Product identification system and a product identification procedure

Abstract

The invention relates to a product identification system (1) for identifying and comparing product dispensing, particularly in the catering industry, comprising a camera system (2) with at least one camera (3) for capturing at least one product (200) in a transfer area (100), a processing unit (5) for analyzing the camera data (4) captured by the camera system (2), and a cash register interface (10) for receiving transaction data (21) from a cash register system (20) for booked products, wherein the processing unit (5) is configured to identify the at least one captured product (200) based on the camera data (4) and to compare it with the transaction data (21) and to report deviations, particularly in real time.

Description

The invention relates to a product identification system and a method for identifying and comparing product dispensing data, particularly in the food service industry. Furthermore, the invention relates to a camera system for capturing product dispensing data, a processing unit for analyzing image data from a camera system, a computer-implemented method for identifying and comparing product dispensing data, a computer program product, a data processing device, a computer-readable storage medium, a data carrier signal, and a method for improving the recognition accuracy of a product identification system. Several technical solutions exist for monitoring and reconciling product expenditures in the food service industry. These solutions aim to minimize discrepancies between issued and recorded products, which can arise from various factors such as theft, personal consumption, or human error. Such discrepancies result in financial losses that are difficult to trace. A typical approach to monitoring product spending in the restaurant industry is the use of security cameras and point-of-sale systems. Security cameras record video footage that can later be analyzed to identify potential discrepancies. However, this method is time-consuming and inefficient because it does not allow for real-time monitoring and requires human intervention in the subsequent analysis of the monitored sales processes. Another well-known system uses point-of-sale (POS) systems to record transactions. These systems are capable of capturing sales in real time. The products sold are entered into the POS system as transaction data. However, such POS systems do not provide a direct link between the products actually dispensed and the recorded transactions. It is therefore possible that transaction data for a different product than the product actually dispensed is entered into the POS system. These approaches have therefore proven to be disadvantageous, as they do not allow for a direct comparison between products provided and products actually recorded as transactions. Another option is to use markings such as barcodes or QR codes for product identification, where these markings are captured by a scanner and compared with data stored in the point-of-sale system. However, this method requires that each product be manually marked and scanned, which can be time-consuming and prone to errors. Furthermore, such markings are unsuitable for the catering industry, where customers are served various products, such as food and beverages, on or in reusable, cleanable, and non-product-specific containers like plates and glasses. These non-product-specific containers cannot be marked with product-specific labels for product identification, as is common practice with packaged products in retail stores. This is especially true since, particularly in the food service industry, individual products can be modified according to customer requests, for example, by omitting, adding, or exchanging individual ingredients. Using markers would therefore require storing an individual marker in the POS system for every product and every conceivable variation of every product, which is simply not practically feasible given the exponential workload involved. Despite the known technical solutions, challenges still exist in the area of ​​product identification and the reconciliation of product expenditures, especially in the catering industry. In particular, traditional monitoring solutions such as cameras or point-of-sale systems are inadequate because they do not allow for a direct comparison between products provided and those recorded. These systems also often require manual intervention and subsequent analysis, which is time-consuming and prone to errors. Furthermore, existing solutions lack seamless integration with existing point-of-sale systems, which impairs the efficiency and accuracy of product tracking. The lack of flexibility and adaptability of existing systems to new products or changing environmental conditions also presents a challenge. The object of the present invention is therefore to provide a technical solution that addresses the common problem of discrepancies between dispensed and recorded products, particularly in the food service industry, caused by theft, personal consumption, or accidentally unrecorded orders. This solution aims to enable precise and automated monitoring and detection of product dispensing, thereby overcoming the aforementioned disadvantages. It should significantly improve both detection accuracy and operational efficiency, especially in the food service industry. Furthermore, the technical solution should offer seamless integration with existing point-of-sale systems and exhibit high flexibility and adaptability to new products and changing environmental conditions. This task is solved in a product identification system for identifying and reconciling product expenditures, particularly in the catering industry, of