data, numerical data, tabular data, image data or the like to a numerical representation of the input data. In particular, at least one first type of input embedding may be applied to at least a part of the input data associated with one first type of input data. Further, at least one second type of input embedding may be applied to at least a part of the input data associated with one second type of input data. The model associated with the input embedding comprising the at least one first and at least one second type of input data may be referred to as multimodal model. The type of the input data may correspond to a modality. An241318WO01 - Secondary Filing Text BASF Coatings GmbH example of input embedding associated with text data can be found in the context of FIG. 8. An example of input embedding associated with numerical and / or tabular data can be found in the context of FIG. 12. of input embedding associated with image data can be found in the context of FIG. 13.Receiving and / or providing the input data may comprise identifying two or more elements of the input data. This may be referred to as tokenization. For this purpose, a vocabulary may be available. The vocabulary may specify a plurality of elements, in particular elements typically repeating in data of the type of the input data. For example, where the input data may be text data, the vocabulary may comprise several endings and / or word stems. In an embodiment, the elements of the input data may be specified by a selection indicative of the plurality of elements provided.The encoder input 978 may apply an input embedding 902, in particular to the two or more elements of the input data. Applying the input embedding 902 may refer to passing the input data, in particular the two or more elements of the input data preferably separately, through one or more embedding layer e.g. as described within the context of FIG. 8. Applying the input embedding may comprise mapping the input data, in particular the two or more elements of the input data to a numerical representation of the input data. The numerical representation may be indicative and / or may be related to the input data. Mapping the input data to the numerical representation of the input data may comprise identifying two or more elements of the input data. For example, where the input data may be text data, the text may be divided into one or more token(s). The one or more element(s) may be mapped to a numerical representation of the one or more part(s). In particular, the number of element(s) may be equal to the number of numerical representation of the element(s). The numerical representation may be a tensor, in particular a vector and / or a matrix.Further, the numerical representation of the two or more elements may be mapped to a numerical representation of a predefined size related to the numerical representation of the two or more elements. This may be referred to as padding. Data-driven model(s) may require data input of a predefined size. Hence, padding may allow for processing241318WO01 - Secondary Filing TextBASF Coatings GmbH of input data of irregular size by the data-driven model. Padding may include concatenating a numerical representation independent of the input data with the numerical representation of the two or more elements to generate the numerical representation of predefined size related to the numerical representation of the two or more elements. The numerical representation independent of the input data may be indicative of a zero.Further, the encoder input 978 may apply positional encoding 904. Applying positional encoding 904 may refer to adding a positional factor to the embedded input obtained via input embedding. Applying positional encoding 904 may comprise mapping the numerical representation of the predefined size related to the numerical representation of the two or more elements to a numerical representation of the two or more elements and a relation between the two or more elements. Preferably, the input data may specify a sequence of elements. The positional factor Pp° may be indicative of the position of the elements within the sequence. For example, the positional factor Pi may be obtained based on the following equation:where pos may refer to the position of the element within the sequence, / may refer to the dimension associated with the input embedding and d may refer to the dimension of the model, e.g. transformer decoder, transformer encoder or transformer encoderdecoder. This may be referred to as absolute positional embeddings. Alternatively, the positional encoding may be based on rotary positional embeddings (RoPE). Positional encoding is beneficial since it enables the processing of sequential data without requiring further dimensions indicating the position of each element. Followingly, the positional encoding 904 reduces the computational resources needed for embedding the input data. By passing the input data through the encoder input, the input data may be transformed into a second-rank tensor representing the sequence of elements. This241318WO01 - Secondary Filing Text BASF Coatings GmbH second-rank tensor may be referred to as embedded input data. The embedded input data may be processed by the encoder block. The embedded input data may be provided to the layer normalization 908 by a residual connection. Multi-head selfattention 906 may be applied to the embedded input data. Multi-head self-attention 906 may comprise the two components multi-head and self-attention. Self-attention may be understood as being a filter applied to the embedded input data. By applying the filter to the embedded input data, the elements associated with the embedded input data contributing to the to be generated output data may be identified for generating the output data. Hence, the filter may represent the degree of contributing to the to be generated output data by the elements associated with the embedded input data. Applying the filter may be referred to as weighting the elements associated with the embedded input data. This is advantageous specifically regarding long sequences of elements. The filter may be learned and improved during the training by learning to identify the contribution of elements associated with the embedded input data. For example, in the partial sentence “I went to the bakery to buy a” the last word may be generated by the data-driven model such as the transformer encoder. The selfattention may focus the transformer encoder to attend to the word “bakery” and “buy” mostly to generate the word “bread”. Self-attention may refer to attention generated based on the input data. Hence, the filter may be determined based on the input data, preferably the embedded input data. The embedded input data may serve as query Q, key K and value V with respect to the self-attention operation. The self-attention may refer to attention based on the received input data. Hence, the filter may be calculated based on the following formula by inserting the respective tensors based on the embedded input data:where dkcorresponds to the dimension of the key.For improving the efficiency of the transformer encoder further, the multiple heads are used to apply the filter resulting in the multi-head self-attention 906. Multi-head selfattention 906 may comprise applying the filter to two or more elements of the embedded input data. Hence, the tensor may be split into two or more elements and241318WO01 - Secondary Filing Text BASF Coatings GmbH the filter may be applied to the two or more elements separately by two or more heads according to the following equation:head i =Attentian QWiQ,KWiK, VWiV) with parameter matrices ■ y.Qwhere i may refer to the number of heads, dvdKanddQ may refer to the dimensions of the value, key and query.The result of the two or more head may be concatenated according to the following equation: MultiHead(Q, K, V) = Concat(head 1, . . . , headh)W° whereWq e^hdvxd and h may refer to the number of heads.The embedded input data may be transformed via the multi-head self-attention 906 into a context tensor. The context tensor may represent the sequence of elements and the relation between two or more elements of the input data. The context tensor may be a second rank tensor and / or may comprise one or more first rank tensor(s). After the multi-head self-attention 906 layer normalization 908 may be applied based on the context tensor and / or the embedded input data from the residual connection. Applying layer normalization 908 may refer to normalizing the context tensor. Normalizing the context tensor may lower the values of the entries of the context tensor. This reduces the computational cost associated with processing the context tensor. Further, it improves the training by contributing the loss to converge and preventing instabilities.Layer normalization 908 may be followed by passing the context tensor to a feedforward layer 910 again followed by layer normalization 912 based on the residual connection to the context tensor and / or the output of the feed-forward layer 910. The feed-forward layer 910 may be a feed-forward neural network. The feed-forward neural network may comprise of a plurality of fully connected neurons. Passing the context tensor through the feed-forward neural network may result in transforming the context tensor linearly. Additionally or alternatively, the neural network may comprise one or more activation functions such as a rectified linear unit (ReLU). Hence, the neural network may be configured for performing one or more non-linear operations to the context tensor and / or transforming the context tensor non-linearly. After the context tensor has been transformed and / or normalized by the feed-forward layer 910 and the layer normalization 912, the context tensor may be provided to one or more further241318WO01 - Secondary Filing Text BASF Coatings GmbH encoder blocks 914. Having passed the context tensor through the feed-forward layer 910 may adapt the context tensor for the processing by a further attention layer of the one or more further encoder blocks 914 for applying a self-attention filter, preferably multi-head self-attention 906. The context vector after being transformed by the layer normalization 912 and the feed-forward layer 910 may be referred to as hidden state.The encoder output 976 comprises of a linear layer 916 and a softmax layer 918. The linear layer 916 may transform the context vector into a logits vector. The linear layer may be fully-connected. The logits vector obtained by passing the context tensor through the linear layer 916 may be passed through the softmax layer 918. Passing the logits vector through the softmax layer 918 may refer to applying the softmax function to the logits vector. Applying the softmax function to the logits vector may result in a probability distribution of one or more elements corresponding to the sequence of elements in the input data. From the probability distribution based on predefined selection criteria, one or more elements may be chosen. The one or more chosen elements may be referred to as the one or more elements generated by the transformer encoder. The one or more generated elements may be provided to the encoder input for generating further one or more elements corresponding to the sequence of the input data and the one or more elements generated by the transformer encoder as described within the context of FIG. 11 .Hence, processing the numerical representation of the two or more elements and the relation between the two or more elements by the data-driven model may comprise at least one of generating two or more numerical representations of the two or more elements and the relation between the two or more elements from the numerical representation of the two or more elements and the relation between the two or more elements, modifying the two or more numerical representation of the two or more elements and the relation between the two or more elements by applying a filter to the two or more numerical representations of the two or more elements and the relation between the two or more elements , wherein the filter may be configured to241318WO01 - Secondary Filing Text BASF Coatings GmbH modify the contribution of the two or more elements to the numerical representations of the two or more elements and the relation between the two or more elements, concatenating the two or more numerical representations of the two or more elements and the relation between the two or more elements mapping the concatenated numerical representation of the two or more elements and the relation between the two or more elements to a numerical representation of the output data or a combination thereof.In particular the encoder block may be configured to split the numerical representation of the two or more elements and the relation between the two or more elements into two or more numerical representations of the two or more elements and the relation between the two or more elements, modify the two or more numerical representation of the two or more elements and the relation between the two or more elements by applying a filter to the two or more numerical representations of the two or more elements and the relation between the two or more elements , wherein the filter may be configured to modify the contribution of the two or more elements to the numerical representations of the two or more elements and the relation between the two or more elements, concatenate the two or more numerical representations of the two or more elements and the relation between the two or more elements or a combination thereof. Applying self-attention may comprise modifying the two or more numerical representation of the two or more elements and the relation between the two or more elements by applying a filter to the two or more numerical representations of the two or more elements and the relation between the two or more elements , wherein the filter may be configured to modify the contribution of the two or more elements to the numerical representations of the two or more elements and the relation between the two or more elements. The filter may be obtained during training of the data-driven model. The filter may be obtained based on, in particular related to the input data. Multi-head self-attention241318WO01 - Secondary Filing TextBASF Coatings GmbH may comprise generating two or more numerical representations of the two or more elements and the relation between the two or more elements from the numerical representation of the two or more elements and the relation between the two or more elements, modifying the two or more numerical representation of the two or more elements and the relation between the two or more elements by applying a filter to the two or more numerical representations of the two or more elements and the relation between the two or more elements , wherein the filter may be configured to modify the contribution of the two or more elements to the numerical representations of the two or more elements and the relation between the two or more elements and / or concatenating the two or more numerical representations of the two or more elements and the relation between the two or more elements.The encoder output may be configured to map the concatenated numerical representation of the two or more elements and the relation between the two or more elements to a numerical representation of the output data. The numerical representation of the output data may be mapped to output data, e.g. by providing a vocabulary indicative of a relation between numerical representations and data of a type according to the input data. Additionally or alternatively, a decoding model may be used to map the concatenated numerical representation of the two or more elements and the relation between the two or more elements to a numerical representation of the output data. The decoding model may be trained to relate a numerical representation of data of a type according to the input data.FIG. 9B illustrates an embodiment of a transformer decoder architecture.The transformer decoder comprises a decoder input 984, one or more decoder blocks 980, 932 and a decoder output 992. The transformer decoder architecture may be derived from the transformer encoder-decoder architecture as known in the art and shown in FIG. 9C. The transformer decoder may be referred to as X-former. The transformer decoder architecture may correspond to the decoder architecture associated with the transformer