Procedure, goods allocation device and system for packaging goods

Abstract

The disclosure relates to a computer-implemented method for packaging one or more goods in a, in particular predefined, package and / or in a corresponding goods receiving area, in particular a goods receiving opening, a packaging station, comprising: generating, based on image data representing a plurality of goods with at least two, in particular different or identical, characteristics, an individualized representation of the plurality of goods; determining, in particular identifying, based on the individualized representation, an individual good from the plurality of goods, and / or of at least one characteristic assigned to the individual good; and outputting a control signal based on the at least one determined characteristic.Furthermore, the disclosure relates to a computer-implemented method for assigning and / or packaging one or more goods from a plurality of packaging stations, comprising: generating, based on image data representing a plurality of goods with at least two, in particular different or identical, characteristics, an individualized representation of the plurality of goods; determining, in particular identifying, based on the individualized representation, an individual good from the plurality of goods and / or of at least one characteristic assigned to the individual good; and outputting a control signal based on the at least one determined characteristic. The disclosure also relates to a system for packaging goods and a corresponding goods assignment device, which in particular implements the methods described herein.In addition, a computer program product and a computer-readable storage medium are provided.

Description

[0001] The present invention relates to the packaging of goods, and in particular to a method and a system for packaging goods, as well as a corresponding goods allocation device that particularly implements the inventive method. Furthermore, a computer-implemented method, a computer program product, and a computer-readable storage medium are provided.

[0002] Conventional processes and systems for packaging goods usually require a large number of workers, for example to sort and / or classify a large number of incoming goods, for example to assign them an identifier so that the goods can then be assigned to a specific customer for packaging. However, such processes and systems are not only extremely time-consuming, for example when large quantities of goods are involved, but also cost-intensive. Employing a large number of workers for manual packing can significantly increase operating expenses and have a negative impact on profitability, particularly when large quantities of goods need to be packed. Furthermore, the throughput rate and / or performance of such processes and systems is limited, in particular by the performance of the workforce.

[0003] Another problem with manual processes is their limited flexibility. Employees require specialized training to identify, sort, and package or route different types of goods. Changes to the product range or the introduction of new items may require retraining, requiring additional time and / or resources.

[0004] Furthermore, manual packaging processes reach their limits as demand increases. Scalability is challenging, as it can be challenging to find enough workers to keep up with the increased production rate, especially during peak periods.

[0005] There are also approaches to methods and systems for packaging goods that are at least partially automated, for example, through the use of robots and / or goods distribution systems, e.g., conveyor belts. However, these methods and systems still require employees, as the automated detection, identification, selection, picking, and / or grasping of a single item, e.g., from the conveyor belt and / or from a box or crate containing multiple goods or products, e.g., in a disorganized / unsorted manner, presents a particular challenge.

[0006] It is an object of the present invention to overcome the disadvantages of the known prior art and in particular to provide an improved method or an improved system for packaging goods, as well as a corresponding goods allocation device.

[0007] The problem is solved by the features of the independent claims. The dependent claims describe preferred embodiments. Further aspects, advantages, and features emerge from the dependent claims, the description, and the accompanying drawings.

[0008] The invention relates to a computer-implemented method for packaging one or more goods in a, in particular predefined, packaging and / or in a corresponding goods receptacle, in particular a goods input opening, comprising: - generating, on the basis of image data representing a plurality of goods with at least two, in particular different or identical, properties, an individualised representation of the plurality of goods, - Determine, in particular identify, on the basis of the individualized representation, -- an individual product from the plurality of products, and / or -- of at least one characteristic associated with the individual product; and, - Outputting a control signal based on the at least one specific property.

[0009] The invention relates to a computer-implemented method, in particular according to one or more of the aspects and / or embodiments described herein, for allocating and / or packaging one or more goods by means of a specific one of a plurality of packaging stations, comprising: - generating, on the basis of image data representing a plurality of goods with at least two, in particular different or identical, properties, an individualised representation of the plurality of goods, - Determine, in particular identify, on the basis of the individualized representation, -- an individual product from the plurality of products, and / or -- of at least one characteristic associated with the individual product; and, - Outputting a control signal based on the at least one specific property.

[0010] The invention also relates to a goods allocation device according to the invention, in particular a robot, in particular a pick-and-place robot, for packaging one or more goods, which implements the method according to the invention according to one or more of the aspects and / or embodiments described herein.

[0011] The invention relates to a system for packaging one or more goods, which comprises the goods allocation device according to the invention and at least one image recording device, in particular with at least one camera, for obtaining the image data.

[0012] The invention relates to a computer program product comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method according to the invention according to one or more of the aspects and / or embodiments described herein.

[0013] The invention relates to a computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to carry out the method according to the invention according to one or more of the aspects and / or embodiments described herein.

[0014] It should be understood that the embodiments described in the claims and in the description in connection with the system and / or the goods allocation device can preferably also be used in the method according to the invention as such, and vice versa. Reference herein to the method, the goods allocation device, or the system refers to the method according to the invention, the goods allocation device according to the invention, or the system according to the invention.

[0015] “Packaging” can be understood as inserting, introducing, inserting, inserting, pushing in, placing and / or arranging a product in and / or on a packaging of a certain kind and / or type, e.g. a container, a pouch, a bag, a carton and / or paper, a film, a can, and / or a packaging made of a glass, a metal, a plastic, e.g. plastic, and / or a sleeve etc., for example in order to protect, store, transport and / or contain a product and / or product.

[0016] "Packaging" can therefore refer, for example, to inserting, introducing, inserting, inserting, pushing, placing, and / or arranging a product into a final package, which can, for example, be a carton, box, mailing bag, envelope, and / or parcel formed and / or folded from a packaging raw material. These terms can be used interchangeably herein. The product can, for example, be inserted into at least one product receptacle formed in the final packaging, for example a product entry opening. This can then be closed, for example, to ship the package.Alternatively or additionally, “packaging” can also mean laying down, depositing, placing, aligning and / or arranging the goods i) in a specific position and / or location on the raw packaging material itself, for example before the final packaging is formed and / or folded therefrom, and / or ii) for example on a tray (for example a storage tray with or without side limitations, e.g. an open surface) on which the goods can be placed, whereby the packaging can be carried out by, for example, folding a box, e.g. a carton, around the goods. The same applies to the other packaging types and / or kinds mentioned herein.

[0017] Packaging can be used to protect the product from damage, contamination and / or other influences (e.g. temperature), for example during transport, and / or to avoid information about the product and / or the goods packed therein. The state when the package is not yet completely, or only partially, formed from the material can be referred to as unformed packaging, e.g. packaging raw material. At this stage, the packaging can often consist of a flat, unprinted material such as paper, cardboard, plastic film or a similar material. The packaging, in particular a flat one, can then be folded, shaped, formed and / or optionally printed according to various specifications to obtain the final packaging, e.g. the final packaging. The final packaging can comprise at least one goods receptacle, in particular a goods input opening for one or more goods and / or products, e.g.for bringing in goods.

[0018] “Predefined” packaging can be understood to mean final packaging and / or raw packaging material with a specific, defined and / or predetermined dimension, for example length, width, height, depth, thickness, in particular (packaging) wall thickness, a specific material and / or a specific material property, a specific shape, a specific weight, a specific surface finish and / or a specific robustness, strength and / or stability. Predefined packaging can also mean specific and / or predetermined customer-specific information, e.g. whether the package is an individual order or a collective order, e.g. several goods in one package or a partial order, e.g. a division of multi-part goods into different packages.

[0019] The final packaging can, for example after it has been formed and / or folded, comprise at least one goods receptacle, in particular a goods input opening, into which the goods can be inserted. Alternatively, the final packaging can comprise at least one goods receptacle, in particular a goods input opening, while it is being created. As already mentioned, the final packaging can be predefined, e.g. its external dimensions can be predetermined. Example external dimensions can be in the range of approximately 35cm × 25cm × 2cm to approximately 120cm × 60cm × 60cm for the final packaging. This information on the packaging size is only to be seen as an example, although it corresponds to the typical sizes of most shipments of goods. The external dimensions of the final packaging can, for example, correspond to and / or be coordinated with a goods receptacle, in particular a goods input opening, e.g.in terms of size and / or shape, for example, with a goods receptacle, in particular a goods input opening of a packaging station, e.g., a goods insertion slot of the packaging station. Other types of goods receptacles, in particular goods input openings, are also conceivable and depend on the packaging used or the packaging station.

[0020] The goods can therefore, for example, be packaged (itself) directly in a final packaging or can also be transferred directly to a packaging station and, for example, inserted, introduced, inserted, placed, arranged and / or plugged into its goods receiving device, in particular its goods input opening.

