system

Abstract

The system according to this embodiment aims to perform age verification quickly and accurately and to properly manage purchase history. [Solution] The system according to the embodiment comprises a facial recognition unit, a supply unit, a management unit, and a transmission unit. The facial recognition unit scans the customer's face to verify their age. The supply unit provides products based on the age verified by the facial recognition unit. The management unit manages the purchase history of the products provided by the supply unit. The transmission unit transmits the purchase history managed by the management unit to the relevant organization.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the age confirmation process takes time and the risk of forged identity cards is high. [[ID=3⑥]]

[0005] The system according to the embodiment aims to perform age confirmation quickly and accurately and manage purchase histories appropriately.

Means for Solving the Problems

[0006] The system according to the embodiment includes a face authentication unit, a provision unit, a management unit, and a transmission unit. The face authentication unit scans the face of a customer to confirm the age. The provision unit provides products based on the age confirmed by the face authentication unit. The management unit manages the purchase histories of the products provided by the provision unit. The transmission unit transmits the purchase histories managed by the management unit to the responsible agency. [Effects of the Invention]

[0007] The system according to this embodiment can perform age verification quickly and accurately and manage purchase history appropriately. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The facial recognition system according to an embodiment of the present invention is a system that utilizes facial recognition technology and vending machine infrastructure to streamline the age verification process and manage purchase history. First, this facial recognition system instantly verifies the customer's age using facial recognition technology. Next, it integrates the facial recognition system into existing vending machines to automate age verification. Furthermore, it introduces a purchase history management system to automatically transmit the purchase history of welfare recipients to the relevant agency. This mechanism shortens the purchase process, prevents fraudulent purchases, and improves the transparency of welfare management. For example, when a customer stands in front of a vending machine, the facial recognition system activates, scanning the customer's face to verify their age. This eliminates the need to present identification, speeding up the age verification process. Next, the facial recognition system integrates it into existing vending machines. This automates age verification compared to conventional vending machines. For example, in a vending machine with an integrated facial recognition system, age verification is performed via facial recognition when a customer purchases a product. This reduces the risk of forged identification and prevents fraudulent purchases. Furthermore, the facial recognition system introduces a purchase history management system. This system has a function to automatically transmit the purchase history of welfare recipients to the relevant agency. For example, when a welfare recipient purchases an item from a vending machine, the purchase history is automatically recorded and transmitted to the agency. This improves the transparency of welfare management and ensures appropriate support is provided. This shortens the time required for the purchase process. Customers can purchase items smoothly because their age is quickly verified through facial recognition. Furthermore, improved security is expected by preventing fraudulent purchases. By using facial recognition technology, fraudulent purchases using forged identification documents are prevented, allowing customers to purchase items with peace of mind. In addition, the transparency of welfare management is improved. The purchase history management system automatically transmits the purchase history of welfare recipients to the relevant agency, ensuring appropriate support is provided. As a result, the facial recognition system can efficiently perform customer age verification, product provision, purchase history management, and history transmission.

[0029] The facial recognition system according to this embodiment comprises a facial recognition unit, a supply unit, a management unit, and a transmission unit. The facial recognition unit scans the customer's face to verify their age. The facial recognition unit verifies the customer's age using, for example, high-precision facial recognition technology using AI. For example, the facial recognition unit can analyze facial features using deep learning technology to estimate age. The facial recognition unit can also extract facial contours and feature points using image processing technology to verify age. Furthermore, the facial recognition unit can refer to a facial recognition database to verify age based on past authentication history. The supply unit provides products based on the age verified by the facial recognition unit. For example, in a vending machine with an integrated facial recognition system, the supply unit verifies age by facial recognition when a customer purchases a product. For example, in a vending machine with an integrated facial recognition system, when a customer selects a product, the facial recognition unit verifies their age and provides an appropriate product. The supply unit can also have the facial recognition unit verify the customer's age and authorize the purchase when a customer purchases a product in a vending machine with an integrated facial recognition system. Furthermore, the supply unit, using vending machines with integrated facial recognition systems, can verify the age of customers and record their purchase history when they purchase products. The management unit manages the purchase history of products provided by the supply unit. For example, the management unit automatically records the purchase history when a welfare recipient purchases a product from a vending machine. For example, the management unit saves the purchase history in a database when a welfare recipient purchases a product from a vending machine. The management unit can also analyze the purchase history when a welfare recipient purchases a product from a vending machine and provide appropriate support. In addition, the management unit can transmit the purchase history when a welfare recipient purchases a product from a vending machine to the relevant agency. The transmission unit transmits the purchase history managed by the management unit to the relevant agency. For example, the transmission unit periodically transmits the purchase history managed by the management unit to the relevant agency. For example, the transmission unit transmits the purchase history managed by the management unit to the relevant agency monthly. The transmission unit can also transmit the purchase history managed by the management unit to the relevant agency in real time. Furthermore, the transmission unit can also transmit purchase history managed by the management unit to the relevant agency based on specific conditions.This allows the facial recognition system to efficiently verify customer age, provide products, manage purchase history, and transmit history.

[0030] The facial recognition unit scans the customer's face to determine their age. The facial recognition unit uses, for example, high-precision facial recognition technology using AI to determine the customer's age. Specifically, the facial recognition unit can analyze facial features using deep learning technology to estimate age. Deep learning technology builds a model that accurately extracts facial features and estimates age by learning from a large amount of facial image data. For example, a convolutional neural network (CNN) is used to extract features from facial images and estimate age. Furthermore, the facial recognition unit can also use image processing technology to extract facial contours and feature points to determine age. Image processing technology detects facial contours and feature points such as eyes, nose, and mouth, and estimates age by analyzing the position and shape of these feature points. For example, age can be estimated by analyzing the shape of the facial contour, skin texture, and the presence or absence of wrinkles. The facial recognition unit can also refer to a facial recognition database to determine age based on past authentication history. The facial recognition database stores previously authenticated facial images and their age information, and age is determined by comparing them with newly scanned facial images. This allows the facial recognition unit to perform highly accurate age verification by combining multiple technologies.

[0031] The supply unit provides products based on the age verified by the facial recognition unit. For example, in a vending machine with an integrated facial recognition system, the supply unit verifies the customer's age through facial recognition when they purchase a product. Specifically, when a customer selects a product in a vending machine with an integrated facial recognition system, the facial recognition unit verifies their age and provides the appropriate product. For example, when purchasing age-restricted products such as alcoholic beverages or tobacco, the facial recognition unit verifies the customer's age and authorizes the purchase. The supply unit can also have the facial recognition unit verify the customer's age and record the purchase history when they purchase a product in a vending machine with an integrated facial recognition system. This allows the supply unit to properly manage the sale of age-restricted products and prevent purchases by minors. Furthermore, when a customer purchases a product in a vending machine with an integrated facial recognition system, the supply unit has the facial recognition unit verify their age and save the purchase history to a database. This allows the supply unit to centrally manage customer purchase history and utilize it for later analysis and management.

[0032] The Management Department manages the purchase history of products provided by the Supply Department. For example, when a welfare recipient purchases a product from a vending machine, the Management Department automatically records the purchase history. Specifically, when a welfare recipient purchases a product from a vending machine, the Management Department saves the purchase history to a database. The database records information such as the date and time of purchase, the purchased product, and the purchaser's information, and is used for later analysis and management. In addition, when a welfare recipient purchases a product from a vending machine, the Management Department can analyze the purchase history and provide appropriate support. For example, based on the purchase history, they can understand the purchasing trends and needs of welfare recipients and provide the necessary support. Furthermore, when a welfare recipient purchases a product from a vending machine, the Management Department can also transmit the purchase history to the relevant agency. This allows the Management Department to centrally manage the purchase history of welfare recipients and share information with the relevant agency to provide appropriate support.

[0033] The transmission unit sends purchase history managed by the management unit to the responsible agency. For example, the transmission unit periodically sends purchase history managed by the management unit to the responsible agency. Specifically, the transmission unit sends purchase history managed by the management unit to the responsible agency every month. By periodically sending purchase history, the transmission unit provides the responsible agency with information to understand the purchasing situation of welfare recipients and provide appropriate support. The transmission unit can also transmit purchase history managed by the management unit to the responsible agency in real time. For example, when a welfare recipient purchases an item, the purchase history is immediately sent to the responsible agency. This allows the responsible agency to understand the welfare recipient's purchasing situation in real time and respond quickly. Furthermore, the transmission unit can also transmit purchase history managed by the management unit to the responsible agency based on specific conditions. For example, the purchase history is sent to the responsible agency when a specific item is purchased or when a certain amount is exceeded. This allows the transmission unit to provide information flexibly based on specific conditions, enabling the responsible agency to appropriately receive the necessary information.