the type mentioned above, by having a camera system with at least one camera for capturing at least one product in a transfer area, a processing unit for analyzing the camera data captured by the camera system, and a POS interface for receiving transaction data from a POS system for booked products, wherein the processing unit is configured to identify the at least one captured product based on the camera data and to compare it with the transaction data and to report deviations, particularly in real time. The camera system can capture data about the product located in the transfer area, in particular multiple products located there, as camera data. The processing unit can receive this camera data, whereby the processing unit itself can be wholly or partially part of the camera system, in particular of the at least one camera, or it can be a separate structural unit from the camera system and connected to it by data. The processing unit can analyze the camera data and identify at least one product captured by the camera system, particularly through the use of AI. Furthermore, the processing unit can receive transaction data from the point-of-sale (POS) system via a POS interface, providing information about the product(s) recorded in the POS system. The processing unit can then compare this transaction data with the identified product to detect discrepancies if the identified product does not match the product recorded in the point-of-sale system. Similarly, if there are multiple products in the transfer area, the product identification system can check whether all are correctly recorded in the point-of-sale system and / or whether the products in the transfer area correspond to the recorded products. The product identification system can then report any such discrepancy. The product identification system can give the user the opportunity, in the event of a discrepancy, to adjust the booking in the POS system and / or to correct the at least one product located in the handover area. In this way, the product identification system enables precise and automated monitoring and reconciliation of product dispensing, particularly in real time, allowing discrepancies between dispensed and recorded products to be detected and reported immediately. This can significantly reduce shrinkage and financial losses, ensuring greater accuracy and efficiency, especially in the food service industry. The handover area can be the area where products, especially consumables like food and meals, are prepared for service to a customer, particularly before they reach the guest or customer. These products may already be recorded in the point-of-sale system or will be recorded as soon as they are placed in the handover area. The handover area can be a kitchen pass, a counter, or a bar. A kitchen pass is the boundary between the restaurant area and the kitchen, or the area where food and drinks are prepared for subsequent service in the restaurant or guest area. The kitchen pass can separate two different areas and teams, such as the service staff and the kitchen staff. The kitchen pass is also referred to as the serving counter or food dispensing point of the kitchen. The camera system, including at least one camera, and in particular several cameras, can be installed and aligned in such a way that the transfer area lies within its field of view or detection range. Real-time notification of deviations can enable an effective reduction of shrinkage and economic losses through immediate corrective action. In particular, the AI ​​can also distinguish between foods or drinks that look very similar to the human eye. Artificial intelligence (AI) can, for example, analyze which ingredients were combined in a mixing process to create a product. The identified products can then be compared with items sold, such as those from the last few minutes. If this sales comparison doesn't yield a clear match, a check can be performed against the menu recipes. Should this comparison also fail to produce a definitive result, the AI ​​can access an expanded recipe database. Once a product is clearly identified, the AI ​​transmits this information as camera data to the processing unit (if the AI ​​is part of the camera system) or to the matching unit (if the AI ​​is part of the processing unit) for further processing. If a match is not possible, the product is marked as an "unrecognized product" and also forwarded to the software. The identification can be based on feature recognition, in particular color, shape and / or pattern analysis, as well as a comparative database of products, especially food and beverages. In a further development of the invention, the at least one camera can be configured to include AI-supported object recognition. This enables increased accuracy in product identification. The object recognition can be part of the processing unit, which can be integrated, in particular, into the camera itself. This reduces the need for external hardware and lowers latency. Using AI-supported object recognition, the camera can identify the type and / or number of products, especially dispensed food and beverages. It is possible that AI-based object recognition is integrated into the camera. The resulting benefit is a reduction in the need for additional external hardware. This can lead to lower data processing latency and improve system efficiency. Furthermore, it can reduce the complexity of system setup and maintenance. Another embodiment provides that at least one camera is sensitive in a non-visible spectral range to detect differences between products that are not, or are difficult to, perceive in the visible spectral range. This can enable the use of product characteristics for identification and differentiation from other products