System

Abstract

To provide a system that rapidly and accurately creates original dishes on the basis of customer requests.SOLUTION: A system according to an embodiment comprises a question unit, an analysis unit, a generation unit, a command unit, and a creation unit. The question unit acquires responses from a customer. The analysis unit analyzes the responses acquired by the question unit. The generation unit generates an image of a dish on the basis of the result analyzed by the analysis unit. The command unit issues commands to a 3D food printer on the basis of the image generated by the generation unit. The creation unit creates a dish on the basis of the commands issued by the command unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding the chatbot character, 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have had the problem that it is difficult to quickly and accurately prepare original dishes based on customer requests.

[0005] The system according to the embodiment aims to quickly and accurately create original dishes based on customer requests. [Means for solving the problem]

[0006] The system according to the embodiment includes a questioning unit, an analysis unit, a generation unit, a command unit, and a creation unit. The questioning unit acquires a customer's answer. The analysis unit analyzes the answer acquired by the questioning unit. The creation unit generates an image of a dish based on the result of the analysis by the analysis unit. The command unit issues a command to a 3D food printer based on the image generated by the creation unit. The creation unit creates the dish based on the command issued by the command unit. Effect of the Invention

[0007] The system according to the embodiment can quickly and accurately create original dishes based on customer requests. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

[0017] 1, a 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. The database 24 and the communication I / F 26 are also 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. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

[0025] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

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

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The food creation system according to the embodiment of the present invention is a system that combines a generation AI and a 3D food printer to create a dish that the customer wants to eat at that time. This food creation system asks the customer several questions and generates an image of the dish based on the answers. The generation AI asks the customer about their preferences, allergy information, and food ingredient preferences, and analyzes the answers. Next, the image generation AI generates an image of the dish based on the analysis results. This image is customized based on the customer's preferences and requests. For example, if the customer answers "I want to eat spicy curry," the image generation AI generates an image of spicy curry. Based on the generated image of the dish and the results of the question and answer session, a command is issued to the 3D food printer to create the dish. The 3D food printer creates the dish based on the generated image using the specified ingredients. For example, if an image of spicy curry is generated, the 3D food printer creates spicy curry using spices and curry ingredients. This mechanism allows the customer to enjoy original dishes that match their preferences and requests. Furthermore, by using a 3D food printer, the food can be created quickly and accurately, so the customer can reduce their waiting time. This allows the food creation system to quickly and accurately create original dishes based on the customer's preferences and requests.

[0029] The dish creation system according to the embodiment includes a questioning unit, an analysis unit, a generation unit, an instruction unit, and a creation unit. The questioning unit acquires a customer's answer. The customer's answer may be in a text format, a multiple choice format, or the like, but is not limited to such examples. The questioning unit acquires, for example, the customer's preferences, allergy information, and ingredient preferences. For example, the questioning unit asks the customer questions such as "What kind of food do you want to eat?", "Do you have any allergies?", and "What are your favorite ingredients?". The analysis unit analyzes the answer acquired by the questioning unit. The analysis may be performed using a method such as text mining or statistical analysis, but is not limited to such examples. For example, the analysis unit analyzes the customer's answer and identifies the characteristics of the dish. For example, the analysis unit extracts information such as "I want to eat spicy curry" from the customer's answer and identifies the characteristics of the dish. The generation unit uses a generation AI to generate an image of the dish based on the characteristics identified by the analysis unit. The generation AI generates an image of the dish using a technology such as an image generation algorithm or 3D modeling. For example, the generation unit uses a generation AI to generate an image of the dish. The command unit issues a command to the 3D food printer based on the image generated by the generation unit. The command may be, for example, a printer operation command or an ingredient distribution command, but is not limited to such examples. For example, the command unit issues commands such as "add spices" or "mix curry ingredients" to the 3D food printer. The creation unit creates a dish based on the command issued by the command unit. The creation unit creates a dish using, for example, specified ingredients. For example, the creation unit creates a spicy curry using spices and curry ingredients. In this way, the dish creation system according to the embodiment can create original dishes based on the customer's preferences and requests.

[0030] The question unit acquires the customer's answer. The customer's answer may be in a text format, a multiple choice format, or the like, but is not limited to such examples. The question unit acquires, for example, the customer's preferences, allergy information, and food ingredient preferences. Specifically, the question unit asks the customer questions such as "What kind of food do you want to eat?", "Do you have any allergies?", and "What are your favorite food ingredients?". These questions are important for understanding the customer's detailed preferences and health condition. The question unit can use a touch panel or a voice recognition system as an interface, and is designed to allow the customer to answer intuitively. For example, when a touch panel is used, the customer can input an answer simply by tapping an option on the screen. When a voice recognition system is used, the customer answers the question verbally, and the voice data is automatically converted into text data. Furthermore, the question unit can save the customer's past answer history and reuse it. This can save the customer the trouble of answering the same question every time. For example, for a customer who has answered "I like spicy food" in the past, spicy food can be suggested in the next and subsequent questions. The question unit also takes security measures to safely manage the customer's answer data and protect privacy. This allows customers to confidently provide their preferences and allergy information.

[0031] The analysis unit analyzes the answer acquired by the question unit. The analysis is performed by, for example, a method such as text mining or statistical analysis, but is not limited to such examples. Specifically, the analysis unit analyzes the customer's answer and identifies the characteristics of the dish. For example, the analysis unit extracts information such as "I want to eat spicy curry" from the customer's answer and identifies the characteristics of the dish. The analysis unit uses natural language processing technology to analyze the text data and accurately grasp the customer's intentions and preferences. For example, if the customer answers "I like spicy food," the analysis unit extracts the keyword "spicy" and suggests a dish with a strong spiciness. In addition, the analysis unit can use statistical analysis to aggregate the answer data of multiple customers and grasp common preferences and trends. This allows the analysis unit to grasp not only individual customers but also overall trends and make more appropriate dish suggestions. Furthermore, the analysis unit can use machine learning algorithms to learn customer preference patterns and improve the accuracy of analysis from the next time onwards. For example, based on past answer data, it is possible to predict what kind of food a customer will like and make more personalized suggestions. The analysis department utilizes these technologies to quickly and accurately analyze customer response data and identify the characteristics of the dishes.

[0032] The generation unit uses the generation AI to generate an image of the dish based on the characteristics identified by the analysis unit. The generation AI generates the image of the dish using technologies such as image generation algorithms and 3D modeling. Specifically, the generation unit uses the generation AI to generate an image of a spicy curry. The generation AI generates visuals of dishes that match the customer's preferences based on a large amount of food image data that it has learned in advance. For example, when generating an image of a spicy curry, it creates a realistic image by taking into account the color of the curry, the arrangement of ingredients, the amount of spices, etc. In addition, the generation unit can also generate a three-dimensional image of the dish using 3D modeling technology. This allows the customer to check the appearance of the completed dish in advance, increasing anticipation. The generation unit can also present the generated image to the customer and receive feedback. For example, if a customer requests, "I would like a little more spice," the generation unit regenerates the image according to that request. This increases customer satisfaction. The generation unit makes full use of these technologies to generate images of dishes that match the customer's preferences with high accuracy and provide them to the customer.

[0033] The command unit issues commands to the 3D food printer based on the image generated by the generation unit. The commands include, for example, printer operation commands and ingredient distribution commands, but are not limited to such examples. Specifically, the command unit issues commands such as "add spices" and "mix curry ingredients" to the 3D food printer. The command unit controls the operation of the 3D food printer in detail based on the generated image of the dish. For example, the printer nozzle movement, ingredient supply speed, temperature setting, etc. are finely adjusted to achieve the optimal cooking process. The command unit can also monitor the operation of the printer in real time and correct the commands as necessary. For example, if the supply of ingredients becomes uneven, the command unit immediately issues a correction command to achieve uniform supply. The command unit can also handle the creation of multiple dishes at the same time. For example, multiple printers can be linked and different commands can be issued to each printer to efficiently create multiple dishes. The command unit makes full use of these functions to accurately control the 3D food printer based on the generated image of the dish and create high-quality dishes.

[0034] The creation unit creates dishes based on the commands issued by the command unit. The creation unit creates dishes using, for example, specified ingredients. Specifically, the creation unit creates spicy curry using spices and curry ingredients. The creation unit uses a 3D food printer to accurately arrange ingredients and automate the cooking process. For example, the printer nozzle is used to evenly distribute spices and arrange curry ingredients in appropriate positions. Furthermore, the creation unit accurately controls the cooking temperature and time to achieve optimal cooking conditions. For example, the simmering time and temperature of the curry are finely adjusted to maximize the flavor and texture. The creation unit can also monitor the progress of the cooking process in real time and make adjustments as necessary. For example, the sensor detects the state of ingredients and the progress of cooking, and proceeds to the next step at the appropriate time. The creation unit makes full use of these functions to efficiently create high-quality dishes based on the instructions of the command unit. Furthermore, the creation unit can also perform quality checks on the completed dishes before serving them to customers. For example, the sensor evaluates the appearance, aroma, and taste to check whether they meet standards. This allows the preparation department to consistently provide high quality food to customers.