[0034] The facial recognition unit can verify a customer's age using high-precision facial recognition technology based on AI. For example, the facial recognition unit can analyze facial features using deep learning technology to estimate age. For instance, it can receive a facial image as input and estimate age using a deep learning model. The facial recognition unit can also extract facial contours and feature points using image processing technology to verify age. For example, it can receive a facial image as input, extract facial contours and feature points using an image processing algorithm, and verify age. Furthermore, the facial recognition unit can refer to a facial recognition database to verify age based on past authentication history. For example, it can refer to past authentication history stored in the facial recognition database and compare it with the current facial image to verify age. This improves the accuracy of age verification through high-precision facial recognition technology. Some or all of the above-described processes in the facial recognition unit may be performed using, for example, generative AI, or without generative AI. For example, the facial recognition unit can input a facial image into a generative AI, which can analyze facial features and estimate age.

[0035] The service provider can use a vending machine with an integrated facial recognition system to verify the age of a customer when they purchase a product. For example, when a customer selects a product in a vending machine with an integrated facial recognition system, the facial recognition system verifies their age and provides an appropriate product. For example, when a customer selects a product in a vending machine with an integrated facial recognition system, the facial recognition system verifies their age and provides an age-restricted product. The service provider can also use a vending machine with an integrated facial recognition system to verify the age of a customer when they purchase a product and authorize the purchase. For example, when a customer purchases a product in a vending machine with an integrated facial recognition system, the facial recognition system verifies their age and authorizes the purchase. The service provider can also use a vending machine with an integrated facial recognition system to verify the age of a customer when they purchase a product and record the purchase history. For example, when a customer purchases a product in a vending machine with an integrated facial recognition system, the facial recognition system verifies their age and records the purchase history. This automates age verification at vending machines, preventing fraudulent purchases. Some or all of the above-described processes in the dispensing unit may be performed using, for example, a generating AI, or without using a generating AI. For example, in a vending machine with an integrated facial recognition system, the dispensing unit can input a customer's facial image into a generating AI, which can verify the customer's age and dispense the product.

[0036] The management department can automatically record the purchase history of welfare recipients when they purchase items from vending machines. For example, when a welfare recipient purchases an item from a vending machine, the management department can save that purchase history to a database. For example, when a welfare recipient purchases an item from a vending machine, the management department can save that purchase history to a database for later reference. The management department can also analyze the purchase history of welfare recipients when they purchase items from vending machines and provide appropriate support. For example, when a welfare recipient purchases an item from a vending machine, the management department can analyze the purchase history and provide the necessary support. Furthermore, the management department can transmit the purchase history of welfare recipients to the relevant agency. For example, when a welfare recipient purchases an item from a vending machine, the management department can transmit that purchase history to the relevant agency, improving the transparency of welfare management. This makes it easier to manage welfare recipients' purchase history as it is automatically recorded. Some or all of the above-described processes in the management department may be performed using, for example, a generative AI, or without using a generative AI. For example, the management department can input the purchase history of welfare recipients into a generative AI, which can then analyze the purchase history to provide appropriate support.

[0037] The transmission unit can transmit purchase history managed by the management unit to the responsible agency. For example, the transmission unit can periodically transmit purchase history managed by the management unit to the responsible agency. For example, the transmission unit can transmit purchase history managed by the management unit to the responsible agency monthly. The transmission unit can also transmit purchase history managed by the management unit to the responsible agency in real time. For example, the transmission unit can transmit purchase history managed by the management unit to the responsible agency in real time. Furthermore, the transmission unit can transmit purchase history managed by the management unit to the responsible agency based on specific conditions. For example, the transmission unit can transmit purchase history managed by the management unit to the responsible agency based on specific conditions. This ensures that purchase history is automatically transmitted to the responsible agency, improving the transparency of welfare management. Some or all of the above processing in the transmission unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the transmission unit can input purchase history managed by the management unit into a generating AI, which can then analyze the purchase history and transmit it to the responsible agency.

[0038] The management department can automatically record purchase history and use it for marketing and customer analysis. For example, the management department can save purchase history to a database for later reference. For example, the management department can save purchase history to a database and use it for marketing and customer analysis. The management department can also analyze purchase history to understand customer purchasing trends. For example, the management department can analyze purchase history to understand customer purchasing trends and develop marketing strategies. Furthermore, the management department can predict customer needs based on purchase history and provide appropriate products. For example, the management department can predict customer needs based on purchase history and provide appropriate products. This allows purchase history to be used for marketing and customer analysis, optimizing the business. Some or all of the above processes in the management department may be performed using, for example, generative AI, or not using generative AI. For example, the management department can input purchase history into generative AI, which can analyze the purchase history and use it for marketing and customer analysis.

[0039] The facial recognition unit can analyze a customer's past facial recognition history and select the optimal authentication method. For example, the facial recognition unit can adjust the authentication method considering the number of times a customer has failed facial recognition in the past. The facial recognition unit can also select the authentication method with the highest success rate from the customer's past facial recognition history. Furthermore, the facial recognition unit can optimize the timing of authentication based on the customer's past facial recognition history. This allows the optimal authentication method to be selected based on past facial recognition history, improving the success rate of authentication. Some or all of the above processing in the facial recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the facial recognition unit can input the customer's past facial recognition history into a generative AI, which can then select the optimal authentication method.

[0040] The facial recognition unit can improve the accuracy of authentication based on the customer's current health status and facial expression during facial recognition. For example, if the customer is tired, the facial recognition unit may perform additional scans to improve the accuracy of facial recognition. The facial recognition unit can also perform facial recognition with the normal number of scans if the customer is healthy. Furthermore, if the customer's facial expression changes frequently, the facial recognition unit may adjust the accuracy of authentication by taking into account the changes in facial expression. This improves the accuracy of authentication according to the customer's health status and facial expression, and increases the reliability of authentication. Some or all of the above processing in the facial recognition unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the facial recognition unit can input the customer's health status and facial expression data into a generative AI, which can then improve the accuracy of authentication.

[0041] The facial recognition unit can improve the accuracy of authentication by considering the customer's geographical location information during facial recognition. For example, if the customer is in a specific region, the facial recognition unit can adjust the accuracy of authentication by considering the characteristics of that region. The facial recognition unit can also improve the accuracy of authentication based on geographical location information if the customer is on the move. Furthermore, if the customer is inside a specific facility, the facial recognition unit can adjust the accuracy of authentication by considering the environment of that facility. In this way, the accuracy of authentication is improved by considering geographical location information. Some or all of the above processing in the facial recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the facial recognition unit can input the customer's geographical location information into a generative AI, which can then improve the accuracy of authentication.

[0042] The facial recognition unit can analyze the customer's social media activity during facial recognition and use the relevant information for authentication. For example, the facial recognition unit can improve the accuracy of facial recognition by referencing recent photos from the customer's social media activity. The facial recognition unit can also estimate the customer's current emotional state from their social media activity and adjust the accuracy of authentication. Furthermore, the facial recognition unit can analyze recent behavioral patterns from the customer's social media activity to improve the accuracy of authentication. In this way, the accuracy of authentication is improved by analyzing social media activity. Some or all of the above processing in the facial recognition unit may be performed using, for example, generative AI, or without generative AI. For example, the facial recognition unit can input the customer's social media activity data into generative AI, which can then analyze the relevant information and use it for authentication.

[0043] The delivery unit can adjust the level of detail provided based on the importance of the product at the time of delivery. For example, the delivery unit can provide detailed descriptions for high-importance products. The delivery unit can also provide concise descriptions for low-importance products. Furthermore, the delivery unit can adjust the level of detail provided in stages according to importance. This ensures that the level of detail provided is adjusted according to the importance of the product, and that the customer receives appropriate information. Some or all of the above processing in the delivery unit may be performed using, for example, a generating AI, or without a generating AI. For example, the delivery unit can input product importance data into a generating AI, and the generating AI can adjust the level of detail provided.