that are not detectable in the visible spectral range. This ensures reliable differentiation of visually similar-looking products, particularly without relying on markings or manual labeling systems. In this way, similar-looking foods or beverages, such as cola and a rum and cola mix, can be more easily distinguished. Furthermore, it can be advantageous if at least one camera is a multispectral camera capable of capturing light in multiple spectral ranges. This can improve the differentiation accuracy of visually similar products. The multispectral camera can cover various wavelength ranges to precisely detect differences between products. This can increase the detection accuracy and reliability of the system. In an advantageous configuration, the at least one camera can be a NIR camera, VIS-NIR camera, SWIR camera, UV camera, or IR camera. This can offer flexibility in adapting to different detection requirements. Different camera types can be used to capture specific product characteristics. This can enable more comprehensive and precise detection. A NIR camera (near-infrared camera) can detect light in the near-infrared (NIR) range, typically with wavelengths from 780 nm to 1400 nm. A VIS-NIR camera combines the visible (VIS) and near-infrared (NIR) ranges in a single device. This camera can cover both the visible and near-infrared spectrum, typically with wavelengths from 400 nm to 1400 nm. A SWIR (Short-Wave Infrared) camera can detect light in the short-wave infrared range, typically with wavelengths from 1400 nm to 3000 nm. A UV camera (ultraviolet camera) can detect ultraviolet light, typically with wavelengths from 200 nm to 400 nm. IR camera (infrared camera): This covers the entire infrared range and is often used for thermal imaging. Preferably, the camera system is designed to include multiple cameras positioned in different locations and / or orientations relative to the transfer area. This allows for the capture of products from various angles and avoids obstructed views. The cameras can be of different types and / or sensitive in different spectral ranges. This ensures the complete capture and identification of all products in the transfer area. In a further development of the invention, the camera system can comprise several identical cameras positioned in different positions and / or orientations relative to the transfer area. This can enable the detection of products from different angles and avoid obstructed fields of view. The identical cameras can operate synchronously to ensure seamless monitoring. This can lead to higher reliability in product identification. It is proposed that the processing unit include an identification unit for identifying the at least one captured product based on the camera data and / or a matching unit for comparing the product identified, particularly by means of the identification unit, with the transaction data. This can enable effective identification of the product or multiple products by means of the identification unit. Likewise, this can ensure effective matching of the identified product or multiple identified products by means of the matching unit. The identification unit can use AI to perform the identification of the product or products. The identification unit can access a comparison database containing the properties of different products. The identification unit can use the data stored in the comparison database to identify the product detected by the camera system. As part of the identification process, the ingredients incorporated into the product can also be identified. For this purpose, the comparison database can also contain properties of different ingredients, which the identification unit can access to recognize the ingredients. Particularly in a central identification unit, simpler product identification and / or quantity recognition can be achieved. All camera data from the camera system can be collected and jointly evaluated for identification purposes within the identification unit. This evaluation can be performed with the assistance of AI. The calibration unit can enable centralized calibration and detection of deviations. The reconciliation unit can exchange data with the identification unit and the POS interface to obtain data on the identified product and transaction data for the products entered into the POS system. Using this data, the reconciliation unit can verify whether the identified product(s) in the transfer area have been correctly entered into the POS system or if there are any discrepancies. Alternatively or additionally, the reconciliation unit can also use the transaction data to check whether the identified products have been prepared correctly or whether they contain or are missing any ingredients. The reconciliation unit can report detected discrepancies or trigger a notification. According to one embodiment of the invention, the processing unit, in particular the identification unit, can be configured to determine the number of products detected. This can enable more accurate monitoring of the quantities dispensed. Precise recording of the product count can improve inventory control and reduce shrinkage. This can increase operational efficiency. It is further advantageous if the processing unit uses a comparison database that stores the properties of different products. This can enable more efficient and precise product identification. The comparison database can be continuously updated to store new product information. This can improve the accuracy and flexibility of the recognition system. In a further embodiment of the invention, the processing unit can be configured to recognize the ingredients of the detected products. This can enable the detection of incorrectly prepared products and the