encoder-decoder architecture independent of receiving241318WO01 - Secondary Filing Text BASF Coatings GmbH one or more hidden states from the encoder of the transformer encoder-decoder. A plurality of transformer decoder architectures are available in the art such as the generative pretrained transformers (GPT).The decoder input 984 may apply input embedding 920 and positional encoding 922 analogous to analogous to the input embedding 902 and the positional encoding 904 as described within the context of FIG. 9A.The decoder block 980 may comprise the layer normalizations 926, the masked multihead self-attention 924, the feed-forward layers 928 and / or the layer normalization 930. The embedded input data resulting from passing the input data through the decoder input 984 may be provided to the layer normalization 926 via a residual connection. Further, masked multi-head self-attention 924 may be applied to the embedded input data. Masked multi-head self-attention 924 corresponds to the multihead self-attention 906 as described within the context of FIG. 9A with additionally masking a part of the embedded input data associated with elements later in the sequence than the element to be generated. Additionally or alternatively, the part of the input data associated with elements later in the sequence than the element to be generated may not be received and / or transformed into the embedded input data.Thus, the transformer decoder may be suitable for generating a subsequent element to a sequence, whereas the transformer encoder may be suitable for generating a missing element in within one sequence and / or between two or more sequences. Therefore, the transformer encoder may be configured for classification tasks. The transformer decoder may be configured for text generation. Masked multi-head selfattention may comprise applying a filter obtained based on elements of the sequence of the input data appearing previously to the to be generated part of the sequence. Similar to the transformer encoder as described within the context of FIG. 9A, a context tensor may be generated by applying the masked multi-head self-attention 924 and the layer normalization 926. The context tensor may be provided to the layer normalization 930 via a residual connection. Further, the feed-forward layer 928 and the layer normalization 930 may be analogous to the feed-forward layer 910 and the layer241318WO01 - Secondary Filing Text BASF Coatings GmbH normalization 912 as described within the context of FIG. 9A. The context tensor may be provided to one or more further decoder blocks 932.The decoder output 992 may comprise of a linear layer 934 and a softmax layer 936. The linear layer 934 and the softmax layer 936 may be analogous to the linear layer 916 and the softmax layer 918 as described within the context of FIG. 9A.FIG. 9C illustrates an embodiment of a transformer encoder-decoder architecture. The transformer encoder-decoder may comprise the encoder input 988, the one or more encoder blocks 986, 964, the decoder input 994, the decoder block 990 and the decoder output 992. The encoder input 988 may correspond to the encoder input 978 of FIG. 9A. The one or more encoder block 986, 964 may correspond to the one or more encoder blocks 974, 914 of FIG. 9A. The decoder input 994 may correspond to the decoder input 984 of FIG. 9B.The decoder block 990 may comprise a masked multi-head self-attention 970, a layer normalization 972, a feed-forward layer 938 and a layer normalization 940 analogous to the masked multi-head self-attention 924, the layer normalization 926, the feedforward layer 928 and the layer normalization 930 as described within the context of FIG. 9B. The decoder block 990 may further comprise a multi-head self-attention 950 and a layer normalization 948. Analogous to the description of FIG. 9B, the context tensor may be obtained from the masked multi-head self-attention 970 and the layer normalization 972. Multi-head self-attention 950 analogous to the multi-head selfattention 906 of FIG. 9A may be applied to the context vector obtained from the layer normalization 972 and the hidden states of the one or more encoder blocks 986, 964. Layer normalization 948 may be applied to the context vector obtained from the multihead self-attention 950 and the context vector obtained from the layer normalization 972 provided via a residual connection. The context vector resulting from the layer normalization 948 may be processed via the feed-forward layer 938 and the layer normalization 940 analogous to the description of FIG. 9B. The context vector resulting from the layer normalization 940 may be provided to further decoder blocks 942 analogous to the decoder block 990. The context vector obtained from the one or more241318WO01 - Secondary Filing Text BASF Coatings GmbH decoder blocks 990, 942 may be provided to the decoder output 992. The decoder output 992 may correspond to the decoder output 982 of FIG. 9B.With the above-described architecture, the transformer encoder-decoder may receive and process input data at the encoder input 988 and the one or more encoder blocks 986, 964 and the decoder block 990 and the decoder output 992. Based on the input data, the transformer encoder-decoder may generate output data part by part or sequentially. The sequentially generated output data may be provided to and / or may be processed by the decoder input 994, the one or more decoder blocks 990, 942 and the decoder output 992. Preferably, a sequence may be provided to the encoder input 988 and after having generated at least a part of the output data, the decoder input 994 may be provided with at least the part of the elements of the output data already generated. By doing so, the next elements of the output data may be generated with a higher accuracy by taking the input data and the generated output data into account since more data is received by the transformer encoder-decoder may be received over time.Because of the transformer encoder-decoder architecture, the transformer encoderdecoder may be configured for transforming a sequence into another representation of the sequence. An example for transforming one sequence into another representation may be translation of one sentence into another language. A plurality of transformer encoder-decoders are available in the art such as BART, T5 or the like.In an embodiment, the layer normalization 908, 912 may be applied prior to the masked multi-head self-attention 924, multi-head self-attention 906 and / or the feedforward layer 910 in the transformer decoder, the transformer encoder and / or the transformer encoder-decoder. By doing so, the computational resources for applying the multi-head self-attention 906 and / or the feed-forward layer 910 to the embedded input data and / or the context tensor may be decreased as the entries of the respective tensors may be lower after normalization.241318WO01 - Secondary Filing Text BASF Coatings GmbHIn an embodiment, the decoder output 992 may comprise of a classification neural network, further feedforward layers, convolutional layers, fully connected layers or the like. For example, the transformer encoder-decoder may be configured for choosing between a plurality of options. For this purpose, the transformer encoder-decoder may be provided with three different input data sets and may classify the context vectors obtained from the one or more decoder blocks 990 via one or more linear layers. Followingly, the architecture may be extended depending on the use case to be solved.FIG. 10 illustrates an embodiment of a Mamba architecture. The mamba architecture may be used as data-driven model. A Mamba architecture may enhance inference speed in relation to a transformer based model.The Mamba architecture with its layered structure may be similar to the transformer decoder architecture discussed in relation to FIG. 10. However, instead of decoder blocks mamba blocks 1032, 1004 are stacked. Mamba block 1032 may be based on a selective space state sequence model (S6).An input token may be linearly projected via linear layer 1012, 1020 into an expanded latent space (which may allow to capture more information during processing in the selective state space layer 1010), followed by a convolution