[0044] The supply unit can apply different supply algorithms depending on the product category at the time of supply. For example, the supply unit can apply a rapid supply algorithm to beverage products. For example, the supply unit can apply a quality-focused supply algorithm to food products. Furthermore, the supply unit can apply a supply algorithm that takes inventory status into account to general merchandise products. For example, the supply unit can apply a supply algorithm that takes inventory status into account to general merchandise products. This ensures that a supply algorithm appropriate to the product category is applied, improving the efficiency of supply. Some or all of the above processing in the supply unit may be performed using, for example, a generation AI, or without a generation AI. For example, the supply unit can input product category data into a generation AI, and the generation AI can apply a supply algorithm.

[0045] The supply unit can determine the priority of product delivery based on the product delivery period. For example, the supply unit can determine the priority of seasonal products according to their delivery period. The supply unit can also determine the priority of new products according to their launch date. Furthermore, the supply unit can also determine the priority of limited-time products according to their delivery period. This ensures that the priority of product delivery is determined according to the product delivery period, and that products are delivered at the appropriate time. Some or all of the above processing in the supply unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the supply unit can input product delivery period data into a generating AI, and the generating AI can determine the priority of product delivery.

[0046] The service provider can adjust the order of service based on the relevance of the products at the time of service. For example, the service provider may prioritize providing products related to products the customer has purchased. The service provider may also provide products related to products the customer has purchased in the past. Furthermore, the service provider may provide highly relevant products based on the customer's purchase history. This adjusts the order of service according to the relevance of the products, ensuring that the customer receives the appropriate products. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider may input product relevance data into a generative AI, which can then adjust the order of service.

[0047] The management department can analyze a customer's past purchase history and select the optimal management method during management. For example, the management department can select the optimal management method based on the customer's past purchase history. The management department can also adjust the management method according to the customer's purchase frequency. For example, the management department can adjust the management method according to the customer's purchase frequency. Furthermore, the management department can analyze the customer's purchase pattern and select the optimal management method. For example, the management department can analyze the customer's purchase pattern and select the optimal management method. This allows the optimal management method to be selected based on past purchase history, improving management efficiency. Some or all of the above processes in the management department may be performed using, for example, a generative AI, or without a generative AI. For example, the management department can input the customer's past purchase history data into a generative AI, which can then select the optimal management method.

[0048] The management department can customize the means of management based on the customer's current living situation during management. For example, the management department can customize the means of management according to the customer's living situation. The management department can also adjust the means of management to match the customer's daily rhythm. For example, the management department can adjust the means of management to match the customer's daily rhythm. Furthermore, the management department can customize the means of management considering the customer's living environment. For example, the management department can customize the means of management considering the customer's living environment. This customizes the means of management according to the customer's living situation, improving the accuracy of management. Some or all of the above processing in the management department may be performed using, for example, a generative AI, or without using a generative AI. For example, the management department can input customer living situation data into a generative AI, and the generative AI can customize the means of management.

[0049] The management department can select the optimal management method when managing customers, taking into account their geographical location information. For example, the management department can select the optimal management method based on the customer's geographical location information. The management department can also adjust the management method when a customer is in a specific region, taking into account the characteristics of that region. Furthermore, the management department can adjust the management method based on geographical location information when a customer is on the move. For example, the management department can adjust the management method based on geographical location information when a customer is on the move. This improves the accuracy of management by taking geographical location information into consideration. Some or all of the above processing in the management department may be performed using, for example, a generative AI, or without a generative AI. For example, the management department can input customer geographical location data into a generative AI, which can then select the optimal management method.

[0050] The management department can analyze customers' social media activity and propose management methods during management. For example, the management department can propose a method for managing purchase history based on customers' social media activity. The management department can also propose the optimal management method based on customers' social media activity. Furthermore, the management department can analyze customers' social media activity and customize the management methods. For example, the management department analyzes customers' social media activity and customizes the management methods. This optimizes the management methods by analyzing social media activity. Some or all of the above processes in the management department may be performed using, for example, generative AI, or not using generative AI. For example, the management department can input customer social media activity data into generative AI, and the generative AI can propose management methods.

[0051] The transmission unit can select the optimal transmission method by analyzing the customer's past transmission history at the time of transmission. For example, the transmission unit can select the optimal transmission method based on the customer's past transmission history. The transmission unit can also adjust the transmission method according to the customer's transmission frequency. For example, the transmission unit can adjust the transmission method according to the customer's transmission frequency. Furthermore, the transmission unit can analyze the customer's transmission pattern and select the optimal transmission method. For example, the transmission unit can analyze the customer's transmission pattern and select the optimal transmission method. This ensures that the optimal transmission method is selected based on past transmission history, making information transmission more efficient. Some or all of the above processing in the transmission unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the transmission unit can input the customer's past transmission history data into a generative AI, which can then select the optimal transmission method.

[0052] The transmitting unit can customize the means of transmission based on the customer's current living situation at the time of transmission. For example, the transmitting unit can customize the means of transmission according to the customer's living situation. The transmitting unit can also adjust the means of transmission to match the customer's daily rhythm. For example, the transmitting unit can adjust the means of transmission to match the customer's daily rhythm. Furthermore, the transmitting unit can also customize the means of transmission considering the customer's living environment. For example, the transmitting unit can customize the means of transmission considering the customer's living environment. This customizes the means of transmission according to the customer's living situation, making information transmission more efficient. Some or all of the above processing in the transmitting unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the transmitting unit can input customer living situation data into a generative AI, which can then customize the means of transmission.

[0053] The transmission unit can select the optimal transmission method at the time of transmission, taking into account the customer's geographical location information. For example, the transmission unit selects the optimal transmission method based on the customer's geographical location information. The transmission unit can also adjust the transmission method if the customer is in a specific region, taking into account the characteristics of that region. For example, the transmission unit adjusts the transmission method if the customer is in a specific region, taking into account the characteristics of that region. Furthermore, the transmission unit can adjust the transmission method based on geographical location information if the customer is on the move. For example, the transmission unit adjusts the transmission method based on geographical location information if the customer is on the move. In this way, the transmission method is optimized by taking geographical location information into account. Some or all of the above processing in the transmission unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the transmission unit can input the customer's geographical location information data into a generative AI, and the generative AI can select the optimal transmission method.

[0054] The transmission unit can analyze the customer's social media activity and suggest a transmission method at the time of transmission. For example, the transmission unit can suggest a transmission method based on the customer's social media activity. The transmission unit can also suggest the optimal transmission method based on the customer's social media activity. Furthermore, the transmission unit can analyze the customer's social media activity and customize the transmission method. For example, the transmission unit analyzes the customer's social media activity and customizes the transmission method. This optimizes the transmission method by analyzing social media activity. Some or all of the above processing in the transmission unit may be performed using, for example, a generative AI, or without a generative AI. For example, the transmission unit can input customer social media activity data into a generative AI, which can then suggest a transmission method.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] A facial recognition system can also be equipped with a biometric authentication unit. This unit can authenticate customers using biometric information such as fingerprints or irises. For example, when a customer scans their fingerprints in front of a vending machine, the biometric authentication unit analyzes the fingerprint information to verify their age. The biometric authentication unit can also scan the customer's iris and work in conjunction with the facial recognition unit to improve the accuracy of authentication. Furthermore, the biometric authentication unit can manage the customer's purchase history based on their biometric information and transmit it to the relevant authorities. This improves the accuracy of age verification and purchase history management by utilizing biometric authentication technology.

[0057] The facial recognition system may also include an environmental recognition unit. This unit can acquire information about the surrounding environment and improve the accuracy of authentication. For example, when a customer stands in front of a vending machine, the environmental recognition unit analyzes the surrounding lighting and background and adjusts the authentication accuracy of the facial recognition unit. The environmental recognition unit can also improve authentication accuracy by considering environmental factors such as weather and time of day. Furthermore, the environmental recognition unit can select the optimal authentication method based on the customer's location information. This allows for improved facial recognition accuracy through the use of environmental information.

[0058] The facial recognition system can also be equipped with a behavior recognition unit. This unit can analyze customer behavior patterns and improve authentication accuracy. For example, it can analyze how a customer moves in front of a vending machine and adjust the authentication accuracy of the facial recognition unit accordingly. Furthermore, the behavior recognition unit can select the optimal authentication method based on the customer's past behavioral history. Additionally, it can manage purchase history based on customer behavior patterns and transmit this information to the relevant authorities. This allows for improved facial recognition accuracy through the use of behavioral information.

[0059] The facial recognition system can also be equipped with a health status recognition unit. This unit can analyze the customer's health status and improve the accuracy of authentication. For example, if the customer is fatigued, the health status recognition unit can perform additional scans to improve the accuracy of facial recognition. Alternatively, if the customer is healthy, the health status recognition unit can perform facial recognition with the normal number of scans. Furthermore, the health status recognition unit can manage purchase history based on the customer's health status and transmit it to the relevant authorities. This allows for improved facial recognition accuracy by leveraging health information.