improvement of product quality. Incorrectly prepared products can be identified and replaced with correctly prepared products before the customer receives the product. This prevents the customer from receiving an incorrect or inadequate product, thereby increasing customer satisfaction. In particular, AI can analyze which ingredients have been incorporated into the product to precisely determine its type. This can enable more accurate matching to the items recorded at the point of sale and help to identify incorrect sales or faulty preparations. If a product is not recognized, the processing unit can categorize it as an "unrecognized product." The user may then be given the option to manually select the product and subsequently enter it into the POS system to improve the recognition database. Furthermore, it can be advantageous if the POS interface uses an API or middleware solution for data exchange. This allows the system to communicate with common POS (Point of Sale) systems. This can facilitate easier integration with existing POS systems and offer more flexible customization options. Using such solutions can accelerate data exchange and increase system compatibility, thereby improving the efficiency of business processes. It is possible that the POS interface is configured to support real-time data transmission. This can enable the immediate detection and reporting of anomalies. Real-time data transmission can reduce response times for error detection, thereby increasing the accuracy and efficiency of the system. A machine learning interface is preferred, allowing the product identification system to adapt to new products or changing environmental conditions. This enables continuous improvement in recognition accuracy and adaptability to new products and conditions. Recognition algorithms can also be continuously adapted based on new data and changing environmental conditions, increasing the system's flexibility and future-proofing. The product identification system can be adapted to new products or changed environmental conditions via machine learning through the machine learning interface. In a further development of the invention, it can be provided that the product identification system, particularly via the machine learning interface, can be put into a learning state in order to learn new products autonomously. In this learning state, the product identification system can learn new products and their properties. This can enable the independent expansion of the recognition database without manual intervention. The system can continuously acquire new products and their properties. This can increase the recognition accuracy and efficiency of the system. Alternatively or additionally, externally trained information and / or routines can be loaded into the identification unit and / or the comparison database via the machine learning interface. Another configuration provides for an output unit for reporting deviations, equipped with a visual and / or audible signaling unit for the immediate notification of personnel upon detection of anomalies, and / or an interface for creating and storing reports or log files about detected deviations. This enables immediate and effective communication of errors for rapid resolution and documentation. The signaling unit allows for immediate notification of personnel about detected anomalies, thus facilitating the correction of the error or deviation. Storing reports or log files ensures long-term traceability and analysis of discrepancies. The output unit can include a dashboard. A dashboard can enable the visualization and structured listing of the collected data. In a camera system for recording product outputs, particularly in the catering industry, it is proposed to solve the aforementioned problem that it includes at least one camera installed and configured in a transfer area to capture image data of products, as well as an AI-supported object recognition unit that is integrated into the camera or operated externally. This can enable precise product detection and identification through advanced image processing technology. Integrated AI-supported object recognition can increase detection accuracy and improve system efficiency. The features described in connection with the product identification system according to the invention can also be applied individually or in combination to the camera system. The same advantages arise as already described. In a processing unit for analyzing image data from a camera system, particularly in the catering industry, it is proposed to solve the aforementioned task that it includes an identification unit for processing camera data captured by a camera system, a comparison database for storing product characteristics, a POS interface for communicating with a POS system and receiving transaction data, and a reconciliation unit configured to detect and report discrepancies between identified products and the transaction data, especially the recorded transactions. This can enable reliable analysis and real-time reconciliation of product expenditures with point-of-sale data. Comprehensive integration of the various units can increase the system's efficiency and accuracy. The features described in connection with the product identification system according to the invention can also be applied individually or in combination to the processing unit. The same advantages arise as already described. In a procedure for identifying and reconciling product expenditures, particularly in the catering industry, it is proposed to solve the aforementioned problem that it comprises the following steps: capturing camera data on at least one product in a transfer area using a camera system with at least one camera, receiving transaction data from