via a convolutional layer 1014 and a non-linear function (e.g. a sigmoid linear unit (SiLu) or swish activation function). The convolution before the selective state space layer 1010 may prevent independent token calculations. The selective state space layer 1010 performs a selective state space operation. Further, a learnable skip connection may be provided via linear layer 1020, this may use a linear transformation to map the input to the output, similar to a residual connection in a transformer model this may help to mitigate vanishing gradient effects.A selective state space layer 1010 may be a linear recurrent network that selectively process data based on the input token, which may allow to focus on relevant data and discard irrelevant data. For instance in each step a separate weight vector may be determined based on the respective input token. The determined weight vector may then be used in a selective scan.A selective state space layer 1010 may be used in a convolutional mode e.g. for parallelizable training and a recurrent mode for near-constant time generation of output241318WO01 - Secondary Filing TextBASF Coatings GmbH data. A state space operation may be based on solving the state and output equations, wherein a state equation may describe how a state changes based on how the input influences the state and an output equation may describe how the state is translated to the output. Further how the input influences the output may be represented by a learnable linear transformation, e.g. a matrix D, used in a learnable skip connection. The state equation for a hidden state may be (in discretized form):The output may be expressed by (in discretized form):Vk = ChkThis discretized space state model may be unfolded into a recurrent form similar to a recurrent network, exemplifying that a selective state space model may be or comprise a linear recurrent model. However, here matrices A, B, and C may also be used as a kernel of a convolution of the state space model. Kernel K for this may e.g. be:K = (CA2B, CAB, CB) which may allow to determine an output:So, in this representation of the state space model training may be performed in a parallel manner like in convolutional neural networks.Matrix A may be a matrix that represents recent tokens well and decays older tokens and may be initialized using HiPPO:241318WO01 - Secondary Filing Text BASF Coatings GmbH where every entry below the diagonal is set to 0. This may allow to create a long-term memory for the selective state space model.For a Mamba block 1032, the matrices B and C as well as the step size A used for discretization of the matrices may be dependent on the input token and may be trained during training, so that for each input token different matrices B and C are determined, which may enhance the content-awareness and may act similar to a multi-head selfattention in a transformer model. However, unlike in space state models with fixed matrices A, B, and C, here the convolutional representation may not be easily determined. Hence, to operate the selective state space layer 1010 in convolutional mode a selective scan may be applied utilizing associative properties of the hidden states calculation, allowing parallel determination of the sequence in parts and iteratively combining them, so that parallel training may be used. Further reading and writing operations may be decreased by using kernel fusion of the described step size, the selective scan, and the multiplication with C.Linear layer 1002 may project the generated output back into the same dimension as the input.Mamba blocks may be used together with transformer decoder blocks or mixture of expert blocks (e.g. decoder blocks wherein the feed-forward layer is exchanged for a gating network and a number of parallel feed-forward layers, wherein the gating network switches between the feed-forward layers depending on the input), which may allow leveraging advantages of the different architectures.An example of the architecture of a mamba block may be found in “Mamba: Linear- Time Sequence Modeling with Selective State Spaces” by Albert Gu and Tri Dao arXiv:2312.00752v2 [cs.LG] 31 May 2024, , which is incorporated herein by reference.FIG. 11 illustrates an embodiment of training and / or deploying the transformer encoder, the transformer decoder and / or the transformer encoder-decoder.The encoder / decoder / encoder-decoder architecture 1102 may correspond to the transformer decoder, the transformer encoder and / or the transformer encoder-decoder as described within the context of FIG. 9A- FIG. 9C.241318WO01 - Secondary Filing Text BASF Coatings GmbHThe output data generated by the encoder / decoder / encoder-decoder architecture 1102 may comprise of one or more elements, in particular a sequence of elements. The previously generated elements of the output data may be provided as input for generating the next element in the sequence of the output data.In the example of FIG. 11 , the input data may comprise of N elements, in particular input tokens. An input token may be a token dedicated to be inputted into a data-driven model such as the transformer decoder, the transformer encoder or the transformer encoder-decoder. The output data to be generated may comprise of M elements. The encoder / decoder / encoder-decoder architecture 1102 may generate one element of the output data based on receiving the input data and optionally previously generated elements of the output data at a timestep. Hence, for generating M elements M time steps are required. A time step comprises of providing input 1110, 1112, 1114 to the encoder / decoder / encoder-decoder architecture 1102 and receiving output data 1104, 1108, 1106 from the encoder / decoder / encoder-decoder architecture 1102. In a first timestep, the input 1110 may comprise of N input tokens. The N input tokens may be associated e.g. with N words, stems or endings. Preferably, the N input tokens may specify a question. One or more input tokens may specify the beginning of the sequence of tokens and / or the end of the sequence of tokens. The input 1110 may be processed by the encoder / decoder / encoder-decoder architecture 1102. Based on the input 1110 at least a part of the output data 1104 may be generated. The at least a part of the output data may comprise a first output token. In the next timestep, the generated first output token may be provided together with the input 1112. Specifically, where the input 1112 may be received by a transformer encoder-decoder the input tokens may be received at the encoder input 988 and the first output token may be received at the decoder input 994. Where the input 1112 may be received by the transformer encoder, the input 1112 may be received by the encoder input 978 and analogously regarding the transformer decoder and the decoder input 984. Based on the input 1112, the output data 1108 comprising the first output token and a second output token may be generated. Generating the output data 1108 based on the input 1112 may refer to generating the second token based on the first token and the N input tokens, wherein the first token may have been generated based on the N input tokens.241318WO01 - Secondary Filing Text BASF Coatings GmbHThis process may be repeated until the last token in the sequence of the output data 1106 may be generated. Preferably, the last token may be an end token. The end token may terminate the generation of a further output token.Similarly, to the data processing during deployment of the encoder / decoder / encoder- decoder architecture 1102, the encoder / decoder / encoder-decoder architecture 1102 may be trained. The training data set may comprise a plurality of sequences comprising a plurality of elements. The sequences may be associated with the input data and / or the output data. Additionally or alternatively, the sequences may be independent of the input data and / or the output data. For example, where the input data and the output data may refer to chemical compositions represented via text, the training data set may comprise sequential text data independent of chemical compositions. In this example, the training data set may comprise sequences of words originating from a conversation. In an embodiment, the training