[0035] The questioning unit can acquire the customer's preferences, allergy information, and ingredient preferences. The questioning unit acquires, for example, the customer's preferences, allergy information, and ingredient preferences. For example, the questioning unit asks the customer questions such as "What kind of food would you like to eat?", "Do you have any allergies?", and "What are your favorite ingredients?". By acquiring detailed information about the customer, more appropriate dishes can be suggested. Some or all of the above-mentioned processing in the questioning unit may be performed, for example, using AI, or may be performed without using AI. For example, the questioning unit can input the customer's answers to AI, which can analyze the answers to identify the customer's preferences and allergy information.

[0036] The analysis unit can analyze the answer acquired by the question unit and identify the characteristics of the dish. The analysis unit can, for example, analyze the answer acquired by the question unit and identify the characteristics of the dish. The analysis can be performed by, for example, text mining or statistical analysis, but is not limited to such examples. For example, the analysis unit can analyze the customer's answer and identify the characteristics of the dish. For example, the analysis unit can extract information such as "I want to eat spicy curry" from the customer's answer and identify the characteristics of the dish. In this way, the characteristics of the dish can be identified by analyzing the customer's answer. A part or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI, or can be performed without using AI. For example, the analysis unit can input the customer's answer to AI, which can analyze the answer and identify the characteristics of the dish.

[0037] The generation unit can generate an image of a dish based on the features identified by the analysis unit. The generation unit uses a generation AI to generate an image of a dish based on the features identified by the analysis unit. The generation AI generates an image of a dish using a technique such as an image generation algorithm or 3D modeling. For example, the generation unit uses a generation AI to generate an image of a spicy curry. This makes it possible to provide a dish according to a customer's request by generating an image of a dish based on the analysis result. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the features identified by the analysis unit to the generation AI, which then generates an image of a dish.

[0038] The command unit can issue commands to the 3D food printer based on the image generated by the generation unit. The command unit issues commands to the 3D food printer based on the image generated by the generation unit, for example. The commands include, but are not limited to, printer operation commands and ingredient distribution commands. For example, the command unit issues commands such as "add spices" and "mix curry ingredients" to the 3D food printer. This allows accurate cooking to be created by issuing commands to the 3D food printer based on the generated image. Some or all of the above-mentioned processing in the command unit may be performed using, for example, AI, or may be performed without using AI. For example, the command unit can input the image generated by the generation unit to AI, and the AI ​​can issue commands to the 3D food printer.

[0039] The creation unit can create a dish using ingredients specified based on the command issued by the command unit. The creation unit, for example, creates a dish using ingredients specified based on the command issued by the command unit. For example, the creation unit creates a spicy curry using spices and curry ingredients. In this way, by creating a dish based on the command, a dish can be provided according to a customer's request. Some or all of the above-mentioned processing in the creation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the creation unit can input the command issued by the command unit to an AI, which then creates a dish using the specified ingredients.

[0040] The questioning unit can analyze the customer's past answer history and automatically generate appropriate questions. The questioning unit, for example, analyzes the customer's past answer history and automatically generates appropriate questions. For example, the questioning unit prioritizes questions about dishes that the customer liked in the past. The questioning unit can also omit questions about ingredients that the customer avoided in the past. The questioning unit can also ask questions about dishes according to the season from the customer's past answer history. This makes it possible to ask more appropriate questions by analyzing the past answer history. A part or all of the above-mentioned processing in the questioning unit may be performed, for example, using AI, or may be performed without using AI. For example, the questioning unit can input the customer's past answer history into AI, which can analyze the answer history and automatically generate appropriate questions.

[0041] The questioning unit can customize questions based on the customer's current health condition and nutritional balance. The questioning unit customizes questions, for example, taking into account the customer's current health condition and nutritional balance. For example, if the customer is on a diet, the questioning unit prioritizes questions about low-calorie dishes. In addition, if the customer has an allergy, the questioning unit can also ask questions about dishes that do not contain allergens. In addition, if the customer needs a specific nutrient, the questioning unit can also ask questions about dishes that contain the nutrient. This allows for more appropriate questions to be asked by taking into account the health condition and nutritional balance. Part or all of the above-mentioned processing in the questioning unit may be performed, for example, using AI, or may be performed without using AI. For example, the questioning unit can input the customer's health condition and nutritional balance into AI, which can then customize the questions.

[0042] The questioning unit can add questions about ingredients and dishes specific to a region in consideration of the geographical location information of the customer. The questioning unit can add questions about ingredients and dishes specific to a region in consideration of the geographical location information of the customer, for example. For example, if the customer is in a specific region, the questioning unit can ask questions about local specialties of the region. In addition, if the customer is traveling, the questioning unit can also ask questions about specialty dishes of the travel destination. In addition, if the customer prefers local ingredients, the questioning unit can also ask questions about dishes using ingredients of the region. This makes it possible to ask questions specific to a region by considering the geographical location information. Part or all of the above-mentioned processing in the questioning unit may be performed, for example, using AI, or may be performed without using AI. For example, the questioning unit can input the geographical location information of the customer to AI, and the AI ​​can generate questions specific to the region.

[0043] The questioning unit can analyze the social media activity of the customer and generate relevant questions. The questioning unit can, for example, analyze the social media activity of the customer and generate relevant questions. For example, the questioning unit can ask questions about dishes shared by the customer on social media. The questioning unit can also ask questions based on posts of cooking accounts that the customer follows. The questioning unit can also ask questions about dishes that the customer has "liked." This allows for more appropriate questions to be asked by analyzing social media activities. Some or all of the above-mentioned processing in the questioning unit can be performed, for example, using AI or without using AI. For example, the questioning unit can input the social media activity of the customer into an AI, which can analyze the activity and generate relevant questions.

[0044] The analysis unit can improve the accuracy of the analysis by referring to the customer's past meal history. The analysis unit can improve the accuracy of the analysis by referring to the customer's past meal history, for example. For example, the analysis unit reflects the characteristics of dishes that the customer liked in the past in the analysis. The analysis unit can also reflect and exclude ingredients that the customer avoided in the past in the analysis. The analysis unit can also reflect the characteristics of dishes according to the season in the analysis from the customer's past meal history. In this way, the accuracy of the analysis is improved by referring to the past meal history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the customer's past meal history into AI, and the AI ​​can improve the accuracy of the analysis by referring to the history.

[0045] The analysis unit can customize the analysis result by considering the health data of the customer. The analysis unit customizes the analysis result by considering, for example, the health data of the customer. For example, if the customer is on a diet, the analysis unit reflects the characteristics of low-calorie dishes in the analysis. In addition, if the customer has an allergy, the analysis unit can also reflect the characteristics of dishes that do not contain allergens in the analysis. In addition, if the customer needs a specific nutrient, the analysis unit can also reflect the characteristics of dishes that contain the nutrient in the analysis. In this way, by considering the health data, more appropriate analysis results can be obtained. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the health data of the customer into AI, and the AI ​​can customize the analysis result by considering the data.

[0046] The analysis unit can associate the analysis result with ingredients and dishes specific to the region by considering the geographical location information of the customer. The analysis unit, for example, associates the analysis result with ingredients and dishes specific to the region by considering the geographical location information of the customer. For example, when the customer is in a specific region, the analysis unit reflects the specialty of the region in the analysis result. In addition, when the customer is traveling, the analysis unit can also reflect the specialty dish of the travel destination in the analysis result. In addition, when the customer prefers local ingredients, the analysis unit can also reflect the dish using ingredients of the region in the analysis result. In this way, by considering the geographical location information, a region-specific analysis result can be obtained. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the geographical location information of the customer into AI, and the AI ​​can consider the information and associate the analysis result with ingredients and dishes specific to the region.

[0047] The analysis unit can analyze the social media activity of the customer and provide related analysis results. The analysis unit can, for example, analyze the social media activity of the customer and provide related analysis results. For example, the analysis unit reflects the characteristics of the dishes shared by the customer on social media in the analysis results. The analysis unit can also reflect the posts of the cooking accounts followed by the customer in the analysis results. The analysis unit can also reflect the characteristics of the dishes that the customer "liked" in the analysis results. In this way, a more appropriate analysis result can be obtained by analyzing the social media activity. A part or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI or can be performed without using AI. For example, the analysis unit can input the social media activity of the customer into AI, which analyzes the activity and provides related analysis results.

[0048] The generation unit can customize the dish image by referring to the customer's past preferences. The generation unit customizes the dish image by referring to the customer's past preferences, for example. For example, the generation unit generates a dish image that reflects the characteristics of dishes that the customer liked in the past. The generation unit can also generate a dish image that excludes ingredients that the customer avoided in the past. The generation unit can also generate a dish image according to the season from the customer's past preferences. In this way, a more appropriate dish image is generated by referring to the past preferences. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the customer's past preferences into the generation AI, and the generation AI can customize the dish image by referring to the preferences.

[0049] The generation unit can generate a nutritionally balanced dish image in consideration of the health data of the customer. The generation unit generates a nutritionally balanced dish image in consideration of the health data of the customer, for example. For example, if the customer is on a diet, the generation unit generates a low-calorie dish image. In addition, if the customer has an allergy, the generation unit can also generate a dish image that does not contain allergens. In addition, if the customer needs a specific nutrient, the generation unit can also generate a dish image that includes the nutrient. In this way, a more appropriate dish image is generated by taking the health data into consideration. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the health data of the customer into the generation AI, and the generation AI can generate a nutritionally balanced dish image in consideration of the data.