a cash register system, identifying the at least one product based on the camera data, reconciling the at least one identified product with transaction data, and reporting discrepancies, particularly in real time, between the identified products and the transaction data. This can enable efficient real-time monitoring and correction of discrepancies. The method can improve operational efficiency and accuracy in the food service industry. The features described in connection with the product identification system according to the invention can also be applied individually or in combination to the method for identifying and comparing product outputs. The same advantages arise as already described. In a computer-implemented method for identifying and reconciling product expenditures, particularly in the catering industry, it is proposed that the aforementioned task be solved by the following steps: receiving camera data, which represents data captured by a camera system relating to at least one product in a transfer area; receiving transaction data from a cash register system; identifying the at least one product based on the camera data; reconciling the at least one identified product with transaction data; and reporting discrepancies, particularly in real time, between the identified products and the transaction data. This can enable efficient and precise monitoring and reconciliation of product expenditures. The computer-implemented method can increase the accuracy and efficiency of product tracking. The features described in connection with the product identification system according to the invention can also be applied individually or in combination to the computer-implemented method. The same advantages arise as already described. In order to solve the aforementioned problem, a computer program product is proposed to include instructions which, when the program is executed by a computer, cause it to perform the steps of the computer-implemented procedure. This can enable easy implementation of the process on existing computer systems. The computer program product can improve the efficiency and accuracy of product tracking. The features described in connection with the product identification system according to the invention and the methods described above can also be applied individually or in combination to the computer program product. The same advantages arise as already described. In order to solve the aforementioned problem, a data processing device is proposed to include means for carrying out the computer-implemented procedure. This can enable the hardware implementation of the process. The data processing device can increase the efficiency and accuracy of the system and allow seamless integration into existing infrastructures. The features described in connection with the product identification system according to the invention and the methods described above can also be applied individually or in combination to the data processing device. The same advantages arise as already described. In the case of a computer-readable storage medium, it is proposed to solve the aforementioned problem that it include instructions which, when executed by a computer, cause it to perform the steps of the computer-implemented procedure. This can enable the storage and distribution of the procedure across different computer systems. The computer-readable storage medium can facilitate the implementation and use of the procedure. The features described in connection with the product identification system according to the invention and the methods described above can also be applied individually or in combination to the computer-readable storage medium. The same advantages arise as already described. In the case of a data carrier signal, it is proposed to transmit the computer program product in order to solve the aforementioned problem. This can enable the transfer and distribution of the computer program to different systems. The data carrier signal can improve the availability and implementation of the procedure. The features described in connection with the product identification system according to the invention, the computer program product, and the methods described above can also be applied individually or in combination to the data carrier signal. The same advantages arise as already described. In a method for improving the recognition accuracy of a product identification system, particularly in the catering industry, it is proposed to solve the aforementioned problem by including the following steps: capturing image data of several products in a transfer area using a camera system, analyzing the captured image data by an AI-controlled processing unit, storing the analysis results in a comparison database, and continuously adapting the recognition algorithms through machine learning based on the stored analysis results. This can enable continuous improvement in identification accuracy and adaptation to new products. The process can increase the efficiency and accuracy of product identification. The features described in connection with the product identification system according to the invention and the methods described above can also be applied individually or in combination to the method for improving recognition accuracy. The same advantages arise as already described. The following sections explain embodiments, further developments, and examples of the invention in more detail with reference to the accompanying drawings. The figures show: Fig. 1 a schematic diagram of the product identification system and Fig. 2 a schematic representation of the data transfer and data processing in the product identification system. Fig. 1 shows the schematic structure of the product identification system 1 and