[0060] The facial recognition system can also be equipped with a behavioral feedback unit. This unit can provide real-time feedback on customer behavior to improve authentication accuracy. For example, it can analyze how a customer moves in front of a vending machine and adjust authentication accuracy in conjunction with the facial recognition unit. The behavioral feedback unit can also select the optimal authentication method based on the customer's behavior patterns. Furthermore, it can manage purchase history based on customer behavior and transmit it to the relevant authorities. This allows for improved facial recognition accuracy through the use of behavioral feedback.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The facial recognition unit scans the customer's face to determine their age. The facial recognition unit uses high-precision AI-based facial recognition technology to determine the customer's age. For example, it uses deep learning technology to analyze facial features and estimate age. It can also use image processing technology to extract facial contours and feature points to determine age. Furthermore, it can refer to a facial recognition database to determine age based on past authentication history. Step 2: The dispensing unit provides the product based on the age verified by the facial recognition unit. For example, in a vending machine with an integrated facial recognition system, age verification is performed via facial recognition when a customer purchases a product. Once the customer selects a product, the facial recognition unit verifies their age and provides the appropriate product. It can also authorize the purchase. Furthermore, it can record the purchase history. Step 3: The management department manages the purchase history of products provided by the service provider. For example, when a welfare recipient purchases a product from a vending machine, the purchase history is automatically recorded and stored in the database. The purchase history can also be analyzed to provide appropriate support. Furthermore, the purchase history can be transmitted to the relevant agency. Step 4: The sending unit transmits the purchase history managed by the management unit to the responsible agency. For example, the purchase history managed by the management unit is transmitted to the responsible agency on a regular basis. This can be done monthly, in real time, or based on specific conditions.

[0063] (Example of form 2) The facial recognition system according to an embodiment of the present invention is a system that utilizes facial recognition technology and vending machine infrastructure to streamline the age verification process and manage purchase history. First, this facial recognition system instantly verifies the customer's age using facial recognition technology. Next, it integrates the facial recognition system into existing vending machines to automate age verification. Furthermore, it introduces a purchase history management system to automatically transmit the purchase history of welfare recipients to the relevant agency. This mechanism shortens the purchase process, prevents fraudulent purchases, and improves the transparency of welfare management. For example, when a customer stands in front of a vending machine, the facial recognition system activates, scanning the customer's face to verify their age. This eliminates the need to present identification, speeding up the age verification process. Next, the facial recognition system integrates it into existing vending machines. This automates age verification compared to conventional vending machines. For example, in a vending machine with an integrated facial recognition system, age verification is performed via facial recognition when a customer purchases a product. This reduces the risk of forged identification and prevents fraudulent purchases. Furthermore, the facial recognition system introduces a purchase history management system. This system has a function to automatically transmit the purchase history of welfare recipients to the relevant agency. For example, when a welfare recipient purchases an item from a vending machine, the purchase history is automatically recorded and transmitted to the agency. This improves the transparency of welfare management and ensures appropriate support is provided. This shortens the time required for the purchase process. Customers can purchase items smoothly because their age is quickly verified through facial recognition. Furthermore, improved security is expected by preventing fraudulent purchases. By using facial recognition technology, fraudulent purchases using forged identification documents are prevented, allowing customers to purchase items with peace of mind. In addition, the transparency of welfare management is improved. The purchase history management system automatically transmits the purchase history of welfare recipients to the relevant agency, ensuring appropriate support is provided. As a result, the facial recognition system can efficiently perform customer age verification, product provision, purchase history management, and history transmission.

[0064] The facial recognition system according to this embodiment comprises a facial recognition unit, a supply unit, a management unit, and a transmission unit. The facial recognition unit scans the customer's face to verify their age. The facial recognition unit verifies the customer's age using, for example, high-precision facial recognition technology using AI. For example, the facial recognition unit can analyze facial features using deep learning technology to estimate age. The facial recognition unit can also extract facial contours and feature points using image processing technology to verify age. Furthermore, the facial recognition unit can refer to a facial recognition database to verify age based on past authentication history. The supply unit provides products based on the age verified by the facial recognition unit. For example, in a vending machine with an integrated facial recognition system, the supply unit verifies age by facial recognition when a customer purchases a product. For example, in a vending machine with an integrated facial recognition system, when a customer selects a product, the facial recognition unit verifies their age and provides an appropriate product. The supply unit can also have the facial recognition unit verify the customer's age and authorize the purchase when a customer purchases a product in a vending machine with an integrated facial recognition system. Furthermore, the supply unit, using vending machines with integrated facial recognition systems, can verify the age of customers and record their purchase history when they purchase products. The management unit manages the purchase history of products provided by the supply unit. For example, the management unit automatically records the purchase history when a welfare recipient purchases a product from a vending machine. For example, the management unit saves the purchase history in a database when a welfare recipient purchases a product from a vending machine. The management unit can also analyze the purchase history when a welfare recipient purchases a product from a vending machine and provide appropriate support. In addition, the management unit can transmit the purchase history when a welfare recipient purchases a product from a vending machine to the relevant agency. The transmission unit transmits the purchase history managed by the management unit to the relevant agency. For example, the transmission unit periodically transmits the purchase history managed by the management unit to the relevant agency. For example, the transmission unit transmits the purchase history managed by the management unit to the relevant agency monthly. The transmission unit can also transmit the purchase history managed by the management unit to the relevant agency in real time. Furthermore, the transmission unit can also transmit purchase history managed by the management unit to the relevant agency based on specific conditions.This allows the facial recognition system to efficiently verify customer age, provide products, manage purchase history, and transmit history.

[0065] The facial recognition unit scans the customer's face to determine their age. The facial recognition unit uses, for example, high-precision facial recognition technology using AI to determine the customer's age. Specifically, the facial recognition unit can analyze facial features using deep learning technology to estimate age. Deep learning technology builds a model that accurately extracts facial features and estimates age by learning from a large amount of facial image data. For example, a convolutional neural network (CNN) is used to extract features from facial images and estimate age. Furthermore, the facial recognition unit can also use image processing technology to extract facial contours and feature points to determine age. Image processing technology detects facial contours and feature points such as eyes, nose, and mouth, and estimates age by analyzing the position and shape of these feature points. For example, age can be estimated by analyzing the shape of the facial contour, skin texture, and the presence or absence of wrinkles. The facial recognition unit can also refer to a facial recognition database to determine age based on past authentication history. The facial recognition database stores previously authenticated facial images and their age information, and age is determined by comparing them with newly scanned facial images. This allows the facial recognition unit to perform highly accurate age verification by combining multiple technologies.

[0066] The supply unit provides products based on the age verified by the facial recognition unit. For example, in a vending machine with an integrated facial recognition system, the supply unit verifies the customer's age through facial recognition when they purchase a product. Specifically, when a customer selects a product in a vending machine with an integrated facial recognition system, the facial recognition unit verifies their age and provides the appropriate product. For example, when purchasing age-restricted products such as alcoholic beverages or tobacco, the facial recognition unit verifies the customer's age and authorizes the purchase. The supply unit can also have the facial recognition unit verify the customer's age and record the purchase history when they purchase a product in a vending machine with an integrated facial recognition system. This allows the supply unit to properly manage the sale of age-restricted products and prevent purchases by minors. Furthermore, when a customer purchases a product in a vending machine with an integrated facial recognition system, the supply unit has the facial recognition unit verify their age and save the purchase history to a database. This allows the supply unit to centrally manage customer purchase history and utilize it for later analysis and management.

[0067] The Management Department manages the purchase history of products provided by the Supply Department. For example, when a welfare recipient purchases a product from a vending machine, the Management Department automatically records the purchase history. Specifically, when a welfare recipient purchases a product from a vending machine, the Management Department saves the purchase history to a database. The database records information such as the date and time of purchase, the purchased product, and the purchaser's information, and is used for later analysis and management. In addition, when a welfare recipient purchases a product from a vending machine, the Management Department can analyze the purchase history and provide appropriate support. For example, based on the purchase history, they can understand the purchasing trends and needs of welfare recipients and provide the necessary support. Furthermore, when a welfare recipient purchases a product from a vending machine, the Management Department can also transmit the purchase history to the relevant agency. This allows the Management Department to centrally manage the purchase history of welfare recipients and share information with the relevant agency to provide appropriate support.