data set may comprise at least partially input data sets and / or output data sets.The training may be initialized by initializing the encoder / decoder / encoder-decoder architecture 1102. In an embodiment, the parameters associated with the encoder / decoder / encoder-decoder architecture 1102 may be initialized randomly. Additionally or alternatively, the input embedding of the encoder / decoder / encoder- decoder architecture 1102 may be obtained by training a CBOW model or a skip gram model as described within the context of FIG. 8. The trained embedding layer may be used during training. The parameters associated with the embedding layer may be kept constant and / or may be updated after a predefined number of training epochs. By doing so, the number of parameters to be updated is lower enabling a faster and less computational resources-consuming training. Further, the accuracy associated with the embedding layer may be constant and / or may be increased by avoiding error compensation in relation to the just initialized encoder / decoder / encoder-decoder architecture 1102.During the training of the encoder / decoder / encoder-decoder architecture 1102, at least a part of the sequences of the training data set may be provided to the241318WO01 - Secondary Filing Text BASF Coatings GmbH encoder / decoder / encoder-decoder architecture 1102 one by another and one or more elements may be generated based on the sequences of the training data set one by another. The elements generated based on the sequences may follow the elements of the parts of sequences the encoder / decoder / encoder-decoder architecture 1102 may have been provided with. The generated one or more elements may be compared to the one or more elements following the at least a part of the sequences provided to the encoder / decoder / encoder-decoder architecture 1102 as specified by the training data set. Hence, during the training the encoder / decoder / encoder-decoder architecture 1102 may generate a guess on the next element and the guess on the next element in a sequence may be compared to the ground truth specifying the actual next element according to the training data set. Based on the guess on the next element and the ground truth a loss may be determined. The loss may define the similarity between the guess on the next element and the ground truth. The loss may be determined by forming a vector dot product between the token associated with the one or more elements and the token associated with the ground truth. A loss unequal to zero may result in updating the parameters associated with encoder / decoder / encoder-decoder architecture 1102. Preferably the parameters associated with the encoder / decoder / encoder-decoder architecture 1102 may be independent of the embedding layer. For example, the parameters associated with the encoder / decoder / encoder-decoder architecture 1102 may be weights of the neurons of the encoder / decoder / encoder-decoder architecture 1102.Based on the determined loss, backpropagation may be applied to determine the gradients associated with the parameters of the parameters associated with encoder / decoder / encoder-decoder architecture 1102 to lower the loss. According to the determined gradients, the parameters associated with the encoder / decoder / encoder-decoder architecture 1102, preferably the weights of the neurons associated with the encoder / decoder / encoder-decoder architecture 1102, may be updated by using a gradient descent algorithm.The training data set may be unlabeled. The sequences of elements within the training data set may inherently comprise the ground truth for determining the loss with respect241318WO01 - Secondary Filing Text BASF Coatings GmbH to the one or more elements generated during the training of the encoder / decoder / encoder-decoder architecture 1102. Hence, the encoder / decoder / encoder-decoder architecture 1102 may be trained self-supervised. This is advantageous since time and resources for creating a labeled training data set may be saved. Furthermore, this enables the usage of large training data sets associated with a size of several tera bytes. Consequently, the data-driven model may be accurate in generating elements of a sequence. In addition, the large training data set enables few shot predictions or even zero shot predictions. Hence, the data-driven model(s) trained as described above are versatile contributing to saving resources needed for training and / or hosting a plurality of purpose-driven models such as convolutional neural networks. The training described above may be referred to as pretraining. Pretraining may refer to training a data-driven model based on data with a plurality of contextsThe data-driven model may be configured for performing few shot or even zero shot predictions with respect to a plurality of use cases after pretraining. The performance of the data-driven model may be increased further by additional training referred to as finetuning. Finetuning may refer to training a pretrained data-driven model for a concrete task, e.g. by providing task instructions to the pretrained data-driven model and adapting the parameters of the pretrained data-driven model to decrease the distance of the generated output data by the pretrained data-driven model in response to receiving the task instructions from predefined output data corresponding to the provided task instructions.Models based on the architecture according to FIG. 9A to FIG. 9C and / or pretrained data-driven model(s) and / or finetuned data-driven model(s) may be referred to as large language models. Famous examples include GPT models, BERT models or the like. Such models have been tested. Testing data-driven model(s), in particular pretrained and / or finetuned data-driven model(s), may include comparing output data generated by the one or more data-driven model(s) in response to receiving the input data with target data, e.g. obtained from domain experts. These domain experts may be a current bar for performing tasks the data-driven model(s) may be parametrized and / or241318WO01 - Secondary Filing Text BASF Coatings GmbH trained for. The target data may specify output data desired to be generated in response to receiving the input data. In an example, Bran et al evaluated use of GPT-4 for chemical tasks such as organic synthesis tasks, molecular design tasks and / or chemical logic and knowledge tasks in the publication “Augmenting large language models with chemistry tools” (doi 10.48550 / arXiv.2304.05376). Bran et al showed that a model such as GPT-4 can solve chemical tasks.FIG. 12 illustrates an embodiment of input embedding. Where the sequence of elements associated with the input data, preferably comprised in the input data, may be of one type, the input embedding 902, 920, 952, 966 as described within the context of FIG. 9A - 2C may be used. For example, a type of input data may be text where the elements may be associated with at least a part of a word, a punctuation character, a start token specifying the beginning of one or more sequences associated with the input data and / or the end token. In another example, the input data may be at least partially numerical. Hence, the input data may comprise a plurality of numbers. Numerical input data may be for example tabular data. Tabular data may specify one or more rows and / or one or more columns. Hence, the tabular data may comprise one or more cells, wherein the cells may be associated with one or more numerical values.Numerical input data may require a different embedding than text input data. Input embeddings for numerical input data may comprise a token embedding, a positional embedding, a column embedding, a row embedding or a combination thereof.Applying a token embedding to one or more elements, in particular tokens associated with the input data may result in a machine-processable representation associated with the one or more elements, in particular tokens. Applying the token embedding to one or more elements may refer to passing the one or more elements through the embedding layer, e.g. as described within the context of FIG. 8. Hence, token embeddings may specify the one or more elements, in particular tokens in a machine- processable