[0050] The generation unit can generate a region-specific food image by taking into account the geographical location information of the customer. The generation unit, for example, generates a region-specific food image by taking into account the geographical location information of the customer. For example, when the customer is in a specific region, the generation unit generates a food image using a specialty of the region. In addition, when the customer is traveling, the generation unit can also generate a specialty food image of the travel destination. In addition, when the customer prefers local ingredients, the generation unit can also generate a food image using ingredients of the region. In this way, a region-specific food image is generated by taking into account the geographical location information. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the geographical location information of the customer to the generation AI, and the generation AI can generate a region-specific food image by taking the information into account.

[0051] The generation unit can analyze the social media activity of the customer and generate a related dish image. The generation unit can, for example, analyze the social media activity of the customer and generate a related dish image. For example, the generation unit can generate a dish image that reflects the characteristics of a dish shared by the customer on social media. The generation unit can also generate a dish image that reflects a post of a cooking account that the customer follows. The generation unit can also generate a dish image that reflects the characteristics of a dish that the customer "liked." In this way, a more appropriate dish image is generated by analyzing the social media activity. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the social media activity of the customer to the generation AI, which can analyze the activity and generate a related dish image.

[0052] The command unit can optimize the command contents by referring to the customer's past cooking history. The command unit can optimize the command contents by referring to the customer's past cooking history, for example. For example, the command unit issues a command reflecting the cooking procedure of a dish that the customer liked in the past. The command unit can also issue a command that excludes ingredients that the customer avoided in the past. The command unit can also issue a command that reflects the optimal cooking method from the customer's past cooking history. This makes it possible to issue more appropriate commands by referring to the past cooking history. Some or all of the above-mentioned processing in the command unit may be performed, for example, using AI, or may be performed without using AI. For example, the command unit can input the customer's past cooking history into AI, and the AI ​​can optimize the command contents by referring to the history.

[0053] The command unit can customize the command contents in consideration of the health data of the customer. The command unit customizes the command contents in consideration of, for example, the health data of the customer. For example, if the customer is on a diet, the command unit issues a command to prepare a low-calorie dish. Also, if the customer has an allergy, the command unit can issue a command to prepare a dish that does not contain allergens. Also, if the customer needs a specific nutrient, the command unit can issue a command to prepare a dish that contains the nutrient. This allows for more appropriate commands by taking the health data into consideration. Part or all of the above-mentioned processing in the command unit may be performed, for example, using AI, or may be performed without using AI. For example, the command unit can input the customer's health data into AI, and the AI ​​can customize the command contents in consideration of the data.

[0054] The command unit can generate a command to use ingredients specific to a region in consideration of the geographical location information of the customer. The command unit, for example, generates a command to use ingredients specific to a region in consideration of the geographical location information of the customer. For example, if the customer is in a particular region, the command unit issues a command to use a specialty of the region. Also, if the customer is traveling, the command unit can issue a command to create a specialty dish of the travel destination. Also, if the customer prefers local ingredients, the command unit can issue a command to use ingredients of the region. This makes it possible to issue region-specific commands by considering the geographical location information. Part or all of the above-mentioned processing in the command unit may be performed, for example, using AI, or may be performed without using AI. For example, the command unit can input the geographical location information of the customer into AI, and the AI ​​can generate a command to use ingredients specific to the region in consideration of the information.

[0055] The command unit can analyze the social media activity of the customer and generate relevant commands. The command unit can, for example, analyze the social media activity of the customer and generate relevant commands. For example, the command unit issues commands that reflect the characteristics of dishes shared by the customer on social media. The command unit can also issue commands that reflect posts from cooking accounts that the customer follows. The command unit can also issue commands that reflect the characteristics of dishes that the customer has "liked." This allows for more appropriate commands by analyzing social media activity. Some or all of the above-mentioned processing in the command unit can be performed, for example, using AI or without AI. For example, the command unit can input the social media activity of the customer into an AI, which analyzes the activity and generates relevant commands.

[0056] The creation unit can optimize the creation procedure by referring to the customer's past cooking history. The creation unit optimizes the creation procedure by referring to the customer's past cooking history, for example. For example, the creation unit reflects the creation procedure of a dish that the customer liked in the past. The creation unit can also exclude ingredients that the customer avoided in the past. The creation unit can also reflect the optimal cooking method from the customer's past cooking history. This makes it possible to provide a more appropriate creation procedure by referring to the past cooking history. Some or all of the above-mentioned processing in the creation unit may be performed, for example, using AI, or may be performed without using AI. For example, the creation unit can input the customer's past cooking history into AI, and the AI ​​can optimize the creation procedure by referring to the history.

[0057] The creation unit can create a nutritionally balanced dish by taking into account the health data of the customer. The creation unit, for example, creates a nutritionally balanced dish by taking into account the health data of the customer. For example, if the customer is on a diet, the creation unit creates a low-calorie dish. In addition, if the customer has an allergy, the creation unit can also create a dish that does not contain allergens. In addition, if the customer needs a specific nutrient, the creation unit can also create a dish that contains the nutrient. In this way, a more appropriate dish is created by taking into account the health data. A part or all of the above-mentioned processing in the creation unit may be performed, for example, using AI, or may be performed without using AI. For example, the creation unit can input the customer's health data into AI, and the AI ​​can create a nutritionally balanced dish by taking the data into account.

[0058] The creation unit can use ingredients specific to the region in consideration of the geographical location information of the customer. The creation unit can use ingredients specific to the region in consideration of the geographical location information of the customer, for example. For example, if the customer is in a particular region, the creation unit uses local specialty foods of that region. Also, if the customer is traveling, the creation unit can create a specialty dish of the travel destination. Also, if the customer prefers local ingredients, the creation unit can use ingredients of that region. In this way, a dish specific to the region is created by considering the geographical location information. A part or all of the above-mentioned processing in the creation unit can be performed, for example, using AI, or can be performed without using AI. For example, the creation unit can input the geographical location information of the customer to AI, and the AI ​​can use ingredients specific to the region in consideration of the information.

[0059] The creation unit can analyze the social media activity of the customer and create a related dish. The creation unit can, for example, analyze the social media activity of the customer and create a related dish. For example, the creation unit reflects the characteristics of the dish shared by the customer on social media. The creation unit can also reflect posts from cooking accounts that the customer follows. The creation unit can also reflect the characteristics of the dish that the customer "liked." In this way, a more appropriate dish is created by analyzing the social media activity. A part or all of the above-mentioned processing in the creation unit can be performed, for example, using AI, or can be performed without using AI. For example, the creation unit can input the social media activity of the customer into AI, which can analyze the activity and create a related dish.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The questioning unit can also obtain information regarding the customer's meal frequency and meal times. For example, the questioning unit can ask the customer questions such as "How many times a day do you eat?" and "What time do you usually eat?" This allows the customer's eating patterns to be understood and more appropriate dishes can be suggested. The questioning unit can also adjust the amount of food and nutritional balance based on the customer's meal frequency and time of day. For example, if a customer eats multiple times a day, lighter dishes can be suggested. This allows dishes to be suggested that match the customer's eating habits.

[0062] The analysis unit can perform analysis based on the frequency and time of meals of the customer. For example, if the customer places importance on breakfast, the analysis unit can reflect the characteristics of dishes suitable for breakfast in the analysis. In addition, if the customer eats late at night, the analysis unit can also reflect the characteristics of dishes that are easy to digest in the analysis. This makes it possible to perform analysis according to the frequency and time of meals of the customer. In addition, the analysis unit can adjust the amount of food and nutritional balance based on the frequency and time of meals of the customer. For example, if the customer eats multiple times a day, the characteristics of lighter dishes can be reflected in the analysis. This makes it possible to perform analysis tailored to the eating habits of the customer.

[0063] The generation unit can generate dish images based on the frequency and time of meals of the customer. For example, if the customer places importance on breakfast, the generation unit can generate images of dishes suitable for breakfast. In addition, if the customer eats late at night, the generation unit can generate images of dishes that are easy to digest. In this way, dish images are generated according to the frequency and time of meals of the customer. In addition, the generation unit can adjust the amount of food and nutritional balance based on the frequency and time of meals of the customer. For example, if the customer eats multiple times a day, an image of a light dish can be generated. In this way, dish images are generated that match the eating habits of the customer.

[0064] The command unit can issue commands to the 3D food printer based on the frequency and time of the customer's meals. For example, if a customer places importance on breakfast, the command unit can issue a command to create a dish suitable for breakfast. In addition, if a customer eats late at night, the command unit can issue a command to create a dish that is easy to digest. This makes it possible to issue commands according to the frequency and time of the customer's meals. In addition, the command unit can issue a command to adjust the amount of food and nutritional balance based on the frequency and time of the customer's meals. For example, if a customer eats multiple times a day, a command to create a lighter dish can be issued. This makes it possible to issue commands according to the customer's eating habits.