[0068] The transmission unit sends purchase history managed by the management unit to the responsible agency. For example, the transmission unit periodically sends purchase history managed by the management unit to the responsible agency. Specifically, the transmission unit sends purchase history managed by the management unit to the responsible agency every month. By periodically sending purchase history, the transmission unit provides the responsible agency with information to understand the purchasing situation of welfare recipients and provide appropriate support. The transmission unit can also transmit purchase history managed by the management unit to the responsible agency in real time. For example, when a welfare recipient purchases an item, the purchase history is immediately sent to the responsible agency. This allows the responsible agency to understand the welfare recipient's purchasing situation in real time and respond quickly. Furthermore, the transmission unit can also transmit purchase history managed by the management unit to the responsible agency based on specific conditions. For example, the purchase history is sent to the responsible agency when a specific item is purchased or when a certain amount is exceeded. This allows the transmission unit to provide information flexibly based on specific conditions, enabling the responsible agency to appropriately receive the necessary information.

[0069] The facial recognition unit can verify a customer's age using high-precision facial recognition technology based on AI. For example, the facial recognition unit can analyze facial features using deep learning technology to estimate age. For instance, it can receive a facial image as input and estimate age using a deep learning model. The facial recognition unit can also extract facial contours and feature points using image processing technology to verify age. For example, it can receive a facial image as input, extract facial contours and feature points using an image processing algorithm, and verify age. Furthermore, the facial recognition unit can refer to a facial recognition database to verify age based on past authentication history. For example, it can refer to past authentication history stored in the facial recognition database and compare it with the current facial image to verify age. This improves the accuracy of age verification through high-precision facial recognition technology. Some or all of the above-described processes in the facial recognition unit may be performed using, for example, generative AI, or without generative AI. For example, the facial recognition unit can input a facial image into a generative AI, which can analyze facial features and estimate age.

[0070] The service provider can use a vending machine with an integrated facial recognition system to verify the age of a customer when they purchase a product. For example, when a customer selects a product in a vending machine with an integrated facial recognition system, the facial recognition system verifies their age and provides an appropriate product. For example, when a customer selects a product in a vending machine with an integrated facial recognition system, the facial recognition system verifies their age and provides an age-restricted product. The service provider can also use a vending machine with an integrated facial recognition system to verify the age of a customer when they purchase a product and authorize the purchase. For example, when a customer purchases a product in a vending machine with an integrated facial recognition system, the facial recognition system verifies their age and authorizes the purchase. The service provider can also use a vending machine with an integrated facial recognition system to verify the age of a customer when they purchase a product and record the purchase history. For example, when a customer purchases a product in a vending machine with an integrated facial recognition system, the facial recognition system verifies their age and records the purchase history. This automates age verification at vending machines, preventing fraudulent purchases. Some or all of the above-described processes in the dispensing unit may be performed using, for example, a generating AI, or without using a generating AI. For example, in a vending machine with an integrated facial recognition system, the dispensing unit can input a customer's facial image into a generating AI, which can verify the customer's age and dispense the product.

[0071] The management department can automatically record the purchase history of welfare recipients when they purchase items from vending machines. For example, when a welfare recipient purchases an item from a vending machine, the management department can save that purchase history to a database. For example, when a welfare recipient purchases an item from a vending machine, the management department can save that purchase history to a database for later reference. The management department can also analyze the purchase history of welfare recipients when they purchase items from vending machines and provide appropriate support. For example, when a welfare recipient purchases an item from a vending machine, the management department can analyze the purchase history and provide the necessary support. Furthermore, the management department can transmit the purchase history of welfare recipients to the relevant agency. For example, when a welfare recipient purchases an item from a vending machine, the management department can transmit that purchase history to the relevant agency, improving the transparency of welfare management. This makes it easier to manage welfare recipients' purchase history as it is automatically recorded. Some or all of the above-described processes in the management department may be performed using, for example, a generative AI, or without using a generative AI. For example, the management department can input the purchase history of welfare recipients into a generative AI, which can then analyze the purchase history to provide appropriate support.

[0072] The transmission unit can transmit purchase history managed by the management unit to the responsible agency. For example, the transmission unit can periodically transmit purchase history managed by the management unit to the responsible agency. For example, the transmission unit can transmit purchase history managed by the management unit to the responsible agency monthly. The transmission unit can also transmit purchase history managed by the management unit to the responsible agency in real time. For example, the transmission unit can transmit purchase history managed by the management unit to the responsible agency in real time. Furthermore, the transmission unit can transmit purchase history managed by the management unit to the responsible agency based on specific conditions. For example, the transmission unit can transmit purchase history managed by the management unit to the responsible agency based on specific conditions. This ensures that purchase history is automatically transmitted to the responsible agency, improving the transparency of welfare management. Some or all of the above processing in the transmission unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the transmission unit can input purchase history managed by the management unit into a generating AI, which can then analyze the purchase history and transmit it to the responsible agency.

[0073] The management department can automatically record purchase history and use it for marketing and customer analysis. For example, the management department can save purchase history to a database for later reference. For example, the management department can save purchase history to a database and use it for marketing and customer analysis. The management department can also analyze purchase history to understand customer purchasing trends. For example, the management department can analyze purchase history to understand customer purchasing trends and develop marketing strategies. Furthermore, the management department can predict customer needs based on purchase history and provide appropriate products. For example, the management department can predict customer needs based on purchase history and provide appropriate products. This allows purchase history to be used for marketing and customer analysis, optimizing the business. Some or all of the above processes in the management department may be performed using, for example, generative AI, or not using generative AI. For example, the management department can input purchase history into generative AI, which can analyze the purchase history and use it for marketing and customer analysis.

[0074] The facial recognition unit can estimate the customer's emotions and adjust the accuracy of facial recognition based on the estimated emotions. For example, if the customer is nervous, the facial recognition unit will perform multiple scans to improve the accuracy of facial recognition. The facial recognition unit can also perform facial recognition with the normal number of scans if the customer is relaxed. Furthermore, if the customer is in a hurry, the facial recognition unit can shorten the scan time to perform facial recognition. This adjusts the accuracy of facial recognition according to the customer's emotions, improving the reliability of authentication. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the facial recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the facial recognition unit can input customer facial expression data into a generative AI, which can then estimate emotions and adjust the accuracy of facial recognition.

[0075] The facial recognition unit can analyze a customer's past facial recognition history and select the optimal authentication method. For example, the facial recognition unit can adjust the authentication method considering the number of times a customer has failed facial recognition in the past. The facial recognition unit can also select the authentication method with the highest success rate from the customer's past facial recognition history. Furthermore, the facial recognition unit can optimize the timing of authentication based on the customer's past facial recognition history. This allows the optimal authentication method to be selected based on past facial recognition history, improving the success rate of authentication. Some or all of the above processing in the facial recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the facial recognition unit can input the customer's past facial recognition history into a generative AI, which can then select the optimal authentication method.

[0076] The facial recognition unit can improve the accuracy of authentication based on the customer's current health status and facial expression during facial recognition. For example, if the customer is tired, the facial recognition unit may perform additional scans to improve the accuracy of facial recognition. The facial recognition unit can also perform facial recognition with the normal number of scans if the customer is healthy. Furthermore, if the customer's facial expression changes frequently, the facial recognition unit may adjust the accuracy of authentication by taking into account the changes in facial expression. This improves the accuracy of authentication according to the customer's health status and facial expression, and increases the reliability of authentication. Some or all of the above processing in the facial recognition unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the facial recognition unit can input the customer's health status and facial expression data into a generative AI, which can then improve the accuracy of authentication.

[0077] The facial recognition unit can estimate the customer's emotions and adjust the timing of facial recognition based on the estimated emotions. For example, if the customer is nervous, the facial recognition unit can delay the timing of facial recognition until the customer is relaxed. For example, if the facial recognition unit is nervous, the facial recognition unit can delay the timing of facial recognition until the customer is relaxed. For example, if the facial recognition unit is relaxed, the facial recognition unit can perform facial recognition immediately. Furthermore, if the customer is in a hurry, the facial recognition unit can speed up the timing of facial recognition. For example, if the facial recognition unit is in a hurry, the facial recognition unit can speed up the timing of facial recognition. This adjusts the timing of facial recognition according to the customer's emotions, improving the efficiency of authentication. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the facial recognition unit may be performed using, for example, generative AI, or without generative AI. For example, the facial recognition unit can input customer facial expression data into a generating AI, which can then estimate emotions and adjust the timing of facial recognition.