[0021] The packaging, especially predefined packaging, can - a packaging raw material, in particular a substantially flat one; and / or - comprise a final packaging, in particular a package, formed from the packaging raw material, in particular folded. The final packaging can comprise at least one goods receptacle, in particular a goods input opening.

[0022] According to the invention, image data are generated and / or obtained which represent a plurality of goods with at least two, in particular different or identical, properties.

[0023] For example, the plurality of goods may be located on a conveyor belt and / or in a container, box, and / or carton. The terms container, box, and / or carton may be used interchangeably herein.

[0024] The plurality of goods, in particular at least two, can either i) have the same properties, e.g. if the container exclusively comprises, e.g. razors, scissors, calculators, ballpoint pens, contact lenses, etc., e.g. of the same type, for example razors of the same brand, size, type, etc.; or ii) at least two different properties, e.g. a razor and scissors; or two razors of different types, e.g. of different brands, sizes, type, etc. The plurality of goods (identical or different) can be arranged and / or distributed in the container, in particular chaotically, in particular on top of one another, next to one another, underneath one another, overlapping, concealing one another and having a, in particular random, alignment, position, location and / or orientation in the container or box. Such a container can also be referred to as a “chaotic box.” The goods therein can, for example,be referred to as bulk material.

[0025] According to the invention, the image data can be obtained and / or captured by an image recording device, for example at least one camera. Preferably, however, a plurality of cameras can be used, for example to capture and / or record the contents of a, for example, chaotic, box, for example from different (viewing) angles and / or directions. The camera can be a 2D and / or a 3D camera, which can, for example, also capture spatial depth. Infrared cameras are also possible. The image data can comprise a two-dimensional representation (2D) and / or a three-dimensional representation (3D), which can, for example, comprise one or more pixels and / or one or more images of the, in particular chaotic, box, in particular of its contents, in particular of the plurality of goods.

[0026] According to the invention, an individualized representation of the plurality of goods can be generated, in particular on the basis of the image data.

[0027] An “individualized representation” can be understood as a (2D and / or 3D) representation, e.g. of the plurality of goods, e.g. within the chaotic box, in which a, e.g. first, good is recognizable, identifiable and / or detectable separately, individualized, segmented, rasterized, isolated and / or detached from a, e.g. second, in particular identical or different, good. The generation of the individualized representation can therefore, for example, be referred to as object, product and / or goods recognition or segmentation and / or take place automatically. The individualized representation can, for example, comprise a three-dimensional representation (3D), e.g. including a spatial depth, which comprises, for example, one or more pixels and / or one or more images, e.g. of the chaotic box, in particular its contents, in particular of the plurality of goods. For example,the goods are presented individually and separately from one another, including in terms of depth, e.g. when the goods are placed on top of one another.

[0028] The generation of the individualized representation can include processing the image data using an artificial intelligence (AI) system.

[0029] An AI (artificial intelligence) system can refer to a technological application based at least on artificial intelligence and / or a system equipped with AI to automate and / or support human-like skills and tasks. According to the invention, the AI ​​system can be used, e.g., for image, object, or product recognition and / or segmentation, e.g., in a chaotic box, for picking up and / or passing on the object or product, and / or for packaging the object or product.

[0030] An AI system can use algorithms, models, networks, and / or data to process information, recognize patterns and / or relationships, make decisions, and / or solve problems. An AI system can include a neural network, a deep learning system such as a convolutional neural network (CNN), and / or machine learning. An AI system can have a learning process in which it learns from provided training data and improves its performance over time. This can be done, for example, through supervised learning, unsupervised learning, and / or reinforcement learning.

[0031] Training data can, for example, correspond to the image data and / or the individualized representation and be provided to the AI ​​system as input.

[0032] For example, one or more images of the, in particular chaotic, box, in particular its contents, in particular the plurality of goods, can be provided to the AI ​​system, wherein the image data can represent a plurality of goods with at least two, in particular different or identical, properties. On the basis of the image data, the AI ​​system can then generate one or more individualized representations, which can be evaluated, e.g., by means of feedback. Possible feedback methods include, for example, user feedback, in which users can give the AI ​​system direct feedback on the generated individualized representation, e.g., confirmation that the individualized representation was generated correctly (e.g., all goods in the box were recognized); automatic feedback, for example, from the AI ​​system itself, e.g., based on orAnalysis of user interaction and / or behavior and / or if the AI ​​system notices, for example, that a product was not processed correctly, e.g., it was not tangible and / or could not be inserted into packaging and / or a product receptacle, in particular a product entry opening; and / or error detection, for example, if the AI ​​system (itself) draws the user's attention to problematic, uncertain, difficult, and / or critical data, e.g., if the AI ​​system recognizes products incorrectly, incorrectly, ambiguously, only partially, and / or not at all, and / or generates an incorrect or dubious individualized representation.

[0033] Alternatively or additionally, one or more generated individualized representations can also be provided to the AI ​​system, whereby the individualized representation can be the plurality of goods, e.g. within the chaotic box, in which one, e.g. first, item of goods is recognizable, identifiable and / or detectable separately, individualized, segmented, rasterized, isolated and / or detached from a, e.g. second, in particular identical or different, item of goods. Based on the individualized representation, the AI ​​system can decide, e.g. how the item is to be picked up from the chaotic box, e.g. how it is to be grasped and / or how it is to be packaged. This can also be evaluated, e.g. user feedback can be given, in which the user gives the AI ​​system direct feedback on the picked-up item, e.g.confirmation that the goods have been picked up correctly; automatic feedback, for example from the AI ​​system itself, e.g. based on or analysis of the interaction of a gripper and / or at least one sensor with the goods, e.g. if the AI ​​system detects that goods have not been processed correctly, e.g. were not graspable and / or could not be inserted into packaging and / or a goods holder, in particular a goods input opening; and / or error detection, for example if the AI ​​system draws the user's attention to problematic, uncertain, difficult and / or critical data, e.g. if the AI ​​system detects goods incorrectly, faultily, ambiguously, only partially and / or not at all and / or does not know how to grip them, e.g. due to their shape.

[0034] As training data, alternatively or additionally, at least one property of the product, one or more reference values, data from one or more sensor devices and / or even the feedback itself can be provided (see below).

[0035] “Feedback” described herein may be provided for one or more training data in the form of one or more of the following: user feedback, automated feedback, and / or error detection, for example, as described above.

[0036] In supervised learning, the model can be presented with training data, e.g., labeled data sets consisting of input attributes (features) and the corresponding target values ​​(labels). The goal can then be to learn a function that maps the mapping of input attributes to the correct target values. The model can be iteratively adapted during training to make the best predictions for new data, e.g., previously unseen or otherwise used data. Examples of supervised learning are classification and regression problems. In a classification problem, inputs are to be assigned to specific classes (e.g., image recognition: razor or contact lens). In regression problems, continuous values ​​are to be predicted (e.g., house price prediction based on certain properties).

[0037] In contrast, unsupervised learning cannot use target values ​​or labels for the training data. Instead, it can aim to discover patterns, structures, and / or relationships in the data itself, e.g., autonomously and / or unsupervised, without the model having any prior information about how the data should be categorized or grouped. The model learns to group, segment, or reduce the data based on similarities or other features, e.g., exploratory. Examples of unsupervised learning include cluster analysis, dimensionality reduction, and association rules.

[0038] A neural network can be a model, particularly an algorithmic one, that is inspired by and / or modeled on the functioning of the human brain. The neural network can comprise a plurality or collection of interconnected neurons that can process and transmit information and / or recognize and / or learn patterns or relationships. By training on large amounts of data, a neural network can be capable of learning complex tasks such as image recognition, segmentation, language processing and / or decision-making. A special type of neural network, e.g. the CNN, can use specific layers, such as convolutional layers and / or pooling layers, to extract features and / or properties from images and to recognize patterns and / or relationships. Bounding boxes can also be used to obtain an individualized representation.For example, after the goods have been detected, e.g. in the image data, a frame, e.g. a bounding box, can be automatically placed around each individual object or each item of goods (e.g. detected in the chaotic box), which defines the position, location, alignment, orientation and / or extent of the goods in the image data. In particular, 2D and / or 3D bounding boxes that additionally take spatial depth into account are conceivable. The AI ​​system can also be trained with a large amount of training data (e.g. in advance and / or during operation), which includes, e.g., images of goods, e.g. in a chaotic box. In this way, the CNN can learn to identify features, relationships and / or patterns that are relevant or useful for the detection, detection and / or segmentation of the goods. A CNN can also be based on convolutional operations.These convolutional operations can enable effective capture of features in images, such as edges, textures, and / or shapes, which are useful for object recognition and discrimination. A CNN can also extract hierarchical features by using different layers of neurons to learn increasingly complex information. For example, the AI ​​system described herein may be a CNN.