[0065] The creation unit can create dishes based on the frequency and time of day of the customer's meals. For example, if the customer places importance on breakfast, the creation unit can create dishes suitable for breakfast. In addition, if the customer eats late at night, the creation unit can create dishes that are easy to digest. In this way, dishes are created according to the frequency and time of day of the customer's meals. In addition, the creation unit can adjust the amount of food and nutritional balance based on the frequency and time of day of the customer's meals. For example, if the customer eats multiple times a day, lighter dishes can be created. In this way, dishes are created that match the customer's eating habits.

[0066] The process flow of the first embodiment will be briefly described below.

[0067] Step 1: The questioning unit obtains the customer's answer. The customer's answer may be in text format, multiple choice format, or the like. The questioning unit obtains the customer's preferences, allergy information, food preferences, and the like. For example, it asks questions such as "What kind of food would you like to eat?", "Do you have any allergies?", and "What is your favorite food?". Step 2: The analysis unit analyzes the answers obtained by the question unit. The analysis is performed using methods such as text mining and statistical analysis. For example, information such as "I want to eat spicy curry" is extracted from the customer's answer, and the characteristics of the dish are identified. Step 3: The generator uses a generative AI to generate an image of a dish based on the characteristics identified by the analyzer. The generative AI uses techniques such as image generation algorithms and 3D modeling. For example, it generates an image of a spicy curry. Step 4: The instruction unit issues commands to the 3D food printer based on the image generated by the generation unit. The commands include printer operation instructions and ingredient distribution instructions. For example, commands such as "add spices" and "mix curry ingredients" are issued. Step 5: The creation unit creates the dish based on the command issued by the command unit. The creation unit creates the dish using the specified ingredients. For example, the creation unit creates a spicy curry using spices and curry ingredients.

[0068] (Example 2) The food creation system according to the embodiment of the present invention is a system that combines a generation AI and a 3D food printer to create a dish that the customer wants to eat at that time. This food creation system asks the customer several questions and generates an image of the dish based on the answers. The generation AI asks the customer about their preferences, allergy information, and food ingredient preferences, and analyzes the answers. Next, the image generation AI generates an image of the dish based on the analysis results. This image is customized based on the customer's preferences and requests. For example, if the customer answers "I want to eat spicy curry," the image generation AI generates an image of spicy curry. Based on the generated image of the dish and the results of the question and answer session, a command is issued to the 3D food printer to create the dish. The 3D food printer creates the dish based on the generated image using the specified ingredients. For example, if an image of spicy curry is generated, the 3D food printer creates spicy curry using spices and curry ingredients. This mechanism allows the customer to enjoy original dishes that match their preferences and requests. Furthermore, by using a 3D food printer, the food can be created quickly and accurately, so the customer can reduce their waiting time. This allows the food creation system to quickly and accurately create original dishes based on the customer's preferences and requests.

[0069] The dish creation system according to the embodiment includes a questioning unit, an analysis unit, a generation unit, an instruction unit, and a creation unit. The questioning unit acquires a customer's answer. The customer's answer may be in a text format, a multiple choice format, or the like, but is not limited to such examples. The questioning unit acquires, for example, the customer's preferences, allergy information, and ingredient preferences. For example, the questioning unit asks the customer questions such as "What kind of food do you want to eat?", "Do you have any allergies?", and "What are your favorite ingredients?". The analysis unit analyzes the answer acquired by the questioning unit. The analysis may be performed using a method such as text mining or statistical analysis, but is not limited to such examples. For example, the analysis unit analyzes the customer's answer and identifies the characteristics of the dish. For example, the analysis unit extracts information such as "I want to eat spicy curry" from the customer's answer and identifies the characteristics of the dish. The generation unit uses a generation AI to generate an image of the dish based on the characteristics identified by the analysis unit. The generation AI generates an image of the dish using a technology such as an image generation algorithm or 3D modeling. For example, the generation unit uses a generation AI to generate an image of the dish. The command unit issues a command to the 3D food printer based on the image generated by the generation unit. The command may be, for example, a printer operation command or an ingredient distribution command, but is not limited to such examples. For example, the command unit issues commands such as "add spices" or "mix curry ingredients" to the 3D food printer. The creation unit creates a dish based on the command issued by the command unit. The creation unit creates a dish using, for example, specified ingredients. For example, the creation unit creates a spicy curry using spices and curry ingredients. In this way, the dish creation system according to the embodiment can create original dishes based on the customer's preferences and requests.

[0070] The question unit acquires the customer's answer. The customer's answer may be in a text format, a multiple choice format, or the like, but is not limited to such examples. The question unit acquires, for example, the customer's preferences, allergy information, and food ingredient preferences. Specifically, the question unit asks the customer questions such as "What kind of food do you want to eat?", "Do you have any allergies?", and "What are your favorite food ingredients?". These questions are important for understanding the customer's detailed preferences and health condition. The question unit can use a touch panel or a voice recognition system as an interface, and is designed to allow the customer to answer intuitively. For example, when a touch panel is used, the customer can input an answer simply by tapping an option on the screen. When a voice recognition system is used, the customer answers the question verbally, and the voice data is automatically converted into text data. Furthermore, the question unit can save the customer's past answer history and reuse it. This can save the customer the trouble of answering the same question every time. For example, for a customer who has answered "I like spicy food" in the past, spicy food can be suggested in the next and subsequent questions. The question unit also takes security measures to safely manage the customer's answer data and protect privacy. This allows customers to confidently provide their preferences and allergy information.

[0071] The analysis unit analyzes the answer acquired by the question unit. The analysis is performed by, for example, a method such as text mining or statistical analysis, but is not limited to such examples. Specifically, the analysis unit analyzes the customer's answer and identifies the characteristics of the dish. For example, the analysis unit extracts information such as "I want to eat spicy curry" from the customer's answer and identifies the characteristics of the dish. The analysis unit uses natural language processing technology to analyze the text data and accurately grasp the customer's intentions and preferences. For example, if the customer answers "I like spicy food," the analysis unit extracts the keyword "spicy" and suggests a dish with a strong spiciness. In addition, the analysis unit can use statistical analysis to aggregate the answer data of multiple customers and grasp common preferences and trends. This allows the analysis unit to grasp not only individual customers but also overall trends and make more appropriate dish suggestions. Furthermore, the analysis unit can use machine learning algorithms to learn customer preference patterns and improve the accuracy of analysis from the next time onwards. For example, based on past answer data, it is possible to predict what kind of food a customer will like and make more personalized suggestions. The analysis department utilizes these technologies to quickly and accurately analyze customer response data and identify the characteristics of the dishes.

[0072] The generation unit uses the generation AI to generate an image of the dish based on the characteristics identified by the analysis unit. The generation AI generates the image of the dish using technologies such as image generation algorithms and 3D modeling. Specifically, the generation unit uses the generation AI to generate an image of a spicy curry. The generation AI generates visuals of dishes that match the customer's preferences based on a large amount of food image data that it has learned in advance. For example, when generating an image of a spicy curry, it creates a realistic image by taking into account the color of the curry, the arrangement of ingredients, the amount of spices, etc. In addition, the generation unit can also generate a three-dimensional image of the dish using 3D modeling technology. This allows the customer to check the appearance of the completed dish in advance, increasing anticipation. The generation unit can also present the generated image to the customer and receive feedback. For example, if a customer requests, "I would like a little more spice," the generation unit regenerates the image according to that request. This increases customer satisfaction. The generation unit makes full use of these technologies to generate images of dishes that match the customer's preferences with high accuracy and provide them to the customer.

[0073] The command unit issues commands to the 3D food printer based on the image generated by the generation unit. The commands include, for example, printer operation commands and ingredient distribution commands, but are not limited to such examples. Specifically, the command unit issues commands such as "add spices" and "mix curry ingredients" to the 3D food printer. The command unit controls the operation of the 3D food printer in detail based on the generated image of the dish. For example, the printer nozzle movement, ingredient supply speed, temperature setting, etc. are finely adjusted to achieve the optimal cooking process. The command unit can also monitor the operation of the printer in real time and correct the commands as necessary. For example, if the supply of ingredients becomes uneven, the command unit immediately issues a correction command to achieve uniform supply. The command unit can also handle the creation of multiple dishes at the same time. For example, multiple printers can be linked and different commands can be issued to each printer to efficiently create multiple dishes. The command unit makes full use of these functions to accurately control the 3D food printer based on the generated image of the dish and create high-quality dishes.

[0074] The creation unit creates dishes based on the commands issued by the command unit. The creation unit creates dishes using, for example, specified ingredients. Specifically, the creation unit creates spicy curry using spices and curry ingredients. The creation unit uses a 3D food printer to accurately arrange ingredients and automate the cooking process. For example, the printer nozzle is used to evenly distribute spices and arrange curry ingredients in appropriate positions. Furthermore, the creation unit accurately controls the cooking temperature and time to achieve optimal cooking conditions. For example, the simmering time and temperature of the curry are finely adjusted to maximize the flavor and texture. The creation unit can also monitor the progress of the cooking process in real time and make adjustments as necessary. For example, the sensor detects the state of ingredients and the progress of cooking, and proceeds to the next step at the appropriate time. The creation unit makes full use of these functions to efficiently create high-quality dishes based on the instructions of the command unit. Furthermore, the creation unit can also perform quality checks on the completed dishes before serving them to customers. For example, the sensor evaluates the appearance, aroma, and taste to check whether they meet standards. This allows the preparation department to consistently provide high quality food to customers.