the associated components and elements. The product identification system 1 for identifying and matching product expenditures, especially in the catering industry, comprises a camera system 2 with at least one camera 3 for capturing at least one product 200 in a transfer area 100, a processing unit 5 for analyzing the camera data 4 captured by the camera system 2, and a cash register interface 10 for receiving transaction data 21 from a cash register system 20 for booked products. The camera system 2 is shown in Fig. 1 with two cameras 3a and 3b. These cameras 3a and 3b are positioned so that they can capture the transfer area 100 from different angles. Camera 3a is located in the upper left corner of the transfer area 100, while camera 3b is positioned in the upper right corner. This arrangement allows for comprehensive coverage of the transfer area 100, thus avoiding obstructed views and ensuring accurate identification of products 200a and 200b, represented here as two beverages. In this way, product 200b is also captured by the camera system 2, even though it is obscured from the view of camera 3a by the other product 200a and therefore not visible to camera 3b. Cameras 3a and 3b capture camera data 4, which is forwarded to processing unit 5. Processing unit 5 is responsible for analyzing this camera data 4. Within processing unit 5, there are several specialized units, such as the identification unit 6 and the matching unit 7. The identification unit 6 serves to identify the at least one detected product 200 based on the camera data 4. The matching unit 7 is configured to match the identified product 200 with the transaction data 21 received from the cash register interface 10. Although the identification unit 6 and the matching unit 7 are depicted as two separate units of the processing unit 5, they can nevertheless also be designed as a single unit. In particular, the functions of the identification unit 6 and the matching unit 7 can be performed jointly by an AI. Similarly, the functions of the identification unit 6 and / or the matching unit 7 can be decentralized and distributed across multiple units. In particular, each camera 3 of the camera system 2 can have its own AI for identifying products 200a and 200b. In this case, the data on the already identified products 200 are then transmitted as camera data 4 to the processing unit 5. This POS interface 10 enables communication between the product identification system 1 and the POS system 20, which is responsible for recording the products. The processing unit 5 also includes a comparison database 8, in which the properties of different products 200 are stored. This database is used by the identification unit 6 to identify the recorded products 200. Furthermore, a machine learning interface 9 can be integrated into the processing unit 5, which enables the system to adapt to new products or changing environmental conditions. The POS interface 10 is responsible for receiving the transaction data 21 from the POS system 20. This transaction data 21 contains information about the products sold. The matching unit 7 compares the identified products 200 with these transaction data 21 in order to detect discrepancies and report them in real time. An output unit 11 is also part of the system and serves to immediately notify personnel upon detection of anomalies. This output unit 11 can send visual and / or audible signals and is also capable of generating and saving reports or log files about detected deviations. In Fig. 1, the transfer area 100 is shown as a central area - for example a counter - in which the products 200a and 200b are made available for delivery to the customers. These products may already be recorded in the POS system 20 or will be recorded as soon as they are placed in the transfer area 100. The transfer area 100 can be a kitchen pass, a counter, or a bar. These products are captured by cameras 3a and 3b, and the camera data 4 is sent to the processing unit 5. The processing unit 5 analyzes the camera data 4 and identifies products 200a and 200b. The identification unit 6 can access the comparison database 8 to verify the product characteristics. The matching unit 7 compares the identified products 200a and 200b with the transaction data 21 received from the cash register interface 10. If discrepancies are detected, a message is generated by the output unit 11, which immediately notifies the staff. The arrangement shown in Fig. 1 enables precise and automated monitoring and reconciliation of product dispensing in real time. This leads to a significant reduction in shrinkage and financial losses by immediately detecting and reporting discrepancies between dispensed and recorded products. Seamless integration with existing POS systems and high flexibility and adaptability to new products and changing environmental conditions increase operational efficiency and accuracy, particularly in the food service industry. Another detail shown in Fig. 1 is the machine learning interface 9, which enables the system to continuously improve and adapt to new products or environmental conditions. This interface can load externally trained information and routines into the identification unit 6 and the comparison database 8 to increase recognition accuracy. The system can also be put into a learning state to learn new products independently and expand the recognition database. Additionally, processing unit 5 can be configured to determine the number of products recorded. This allows for more accurate monitoring of dispensed quantities and improves inventory control. The POS interface 10 can use an API or middleware solution for data exchange, which facilitates integration with common POS systems and speeds up data exchange. The POS interface 10 