[0039] As already mentioned, image data, e.g., of a chaotic box, can be provided to the AI ​​system as input. It is also possible for the image data to be provided as training data (see above).

[0040] Based on the individualized representation, an individual product can be determined from the plurality of products, in particular identified, and / or at least one property that is assigned to the individual product. An “individual product” can refer to a product, e.g. a first product, that has been recognized, identified and / or recorded separately, individualized, segmented, rasterized, isolated and / or detached from a product, e.g. a second product (in particular the same or different), e.g. from the plurality of products, e.g. in the chaotic box. The at least one property that is assigned to the individual product can include a position, location, orientation, alignment, e.g. in the chaotic box, a surface quality, dimension, e.g. size, length, height, depth, thickness, shape, weight, stability, strength, hardness and / or a material as well as e.g. a density of the individual product.Other additional properties can also be determined, in particular identified, for example a fragility or a degree of fragility, which can provide information about how fragile a product is, e.g. in the case of glassware; a degree of grip, which can provide information about how a product can be picked up, whether a product has a structure for picking up, the general grip of a product and / or whether a product tends to be easy to grip or rather slippery; and / or a degree of handling reliability, which can provide information about the success of a pick-and-place process, for example whether the product falls out of the product allocation device or not.

[0041] A "control signal" can be understood as a signal, e.g., an electrical, electronic, and / or digital signal, that can initiate, control, regulate, monitor, and / or influence the operation and / or behavior of a system, device, appliance, machine, and / or facility, and / or can signal something to it. It can serve as a means of communication, e.g., for transmitting instructions, commands, instructions, and / or information, e.g., to initiate, execute, or perform certain actions, movements, actions, functions, and / or sequences.

[0042] A control signal can comprise one or more pieces of information, for example at least one property of at least one product, determined in particular on the basis of the individualized representation. The control signal can also comprise one or more pieces of information on: how the product is to be picked up, in particular gripped, e.g. depending on an orientation, dimension, location and / or position in the box, e.g. in relation to other products; how the picked up product is to be inserted, e.g. in relation to an orientation, dimension, location and / or position etc. and / or taking into account goods that have already been inserted and / or are still to be inserted. Further information that the control signal can comprise can be found in the following description.

[0043] According to the invention, the step of determining the at least one property associated with the individual product can comprise processing the generated individualized representation by means of the AI ​​system, wherein, in particular, the individualized representation is provided to the AI ​​system as input. Based on the individualized representation, the AI ​​system can make decisions, in particular how the product is to be picked up from the chaotic box and / or how it is to be further processed, in particular how the product is to be packaged, e.g., whether the picked-up product needs to be rotated in order to be packaged (see also above).

[0044] According to the invention, the determination, in particular identification of the at least one property, can comprise an estimation and / or calculation, in particular based on the image data and / or the individualized representation, in particular based on one or more pixels and / or one or more images therein, in particular by means of the AI ​​system. The calculations or estimates that can be carried out as part of the property determination of the product can be based on mathematical methods, statistical analyses and / or other algorithmic approaches. The AI ​​system can, for example, due to its trained model and its ability to capture complex relationships, carry out precise calculations and / or estimates in order to obtain a statement about the product, e.g. before the product is picked up. In this way, the AI ​​system can, for example,a weight of the item to be picked up and / or identified in the personalized representation can be estimated. This process can be provided to the AI ​​system again, in particular continuously, e.g. for each item, as input, e.g. for training purposes. For example, items with a known weight can be passed to the AI ​​system, for which it carries out a calculation or estimate. In this case, the training data can correspond to the weight of the item, and the AI ​​system can be given feedback as to whether the weight determined, e.g. from the personalized representation, also corresponds to the actual weight, or how large the deviation is. The AI ​​system can learn on this basis and, for example, improve its accuracy. The actual weight can also be determined using at least one sensor, whereby the AI ​​system can, for example, receive feedback automatically, e.g. without user interaction being necessary.The same considerations apply analogously to other characteristics of goods mentioned herein.

[0045] According to the invention, the method may comprise: - Obtaining one or more reference values ​​for the, in particular predefined, packaging and / or for the corresponding goods intake, in particular goods entry opening, in particular from a database, in particular from a cloud system; - Compare whether at least one characteristic assigned to the individual product corresponds to the one or more reference values.

[0046] The one or more reference values ​​may include one or more of: a dimension, shape, material type and / or property, position, strength, stability, surface finish, assigned identifier, and / or weight. The assigned identifier may, for example, be an ID that can indicate whether the order is for an individual or a bulk order.

[0047] According to the invention, the method may comprise: - Determine, based on comparison, i) whether the individual goods are suitable for the packaging, in particular the predefined packaging, and / or for the corresponding goods reception, in particular the goods entry opening, and / or ii) how the individual goods are to be inserted into and / or arranged in the, in particular predefined, packaging and / or the corresponding goods receptacle, in particular the goods entry opening.

[0048] “Suitable” can be understood to mean that the product and the packaging, in particular predefined packaging, are coordinated with one another. For example, this can be understood to mean that the individual product fits into a predefined packaging, e.g. a given dimension, e.g. length / width / depth, in terms of its dimensions, e.g. length / width / depth. For example, a USB stick (product) can be suitable for a particularly predefined packaging in terms of its size, e.g., it fits, whereas a broom handle may not be suitable for the particularly predefined packaging, e.g., a parcel, due to its length, e.g., because it is too long.

[0049] Determining “how” the individual goods are to be inserted can be done by taking into account a) a position, location, orientation, alignment, e.g. of the goods in the chaotic box, e.g. in relation to other goods, which may, for example, only allow limited possibilities for the individual goods to be accommodated by the goods allocation device; and / or b) a surface quality, dimension, shape, weight, stability, strength, hardness and / or a material of the individual goods; and / or c) a final packaging and / or a packaging raw material, in particular with a specific and / or predetermined dimension, e.g. length, width, height, depth, thickness, in particular (packaging) wall thickness, a specific material, a specific shape, a specific weight, a specific surface quality and / or a specific robustness, strength and / or stability. Specific and / or predetermined customer-specific information, e.g.Whether the package is a single order or a bulk order, e.g., multiple items in one package, can also be included. Furthermore, how the individual item was picked up can be taken into account, e.g., taking into account a position, location, orientation, or alignment, e.g., of the picked item on the item allocation device, which can, for example, determine a loading direction. For example, a picked ruler can be rotated before loading so that it can be loaded lengthwise, for example, instead of crosswise.

[0050] According to the invention, the determination of steps i) and / or ii) may further include taking into account - goods already placed in and / or arranged in the, in particular predefined, packaging and / or the corresponding goods receptacle, in particular the goods entry opening, and / or - nor into the, in particular predefined, packaging and / or the corresponding goods receptacle, in particular the goods entry opening, and / or the goods to be placed therein.

[0051] For example, the AI ​​system can, e.g. based on the individualized representation of the plurality of goods in a chaotic box, determine which goods are already arranged in a package, e.g. a number of goods, e.g. one, two, three, etc. and / or a type of goods, e.g. a razor and two contact lenses, etc.; and / or how these are arranged there, e.g. with regard to an orientation, position, location and / or extension direction within the packaging. This can, for example, serve to optimally utilize a packaging size, e.g. for a bulk order. In each example mentioned herein, the AI ​​system can also take into account a gripping position of the goods, e.g. an orientation, e.g. after a goods allocation device has removed or picked up, in particular gripped, an individual good from the, in particular chaotic, box. The gripping position can therefore influence the direction of insertion into the packaging.A gripping position can also be fed back to the AI ​​system (feedback) and serve as a starting point for training. This can be used, for example, to teach the AI ​​system that certain goods should be turned, swiveled, moved, and / or handled more carefully so that they don't fall out of the gripper, and / or that certain goods should be held, secured, and / or gripped more securely, for example, to further secure them.

[0052] According to the invention, the comparing, the determining of steps i) and / or ii) and / or the taking into account a processing -- the image data, -- the generated individualized representation; -- the one or more reference values; and / or -- a feedback signal, in particular from a packaging station and / or a cloud system, using the AI ​​system.

[0053] The “processing” may include the provision -- the image data, -- the generated individualized representation; -- the one or more reference values; and / or -- the feedback signal, as input for the AI ​​system, e.g. for training or learning purposes.