representation. For example, the token embedding may transform a numerical value into a vector. This is advantageous since this representation can be enriched by further information such as the position of the token within the sequence241318WO01 - Secondary Filing Text BASF Coatings GmbH and / or within a table associated with the sequence of tokens. The positional embedding may be analogous to the positional embedding as described within the context of FIG. 8, FIG. 9A-2C. Where the input data may be tabular data, column embedding may be applied. Applying a column embedding to one or more elements, in particular tokens associated with the input data may result in a machine-processable representation specifying the location of the one or more elements within a table 1202, preferably within the columns of the table 1202. Applying the column embedding may refer to adding a column factor to the input data embedded via token embeddings, in particular the embedded input data. The column factor may be the same for elements associated with the same column and / or may differ between two or more elements associated with different columns. Analogous, row embeddings may be applied where the input data may be tabular data. Applying a row embedding to one or more elements, in particular tokens associated with the input data may result in a machine- processable representation specifying the location of the one or more elements within a table 1202, preferably within the rows of the table 1202. Applying the row embedding may refer to adding a column factor to the input data embedded via token embeddings, in particular the embedded input data. The row factor may be the same for elements associated with the same row and / or may differ between two or more elements associated with different rows.In an embodiment, input data may be at least partially numerical and at least partially text. Hence, the input data may comprise two or more types of data. A type of data may refer to a modality. Followingly, different embeddings may be applied to the input data. To parts of the input data comprising text the input embedding referred to in FIG. 8, FIG. 9A-2C may be applied. To parts of the input data being numerical token embeddings, positional embeddings, column embeddings and row embeddings may be applied. Further, segment embeddings may be applied to the input data independent of the type of input data. The segment embedding may specify the type of input data one or more elements may be associated to. For example, if the input data comprises of text and numbers, the input data may comprise of two types of input data. Applying the segment embedding to the input data may refer to adding a segment factor to the input data, preferably the embedded input data and / or the input data after having applied the241318WO01 - Secondary Filing TextBASF Coatings GmbH token embedding. The segment factor may specify the type of data associated with the one or more elements. The segment factor may be the same for one or more elements associated with the same type of input data and / or may differ between two or more elements associated with different types of input data.Applying the token embedding, the positional embedding, the segment embedding, the column embedding, the row embedding or a combination thereof may result in embedded input data and / or may be the output of any one of the encoder input 978, 984, 988 or decoder input 984, 994. The data obtained by applying the token embedding, the positional embedding, the segment embedding, the column embedding, the row embedding or a combination thereof may be processed by the encoder block 974, 986, decoder block 980, 990, encoder output 976, decoder output 992, 982.FIG. 13 illustrates an embodiment of input embedding.Input data to the data-driven model, in particular to the encoder input and / or the decoder input as described in the context of FIG. 9A-C, may comprise image data. The data-driven model may be parametrized to receive image data. For processing image data as input data, the data-driven model may comprise one or more encoder blocks and / or one or more decoder blocks and / or one or more encoder outputs and / or one or more decoder outputs as described within the context of FIG. 9A-C. FIG. 13 may show an embodiment of an encoder input and / or a decoder input. When processing image data, the encoder input and / or the decoder input of the data-driven model may be as described within the context of FIG. 13. The encoder input and / or decoder input may comprise one or more linear projection layers 1314 for a linear projection of one or more images, preferably one or more partial images, more preferably a sequence of two or more partial images. The one or more linear projection layers 1314 may be suitable for changing the dimension of the one or more received images, preferably one or more partial images, preferably passing the one or more images, preferably partial images, through the one or more linear projection layers 1314 may result in241318WO01 - Secondary Filing Text BASF Coatings GmbH applying image embedding, preferably partial image embedding to the one or more images and / or partial images.Furthermore, when a sequence of two or more images and / or partial images may be received, positional embedding may be applied to the sequence, preferably by passing the sequence of one or more images and / or partial images through the one or more linear projection layers 1314. Applying positional embedding may refer to adding a positional factor. The positional factor may be different depending on the position of the image and / or the partial image within the sequence. In particular, the positional factor added to a first element of the sequence may be different to the positional factor added to a second element of the sequence. The first element of the sequence may be a first image and / or first partial image. The second element of the sequence may be a second image and / or a second partial image.The representation of the one or more images, preferably one or more partial images, may be obtained based on the following equation:where xciassis the image class embedding 1328 ,x^pis the n-th image, in particular partial image in the sequence, z0is the representation of the one or more images, preferably one or more partial images, (H,W) are the resolution of the image, in particular the image the partial images are generated on, C is the number of channels associated with the one or more image, in particular the one or more partial images and D is the dimension of the representation of the one or more images, preferably one or more partial images. Applying the partial image embedding may refer to forming the product ofXN with E above-described equation. Applying the positional embedding may refer to adding the factorEP°« according to the above-described equation. By doing so, text-based data, numerical data, tabular data, image data or the like may be processed by one data-driven model. If input data associated with two or more data types may be provided to the data-driven model, the input data may be separated according to their data type into two or more parts. Further, the two or more parts of241318WO01 - Secondary Filing Text BASF Coatings GmbH the input data may be provided to two or more different input embeddings. The input embeddings may tokenize the parts received. Tokenizing the parts may comprise separating the received parts into two or more elements per part. A numerical representation of the parts of the input data may be obtained by determining a numerical representation of the two or more elements. The said numerical representations of the two or more parts, in particular the two or more elements per part, may be concatenated. Hence, at least one numerical representation of the input data may be obtained from the numerical representations of the two or more parts, in particular the two or more elements per part. Said numerical representation of the input data may be processed by the data-driven model, e.g. by applying one or more matrix operation(s).FIG. 14 illustrates an embodiment of providing input data for generating and / or providing output data.The publication Prior Art Disclosure; Issue 684; paragraphs