supports real-time data transmission, enabling the immediate detection and reporting of deviations. This reduces response times for error detection and increases the accuracy and efficiency of the system. The camera system 2 and the processing unit 5 can also identify the ingredients of the detected products 200 to ensure that the products 200 have been prepared correctly. This improves product quality and prevents customers from receiving incorrect or inadequate products. In a preferred embodiment, the product identification system 1 comprises multiple cameras 3 positioned in different positions and / or orientations relative to the transfer area 100. These cameras can operate synchronously to ensure seamless monitoring and enable the detection of products from different viewpoints. The cameras 3 can be sensitive in different spectral ranges to detect differences between products that are not perceptible or difficult to detect in the visible spectrum. This includes the use of multispectral cameras, which can detect light in several spectral ranges, as well as NIR cameras, VIS-NIR cameras, SWIR cameras, UV cameras, or IR cameras, which can detect specific product characteristics. In this way, the ingredients of the detected products 200, i.e., their components, can also be identified. The matching unit 7 can then use the transaction data 21 to check whether the products were all prepared correctly, or whether ingredients are missing or incorrect. Fig. 2 shows a schematic representation of the data transfer and data processing in the product identification system 1. This representation illustrates the various components of the system and their interaction to enable precise and automated monitoring and reconciliation of product outputs. The product identification system 1 comprises several central components, including the camera system 2, the identification unit 6, the matching unit 7, the cash register system 20, and the output unit 11. These components work together to ensure the capture, analysis, and matching of product data and to report deviations in real time. Camera system 2 is responsible for capturing image data of the products 200 in the transfer area 100. This image data is then forwarded to identification unit 6. Identification unit 6 uses artificial intelligence (AI) to identify the captured products based on the image data. The AI ​​in identification unit 6 analyzes various characteristics of the products 200, such as shape, color, and pattern, to enable accurate identification. The identified products 200 are then forwarded to matching unit 7. The matching unit 7 is responsible for matching the identified products 200 with the transaction data 21 received by the cash register system 20. The point-of-sale system 20 records sales in real time and provides the corresponding transaction data 21. This data contains information about the products that were entered into the point-of-sale system 20. The reconciliation unit 7 compares the identified products 200 with these transaction data 21 to ensure that the recorded products match the products actually issued. If deviations are detected, the comparison unit 7 generates a message that is forwarded to the output unit 11. The output unit 11 can send visual and / or audible signals to immediately inform personnel of the deviations. Additionally, the output unit 11 can create and save reports or log files about the detected deviation to enable long-term traceability and analysis. Figure 2 also roughly illustrates the data processing process. After the camera data 4 has been acquired by the camera system 2, this data is forwarded to the AI ​​recognition unit 6. The AI ​​recognition unit 6 analyzes the image data and identifies the products 200. These identified products 200 are then forwarded to the software of the matching unit 7, which is responsible for the matching process. The software receives the transaction data 21 from the cash register system 20 and compares it with the identified products 200. The software detects discrepancies between the identified products and the recorded transactions and reports them immediately. These messages are sent to a dashboard of output unit 11. This enables simple visualization and structured listing of the collected data and evaluation results. Data transfer between the individual components occurs in real time to ensure immediate detection and reporting of deviations. The identification unit 6 and the reconciliation unit 7 are in constant data exchange with the cash register system 20 to maximize the system's accuracy and efficiency. The use of artificial intelligence in identification unit 6 enables precise recognition of products 200, even if they appear visually similar. The AI ​​can distinguish between different products 200a and 200b based on their characteristics and ensure that the correct products are identified. This is particularly important in the food service industry, where similar products such as different drinks or dishes can easily be confused. The comparison database 8, integrated into the processing unit 5, assists in the identification of the 200 products. This database 8 stores the characteristics of various products and is continuously updated to capture new products and their features. The identification unit 6 accesses this comparison database 8 to compare the captured 200 products with the stored data and enable accurate identification. The POS interface 10 uses an API or middleware solution for data exchange, which facilitates integration with common POS (Point of Sale) systems and accelerates data exchange. This real-time data transmission enables immediate detection and reporting of deviations, reducing response times for error detection and increasing the system's accuracy