[0054] According to the invention, the control signal can be configured to control a goods allocation device, in particular a robot, in particular a pick-and-place robot, in particular for packaging the individual goods in the, in particular predefined, packaging and / or the goods receptacle corresponding thereto, in particular the goods input opening.

[0055] According to the invention, the control signal can be configured to cause the goods allocation device to pick up, in particular to grip, the individual goods from the plurality of goods, in particular individually.

[0056] According to the invention, the control signal can indicate how the goods allocation device, on the basis of the at least one property, should pick up, in particular grip, the individual goods from the plurality of goods.

[0057] The indication of “how” the individual goods are to be picked up can be made taking into account a) a position, location, orientation, alignment, e.g. of the goods in the chaotic box, e.g. in relation to other goods, which may only allow limited picking up of the individual goods by means of the goods allocation device, for example if the goods are on top of each other, next to each other, underneath each other, overlapping and / or covering each other; and / or b) a surface quality, dimension, shape, weight, stability, strength, hardness and / or a material of the individual goods. For example, it may be necessary to

[0058] It's best to grasp goods at a stronger or more stable point, e.g., a thicker point, rather than at a fragile or easily breakable / damaged point, e.g., made of glass. It can also be taken into account that goods are more likely to break in some places. The AI ​​system can make such decisions based, for example, on the individualized representation and / or image data and / or previous training data.

[0059] The control signal can indicate how the goods allocation device handles the picked goods - in the, in particular predefined, packaging and / or - the corresponding goods acceptance, in particular the goods entry opening; and / or arrange therein, in particular with regard to the at least one characteristic assigned to the individual product.

[0060] The “how” of introducing the individual goods has already been described above; this also applies here.

[0061] The method according to the invention may further comprise: Receiving a feedback signal, in particular from the packaging station and / or the cloud system, in particular after the individual goods have been introduced and / or arranged in the, in particular predefined, packaging and / or the corresponding goods receptacle, in particular the goods input opening.

[0062] A cloud system can be understood as an infrastructure that can comprise one or more (e.g., virtual or virtualized) components or computing resources, such as a server, a storage device, a database, and / or a network for communication between the components. The cloud system can be centralized or decentralized.

[0063] According to the invention, the feedback signal can indicate whether the packaging of the individual goods in the, in particular predefined, packaging and / or the goods receptacle corresponding thereto and / or formed therein, in particular the goods input opening, was successful and / or how the individual goods were arranged therein, in particular with regard to the at least one property assigned to the individual goods.

[0064] "Successful" can be understood as meaning that the goods are suitable for the packaging, e.g., the final packaging and / or the packaging raw material, e.g., with regard to a dimension, and in particular that they can be packaged in / with it. This can also indicate that the goods have been packaged in the packaging.

[0065] The indication of “how” the individual goods were arranged therein can be determined taking into account a1) a position, location, orientation, alignment, e.g. of the goods in the chaotic box, e.g. in relation to other goods, which may only allow limited possibilities for picking up the individual goods by means of the goods allocation device; and / or a2) a position, location, orientation, alignment, e.g. of the picked up goods on the goods allocation device, which maycan determine an insertion direction and may require a change in position, in particular a rotation; and / or b) a surface quality, dimension, shape, weight, stability, strength, hardness and / or a material of the individual and / or picked up goods and / or c) a final packaging and / or a packaging raw material with a specific and / or predetermined dimension, for example length, width, height, depth, thickness, in particular (packaging) wall thickness, a specific material, a specific shape, a specific weight, a specific surface quality and / or a specific robustness, strength and / or stability. Specific and / or predetermined customer-specific information, e.g. whether the package is an individual order or a collective order, e.g. several goods in one package, can also be included.

[0066] According to the invention, the feedback signal can be provided to the AI ​​system as input, in particular before and / or after generating the personalized representation of the plurality of goods. For example, this can be done automatically and / or manually for training purposes, e.g., through supervised and / or unsupervised training. Other training data can also be used.

[0067] The invention relates to a computer-implemented method, in particular according to one or more of the aspects and / or embodiments described herein, for allocating and / or packaging one or more goods by means of a specific one of a plurality of packaging stations, comprising: - generating, on the basis of image data representing a plurality of goods with at least two, in particular different or identical, properties, an individualised representation of the plurality of goods, - Determine, in particular identify, on the basis of the individualized representation, -- an individual product from the plurality of products, and / or -- of at least one characteristic associated with the individual product; and, - Outputting a control signal based on the at least one specific property.

[0068] A "packaging station" can represent an area, location, and / or zone where specific devices, equipment, machines, and / or facilities for packaging goods are present, e.g., featuring a specific packaging process, a specific packaging material, and / or a specific packaging type. A packaging station can, for example, be an area where an employee performs manual packaging, e.g., because the AI ​​system has determined and / or decided that the goods are easily breakable, e.g., fragile, and should be packaged manually, e.g., by an employee. Another example is a parcel station, where goods are packed, e.g., in a folded box, or a station for letters of specific dimensions, e.g., depending on the postage, especially maxi letters or large letters, etc.

[0069] The control signal can indicate to which of the plurality of packaging stations the one or more goods should be transferred, for example, depending on the at least one property of the goods. The at least one property can include a position, location, orientation, alignment, surface finish, size, dimension, shape, weight, stability, strength, hardness, a degree of fragility, a degree of grip, a degree of handling reliability, and / or a material of the individual goods.

[0070] The step of generating the individualized representation of the plurality of goods may comprise processing the image data by means of an artificial intelligence (AI) system, wherein in particular the image data is provided to the AI ​​system as input and wherein in particular the image data is obtained from an image recording device.

[0071] The step of determining the at least one property associated with the individual product may comprise processing the generated individualized representation by means of the AI ​​system, wherein in particular the individualized representation is provided to the AI ​​system as input.

[0072] Determining, in particular identifying, the at least one property may comprise an estimation and / or calculation, in particular based on the image data and / or the individualized representation, in particular based on one or more pixels and / or one or more images therein, in particular by means of the AI ​​system.

[0073] The method according to the invention may comprise: - Obtaining one or more reference values ​​for at least two of the plurality of packaging stations, in particular from a database, in particular from a cloud system; - Compare whether at least one characteristic assigned to the individual product corresponds to the one or more reference values.

[0074] The plurality of packaging stations may comprise at least two packaging stations that differ in terms of a packaging process, packaging material, and / or packaging type. However, identical / identical packaging stations are also possible.

[0075] The one or more reference values ​​for at least two of the plurality of packaging stations may comprise one or more of: - a dimension of a goods receptacle, in particular a goods input opening of at least two packaging stations, and / or - a shape, material type and / or property, strength, stability, surface quality of a packaging material of the at least two packaging stations, and / or - a dimension and / or shape of a final packaging and / or packaging raw material formed by the at least two packaging stations.

[0076] The method according to the invention may comprise: - Determine, based on comparison, i) for which of the plurality of packaging stations the individual goods are suitable, and / or ii) how the individual goods are to be handed over to the specific packaging station, in particular on the basis of at least one characteristic.

[0077] “Suitable” can be understood to mean that the product and the product holder, in particular the product input opening of the packaging station, are coordinated with one another for packaging. For example, that the individual product fits into a product holder, in particular a product input opening, e.g. a product input slot, e.g. with a predetermined dimension, e.g. in terms of its dimensions, e.g. size. For example, a USB stick (product) can be suitable for a product holder, in particular a product input opening, e.g. in terms of its size, e.g. fitting, whereas a vase may not be suitable for the product holder, in particular the product input opening, e.g. due to its shape / width, e.g. because it is too wide / thick.

[0078] The indication “how” the individual goods are to be handed over to the specific packaging station can be determined taking into account a1) a position, location, orientation, alignment, e.g. of the goods in the chaotic box, e.g. in relation to other goods, which may only allow limited picking up of the individual goods by means of the goods allocation device; and / or a2) a position, location, orientation, alignment, e.g. of the picked up goods at the goods allocation device, which maycan specify an insertion direction; and / or b) a surface quality, dimension, shape, weight, stability, strength, hardness and / or a material of the individual goods; and / or the picked up goods and / or c) the goods receptacle, in particular the goods input opening, with a specific and / or predetermined dimension, for example length, width, height, depth, thickness, in particular (packaging) wall thickness, a specific material, a specific shape, a specific weight, a specific surface quality and / or a specific robustness, strength and / or stability. Specific and / or predetermined customer-specific information, e.g. whether the package is an individual order or a collective order, e.g. several goods in one package, can also be included.