[0075] The questioning unit can acquire the customer's preferences, allergy information, and ingredient preferences. The questioning unit acquires, for example, the customer's preferences, allergy information, and ingredient preferences. For example, the questioning unit asks the customer questions such as "What kind of food would you like to eat?", "Do you have any allergies?", and "What are your favorite ingredients?". By acquiring detailed information about the customer, more appropriate dishes can be suggested. Some or all of the above-mentioned processing in the questioning unit may be performed, for example, using AI, or may be performed without using AI. For example, the questioning unit can input the customer's answers to AI, which can analyze the answers to identify the customer's preferences and allergy information.

[0076] The analysis unit can analyze the answer acquired by the question unit and identify the characteristics of the dish. The analysis unit can, for example, analyze the answer acquired by the question unit and identify the characteristics of the dish. The analysis can be performed by, for example, text mining or statistical analysis, but is not limited to such examples. For example, the analysis unit can analyze the customer's answer and identify the characteristics of the dish. For example, the analysis unit can extract information such as "I want to eat spicy curry" from the customer's answer and identify the characteristics of the dish. In this way, the characteristics of the dish can be identified by analyzing the customer's answer. A part or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI, or can be performed without using AI. For example, the analysis unit can input the customer's answer to AI, which can analyze the answer and identify the characteristics of the dish.

[0077] The generation unit can generate an image of a dish based on the features identified by the analysis unit. The generation unit uses a generation AI to generate an image of a dish based on the features identified by the analysis unit. The generation AI generates an image of a dish using a technique such as an image generation algorithm or 3D modeling. For example, the generation unit uses a generation AI to generate an image of a spicy curry. This makes it possible to provide a dish according to a customer's request by generating an image of a dish based on the analysis result. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the features identified by the analysis unit to the generation AI, which then generates an image of a dish.

[0078] The command unit can issue commands to the 3D food printer based on the image generated by the generation unit. The command unit issues commands to the 3D food printer based on the image generated by the generation unit, for example. The commands include, but are not limited to, printer operation commands and ingredient distribution commands. For example, the command unit issues commands such as "add spices" and "mix curry ingredients" to the 3D food printer. This allows accurate cooking to be created by issuing commands to the 3D food printer based on the generated image. Some or all of the above-mentioned processing in the command unit may be performed using, for example, AI, or may be performed without using AI. For example, the command unit can input the image generated by the generation unit to AI, and the AI ​​can issue commands to the 3D food printer.

[0079] The creation unit can create a dish using ingredients specified based on the command issued by the command unit. The creation unit, for example, creates a dish using ingredients specified based on the command issued by the command unit. For example, the creation unit creates a spicy curry using spices and curry ingredients. In this way, by creating a dish based on the command, a dish can be provided according to a customer's request. Some or all of the above-mentioned processing in the creation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the creation unit can input the command issued by the command unit to an AI, which then creates a dish using the specified ingredients.

[0080] The questioning unit can estimate the customer's emotions and adjust the content and order of questions based on the estimated customer's emotions. The questioning unit, for example, estimates the customer's emotions and adjusts the content and order of questions based on the estimated customer's emotions. For example, if the customer is stressed, the questioning unit can prioritize questions about relaxing dishes. Also, if the customer is excited, the questioning unit can prioritize questions about exciting dishes. Also, if the customer is tired, the questioning unit can prioritize questions about simple and nutritious dishes. This allows for more appropriate questions by adjusting the content and order of questions according to the customer's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the questioning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the questioning unit can input the customer's emotions into an AI, and the AI ​​can estimate the emotions and adjust the content and order of questions.

[0081] The questioning unit can analyze the customer's past answer history and automatically generate appropriate questions. The questioning unit, for example, analyzes the customer's past answer history and automatically generates appropriate questions. For example, the questioning unit prioritizes questions about dishes that the customer liked in the past. The questioning unit can also omit questions about ingredients that the customer avoided in the past. The questioning unit can also ask questions about dishes according to the season from the customer's past answer history. This makes it possible to ask more appropriate questions by analyzing the past answer history. A part or all of the above-mentioned processing in the questioning unit may be performed, for example, using AI, or may be performed without using AI. For example, the questioning unit can input the customer's past answer history into AI, which can analyze the answer history and automatically generate appropriate questions.

[0082] The questioning unit can customize questions based on the customer's current health condition and nutritional balance. The questioning unit customizes questions, for example, taking into account the customer's current health condition and nutritional balance. For example, if the customer is on a diet, the questioning unit prioritizes questions about low-calorie dishes. In addition, if the customer has an allergy, the questioning unit can also ask questions about dishes that do not contain allergens. In addition, if the customer needs a specific nutrient, the questioning unit can also ask questions about dishes that contain the nutrient. This allows for more appropriate questions to be asked by taking into account the health condition and nutritional balance. Part or all of the above-mentioned processing in the questioning unit may be performed, for example, using AI, or may be performed without using AI. For example, the questioning unit can input the customer's health condition and nutritional balance into AI, which can then customize the questions.

[0083] The questioning unit can estimate the customer's emotions and adjust the difficulty of the questions based on the estimated customer's emotions. The questioning unit, for example, estimates the customer's emotions and adjusts the difficulty of the questions based on the estimated customer's emotions. For example, when the customer is relaxed, the questioning unit asks detailed questions to grasp more specific preferences. Also, when the customer is in a hurry, the questioning unit can ask simple questions to quickly collect information. Also, when the customer is excited, the questioning unit can ask interesting questions to provide a fun experience. This allows for more appropriate questions by adjusting the difficulty of the questions according to the customer's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the questioning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the questioning unit can input the customer's emotions into an AI, and the AI ​​can estimate the emotions and adjust the difficulty of the questions.

[0084] The questioning unit can add questions about ingredients and dishes specific to a region in consideration of the geographical location information of the customer. The questioning unit can add questions about ingredients and dishes specific to a region in consideration of the geographical location information of the customer, for example. For example, if the customer is in a specific region, the questioning unit can ask questions about local specialties of the region. In addition, if the customer is traveling, the questioning unit can also ask questions about specialty dishes of the travel destination. In addition, if the customer prefers local ingredients, the questioning unit can also ask questions about dishes using ingredients of the region. This makes it possible to ask questions specific to a region by considering the geographical location information. Part or all of the above-mentioned processing in the questioning unit may be performed, for example, using AI, or may be performed without using AI. For example, the questioning unit can input the geographical location information of the customer to AI, and the AI ​​can generate questions specific to the region.

[0085] The questioning unit can analyze the social media activity of the customer and generate relevant questions. The questioning unit can, for example, analyze the social media activity of the customer and generate relevant questions. For example, the questioning unit can ask questions about dishes shared by the customer on social media. The questioning unit can also ask questions based on posts of cooking accounts that the customer follows. The questioning unit can also ask questions about dishes that the customer has "liked." This allows for more appropriate questions to be asked by analyzing social media activities. Some or all of the above-mentioned processing in the questioning unit can be performed, for example, using AI or without using AI. For example, the questioning unit can input the social media activity of the customer into an AI, which can analyze the activity and generate relevant questions.

[0086] The analysis unit can estimate the customer's emotions and adjust the analysis algorithm based on the estimated customer's emotions. The analysis unit, for example, estimates the customer's emotions and adjusts the analysis algorithm based on the estimated customer's emotions. For example, when the customer is relaxed, the analysis unit performs a detailed analysis to grasp more specific preferences. In addition, when the customer is in a hurry, the analysis unit can perform a simplified analysis to quickly provide results. In addition, when the customer is excited, the analysis unit can perform an interesting analysis to provide a fun experience. This enables more appropriate analysis by adjusting the analysis algorithm according to the customer's emotions. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the customer's emotions into an AI, and the AI ​​can estimate the emotions and adjust the analysis algorithm.

[0087] The analysis unit can improve the accuracy of the analysis by referring to the customer's past meal history. The analysis unit can improve the accuracy of the analysis by referring to the customer's past meal history, for example. For example, the analysis unit reflects the characteristics of dishes that the customer liked in the past in the analysis. The analysis unit can also reflect and exclude ingredients that the customer avoided in the past in the analysis. The analysis unit can also reflect the characteristics of dishes according to the season in the analysis from the customer's past meal history. In this way, the accuracy of the analysis is improved by referring to the past meal history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the customer's past meal history into AI, and the AI ​​can improve the accuracy of the analysis by referring to the history.

[0088] The analysis unit can customize the analysis result by considering the health data of the customer. The analysis unit customizes the analysis result by considering, for example, the health data of the customer. For example, if the customer is on a diet, the analysis unit reflects the characteristics of low-calorie dishes in the analysis. In addition, if the customer has an allergy, the analysis unit can also reflect the characteristics of dishes that do not contain allergens in the analysis. In addition, if the customer needs a specific nutrient, the analysis unit can also reflect the characteristics of dishes that contain the nutrient in the analysis. In this way, by considering the health data, more appropriate analysis results can be obtained. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the health data of the customer into AI, and the AI ​​can customize the analysis result by considering the data.