and efficiency. Output unit 11 is used to notify staff of detected deviations. This output unit 11 can send visual and / or audible signals to immediately inform staff and enable rapid correction. Additionally, reports or log files can be generated and saved to ensure long-term traceability and analysis of the deviations. The arrangement shown in Fig. 2 offers several advantages, including increased recognition accuracy, improved inventory control, seamless integration with existing POS systems, and high flexibility and adaptability to new products and changing environmental conditions. This leads to a significant improvement in operational efficiency and accuracy, particularly in the food service industry. The use of artificial intelligence in the identification unit 6 and the continuous adaptation of system 1 via the machine learning interface 9 ensure that the product identification system 1 remains up-to-date and can adapt to new challenges. This ensures that the system remains precise and efficient in the future and can adapt to the changing requirements of the catering industry. Figure 2 illustrates how the Product Identification System 1, through the seamless integration of its various components, enables precise and automated monitoring and reconciliation of product outputs. Real-time data transfer and immediate notification of deviations help minimize financial losses and maximize operational efficiency. Continuous improvement and adaptability of the system through artificial intelligence and machine learning ensure that the Product Identification System 1 remains a reliable and efficient solution for the food service industry in the future. REFERENCE MARK LIST 1 Product identification system 2 Camera system 3 Camera 4 Camera data 5 Processing unit 6 Identification unit 7 Matching unit 8 Comparison database 9 Machine learning interface 10 POS interface 11 Output unit 20 POS system 21 Transaction data 100 Transfer area 200 Product

Claims

Product identification system (1) for identifying and matching product dispensing, especially in the catering industry, comprising a camera system (2) with at least one camera (3) for capturing at least one product (200) in a transfer area (100), a processing unit (5) for analyzing the camera data (4) captured by the camera system (2), and a cash register interface (10) for receiving transaction data (21) from a cash register system (20) relating to dispensed products, wherein the processing unit (5) is configured to identify the at least one captured product (200) using the camera data (4) and to compare it with the transaction data (21) and to report any discrepancies, especially in real time. Product identification system (1) according to claim 1, wherein the at least one camera (3) comprises AI-supported object recognition. Product identification system (1) according to one of the preceding claims, characterized in that the at least one camera (3) is sensitive in a non-visible spectral range to detect differences between products (200) that are not or are difficult to perceive in the visible spectral range. Product identification system (1) according to one of the preceding claims, characterized in that the at least one camera (3) is a multispectral camera with which light can be detected in several spectral ranges. Product identification system (1) according to one of the preceding claims, characterized in that the camera system (2) comprises several cameras (3) which are positioned in different positions and / or orientations relative to the transfer area (100). Product identification system (1) according to one of the preceding claims, characterized in that the processing unit (5) comprises an identification unit (6) for identifying the at least one detected product (200) on the basis of the camera data (4) and / or a matching unit (7) for matching the product (200) identified, in particular by means of the identification unit (6), with the transaction data (21). Product identification system (1) according to one of the preceding claims, characterized in that the processing unit (5) is configured to recognize ingredients of the detected products (200). Product identification system (1) according to one of the preceding claims, characterized by an output unit (11) for reporting deviations with an optical and / or acoustic signaling unit for immediate notification of personnel upon detection of anomalies, and / or an interface for creating and storing reports or log files about detected deviations Method for identifying and matching product expenditures, particularly in the catering industry, comprising the steps of: • Capturing camera data (4) on at least one product (200) in a transfer area (100) using a camera system (2) with at least one camera (3), • Receiving transaction data (21) from a cash register system (20), • Identifying the at least one product (200) based on the camera data (4), • Matching the at least one identified product (200) with transaction data (21), • Reporting discrepancies, particularly in real time, between the identified products and the transaction data (21). A computer-implemented method for identifying and matching product expenditures, particularly in the catering industry, comprising: • Receiving camera data (4) representing data captured by a camera system (2) relating to at least one product (200) in a transfer area (100), • Receiving transaction data (21) from a cash register system (20), • Identifying the at least one product (200) based on the camera data (4), • Matching the at least one identified product (200) with transaction data (21), • Reporting discrepancies, particularly in real time, between the identified products and the transaction data (21).

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