[0079] According to the invention, the comparing and / or determining of steps i) and / or ii) may involve processing -- the image data, -- the generated individualized representation; -- the one or more reference values; and / or -- a feedback signal, in particular from one or more packaging stations and / or a cloud system, by means of the AI ​​system.

[0080] The control signal can be configured to control a goods allocation device, in particular a robot, in particular a pick-and-place robot, in particular for picking up, in particular gripping, the individual item of goods from the plurality of goods and / or for transferring the individual item of goods to the specific one of the plurality of packaging stations, in particular based on the at least one property. The control signal can also be configured to cause the goods allocation device to introduce the goods into the goods receptacle, in particular the goods input opening, of a packaging station, e.g., to insert it into a goods input slot and / or, if necessary, to bring the picked up goods into the corresponding position, e.g., to align and / or rotate them accordingly.

[0081] A goods allocation device can be a robot, in particular a pick-and-place robot, for packaging one or more goods in a, in particular predefined, packaging and / or in a packaging corresponding thereto and / or for allocating and / or packaging one or more goods by means of a specific one of a plurality of packaging stations, in particular for introducing and / or arranging in a corresponding goods receptacle, in particular a goods input opening, of the specific one of the plurality of packaging stations.

[0082] A system according to the invention for packaging one or more goods can comprise the goods allocation device according to the invention, which implements one or more of the inventive methods described herein, and at least one image recording device, in particular with at least one camera, for obtaining the image data. The system can further comprise the AI ​​system described herein.

[0083] The system may further comprise at least one packaging station, or at least two packaging stations that differ with respect to a packaging process, a packaging material and / or a packaging type.

[0084] The system may further comprise at least one sensor device which is configured -- to determine at least one further characteristic associated with the individual goods, and -- to provide at least one further property to the AI ​​system as, in particular, additional, input.

[0085] The at least one sensor device can be located on the goods allocation device, in particular be attached to a goods pickup end point. For example, as already described, the AI ​​system can perform an estimate and / or calculation before and / or during the pickup of the goods from the chaotic box, e.g., to estimate at least one property of the individual goods. The sensor device can, for example, provide additional information, e.g., after the goods have been picked up, to verify the estimated and / or calculated data or information, in particular by means of the AI ​​system. For example, the at least one sensor device can comprise one or more of: at least one camera, at least one weight measuring device, in particular a scale, and / or at least one laser measuring device.For example, after picking up the goods from the chaotic box, a weight measuring device can perform a weight measurement to confirm the AI ​​system's pre-estimation and / or calculation, or provide the measured result to the AI ​​system as input and / or feedback. The estimation and / or calculation of at least one property of the individual goods can be confirmed (or not) in order to (further) train the AI ​​system in this regard and improve its accuracy.

[0086] The system according to the invention can further comprise a cloud system that is communicatively coupled to the AI ​​system, wherein the cloud system comprises a database. The database can store, for example, customer-related data relevant for the delivery of the package, such as name, address, and / or contents, e.g., an individual order or a bulk order.

[0087] Both the one or more packaging stations and the goods allocation device can each be connected to the cloud system, for example, as can all components of the system according to the invention, to enable communication. For example, a communication connection, e.g., for exchanging information, can comprise one or more of: Ethernet, Internet, LAN (Local Area Network), WLAN (Wireless Local Area Network), VPN (Virtual Private Network), wireless technologies such as Bluetooth, Wi-Fi Direct, Near Field Communication (NFC), the Internet, and / or mobile networks (e.g., GSM, 3G, 4G, 5G, etc.). BRIEF DESCRIPTION OF THE DRAWINGS

[0088] Embodiments of the invention will now be described with reference to the accompanying drawings. In order to understand the above-mentioned features of the present disclosure in detail, a more detailed description of the disclosure, briefly summarized above, can be obtained by reference to embodiments. The accompanying drawings relate to embodiments of the disclosure and are described below: Fig. 1A shows a flowchart of a method according to the invention according to an embodiment; Fig. 1B shows the steps of generating the individualized representation of the method according to the invention according to the embodiment of Fig. 1A; Fig. 1C shows the steps of determining the individual product and / or the at least one property of the method according to the invention according to the embodiment of Fig. 1A; Fig. 1D shows the output of the control signal of the method according to the invention according to the embodiment of Fig. 1A; Fig. 2 shows a flowchart of a method according to the invention according to a further embodiment; Fig. 3 shows image data obtained by means of an image recording device and / or an individualized representation of a plurality of goods obtained by means of the method according to the invention according to one or more embodiments; Fig. 4 shows a system according to the invention for packaging one or more goods with a goods allocation device, in particular a robot, in particular a pick-and-place robot, and a plurality of packaging stations according to one or more embodiments. DESCRIPTION OF PREFERRED EMBODIMENTS

[0089] In the following, the invention will be explained in more detail with reference to embodiments shown in the drawings, wherein in all drawings, essentially functionally identical elements have the same reference numerals.

[0090] The drawings are schematic drawings and are not to scale. Some elements in the drawings may have exaggerated dimensions to emphasize aspects of the present disclosure and / or for presentation clarity. For simplicity, identical reference numerals are used to identify identical elements commonly included in the drawings. It is contemplated that elements and features of one embodiment may be advantageously incorporated into other embodiments without further notice. In general, only the differences with respect to individual embodiments will be described.

[0091] Each embodiment is provided to illustrate the disclosure and is not intended to limit the disclosure. Furthermore, features illustrated or described as part of one embodiment may be used in conjunction with other embodiments to create another embodiment. The description is intended to encompass such modifications and variations.

[0092] Fig. 1A shows a flowchart of a method 100 according to the invention according to an exemplary embodiment for packaging one or more goods in a, in particular predefined, packaging and / or in a corresponding goods receptacle, in particular a goods input opening. The method 100 comprises generating 110, based on image data representing a plurality of goods with at least two, in particular different or identical, properties, an individualized representation of the plurality of goods; determining, in particular identifying, based on the individualized representation, an individual good 120 from the plurality of goods and / or at least one property 130 assigned to the individual good; and outputting 140 a control signal based on the at least one determined property.

[0093] Optionally, the method may also comprise controlling a goods allocation device for packaging the goods in a, in particular predefined, packaging and / or a packaging corresponding thereto.

[0094] Fig. 1B shows the steps of generating 110 the individualized representation of the method 100 according to the invention according to the embodiment of Fig. 1A. For example, the image data 150 can be captured using an image recording device (see Fig. 3), for example at least one camera. The image data 150 can comprise a two-dimensional representation (2D) and / or three-dimensional representation (3D), which for example comprise one or more pixels and / or one or more images of the, in particular chaotic, box, in particular its contents, in particular the plurality of goods. The image data 150 can be provided to the AI ​​system 170 as input. In addition, training data 160 can optionally also be provided, e.g., to train the AI ​​system 170. The training data 160 can show a plurality of goods, e.g., bulk goods, which are arranged, in particular chaotically and / or randomly, in a box.

[0095] In addition, the AI ​​system 170 can be coupled to a cloud system 197, in particular communicatively, for example via a communication connection, e.g., for exchanging information, such as Ethernet, Internet, LAN (Local Area Network), WLAN (Wireless Local Area Network), VPN (Virtual Private Network), wireless technologies such as Bluetooth, Wi-Fi Direct, Near Field Communication (NFC), the Internet, and / or mobile networks (e.g., GSM, 3G, 4G, 5G, etc.). The cloud system can include a database.

[0096] The cloud system 197 can provide the AI ​​system 170 with customer-specific information (e.g., name, address, whether it is a single or bulk order, etc.) and / or training data, in particular additional training data, e.g., image data or other data. Furthermore, reference values ​​can be provided, whereby feedback to the AI ​​system via the cloud system 197 is also conceivable, e.g., based on the generated personalized representation.

[0097] According to the invention, the AI ​​system 170 can generate an individualized representation 180 of the plurality of goods from the image data 150, optionally the training data 160, optionally the data from the cloud system 197. The individualized representation 180 can be understood as a (2D and / or 3D) representation, e.g., of the plurality of goods, e.g., within the chaotic box, in which a, e.g., first, good is recognizable, identifiable, and / or detectable separately, individualized, segmented, rasterized, isolated, and / or detached from a, e.g., second, in particular identical or different, good (see, e.g., Fig. 3). The individualized representation 180 can, for example, comprise a three-dimensional (3D) representation of the plurality of goods, e.g., including a spatial depth comprising, for example, one or more pixels and / or one or more images, e.g., of the chaotic box, in particular its contents, in particular of the plurality of goods. Thus, for example, the goods can be represented individually and separately from one another, also with regard to depth, e.g., when the goods lie on top of one another. The generation of the individualized representation can comprise processing the image data by means of the AI ​​system 170. It is also possible to subsequently transfer the individualized representation as training data to the AI ​​system 170, e.g., in order to improve the generation of the individualized representation, in particular to improve recognition of the individual goods within the box.