[0078] The facial recognition unit can improve the accuracy of authentication by considering the customer's geographical location information during facial recognition. For example, if the customer is in a specific region, the facial recognition unit can adjust the accuracy of authentication by considering the characteristics of that region. The facial recognition unit can also improve the accuracy of authentication based on geographical location information if the customer is on the move. Furthermore, if the customer is inside a specific facility, the facial recognition unit can adjust the accuracy of authentication by considering the environment of that facility. In this way, the accuracy of authentication is improved by considering geographical location information. Some or all of the above processing in the facial recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the facial recognition unit can input the customer's geographical location information into a generative AI, which can then improve the accuracy of authentication.

[0079] The facial recognition unit can analyze the customer's social media activity during facial recognition and use the relevant information for authentication. For example, the facial recognition unit can improve the accuracy of facial recognition by referencing recent photos from the customer's social media activity. The facial recognition unit can also estimate the customer's current emotional state from their social media activity and adjust the accuracy of authentication. Furthermore, the facial recognition unit can analyze recent behavioral patterns from the customer's social media activity to improve the accuracy of authentication. In this way, the accuracy of authentication is improved by analyzing social media activity. Some or all of the above processing in the facial recognition unit may be performed using, for example, generative AI, or without generative AI. For example, the facial recognition unit can input the customer's social media activity data into generative AI, which can then analyze the relevant information and use it for authentication.

[0080] The service provider can estimate the customer's emotions and adjust the types of products offered based on the estimated emotions. For example, if the customer is relaxed, the service provider can offer products with relaxing effects. For example, if the customer is relaxed, the service provider can offer products with relaxing effects. The service provider can also prioritize offering products with relaxing effects if the customer is tense. For example, if the customer is tense, the service provider can prioritize offering products with relaxing effects. Furthermore, if the customer is in a hurry, the service provider can prioritize offering products that can be delivered quickly. For example, if the customer is in a hurry, the service provider can prioritize offering products that can be delivered quickly. This adjusts the types of products offered according to the customer's emotions, thereby improving customer satisfaction. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using, for example, generative AI, or not using generative AI. For example, the service provider can input customer emotion data into a generating AI, which can then estimate the emotion and adjust the type of product offered.

[0081] The delivery unit can adjust the level of detail provided based on the importance of the product at the time of delivery. For example, the delivery unit can provide detailed descriptions for high-importance products. The delivery unit can also provide concise descriptions for low-importance products. Furthermore, the delivery unit can adjust the level of detail provided in stages according to importance. This ensures that the level of detail provided is adjusted according to the importance of the product, and that the customer receives appropriate information. Some or all of the above processing in the delivery unit may be performed using, for example, a generating AI, or without a generating AI. For example, the delivery unit can input product importance data into a generating AI, and the generating AI can adjust the level of detail provided.

[0082] The supply unit can apply different supply algorithms depending on the product category at the time of supply. For example, the supply unit can apply a rapid supply algorithm to beverage products. For example, the supply unit can apply a quality-focused supply algorithm to food products. Furthermore, the supply unit can apply a supply algorithm that takes inventory status into account to general merchandise products. For example, the supply unit can apply a supply algorithm that takes inventory status into account to general merchandise products. This ensures that a supply algorithm appropriate to the product category is applied, improving the efficiency of supply. Some or all of the above processing in the supply unit may be performed using, for example, a generation AI, or without a generation AI. For example, the supply unit can input product category data into a generation AI, and the generation AI can apply a supply algorithm.

[0083] The service provider can estimate the customer's emotions and determine the priority of products to offer based on the estimated emotions. For example, if the customer is relaxed, the service provider will prioritize offering products with a relaxing effect. The service provider can also prioritize offering products with a relaxing effect if the customer is tense. Furthermore, if the customer is in a hurry, the service provider can prioritize offering products that can be delivered quickly. This ensures that the priority of products offered is determined according to the customer's emotions, thereby improving customer satisfaction. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using, for example, generative AI, or without generative AI. For example, the service department can input customer emotion data into a generating AI, which can then estimate the emotion and determine the priority of the products to offer.

[0084] The supply unit can determine the priority of product delivery based on the product delivery period. For example, the supply unit can determine the priority of seasonal products according to their delivery period. The supply unit can also determine the priority of new products according to their launch date. Furthermore, the supply unit can also determine the priority of limited-time products according to their delivery period. This ensures that the priority of product delivery is determined according to the product delivery period, and that products are delivered at the appropriate time. Some or all of the above processing in the supply unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the supply unit can input product delivery period data into a generating AI, and the generating AI can determine the priority of product delivery.

[0085] The service provider can adjust the order of service based on the relevance of the products at the time of service. For example, the service provider may prioritize providing products related to products the customer has purchased. The service provider may also provide products related to products the customer has purchased in the past. Furthermore, the service provider may provide highly relevant products based on the customer's purchase history. This adjusts the order of service according to the relevance of the products, ensuring that the customer receives the appropriate products. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider may input product relevance data into a generative AI, which can then adjust the order of service.

[0086] The management department can estimate the customer's emotions and adjust the method of managing purchase history based on the estimated emotions. For example, if the customer is relaxed, the management department can manage a detailed purchase history. For example, if the customer is relaxed, the management department can manage a detailed purchase history. For example, if the customer is stressed, the management department can manage a concise purchase history. For example, if the customer is stressed, the management department can manage a concise purchase history. Furthermore, if the customer is in a hurry, the management department can manage a rapid purchase history. For example, if the customer is in a hurry, the management department can manage a rapid purchase history. This adjusts the method of managing purchase history according to the customer's emotions, improving the efficiency of management. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management department may be performed using, for example, generative AI, or not using generative AI. For example, the management department can input customer emotion data into a generating AI, which can then estimate the emotion and adjust the purchase history management method accordingly.

[0087] The management department can analyze a customer's past purchase history and select the optimal management method during management. For example, the management department can select the optimal management method based on the customer's past purchase history. The management department can also adjust the management method according to the customer's purchase frequency. For example, the management department can adjust the management method according to the customer's purchase frequency. Furthermore, the management department can analyze the customer's purchase pattern and select the optimal management method. For example, the management department can analyze the customer's purchase pattern and select the optimal management method. This allows the optimal management method to be selected based on past purchase history, improving management efficiency. Some or all of the above processes in the management department may be performed using, for example, a generative AI, or without a generative AI. For example, the management department can input the customer's past purchase history data into a generative AI, which can then select the optimal management method.

[0088] The management department can customize the means of management based on the customer's current living situation during management. For example, the management department can customize the means of management according to the customer's living situation. The management department can also adjust the means of management to match the customer's daily rhythm. For example, the management department can adjust the means of management to match the customer's daily rhythm. Furthermore, the management department can customize the means of management considering the customer's living environment. For example, the management department can customize the means of management considering the customer's living environment. This customizes the means of management according to the customer's living situation, improving the accuracy of management. Some or all of the above processing in the management department may be performed using, for example, a generative AI, or without using a generative AI. For example, the management department can input customer living situation data into a generative AI, and the generative AI can customize the means of management.

[0089] The management department can estimate customer emotions and prioritize purchase history based on the estimated emotions. For example, if a customer is relaxed, the management department can prioritize managing detailed purchase history. For example, if a customer is relaxed, the management department can prioritize managing detailed purchase history. For example, if a customer is stressed, the management department can prioritize managing concise purchase history. For example, if a customer is stressed, the management department can prioritize managing concise purchase history. Furthermore, if a customer is in a hurry, the management department can prioritize managing purchase history quickly. For example, if a customer is in a hurry, the management department can prioritize managing purchase history quickly. This improves management efficiency by prioritizing purchase history according to customer emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management department may be performed using, for example, generative AI, or not using generative AI. For example, the management department can input customer sentiment data into a generating AI, which can then estimate the sentiment and determine the priority of purchase history.

[0090] The management department can select the optimal management method when managing customers, taking into account their geographical location information. For example, the management department can select the optimal management method based on the customer's geographical location information. The management department can also adjust the management method when a customer is in a specific region, taking into account the characteristics of that region. Furthermore, the management department can adjust the management method based on geographical location information when a customer is on the move. For example, the management department can adjust the management method based on geographical location information when a customer is on the move. This improves the accuracy of management by taking geographical location information into consideration. Some or all of the above processing in the management department may be performed using, for example, a generative AI, or without a generative AI. For example, the management department can input customer geographical location data into a generative AI, which can then select the optimal management method.