[0089] The analysis unit can estimate the customer's emotions and adjust the display method of the analysis results based on the estimated customer's emotions. The analysis unit, for example, estimates the customer's emotions and adjusts the display method of the analysis results based on the estimated customer's emotions. For example, the analysis unit provides a simple and highly visible display method when the customer is nervous. The analysis unit can also provide a display method including detailed information when the customer is relaxed. The analysis unit can also provide a display method that focuses on the main points when the customer is in a hurry. This allows for more appropriate display by adjusting the display method according to the customer's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the customer's emotions into an AI, and the AI ​​can estimate the emotions and adjust the display method of the analysis results.

[0090] The analysis unit can associate the analysis result with ingredients and dishes specific to the region by considering the geographical location information of the customer. The analysis unit, for example, associates the analysis result with ingredients and dishes specific to the region by considering the geographical location information of the customer. For example, when the customer is in a specific region, the analysis unit reflects the specialty of the region in the analysis result. In addition, when the customer is traveling, the analysis unit can also reflect the specialty dish of the travel destination in the analysis result. In addition, when the customer prefers local ingredients, the analysis unit can also reflect the dish using ingredients of the region in the analysis result. In this way, by considering the geographical location information, a region-specific analysis result can be obtained. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the geographical location information of the customer into AI, and the AI ​​can consider the information and associate the analysis result with ingredients and dishes specific to the region.

[0091] The analysis unit can analyze the social media activity of the customer and provide related analysis results. The analysis unit can, for example, analyze the social media activity of the customer and provide related analysis results. For example, the analysis unit reflects the characteristics of the dishes shared by the customer on social media in the analysis results. The analysis unit can also reflect the posts of the cooking accounts followed by the customer in the analysis results. The analysis unit can also reflect the characteristics of the dishes that the customer "liked" in the analysis results. In this way, a more appropriate analysis result can be obtained by analyzing the social media activity. A part or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI or can be performed without using AI. For example, the analysis unit can input the social media activity of the customer into AI, which analyzes the activity and provides related analysis results.

[0092] The generation unit can estimate the customer's emotion and adjust the color and design of the food image based on the estimated customer's emotion. The generation unit, for example, estimates the customer's emotion and adjusts the color and design of the food image based on the estimated customer's emotion. For example, the generation unit generates a food image with calm colors when the customer is relaxed. In addition, the generation unit can also generate a food image with vivid colors when the customer is excited. In addition, the generation unit can generate a simple and highly visible food image when the customer is tired. In this way, a more appropriate food image is generated by adjusting the color and design according to the customer's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the customer's emotion into the generation AI, and the generation AI can estimate the emotion and adjust the color and design of the food image.

[0093] The generation unit can customize the dish image by referring to the customer's past preferences. The generation unit customizes the dish image by referring to the customer's past preferences, for example. For example, the generation unit generates a dish image that reflects the characteristics of dishes that the customer liked in the past. The generation unit can also generate a dish image that excludes ingredients that the customer avoided in the past. The generation unit can also generate a dish image according to the season from the customer's past preferences. In this way, a more appropriate dish image is generated by referring to the past preferences. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the customer's past preferences into the generation AI, and the generation AI can customize the dish image by referring to the preferences.

[0094] The generation unit can generate a nutritionally balanced dish image in consideration of the health data of the customer. The generation unit generates a nutritionally balanced dish image in consideration of the health data of the customer, for example. For example, if the customer is on a diet, the generation unit generates a low-calorie dish image. In addition, if the customer has an allergy, the generation unit can also generate a dish image that does not contain allergens. In addition, if the customer needs a specific nutrient, the generation unit can also generate a dish image that includes the nutrient. In this way, a more appropriate dish image is generated by taking the health data into consideration. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the health data of the customer into the generation AI, and the generation AI can generate a nutritionally balanced dish image in consideration of the data.

[0095] The generation unit can estimate the customer's emotion and adjust the level of detail of the food image based on the estimated customer's emotion. The generation unit, for example, estimates the customer's emotion and adjusts the level of detail of the food image based on the estimated customer's emotion. For example, the generation unit generates a detailed food image when the customer is relaxed. The generation unit can also generate a simplified food image when the customer is in a hurry. The generation unit can also generate a visually stimulating food image when the customer is excited. In this way, a more appropriate food image is generated by adjusting the level of detail according to the customer's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the customer's emotion into the generation AI, and the generation AI can estimate the emotion and adjust the level of detail of the food image.

[0096] The generation unit can generate a region-specific food image by taking into account the geographical location information of the customer. The generation unit, for example, generates a region-specific food image by taking into account the geographical location information of the customer. For example, when the customer is in a specific region, the generation unit generates a food image using a specialty of the region. In addition, when the customer is traveling, the generation unit can also generate a specialty food image of the travel destination. In addition, when the customer prefers local ingredients, the generation unit can also generate a food image using ingredients of the region. In this way, a region-specific food image is generated by taking into account the geographical location information. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the geographical location information of the customer to the generation AI, and the generation AI can generate a region-specific food image by taking the information into account.

[0097] The generation unit can analyze the social media activity of the customer and generate a related dish image. The generation unit can, for example, analyze the social media activity of the customer and generate a related dish image. For example, the generation unit can generate a dish image that reflects the characteristics of a dish shared by the customer on social media. The generation unit can also generate a dish image that reflects a post of a cooking account that the customer follows. The generation unit can also generate a dish image that reflects the characteristics of a dish that the customer "liked." In this way, a more appropriate dish image is generated by analyzing the social media activity. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the social media activity of the customer to the generation AI, which can analyze the activity and generate a related dish image.

[0098] The command unit can estimate the customer's emotion and adjust the priority of the command based on the estimated customer's emotion. The command unit, for example, estimates the customer's emotion and adjusts the priority of the command based on the estimated customer's emotion. For example, the command unit can give a detailed command with priority when the customer is relaxed. Also, the command unit can give a simplified command with priority when the customer is in a hurry. Also, the command unit can give a visually stimulating command with priority when the customer is excited. This allows for more appropriate commands by adjusting the priority of the command according to the customer's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the command unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the command unit can input the customer's emotion into an AI, and the AI ​​can estimate the emotion and adjust the priority of the command.

[0099] The command unit can optimize the command contents by referring to the customer's past cooking history. The command unit can optimize the command contents by referring to the customer's past cooking history, for example. For example, the command unit issues a command reflecting the cooking procedure of a dish that the customer liked in the past. The command unit can also issue a command that excludes ingredients that the customer avoided in the past. The command unit can also issue a command that reflects the optimal cooking method from the customer's past cooking history. This makes it possible to issue more appropriate commands by referring to the past cooking history. Some or all of the above-mentioned processing in the command unit may be performed, for example, using AI, or may be performed without using AI. For example, the command unit can input the customer's past cooking history into AI, and the AI ​​can optimize the command contents by referring to the history.

[0100] The command unit can customize the command contents in consideration of the health data of the customer. The command unit customizes the command contents in consideration of, for example, the health data of the customer. For example, if the customer is on a diet, the command unit issues a command to prepare a low-calorie dish. Also, if the customer has an allergy, the command unit can issue a command to prepare a dish that does not contain allergens. Also, if the customer needs a specific nutrient, the command unit can issue a command to prepare a dish that contains the nutrient. This allows for more appropriate commands by taking the health data into consideration. Part or all of the above-mentioned processing in the command unit may be performed, for example, using AI, or may be performed without using AI. For example, the command unit can input the customer's health data into AI, and the AI ​​can customize the command contents in consideration of the data.

[0101] The command unit can estimate the customer's emotion and adjust the level of detail of the command based on the estimated customer's emotion. The command unit, for example, estimates the customer's emotion and adjusts the level of detail of the command based on the estimated customer's emotion. For example, the command unit issues a detailed command when the customer is relaxed. The command unit can also issue a simplified command when the customer is in a hurry. The command unit can also issue a visually stimulating command when the customer is excited. This allows for more appropriate commands by adjusting the level of detail of the command according to the customer's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the command unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the command unit can input the customer's emotion into an AI, and the AI ​​can estimate the emotion and adjust the level of detail of the command.

[0102] The command unit can generate a command to use ingredients specific to a region in consideration of the geographical location information of the customer. The command unit, for example, generates a command to use ingredients specific to a region in consideration of the geographical location information of the customer. For example, if the customer is in a particular region, the command unit issues a command to use a specialty of the region. Also, if the customer is traveling, the command unit can issue a command to create a specialty dish of the travel destination. Also, if the customer prefers local ingredients, the command unit can issue a command to use ingredients of the region. This makes it possible to issue region-specific commands by considering the geographical location information. Part or all of the above-mentioned processing in the command unit may be performed, for example, using AI, or may be performed without using AI. For example, the command unit can input the geographical location information of the customer into AI, and the AI ​​can generate a command to use ingredients specific to the region in consideration of the information.