[0098] Fig. 1C shows the steps of determining 120, 130 the individual product and / or the at least one property of the method 100 according to the invention according to the embodiment of Fig. 1A. As in Fig. 1C, the individualized representation 180 can be provided to the AI ​​system 170 as input, for example the Fig. 1B, in order to determine, in particular to identify, an individual product from the plurality of products and / or to determine, in particular to identify, at least one property that is associated with the individual product. This may include processing the generated individualized representation 180 by means of the AI ​​system 170, wherein in particular the individualized representation 180 is provided to the AI ​​system 170 as input. It should be understood that for the determination 120 of the individual product and / or the at least one property as in Fig. 1C shown, optionally, also the image data 150 from Fig. 1B can be used, e.g. additionally, to provide the AI ​​system 170 with more data / information.

[0099] Based on the individualized representation 180, the AI ​​system 170 can make decisions, in particular how the goods are to be picked up from the chaotic box, e.g. at which point the goods are to be picked up or in relation to other goods, and / or how to proceed with them, in particular how the goods are to be packaged, e.g. whether the picked up goods have to be turned in order to be able to package them or to be able to insert them into the packaging.

[0100] The determination 120, 130, in particular identifying the at least one property, can comprise an estimation and / or calculation, in particular based on the image data and / or the individualized representation 180, in particular based on one or more pixels and / or one or more images therein, in particular by means of the AI ​​system 170. The calculations or estimates that can be carried out as part of the property determination of the product can be based on mathematical methods, statistical analyses and / or other algorithmic approaches. The AI ​​system 170 can, for example, due to its trained model and its ability to capture complex relationships, carry out precise calculations and / or estimates in order to obtain a statement about the product, e.g. before the product is picked up. In this way, the AI ​​system 170 can, for example,a weight of the item to be picked up and / or identified in the individualized representation can be estimated. This process can be provided to the AI ​​system 170 again, in particular continuously, e.g., for each item of goods, as input, e.g., for training purposes.

[0101] Fig. 1D shows the output of the control signal 140 of the method 100 according to the invention according to the embodiment of Fig. 1A. To output the control signal, the AI ​​system 170 may use, in particular process, one or more of: the image data 150, training data 160, the individualized representation 180, one or more reference values ​​190, e.g., information from the cloud system 197, a feedback signal 195.

[0102] The control signal can be set up to control a goods allocation device (see Fig. 4; Fig. 405) to pick up, in particular grip, the individual product from the plurality of products, in particular individually. The control signal can indicate how the product allocation device, based on the at least one property, should pick up, in particular grip, the individual product from the plurality of products. The control signal can indicate how the product allocation device should insert and / or arrange the picked-up product into the, in particular predefined, packaging and / or the corresponding product receptacle, in particular the product input opening, and / or arrange it therein, in particular with reference to the at least one property assigned to the individual product.

[0103] The control signal can comprise one or more pieces of information on: how the goods are to be picked up, in particular gripped, e.g. depending on an orientation, dimension, location and / or position in the box, e.g. in relation to other goods; how the picked up goods are to be inserted into the packaging, e.g. with respect to an orientation, dimension, location and / or position, etc. and / or taking into account goods already inserted and / or still to be inserted.

[0104] The AI ​​system 170 can then compare whether the at least one property assigned to the individual product corresponds to the one or more reference values ​​190, e.g. to determine i) whether the individual product is suitable for the, in particular predefined, packaging, and / or for the corresponding goods receptacle, in particular the goods entry opening, and / or ii) how the individual product is to be introduced into the, in particular predefined, packaging and / or the corresponding goods receptacle, in particular the goods entry opening, and / or arranged therein.

[0105] In this context, goods already introduced into and / or arranged in the, in particular predefined, packaging and / or the corresponding goods receptacle, in particular the goods entry opening, and / or goods still to be introduced into and / or arranged in the, in particular predefined, packaging and / or the corresponding goods receptacle, in particular the goods entry opening, can also be taken into account by the AI ​​system 170.

[0106] For the sake of clarity, reference is made to the above description for details on “how” the goods are to be picked up, inserted and / or arranged and / or whether the goods are “suitable”.

[0107] Fig. 2 shows a flowchart of a method 200 according to the invention according to a further embodiment, which can be combined with any aspect and / or embodiment described herein, for allocating and / or packaging one or more goods by means of a specific one of a plurality of packaging stations, comprising: - generating 210, on the basis of image data representing a plurality of goods with at least two, in particular different or identical, properties, an individualized representation of the plurality of goods, - Determine, in particular identify, on the basis of the individualized representation, -- an individual product 220 from the plurality of products, and / or -- of at least one characteristic 230 associated with the individual product; and, - Outputting 240 a control signal based on the at least one specific property.

[0108] Optionally, the method may also comprise controlling a goods allocation device for allocating and / or packaging one or more goods by means of a specific one of a plurality of packaging stations.

[0109] It should be noted that method 200 differs from method 100, in particular, in the type of control signal. While the control signal generated by method 100 serves to control a goods allocation device for packaging the goods in a package and / or a corresponding goods receptacle, in particular the goods input opening, of a packaging station, the control signal 200 generated by method 200 can be used to control a goods allocation device for allocating and / or packaging one or more goods by means of a specific one of a plurality of packaging stations (see Fig. 4) serve.

[0110] The control signal generated by method 200, in particular by means of AI system 170, can be configured to control a goods allocation device, in particular a robot, in particular a pick-and-place robot, in particular for picking up, in particular gripping, the individual item of goods from the plurality of goods and / or for transferring the individual item of goods to the specific one of the plurality of packaging stations, in particular based on the at least one property. The control signal can also be configured to cause the goods allocation device to insert the goods into the goods receptacle, in particular the goods input opening, of a packaging station, e.g., to insert it into a goods input slot and / or, if necessary, to bring the picked-up goods into the corresponding position, e.g., to align and / or rotate them accordingly.

[0111] Fig. 3 shows image data 150 obtained by means of an image recording device 310, in particular at least one camera 311, and / or an individualized representation 180 of a plurality of goods obtained by means of the method 100, 200 according to the invention, in particular by means of the AI ​​system 170.

[0112] For example, the camera 311 can capture, record, and / or capture 312 the contents of a, in particular, chaotic box 320. The image data 150 shows the contents of the chaotic box, in particular a plurality of goods randomly arranged therein. As can be seen, the goods have different properties; however, they can also have identical properties. For example, the box 320 contains scissors 320A, a pen 320B, and a pack of sticky tape rolls 320C, which are referred to here as examples. In addition, there are also further / different goods in the box 320. The majority of the goods in the box 320 are located on top of one another, next to one another, below one another, overlap one another, cover one another, are located on the back, and / or have a, in particular random, alignment, position, location, and / or orientation in the container 320 or the box 320.For example, additional goods are located below the scissors 320A, the pen 320B, and the pack of adhesive tape rolls 320C. The pack of adhesive tape rolls 320C is also arranged with the back facing up compared to the scissors 320A and the pen 320B.

[0113] As in Fig. 3, the AI ​​system 170 can generate an individualized representation 180 based on the image data 150.

[0114] The individualized representation 180 shows the contents of the box 320, in which a first product, e.g., the scissors 320A, is shown separately, individualized, segmented, rasterized, isolated and / or detached from a second product, e.g., the pen 320B. The products 320A, 320B and 320C are shown individually, for example. In the specific example of the Fig. 3, the individualized representation 180 was generated using bounding boxes 320A', 320B', and 320C'. However, other ways of generating the individualized representation are also conceivable.

[0115] How to continue in Fig. 3, in particular the AI ​​system 170 can determine at least one property associated with the individual product 320A, 320B and / or 320C based on the individualized representation 180. In particular, a position, location, orientation, alignment, surface quality, dimension, shape, weight, stability, strength, hardness, a degree of fragility, a degree of grip, a degree of handling reliability and / or a material of the individual product 320A, 320B and / or 320C can be determined. A special article, e.g., a pair of scissors 320A, can be recognized and distinguished from the pen 320B and / or the pack of adhesive tape rolls 320C.