[1000] to

[8005] ; ISSN: 2198-4786; published: February 12, 2024 will be regarded as Reference RF1 , which is incorporated herein by reference in its entirety. Preferably, the product is a product as described in Reference RF1 ; paragraphs

[1000] to

[8005] , Preferably, the method / process described herein is further a method / process for the production of a product.The converting step to obtain the product preferably comprises one or more step(s) as described below and can be performed by conventional methods well known to a person skilled in the art. The converting step preferably comprises one or more step(s) selected from:• recycling, preferably depolymerizing, gasifying, pyrolyzing, and / or steam cracking; and / or• purifying, preferably crystallizing, (solvent) extracting, distilling, evaporating, hydrotreating, absorbing, adsorbing and / or subjecting to ion exchanger; and / or• assembling, preferably foaming, synthesizing, chemical conversion, chemically transforming, polymerizing and / or compounding; and / or241318WO01 - Secondary Filing Text BASF Coatings GmbH• forming, preferably foaming, extruding and / or molding; and / or• finishing, preferably coating and / or smoothing.In addition, the one or more step(s) are described in detail in Reference RF1 ; paragraphs

[1000] to

[8005] ,The present disclosure has been described in conjunction with preferred embodiments and examples as well. However, other variations can be understood and effected by those persons skilled in the art and practicing the claimed subject-matter, from the studies of the drawings, this disclosure and the claims. Notably, in particular, the any steps presented can be performed in any order, i.e. the present disclosure is not limited to a specific order of these steps. Moreover, it is also not required that the different steps are performed at a certain place or at one node of a distributed system, i.e. each of the steps may be performed at different nodes using different equipment / data processing.As used herein ..determining" also includes ..initiating or causing to determine", “generating" also includes ..initiating and / or causing to generate" and “providing” also includes “initiating or causing to determine, generate, select, send and / or receive”. “Initiating or causing to perform an action” includes any processing signal that triggers a computing node or device to perform the respective action.In the claims as well as in the description the word “comprising” or “including” or similar wording does not exclude other elements or steps and shall not be construed limiting to the elements or steps lined out. The indefinite article “a” or “an” does not exclude a plurality. A single element or other unit may fulfill the functions of several entities or items recited in the claims. The mere fact that certain measures are recited in the mutual different dependent claims does not indicate that a combination of these measures cannot be used in an advantageous implementation or further elements may be included.Providing in the scope of this disclosure may include any interface configured to provide data. This may include an application programming interface, a human-241318WO01 - Secondary Filing TextBASF Coatings GmbH machine interface such as a display and / or a software module interface. Providing may include communication of data or submission of data to the interface, in particular display to a user or use of the data by the receiving entity.Various units, circuits, entities, nodes or other computing components may be described as “configured to” perform a task or tasks. Configured to shall recite structure meaning “having circuitry that” performs the task or tasks on operation. The units, circuits, entities, nodes or other computing components can be configured to perform the task even when the unit / circuit / component is not operating. The units, circuits, entities, nodes or other computing components that form the structure corresponding to “configured to” may include hardware circuits and / or memory storing program instructions executable to implement the operation. The units, circuits, entities, nodes or other computing components may be described as performing a task or tasks, for convenience in the description. Such descriptions shall be interpreted as including the phrase “configured to.” Any recitation of “configured to” is expressly intended not to invoke 35 U.S.C. § 112(f) interpretation.In general, the methods, apparatuses, systems, computer elements, nodes or other computing components described herein may include memory, software components and hardware components. The memory can include volatile memory such as static or dynamic random-access memory and / or nonvolatile memory such as optical or magnetic disk storage, flash memory, programmable read-only memories, etc. The hardware components may include any combination of combinatorial logic circuitry, clocked storage devices such as flops, registers, latches, etc., finite state machines, memory such as static random-access memory or embedded dynamic random-access memory, custom designed circuitry, programmable logic arrays, etc.Any disclosure and embodiments described herein relate to the methods, the systems, devices, the computer program element lined out above and vice versa. Advantageously, the benefits provided by any of the embodiments and examples equally apply to all other embodiments and examples and vice versa.241318WO01 - Secondary Filing TextBASF Coatings GmbHAll terms and definitions used herein are understood broadly and have their general meaning if not indicated otherwise.

Claims

241318WO01 - Secondary Filing Text BASF Coatings GmbHCLAIMSWhat is claimed is:

1. A method for determining a processing step associated with a target product, the method comprising: obtaining a request for processing of a target product, wherein the request is associated with an indication of the target product, providing a task instruction associated with the request to a data-driven model for selecting one or more operating engine(s) configured to determine the processing step associated with the target product from a plurality of operating engines, wherein the data-driven model is configured to follow task instructions, determining the processing step associated with the target product by providing the indication of the target product to the one or more selected operating engine(s) for, providing the determined processing step for monitoring and / or controlling processing of a target product.