[0103] The command unit can analyze the social media activity of the customer and generate relevant commands. The command unit can, for example, analyze the social media activity of the customer and generate relevant commands. For example, the command unit issues commands that reflect the characteristics of dishes shared by the customer on social media. The command unit can also issue commands that reflect posts from cooking accounts that the customer follows. The command unit can also issue commands that reflect the characteristics of dishes that the customer has "liked." This allows for more appropriate commands by analyzing social media activity. Some or all of the above-mentioned processing in the command unit can be performed, for example, using AI or without AI. For example, the command unit can input the social media activity of the customer into an AI, which analyzes the activity and generates relevant commands.

[0104] The creation unit can estimate the customer's emotions and adjust the cooking procedure of the dish based on the estimated customer's emotions. The creation unit, for example, estimates the customer's emotions and adjusts the cooking procedure of the dish based on the estimated customer's emotions. For example, the creation unit creates the dish at a leisurely pace when the customer is relaxed. Also, the creation unit can create the dish quickly when the customer is in a hurry. Also, the creation unit can create the dish with a visually stimulating procedure when the customer is excited. In this way, a more appropriate dish is created by adjusting the cooking procedure according to the customer's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the creation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the creation unit can input the customer's emotions into an AI, and the AI ​​can estimate the emotions and adjust the cooking procedure of the dish.

[0105] The creation unit can optimize the creation procedure by referring to the customer's past cooking history. The creation unit optimizes the creation procedure by referring to the customer's past cooking history, for example. For example, the creation unit reflects the creation procedure of a dish that the customer liked in the past. The creation unit can also exclude ingredients that the customer avoided in the past. The creation unit can also reflect the optimal cooking method from the customer's past cooking history. This makes it possible to provide a more appropriate creation procedure by referring to the past cooking history. Some or all of the above-mentioned processing in the creation unit may be performed, for example, using AI, or may be performed without using AI. For example, the creation unit can input the customer's past cooking history into AI, and the AI ​​can optimize the creation procedure by referring to the history.

[0106] The creation unit can create a nutritionally balanced dish by taking into account the health data of the customer. The creation unit, for example, creates a nutritionally balanced dish by taking into account the health data of the customer. For example, if the customer is on a diet, the creation unit creates a low-calorie dish. In addition, if the customer has an allergy, the creation unit can also create a dish that does not contain allergens. In addition, if the customer needs a specific nutrient, the creation unit can also create a dish that contains the nutrient. In this way, a more appropriate dish is created by taking into account the health data. A part or all of the above-mentioned processing in the creation unit may be performed, for example, using AI, or may be performed without using AI. For example, the creation unit can input the customer's health data into AI, and the AI ​​can create a nutritionally balanced dish by taking the data into account.

[0107] The creation unit can estimate the customer's emotions and adjust the speed of cooking the food based on the estimated customer's emotions. The creation unit, for example, estimates the customer's emotions and adjusts the speed of cooking the food based on the estimated customer's emotions. For example, the creation unit creates the food at a leisurely pace when the customer is relaxed. Also, the creation unit can create the food quickly when the customer is in a hurry. Also, the creation unit can create the food in a visually stimulating procedure when the customer is excited. In this way, a more appropriate dish is created by adjusting the cooking speed according to the customer's emotions. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the creation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the creation unit can input the customer's emotions into an AI, and the AI ​​can estimate the emotions and adjust the cooking speed of the food.

[0108] The creation unit can use ingredients specific to the region in consideration of the geographical location information of the customer. The creation unit can use ingredients specific to the region in consideration of the geographical location information of the customer, for example. For example, if the customer is in a particular region, the creation unit uses local specialty foods of that region. Also, if the customer is traveling, the creation unit can create a specialty dish of the travel destination. Also, if the customer prefers local ingredients, the creation unit can use ingredients of that region. In this way, a dish specific to the region is created by considering the geographical location information. A part or all of the above-mentioned processing in the creation unit can be performed, for example, using AI, or can be performed without using AI. For example, the creation unit can input the geographical location information of the customer to AI, and the AI ​​can use ingredients specific to the region in consideration of the information.

[0109] The creation unit can analyze the social media activity of the customer and create a related dish. The creation unit can, for example, analyze the social media activity of the customer and create a related dish. For example, the creation unit reflects the characteristics of the dish shared by the customer on social media. The creation unit can also reflect posts from cooking accounts that the customer follows. The creation unit can also reflect the characteristics of the dish that the customer "liked." In this way, a more appropriate dish is created by analyzing the social media activity. A part or all of the above-mentioned processing in the creation unit can be performed, for example, using AI, or can be performed without using AI. For example, the creation unit can input the social media activity of the customer into AI, which can analyze the activity and create a related dish.

[0110] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0111] The questioning unit can also obtain information regarding the customer's meal frequency and meal times. For example, the questioning unit can ask the customer questions such as "How many times a day do you eat?" and "What time do you usually eat?" This allows the customer's eating patterns to be understood and more appropriate dishes can be suggested. The questioning unit can also adjust the amount of food and nutritional balance based on the customer's meal frequency and time of day. For example, if a customer eats multiple times a day, lighter dishes can be suggested. This allows dishes to be suggested that match the customer's eating habits.

[0112] The analysis unit can perform analysis based on the frequency and time of meals of the customer. For example, if the customer places importance on breakfast, the analysis unit can reflect the characteristics of dishes suitable for breakfast in the analysis. In addition, if the customer eats late at night, the analysis unit can also reflect the characteristics of dishes that are easy to digest in the analysis. This makes it possible to perform analysis according to the frequency and time of meals of the customer. In addition, the analysis unit can adjust the amount of food and nutritional balance based on the frequency and time of meals of the customer. For example, if the customer eats multiple times a day, the characteristics of lighter dishes can be reflected in the analysis. This makes it possible to perform analysis tailored to the eating habits of the customer.

[0113] The generation unit can generate dish images based on the frequency and time of meals of the customer. For example, if the customer places importance on breakfast, the generation unit can generate images of dishes suitable for breakfast. In addition, if the customer eats late at night, the generation unit can generate images of dishes that are easy to digest. In this way, dish images are generated according to the frequency and time of meals of the customer. In addition, the generation unit can adjust the amount of food and nutritional balance based on the frequency and time of meals of the customer. For example, if the customer eats multiple times a day, an image of a light dish can be generated. In this way, dish images are generated that match the eating habits of the customer.

[0114] The command unit can issue commands to the 3D food printer based on the frequency and time of the customer's meals. For example, if a customer places importance on breakfast, the command unit can issue a command to create a dish suitable for breakfast. In addition, if a customer eats late at night, the command unit can issue a command to create a dish that is easy to digest. This makes it possible to issue commands according to the frequency and time of the customer's meals. In addition, the command unit can issue a command to adjust the amount of food and nutritional balance based on the frequency and time of the customer's meals. For example, if a customer eats multiple times a day, a command to create a lighter dish can be issued. This makes it possible to issue commands according to the customer's eating habits.

[0115] The creation unit can create dishes based on the frequency and time of day of the customer's meals. For example, if the customer places importance on breakfast, the creation unit can create dishes suitable for breakfast. In addition, if the customer eats late at night, the creation unit can create dishes that are easy to digest. In this way, dishes are created according to the frequency and time of day of the customer's meals. In addition, the creation unit can adjust the amount of food and nutritional balance based on the frequency and time of day of the customer's meals. For example, if the customer eats multiple times a day, lighter dishes can be created. In this way, dishes are created that match the customer's eating habits.

[0116] The questioning unit can estimate the customer's emotions and adjust the format of the questions based on the estimated customer's emotions. For example, if the customer is relaxed, the questioning unit can ask detailed questions to grasp more specific preferences. Also, if the customer is in a hurry, the questioning unit can ask simple questions to quickly collect information. Also, if the customer is excited, the questioning unit can ask interesting questions to provide a fun experience. This allows for more appropriate questions by adjusting the format of the questions according to the customer's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the questioning unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the questioning unit can input the customer's emotions into an AI, and the AI ​​can estimate the emotions and adjust the format of the questions.

[0117] The analysis unit can estimate the customer's emotions and adjust the depth of the analysis based on the estimated customer's emotions. For example, when the customer is relaxed, the analysis unit performs a detailed analysis to grasp more specific preferences. In addition, when the customer is in a hurry, the analysis unit can perform a simplified analysis to quickly provide results. In addition, when the customer is excited, the analysis unit can perform an interesting analysis to provide a fun experience. This enables more appropriate analysis by adjusting the depth of the analysis according to the customer's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the customer's emotions into an AI, and the AI ​​can estimate the emotions and adjust the depth of the analysis.

[0118] The generation unit can estimate the customer's emotion and adjust the style of the food image based on the estimated customer's emotion. For example, the generation unit generates a food image in a calm style when the customer is relaxed. Also, the generation unit can generate a food image in a visually stimulating style when the customer is excited. Also, the generation unit can generate a food image in a simple and highly visible style when the customer is tired. In this way, a more appropriate food image is generated by adjusting the style according to the customer's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the customer's emotion into the generation AI, and the generation AI can estimate the emotion and adjust the style of the food image.