[0116] Fig. 4 shows a system 400 according to the invention for packaging one or more goods 402, 404 with a goods allocation device 405, in particular a robot 405, in particular a pick-and-place robot 405, and a plurality of packaging stations 420-450. The system can be used to carry out or implement the aspects and / or embodiments of the invention, in particular the methods of the invention, in particular individually and / or in combination.

[0117] The system 400 according to the invention for packaging one or more goods 402, 404 may comprise the goods allocation device 405 according to the invention, which implements one or more of the inventive methods described herein, and at least one image recording device 310 (see Fig. 3), in particular with at least one camera, for obtaining the image data. The image recording device can, for example, be arranged directly on the goods allocation device 405 or at another location where the at least one camera is capable of capturing, recording and / or recording, for example, the contents of a particularly chaotic box 320. The system 400 can further comprise the AI ​​system 170 described herein. As in Fig. As can be seen in Figure 4, the goods allocation device 405 can be equipped with or comprise an AI system 170. However, the AI ​​system 170 can also be located at a different location. The system 400 further comprises at least two packaging stations 420-450, which differ with regard to a packaging process, a packaging material, and / or a packaging type. However, identical / identical packaging stations are also possible.

[0118] The plurality of goods 402, 404 can be located, for example, in a container, a box and / or a carton, in particular in a chaotic box and / or on a conveyor belt 410, which can transport the goods 402, 404 and / or the container, the box and / or the carton, in particular in the chaotic box, to the goods allocation device 405 and / or can make them available to the latter. This, in particular chaotic, box can therefore be located, for example, on the conveyor belt 410, or at least at a location at which the goods allocation device 405 can access the box, in particular its contents, in particular the plurality of goods 402, 404, e.g., in order to individually remove goods therefrom. It is also conceivable for the box to be located on a shelf and / or to be provided and / or transported by a means of transport, for example, a trolley.

[0119] The system may further comprise at least one sensor device (not shown) configured to determine at least one further property associated with the individual item of goods 402, 404, and to provide the at least one further property to the AI ​​system 170 as, in particular, additional input. The at least one sensor device may be located on the goods allocation device 405, in particular attached to a goods receiving end point, e.g., to a gripper, in particular a goods allocation device, for example, a pick-and-place robot. For example, the AI ​​system 170, as already described, may perform an estimation and / or calculation before and / or during the picking of the goods 402, 404 from the chaotic box, e.g., to estimate or pre-estimate at least one property of the individual item of goods 402, 404. The sensor device may, for example, provide additional information, e.g.,after picking up the goods 402, 404, in order to verify the estimated and / or calculated data or information, in particular by means of the AI ​​system 170. For example, the at least one sensor device can comprise one or more of: at least one camera, at least one weight measuring device, in particular a scale, and / or at least one laser measuring device. For example, after picking up the goods 402, 404, a weight measuring device can perform a weight measurement in order to confirm the pre-estimation and / or pre-calculation of the AI ​​system 170, or provide the measured result to the AI ​​system as input and / or feedback.

[0120] In Fig. 4, a plurality of packaging stations 420-450 are shown, which have a specific packaging process, a specific packaging material and / or a specific packaging type. For example, the packaging station 420 can be an area where an employee performs manual packaging, e.g. because the AI ​​system 170 has detected, determined and / or decided that the goods 402, 404 are easily breakable, e.g. fragile, and should be packaged manually, e.g. by an employee. The goods allocation device 405 can then transfer the goods 402, 404 to the packaging station 420. From this packaging station, for example, manual feedback can be fed back to the AI ​​system, although automatic feedback is also possible. Another example is a parcel station 430, where the goods, e.g.in a final packaging, such as a folded carton, envelope, or the like, or a station for letters of specific dimensions, e.g., depending on the postage, especially Maxi Letters 440 or Large Letters 450, e.g., with different dimensions. From this packaging station, for example, automatic feedback can be fed back to the AI ​​system, although manual feedback is also possible.

[0121] The control signal can indicate to which of the plurality of packaging stations 420-450 the one or more goods 402, 404 should be transferred, for example, depending on the at least one property of the goods. The at least one property can include a position, location, orientation, alignment, surface finish, size, dimension, shape, weight, stability, strength, hardness, a degree of fragility, a degree of grip, a degree of handling reliability, and / or a material of the individual goods 402, 404.

[0122] For example, this can be done by comparing whether the at least one property associated with the individual product matches the one or more reference values. The one or more reference values ​​can be obtained from the cloud system and include one or more of: - a dimension of a goods receptacle, in particular a goods input opening, of the at least two packaging stations 420-450, and / or - a shape, material type and / or property, strength, stability, surface quality of a packaging material of the at least two packaging stations 420-450, and / or - a dimension and / or shape of a final package and / or a packaging raw material formed by the at least two packaging stations 420-450.

[0123] As already mentioned, the one or more packaging stations 420-450, the AI ​​system 170 and / or the goods allocation device 405 can be connected to the cloud system 197, in particular communicatively, e.g. via a communication connection (in Fig.4 (for the sake of clarity, only one connection is shown graphically). In this way, the one or more packaging stations 420-450 can provide the AI ​​system with feedback and / or additional input, in particular whether the packaging of the individual goods by means of the specific one of the plurality of packaging stations 420-450 was successful and / or how the individual goods were arranged therein, in particular with regard to the at least one property associated with the individual goods. This allows the AI ​​system 170 to be improved, in particular its accuracy, reliability and / or speed. The feedback can be provided automatically, for example, via a communication connection, or, as in the case of the packaging station 420, manually, e.g., by the employee. This can be used, in particular, to further train and / or improve the AI ​​system 170.

[0124] While the foregoing is directed to embodiments of the disclosure, other and further embodiments of the disclosure may be devised without departing from the basic scope thereof, the scope being determined by the following claims. List of reference symbols 100 procedures 110 Creating individualized representation 120 Determining individual goods 130 Determining at least one property 140 Output control signal 150 image data 160 training data 170 AI system 180 Individualized display 190 reference values 195 Feedback signal 197 Cloud system 200 procedures 210 Creating individualized representation 220 Determining individual goods 230 Determining at least one property 240 Output control signal 310 Image recording device 311 Camera 312 Capture 320 boxes 320A scissors 320B pen 320C Tesa tape roll 320A'-320C' Bounding Box 400 system 402, 404 goods 405 Goods allocation device 410 boxes / conveyor belt 420-450 packaging stations