[0091] The management department can analyze customers' social media activity and propose management methods during management. For example, the management department can propose a method for managing purchase history based on customers' social media activity. The management department can also propose the optimal management method based on customers' social media activity. Furthermore, the management department can analyze customers' social media activity and customize the management methods. For example, the management department analyzes customers' social media activity and customizes the management methods. This optimizes the management methods by analyzing social media activity. Some or all of the above processes in the management department may be performed using, for example, generative AI, or not using generative AI. For example, the management department can input customer social media activity data into generative AI, and the generative AI can propose management methods.

[0092] The transmission unit can estimate the customer's emotions and determine the priority of information to transmit based on the estimated emotions. For example, if the customer is relaxed, the transmission unit will prioritize transmitting detailed information. For example, if the customer is relaxed, the transmission unit will prioritize transmitting detailed information. The transmission unit can also prioritize transmitting concise information if the customer is tense. For example, if the customer is tense, the transmission unit will prioritize transmitting concise information. Furthermore, if the customer is in a hurry, the transmission unit can prioritize transmitting information quickly. For example, if the customer is in a hurry, the transmission unit will prioritize transmitting information quickly. This ensures that the priority of information transmitted is determined according to the customer's emotions, making information transmission more efficient. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the transmission unit may be performed using, for example, generative AI, or not using generative AI. For example, the transmission unit can input customer emotion data into a generating AI, which can then estimate the emotion and determine the priority of the information to transmit.

[0093] The transmission unit can select the optimal transmission method by analyzing the customer's past transmission history at the time of transmission. For example, the transmission unit can select the optimal transmission method based on the customer's past transmission history. The transmission unit can also adjust the transmission method according to the customer's transmission frequency. For example, the transmission unit can adjust the transmission method according to the customer's transmission frequency. Furthermore, the transmission unit can analyze the customer's transmission pattern and select the optimal transmission method. For example, the transmission unit can analyze the customer's transmission pattern and select the optimal transmission method. This ensures that the optimal transmission method is selected based on past transmission history, making information transmission more efficient. Some or all of the above processing in the transmission unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the transmission unit can input the customer's past transmission history data into a generative AI, which can then select the optimal transmission method.

[0094] The transmitting unit can customize the means of transmission based on the customer's current living situation at the time of transmission. For example, the transmitting unit can customize the means of transmission according to the customer's living situation. The transmitting unit can also adjust the means of transmission to match the customer's daily rhythm. For example, the transmitting unit can adjust the means of transmission to match the customer's daily rhythm. Furthermore, the transmitting unit can also customize the means of transmission considering the customer's living environment. For example, the transmitting unit can customize the means of transmission considering the customer's living environment. This customizes the means of transmission according to the customer's living situation, making information transmission more efficient. Some or all of the above processing in the transmitting unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the transmitting unit can input customer living situation data into a generative AI, which can then customize the means of transmission.

[0095] The transmitting unit can estimate the customer's emotions and adjust how the information it transmits is displayed based on the estimated emotions. For example, if the customer is relaxed, the transmitting unit can display detailed information. For example, if the customer is relaxed, the transmitting unit can display detailed information. The transmitting unit can also display concise information if the customer is tense. For example, if the customer is tense, the transmitting unit can display information quickly. For example, if the customer is in a hurry, the transmitting unit can display information quickly. This adjusts how information is displayed according to the customer's emotions, making information transmission more efficient. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the transmitting unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the transmitting unit can input customer emotion data into a generative AI, and the generative AI can estimate the emotion and adjust how the information it transmits is displayed.

[0096] The transmission unit can select the optimal transmission method at the time of transmission, taking into account the customer's geographical location information. For example, the transmission unit selects the optimal transmission method based on the customer's geographical location information. The transmission unit can also adjust the transmission method if the customer is in a specific region, taking into account the characteristics of that region. For example, the transmission unit adjusts the transmission method if the customer is in a specific region, taking into account the characteristics of that region. Furthermore, the transmission unit can adjust the transmission method based on geographical location information if the customer is on the move. For example, the transmission unit adjusts the transmission method based on geographical location information if the customer is on the move. In this way, the transmission method is optimized by taking geographical location information into account. Some or all of the above processing in the transmission unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the transmission unit can input the customer's geographical location information data into a generative AI, and the generative AI can select the optimal transmission method.

[0097] The transmission unit can analyze the customer's social media activity and suggest a transmission method at the time of transmission. For example, the transmission unit can suggest a transmission method based on the customer's social media activity. The transmission unit can also suggest the optimal transmission method based on the customer's social media activity. Furthermore, the transmission unit can analyze the customer's social media activity and customize the transmission method. For example, the transmission unit analyzes the customer's social media activity and customizes the transmission method. This optimizes the transmission method by analyzing social media activity. Some or all of the above processing in the transmission unit may be performed using, for example, a generative AI, or without a generative AI. For example, the transmission unit can input customer social media activity data into a generative AI, which can then suggest a transmission method.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The facial recognition system can also be equipped with a voice recognition unit. The voice recognition unit can recognize the customer's voice and assist in age verification. For example, when a customer verbally states their age in front of a vending machine, the voice recognition unit analyzes their voice to verify their age. The voice recognition unit can also estimate emotions from the customer's tone of voice and speaking style, and work in conjunction with the facial recognition unit to improve the accuracy of authentication. Furthermore, the voice recognition unit can accept voice commands when the customer selects a product, facilitating smooth product delivery. This allows for improved efficiency in age verification and product delivery by utilizing voice recognition technology.

[0100] A facial recognition system can also be equipped with a biometric authentication unit. This unit can authenticate customers using biometric information such as fingerprints or irises. For example, when a customer scans their fingerprints in front of a vending machine, the biometric authentication unit analyzes the fingerprint information to verify their age. The biometric authentication unit can also scan the customer's iris and work in conjunction with the facial recognition unit to improve the accuracy of authentication. Furthermore, the biometric authentication unit can manage the customer's purchase history based on their biometric information and transmit it to the relevant authorities. This improves the accuracy of age verification and purchase history management by utilizing biometric authentication technology.

[0101] The facial recognition system may also include an environmental recognition unit. This unit can acquire information about the surrounding environment and improve the accuracy of authentication. For example, when a customer stands in front of a vending machine, the environmental recognition unit analyzes the surrounding lighting and background and adjusts the authentication accuracy of the facial recognition unit. The environmental recognition unit can also improve authentication accuracy by considering environmental factors such as weather and time of day. Furthermore, the environmental recognition unit can select the optimal authentication method based on the customer's location information. This allows for improved facial recognition accuracy through the use of environmental information.

[0102] The facial recognition system can also be equipped with a behavior recognition unit. This unit can analyze customer behavior patterns and improve authentication accuracy. For example, it can analyze how a customer moves in front of a vending machine and adjust the authentication accuracy of the facial recognition unit accordingly. Furthermore, the behavior recognition unit can select the optimal authentication method based on the customer's past behavioral history. Additionally, it can manage purchase history based on customer behavior patterns and transmit this information to the relevant authorities. This allows for improved facial recognition accuracy through the use of behavioral information.

[0103] The facial recognition system can also be equipped with an emotional feedback unit. This unit can provide real-time feedback on the customer's emotions, improving the accuracy of authentication. For example, if the customer is tense in front of a vending machine, the emotional feedback unit can provide guidance to help them relax. Conversely, if the customer is relaxed, the emotional feedback unit can expedite the authentication process. Furthermore, the emotional feedback unit can manage purchase history based on the customer's emotional state and transmit this information to the relevant authorities. This leverages emotional feedback to improve the accuracy of facial recognition.

[0104] The facial recognition system can also be equipped with a health status recognition unit. This unit can analyze the customer's health status and improve the accuracy of authentication. For example, if the customer is fatigued, the health status recognition unit can perform additional scans to improve the accuracy of facial recognition. Alternatively, if the customer is healthy, the health status recognition unit can perform facial recognition with the normal number of scans. Furthermore, the health status recognition unit can manage purchase history based on the customer's health status and transmit it to the relevant authorities. This allows for improved facial recognition accuracy by leveraging health information.

[0105] The facial recognition system can also be equipped with a social media integration unit. This unit can analyze the customer's social media activity and improve the accuracy of authentication. For example, it can improve facial recognition accuracy by referencing the customer's recent photos. It can also estimate the customer's current emotional state and adjust the authentication accuracy accordingly. Furthermore, it can analyze the customer's behavioral patterns, manage purchase history, and transmit this information to relevant authorities. This leverages social media activity to improve the accuracy of facial recognition.