2. The method of claim 1 , wherein determining the processing step by the one or more selected operating engine(s) comprises identifying one or more component(s) associated with the target product based on the indication of the target product, obtaining a processing score per identified component of the target product, wherein the processing score is indicative of a processing of the identified component(s) into one or more subcomponent(s), obtaining a processing step per identified component according to the processing score.

3. The method of claim 1 or 2, wherein determining the processing step by the one or more selected operating engine(s) comprises68241318WO01 - Secondary Filing TextBASF Coatings GmbH identifying one or more component(s) associated with the target product based on the indication of the target product, obtaining a processing score per identified component of the target product, wherein the processing score is indicative of a processing of the identified component(s) into one or more subcomponent(s), obtaining a processing step per identified component according to the processing score, and wherein the processing score comprises two or more subscores, wherein the two or more subscores are associated with two or more types of processing the one or more component(s).

4. The method of any one of claims 1 to 3, wherein determining the processing step by the one or more selected operating engine(s) comprises identifying one or more component(s) associated with the target product based on the indication of the target product, obtaining a processing score per identified component of the target product, wherein the processing score is indicative of a processing of the identified component(s) into one or more subcomponent(s), obtaining a processing step per identified component according to the processing score, and wherein the processing score is obtained based on an image of the target product.

5. The method of any one of claims 1 to 4, wherein determining the processing step by the one or more selected operating engine(s) comprises identifying one or more component(s) associated with the target product based on the indication of the target product, obtaining a processing score per identified component of the target product, wherein the processing score is indicative of a processing of the identified component(s) into one or more subcomponent(s), obtaining a processing step per identified component according to the processing score, and wherein the processing score is obtained via a user interface.241318WO01 - Secondary Filing Text BASF Coatings GmbH6. The method of any one of claims 1 to 5, wherein determining the processing step by the one or more selected operating engine(s) comprises identifying one or more component(s) associated with the target product based on the indication of the target product, obtaining a processing score per identified component of the target product, wherein the processing score is indicative of a processing of the identified component(s) into one or more subcomponent(s), obtaining a processing step per identified component according to the processing score, and wherein the processing score is obtained according to a property of the target product.

7. The method of any one of claims 1 to 6, wherein determining the processing step by the one or more selected operating engine(s) comprises identifying one or more component(s) associated with the target product based on the indication of the target product, obtaining a processing score per identified component of the target product, wherein the processing score is indicative of a processing of the identified component(s) into one or more subcomponent(s), identifying one or more subcomponent(s) of associated with the one or more component(s) if the processing score associated with the one or more component(s) is within a dismantling range, obtaining a processing score per identified subcomponent of the target product, obtaining a processing step per identified component and subcomponent according to the processing score.

8. The method of any one of claims 1 to 7, wherein determining the processing step by the one or more selected operating engine(s) comprises identifying one or more component(s) associated with the target product based on the indication of the target product,70241318WO01 - Secondary Filing TextBASF Coatings GmbH obtaining a processing score per identified component of the target product, wherein the processing score is indicative of a processing of the identified component(s) into one or more subcomponent(s), obtaining a processing step per identified component according to the processing score, wherein identifying the one or more component(s) comprises obtaining an indication of the one or more component(s) and / or from a digital twin associated with the target product.

9. The method of any one of claims 1 to 8, wherein determining the processing step by the one or more selected operating engine(s) comprises identifying one or more component(s) associated with the target product based on the indication of the target product, obtaining a processing score per identified component of the target product, wherein the processing score is indicative of a processing of the identified component(s) into one or more subcomponent(s), obtaining a processing step per identified component according to the processing score, wherein determining the processing step per identified component comprises determining if the processing score associated with at least one component may be within a dismantling range and triggering identifying of one or more subcomponent(s) of the at least one component associated with the processing score within the dismantling range.

10. The method of any one of claims 1 to 9, wherein determining the processing step by the one or more selected operating engine(s) comprises identifying one or more component(s) associated with the target product based on the indication of the target product, obtaining a processing score per identified component of the target product, wherein the processing score is indicative of a processing of the identified component(s) into one or more subcomponent(s), obtaining a processing step per identified component according to the processing score and an indication of a location associated with the target product.241318WO01 - Secondary Filing Text BASF Coatings GmbH11 . The method according to any one of claims 1 to 10, wherein determining the processing step by the one or more selected operating engine(s) comprises identifying one or more component(s) associated with the target product based on the indication of the target product, obtaining a processing score per identified component of the target product, wherein the processing score is indicative of a processing of the identified component(s) into one or more subcomponent(s), obtaining a processing step per identified component according to the processing score, determining a processing route by combining the one or more processing step(s), and wherein providing the processing step comprises providing the determined processing route.

12. The method according to any one of claims 1 to 11 , wherein providing the indication of the target product to the one or more selected operating engine(s) triggers the one or more selected operating engine(s) to identifying one or more component(s) associated with the target product based on the indication of the target product, obtaining a processing score per identified component of the target product, wherein the processing score is indicative of a processing of the identified component(s) into one or more subcomponent(s), obtaining a processing step per identified component according to the processing score, determining a processing route score per processing route by combining the at least one processing score associated with the at least one processing route by the one or more operating engine(s), selecting a processing route based on the processing route score by the one or more operating engine(s), and wherein providing the processing step comprises providing the selected processing route.241318WO01 - Secondary Filing TextBASF Coatings GmbH13. The method according to any one of claims 1 to 12, wherein the request comprises unstructured data.

14. A system and / or computing apparatus comprising: a processor configured for performing any one of the methods according to any one of claims 1 to 12.

15. Use of a data-driven model for selecting one or more operating engine(s) configured to determine a processing step associated with the target product from a plurality of operating engines.73

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