Claims

[1] Computer-implemented method for packaging one or more goods in a, in particular predefined, packaging and / or in a corresponding goods receptacle, in particular a goods input opening, a packaging station, comprising: - generating, on the basis of image data representing a plurality of goods with at least two, in particular different or identical, properties, an individualised representation of the plurality of goods, - Determine, in particular identify, on the basis of the individualized representation, -- an individual product from the plurality of products, and / or -- of at least one characteristic associated with the individual product; and, - Outputting a control signal based on the at least one specific property. [2] Method according to claim 1, wherein the particularly predefined packaging - a packaging raw material, in particular a substantially flat one; and / or - a final packaging formed, in particular folded, from the packaging raw material, in particular a shipping bag, preferably an envelope or a package. [3] Method according to one of claims 1-2, wherein the at least one property comprises a position, location, orientation, alignment, surface finish, dimension, shape, weight, stability, strength, hardness, a degree of fragility, a degree of grip, a degree of handling reliability and / or a material of the individual product. [4] A method according to any one of claims 1-3, wherein the step of generating the individualized representation of the plurality of goods comprises a Processing the image data by means of an artificial intelligence, AI, system, wherein in particular the image data is provided to the AI ​​system as input and, wherein in particular the image data are obtained from an image recording device. [5] A method according to any one of claims 1-4, wherein the step of determining the at least one property associated with the individual product comprises a Processing the generated individualized representation by means of the AI ​​system includes, In particular, the individualized representation is provided to the AI ​​system as input. [6] Method according to one of claims 1-5, wherein the determining, in particular identifying, of the at least one property comprises an estimation and / or calculation, in particular on the basis of the image data and / or the individualized representation, in particular on the basis of one or more pixels and / or one or more images therein, in particular by means of the AI ​​system. [7] Method according to any one of claims 1-6, further comprising: - Obtaining one or more reference values ​​for the, in particular predefined, packaging and / or for the corresponding goods intake, in particular the goods input opening, of the packaging station, in particular from a database, in particular from a cloud system; - Compare whether at least one characteristic assigned to the individual product corresponds to the one or more reference values. [8] The method of claim 7, wherein the one or more reference values ​​comprise one or more of: a dimension, shape, material type and / or property, location, position, alignment, orientation, strength, stability, surface finish, associated identifier and / or a weight. [9] A method according to any one of claims 7-8, further comprising: - Determine, based on comparison, i) whether the individual goods are suitable for the packaging, in particular the predefined packaging, and / or for the corresponding goods reception, in particular the goods entry opening, and / or ii) how the individual goods are to be inserted into and / or arranged in the, in particular predefined, packaging and / or the corresponding goods receptacle, in particular the goods entry opening. [10] Method according to claim 9, wherein determining steps i) and / or ii) further comprises taking into account - goods already placed in and / or arranged in the, in particular predefined, packaging and / or the corresponding goods receptacle, in particular the goods entry opening, and / or - nor the goods to be placed in and / or arranged in the, in particular predefined, packaging and / or the corresponding goods receptacle, in particular the goods entry opening. [11] Method according to one of claims 9-10, wherein the comparing, the determining of steps i) and / or ii) and / or the taking into account a processing -- the image data, -- the generated individualized representation; -- the one or more reference values; and / or -- a feedback signal, in particular from a packaging station and / or a cloud system, by means of the AI ​​system. [12] Method according to one of claims 1-11, wherein the control signal is arranged to control a goods allocation device, in particular a robot, in particular a pick-and-place robot, in particular for packaging the individual goods in the, in particular predefined, packaging and / or the goods receptacle corresponding thereto, in particular the goods input opening. [13] Method according to claim 12, wherein the control signal is arranged to cause the goods allocation device to pick up, in particular to grip, the individual goods from the plurality of goods, in particular individually. [14] Method according to one of claims 12-13, wherein the control signal indicates how the goods allocation device, on the basis of the at least one property, should pick up, in particular grip, the individual goods from the plurality of goods. [15] Method according to one of claims 12-14, wherein the control signal indicates how the goods allocation device allocates the picked-up goods - in the, in particular predefined, packaging and / or - the corresponding goods acceptance, in particular the goods entry opening; and / or arrange therein, in particular with regard to the at least one characteristic assigned to the individual product. [16] The method of claim 15, further comprising: Receiving a feedback signal, in particular from the packaging station and / or a cloud system, in particular after the individual goods have been introduced and / or arranged in the, in particular predefined, packaging and / or the corresponding goods receptacle, in particular the goods input opening. [17] Method according to claim 16, wherein the feedback signal indicates whether the packaging of the individual goods in the, in particular predefined, packaging and / or the corresponding goods receptacle, in particular the goods input opening, was successful and / or how the individual goods were arranged therein, in particular with regard to the at least one property assigned to the individual goods. [18] Method according to one of claims 16-17, wherein the feedback signal is provided to the AI ​​system as input, in particular before, during and / or after generating the individualized representation of the plurality of goods. [19] A computer-implemented method, in particular according to any one of claims 1-18, for allocating and / or packaging one or more goods by means of a specific one of a plurality of packaging stations, comprising: - generating, on the basis of image data representing a plurality of goods with at least two, in particular different or identical, properties, an individualised representation of the plurality of goods, - Determine, in particular identify, on the basis of the individualized representation, -- an individual product from the plurality of products, and / or -- of at least one characteristic associated with the individual product; and, - Outputting a control signal based on the at least one specific property. [20] Method according to claim 19, wherein the control signal indicates, in particular selects, to which of the plurality of packaging stations the one or more goods are to be transferred. [21] The method according to any one of claims 19-20, wherein the at least one property comprises a position, location, orientation, alignment, surface finish, size, dimension, shape, weight, stability, strength, hardness, a degree of fragility, a degree of grip, a degree of handling reliability and / or a material of the individual product. [22] Method according to one of claims 19-21, wherein the step of generating the individualized representation of the plurality of goods comprises processing the image data by means of an artificial intelligence (AI) system, wherein in particular the image data is provided to the AI ​​system as input and wherein in particular the image data is obtained from an image recording device. [23] A method according to any one of claims 19-22, wherein the step of determining the at least one property associated with the individual product comprises a Processing the generated individualized representation using the AI ​​system includes In particular, the individualized representations are provided to the AI ​​system as input. [24] Method according to one of claims 19-23, wherein the determining, in particular identifying, of the at least one property comprises an estimation and / or calculation, in particular on the basis of the image data and / or the individualized representation, in particular on the basis of one or more pixels and / or one or more images therein, in particular by means of the AI ​​system. [25] A method according to any one of claims 19-24, further comprising: - Obtaining one or more reference values ​​for at least two of the plurality of packaging stations, in particular from a database, in particular from a cloud system; - Compare whether at least one characteristic assigned to the individual product corresponds to the one or more reference values. [26] A method according to any one of claims 19-25, wherein the plurality of packaging stations comprise at least two packaging stations which differ with respect to a packaging process, a packaging material and / or a packaging type. [27] A method according to any one of claims 25-26, wherein the one or more reference values ​​comprise one or more of: - a dimension of a goods receptacle, in particular a goods input opening, of the at least two packaging stations, and / or - a shape, material type and / or property, strength, stability, surface quality of a packaging material of the at least two packaging stations, and / or - a dimension and / or shape of a final packaging and / or packaging raw material formed by the at least two packaging stations. [28] A method according to any one of claims 25-27, further comprising: - Determine, based on comparison, i) for which of the plurality of packaging stations the individual goods are suitable, and / or ii) how the individual goods are to be handed over to the specific packaging station, in particular on the basis of at least one characteristic. [29] Method according to claim 28, wherein the comparing and / or determining of steps i) and / or ii) comprises processing -- the image data, -- the generated individualized representation; -- the one or more reference values; and / or -- a feedback signal, in particular from one or more packaging stations and / or a cloud system, by means of the AI ​​system. [30] Method according to one of claims 19-29, wherein the control signal is arranged to control a goods allocation device, in particular a robot, in particular a pick-and-place robot, in particular for picking up, in particular for gripping, the individual goods from the plurality of goods and / or for transferring the individual goods to the specific one of the plurality of packaging stations, in particular on the basis of the at least one property. [31] Goods allocation device, in particular robots, in particular pick-and-place robots, for packaging one or more goods, which implements the method according to one of claims 1-18 and / or the method according to one of claims 19-30. [32] System for packaging one or more goods, comprising: - the goods allocation device according to claim 31, and - at least one image recording device, in particular with at least one camera, for obtaining the image data. [33] The system of claim 32, further comprising: at least one packaging station, or at least two packaging stations that differ in terms of a packaging process, a packaging material and / or a packaging type. [34] System according to any one of claims 33-34, further comprising: - at least one sensor device which is arranged -- to determine at least one further characteristic associated with the individual goods, and -- to provide at least one further property to the AI ​​system as, in particular, additional, input. [35] System according to claim 34, wherein the at least one sensor device is located on the goods allocation device, in particular is attached to a goods receiving end point. [36] System according to any one of claims 32-35, wherein the at least one sensor device comprises one or more of: at least one camera, at least one weight measuring device, in particular a scale and / or at least one laser measuring device. [37] System according to any one of claims 32-36, further comprising: a cloud system that is communicatively coupled to the AI ​​system, wherein the cloud system includes a database. [38] A computer program product comprising instructions which, when executed by a computer, cause the computer to carry out the method according to any one of claims 1-18 and / or the method according to any one of claims 19-30. [39] A computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to carry out the method according to any one of claims 1-18 and / or the method according to any one of claims 19-30. [40] Training data set for training an AI system and / or as input for the AI ​​system, comprising: image data, in particular one or more images representing a plurality of goods with at least two, in particular different or identical, properties; and / or one or more generated individualised representation(s) of the plurality of goods; and / or at least one, in particular specific, property associated with the individual goods; and / or Sensor device data; one or more reference values ​​for the, in particular predefined, packaging and / or for the corresponding goods receptacle, in particular the goods input opening, of the packaging station and / or for at least two of the plurality of packaging stations; and / or at least one feedback signal associated with a product, in particular a specific property of the individual product; a product allocation device; a packaging, in particular a predefined packaging, and / or a corresponding product receptacle and / or at least one packaging station. [41] Generating a training data set according to claim 40 for training an AI system. [42] Generating a training data set, in particular according to claim 41, for training an AI system by means of the method according to any one of claims 1-18 and / or the method according to any one of claims 19-30 and / or the system according to any one of claims 32-37. [43] Training an AI system using a training data set according to claim 40 and / or using the method according to any one of claims 1-18 and / or the method according to any one of claims 19-30. [44] AI system, in particular trained AI system that has been trained by means of the method according to any one of claims 1-18 and / or the method according to any one of claims 19-30 and / or the system according to any one of claims 32-37; the training data set according to claim 40; and / or the training set generated according to any one of claims 41-42.

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