[0106] The facial recognition system may also include a purchase intent estimation unit. This unit can estimate the customer's purchase intent and adjust the types of products offered accordingly. For example, if the unit indicates high purchase intent, it can suggest relevant products. Conversely, if the unit indicates low purchase intent, it can suggest discounted products. Furthermore, the unit can manage purchase history based on the customer's purchase intent and transmit it to the relevant authorities. This allows for the adjustment of product offerings based on purchase intent, thereby improving customer satisfaction.

[0107] The facial recognition system can also be equipped with a voice feedback unit. This unit can analyze the customer's voice and improve the accuracy of authentication. For example, the voice feedback unit can analyze what the customer says in front of the vending machine and adjust the authentication accuracy in conjunction with the facial recognition unit. Furthermore, the voice feedback unit can estimate emotions from the customer's tone of voice and speaking style, optimizing the authentication process. In addition, the voice feedback unit can manage purchase history based on the customer's voice and transmit it to the relevant authorities. This allows for improved facial recognition accuracy through the use of voice feedback.

[0108] The facial recognition system can also be equipped with a behavioral feedback unit. This unit can provide real-time feedback on customer behavior to improve authentication accuracy. For example, it can analyze how a customer moves in front of a vending machine and adjust authentication accuracy in conjunction with the facial recognition unit. The behavioral feedback unit can also select the optimal authentication method based on the customer's behavior patterns. Furthermore, it can manage purchase history based on customer behavior and transmit it to the relevant authorities. This allows for improved facial recognition accuracy through the use of behavioral feedback.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The facial recognition unit scans the customer's face to determine their age. The facial recognition unit uses high-precision AI-based facial recognition technology to determine the customer's age. For example, it uses deep learning technology to analyze facial features and estimate age. It can also use image processing technology to extract facial contours and feature points to determine age. Furthermore, it can refer to a facial recognition database to determine age based on past authentication history. Step 2: The dispensing unit provides the product based on the age verified by the facial recognition unit. For example, in a vending machine with an integrated facial recognition system, age verification is performed via facial recognition when a customer purchases a product. Once the customer selects a product, the facial recognition unit verifies their age and provides the appropriate product. It can also authorize the purchase. Furthermore, it can record the purchase history. Step 3: The management department manages the purchase history of products provided by the service provider. For example, when a welfare recipient purchases a product from a vending machine, the purchase history is automatically recorded and stored in the database. The purchase history can also be analyzed to provide appropriate support. Furthermore, the purchase history can be transmitted to the relevant agency. Step 4: The sending unit transmits the purchase history managed by the management unit to the responsible agency. For example, the purchase history managed by the management unit is transmitted to the responsible agency on a regular basis. This can be done monthly, in real time, or based on specific conditions.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements described above, including the facial recognition unit, supply unit, management unit, and transmission unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the facial recognition unit scans the customer's face using the camera 42 of the smart device 14 and verifies their age using the control unit 46A. The supply unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and provides products based on the verified age. The management unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and manages the purchase history of the provided products. The transmission unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and transmits the managed purchase history to the responsible organization. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the facial recognition unit, provision unit, management unit, and transmission unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the facial recognition unit scans the customer's face using the camera 42 of the smart glasses 214 and verifies their age using the control unit 46A. The provision unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and provides products based on the verified age. The management unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and manages the purchase history of the provided products. The transmission unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and transmits the managed purchase history to the responsible organization. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the facial recognition unit, provision unit, management unit, and transmission unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the facial recognition unit scans the customer's face using the camera 42 of the headset terminal 314 and verifies their age using the control unit 46A. The provision unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and provides products based on the verified age. The management unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and manages the purchase history of the provided products. The transmission unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and transmits the managed purchase history to the responsible organization. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the facial recognition unit, supply unit, management unit, and transmission unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the facial recognition unit scans the customer's face using the camera 42 of the robot 414 and verifies their age using the control unit 46A. The supply unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and provides products based on the verified age. The management unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and manages the purchase history of the provided products. The transmission unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and transmits the managed purchase history to the responsible organization. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0164] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0174] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] (Note 1) A facial recognition unit that scans the customer's face to confirm their age, A product provision unit that provides products based on the age confirmed by the aforementioned facial recognition unit, A management unit that manages the purchase history of products provided by the aforementioned provisioning unit, The system includes a transmission unit that transmits the purchase history managed by the aforementioned management unit to the responsible agency. A system characterized by the following features. (Note 2) The aforementioned facial recognition unit is We use AI-powered, highly accurate facial recognition technology to verify the customer's age. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, In a vending machine with an integrated facial recognition system, age verification is performed via facial recognition when a customer purchases a product. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned management department, When a welfare recipient purchases an item from a vending machine, the purchase history is automatically recorded. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned transmitting unit The purchase history managed by the aforementioned management department is transmitted to the responsible agency. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned management department, Automatically record purchase history to aid in marketing and customer analysis. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned facial recognition unit is The system estimates the customer's emotions and adjusts the accuracy of facial recognition based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned facial recognition unit is We analyze the customer's past facial recognition history and select the optimal authentication method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned facial recognition unit is During facial recognition, the accuracy of the authentication process is improved based on the customer's current health status and facial expressions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned facial recognition unit is It estimates the customer's emotions and adjusts the timing of facial recognition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned facial recognition unit is When using facial recognition, the accuracy of authentication is improved by taking into account the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned facial recognition unit is During facial recognition, the system analyzes the customer's social media activity and uses relevant information for authentication. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned supply unit is, We estimate customer emotions and adjust the types of products offered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned supply unit is, When providing the product, adjust the level of detail based on its importance. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned supply unit is, When providing products, different provisioning algorithms are applied depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, We estimate customer emotions and prioritize the products we offer based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, When providing products, we will determine the priority of provision based on the timing of product delivery. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing products, the order of delivery will be adjusted based on the relevance of the products. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned management department, We estimate customer emotions and adjust how we manage purchase history based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned management department, During management, the optimal management method is selected by analyzing the customer's past purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned management department, During management, customize the management methods based on the customer's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned management department, The system estimates customer emotions and prioritizes purchase history based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned management department, During management, the optimal management method is selected considering the customer's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned management department, During management, we analyze customers' social media activity and propose management strategies. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned transmitting unit It estimates customer emotions and prioritizes the information sent based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned transmitting unit When sending a message, the system analyzes the customer's past sending history to select the optimal sending method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned transmitting unit When sending, customize the method of sending based on the customer's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned transmitting unit We estimate customer emotions and adjust how information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned transmitting unit When sending a message, the system selects the optimal sending method, taking into account the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned transmitting unit When sending a message, we analyze the customer's social media activity and suggest a suitable method of sending. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A facial recognition unit that scans the customer's face to confirm their age, A product provision unit that provides products based on the age confirmed by the aforementioned facial recognition unit, A management unit that manages the purchase history of products provided by the aforementioned provisioning unit, The system includes a transmission unit that transmits the purchase history managed by the aforementioned management unit to the responsible agency. A system characterized by the following features.

2. The aforementioned facial recognition unit is We use AI-powered, highly accurate facial recognition technology to verify the customer's age. The system according to feature 1.

3. The aforementioned supply unit is, In a vending machine with an integrated facial recognition system, age verification is performed via facial recognition when a customer purchases a product. The system according to feature 1.

4. The aforementioned management department, When a welfare recipient purchases an item from a vending machine, the purchase history is automatically recorded. The system according to feature 1.

5. The aforementioned transmitting unit The purchase history managed by the aforementioned management department is transmitted to the responsible agency. The system according to feature 1.

6. The aforementioned management department, Automatically record purchase history to aid in marketing and customer analysis. The system according to feature 1.

7. The aforementioned facial recognition unit is The system estimates the customer's emotions and adjusts the accuracy of facial recognition based on those estimated emotions. The system according to feature 1.

8. The aforementioned facial recognition unit is We analyze the customer's past facial recognition history and select the optimal authentication method. The system according to feature 1.

9. The aforementioned facial recognition unit is During facial recognition, the accuracy of the authentication process is improved based on the customer's current health status and facial expressions. The system according to feature 1.

10. The aforementioned facial recognition unit is It estimates the customer's emotions and adjusts the timing of facial recognition based on the estimated emotions. The system according to feature 1.

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