[0119] The command unit can estimate the customer's emotion and adjust the timing of the command based on the estimated customer's emotion. For example, if the customer is relaxed, the command unit issues commands at a leisurely pace. Also, if the customer is in a hurry, the command unit can issue commands quickly. Also, if the customer is excited, the command unit can issue visually stimulating commands. This allows for more appropriate commands by adjusting the timing of commands according to the customer's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the command unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the command unit can input the customer's emotion into an AI, which can estimate the emotion and adjust the timing of the command.

[0120] The creation unit can estimate the customer's emotions and adjust the presentation of the food based on the estimated customer's emotions. For example, when the customer is relaxed, the creation unit presents the food in a calm manner. When the customer is excited, the creation unit can present the food in a visually stimulating manner. When the customer is tired, the creation unit can present the food in a simple and highly visible manner. This allows the food to be served more appropriately by adjusting the presentation according to the customer's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the creation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the creation unit can input the customer's emotions into an AI, which can estimate the emotions and adjust the presentation of the food.

[0121] The process flow of the second embodiment will be briefly described below.

[0122] Step 1: The questioning unit obtains the customer's answer. The customer's answer may be in text format, multiple choice format, or the like. The questioning unit obtains the customer's preferences, allergy information, food preferences, and the like. For example, it asks questions such as "What kind of food would you like to eat?", "Do you have any allergies?", and "What is your favorite food?". Step 2: The analysis unit analyzes the answers obtained by the question unit. The analysis is performed using methods such as text mining and statistical analysis. For example, information such as "I want to eat spicy curry" is extracted from the customer's answer, and the characteristics of the dish are identified. Step 3: The generator uses a generative AI to generate an image of a dish based on the characteristics identified by the analyzer. The generative AI uses techniques such as image generation algorithms and 3D modeling. For example, it generates an image of a spicy curry. Step 4: The instruction unit issues commands to the 3D food printer based on the image generated by the generation unit. The commands include printer operation instructions and ingredient distribution instructions. For example, commands such as "add spices" and "mix curry ingredients" are issued. Step 5: The creation unit creates the dish based on the command issued by the command unit. The creation unit creates the dish using the specified ingredients. For example, the creation unit creates a spicy curry using spices and curry ingredients.

[0123] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the 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 voice data.

[0124] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0125] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements including the question unit, analysis unit, generation unit, command unit, and creation unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the question unit is realized by the reception device 38 of the smart device 14 and acquires the customer's answer. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the answer acquired by the question unit. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an image of a dish based on the characteristics identified by the analysis unit. The command unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and issues a command to the 3D food printer based on the image generated by the generation unit. The creation unit is realized, for example, by the control unit 46A of the smart device 14 and creates a dish based on the command issued by the command unit. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0127] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0128] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, 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. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0134] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0135] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0136] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0137] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0138] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0139] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0140] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0141] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements including the question unit, the analysis unit, the generation unit, the command unit, and the creation unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the question unit is realized by the microphone 238 of the smart glasses 214 and acquires the customer's answer. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the answer acquired by the question unit. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an image of a dish based on the characteristics identified by the analysis unit. The command unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and issues a command to the 3D food printer based on the image generated by the generation unit. The creation unit is realized, for example, by the control unit 46A of the smart glasses 214 and creates a dish based on the command issued by the command unit. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0143] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0144] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0145] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0146] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. 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. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0147] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0149] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0150] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0152] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0153] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0154] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0156] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0157] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements including the question unit, the analysis unit, the generation unit, the command unit, and the creation unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the question unit is realized by the microphone 238 of the headset type terminal 314 and acquires the customer's answer. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the answer acquired by the question unit. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an image of a dish based on the characteristics identified by the analysis unit. The command unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and issues a command to the 3D food printer based on the image generated by the generation unit. The creation unit is realized, for example, by the control unit 46A of the headset type terminal 314 and creates a dish based on the command issued by the command unit. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0159] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0160] 7, a 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.

[0161] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. 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. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0163] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0165] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0166] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0167] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0168] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0169] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0170] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0171] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0172] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0173] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0174] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0175] Each of the multiple elements including the question unit, analysis unit, generation unit, command unit, and creation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the question unit is realized by the microphone 238 of the robot 414 and acquires the answer of the customer. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the answer acquired by the question unit. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an image of a dish based on the characteristics identified by the analysis unit. The command unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and issues a command to the 3D food printer based on the image generated by the generation unit. The creation unit is realized, for example, by the control unit 46A of the robot 414 and creates a dish based on the command issued by the command unit. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0176] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0177] FIG. 9 is a diagram showing an emotion map 400 on 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 emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0178] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0179] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0180] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0181] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0184] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0185] In addition, 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 upon request from the data processing device 12.

[0186] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0187] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0188] The hardware resource that executes the specific process may be one of these various processors, or may be 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). Also, the hardware resource that executes the specific process may be a single processor.

[0189] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0190] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0191] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0192] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0193] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0194] (Appendix 1) a question section for obtaining a customer's answer; an analysis unit that analyzes the answer acquired by the questioning unit; a generation unit that generates an image of a dish based on the result of the analysis by the analysis unit; A command unit that issues a command to a 3D food printer based on the image generated by the generation unit; A preparation unit that prepares a dish based on the command issued by the command unit. A system characterized by: (Appendix 2) The interrogation unit is Obtain customer preferences, allergy information, and food preferences 2. The system of claim 1, (Appendix 3) The analysis unit is Analyzing the answers acquired by the questioning unit to identify characteristics of the dish 2. The system of claim 1, (Appendix 4) The generation unit is Generate an image of a dish based on the characteristics identified by the analysis unit. 2. The system of claim 1, (Appendix 5) The instruction unit includes: issue commands to a 3D food printer based on the image generated by the generating unit 2. The system of claim 1, (Appendix 6) The creation unit is A dish is prepared using ingredients designated based on the command issued by the command unit. 2. The system of claim 1, (Appendix 7) The interrogation unit is Infer customer sentiment and adjust question content and order based on inferred customer sentiment 2. The system of claim 1, (Appendix 8) The interrogation unit is Analyze the customer's past response history and automatically generate appropriate questions 2. The system of claim 1, (Appendix 9) The interrogation unit is Customize questions based on the customer's current health and nutritional status 2. The system of claim 1, (Appendix 10) The interrogation unit is Estimate customer sentiment and adjust question difficulty based on estimated customer sentiment 2. The system of claim 1, (Appendix 11) The interrogation unit is Consider your customers' geographic location and add questions about local ingredients and dishes 2. The system of claim 1, (Appendix 12) The interrogation unit is Analyze your customers' social media activity and generate relevant questions 2. The system of claim 1, (Appendix 13) The analysis unit is Infer customer sentiment and adjust analytics algorithms based on inferred customer sentiment 2. The system of claim 1, (Appendix 14) The analysis unit is Improve the accuracy of analysis by referencing the customer's past meal history 2. The system of claim 1, (Appendix 15) The analysis unit is Customize analytics with customer health data in mind 2. The system of claim 1, (Appendix 16) The analysis unit is Infer customer sentiment and adjust how analytics results are displayed based on the inferred customer sentiment 2. The system of claim 1, (Appendix 17) The analysis unit is Considering the geographic location of customers, linking analytics results to local ingredients and dishes 2. The system of claim 1, (Appendix 18) The analysis unit is Analyze your social media activity and provide relevant analytics 2. The system of claim 1, (Appendix 19) The generation unit is Estimate customer emotions and adjust the color and design of food images based on the estimated customer emotions 2. The system of claim 1, (Appendix 20) The generation unit is Customize your food image based on your customers' past preferences 2. The system of claim 1, (Appendix 21) The generation unit is Generate nutritionally balanced meal images based on customer health data 2. The system of claim 1, (Appendix 22) The generation unit is Estimate customer sentiment and adjust the detail of food images based on the estimated customer sentiment 2. The system of claim 1, (Appendix 23) The generation unit is Generate localized food images based on customer geographic location 2. The system of claim 1, (Appendix 24) The generation unit is Analyze customers' social media activity and generate relevant food images 2. The system of claim 1, (Appendix 25) The instruction unit includes: Estimate customer sentiment and adjust order priorities based on estimated customer sentiment 2. The system of claim 1, (Appendix 26) The instruction unit includes: Optimize your orders by referencing your customer's past cooking history 2. The system of claim 1, (Appendix 27) The instruction unit includes: Customize instructions taking into account customer health data 2. The system of claim 1, (Appendix 28) The instruction unit includes: Infer customer sentiment and adjust the detail of the command based on the inferred customer sentiment 2. The system of claim 1, (Appendix 29) The instruction unit includes: Generate orders using local ingredients based on the customer's geographic location 2. The system of claim 1, (Appendix 30) The instruction unit includes: Analyze customers' social media activity and generate relevant instructions 2. The system of claim 1, (Appendix 31) The creation unit is Infer customer sentiment and adjust food preparation steps based on the inferred customer sentiment 2. The system of claim 1, (Appendix 32) The creation unit is Optimize the cooking process by referencing the customer's past cooking history 2. The system of claim 1, (Appendix 33) The creation unit is Create nutritionally balanced meals taking into account customers' health data 2. The system of claim 1, (Appendix 34) The creation unit is Estimate customer sentiment and adjust food preparation speed based on estimated customer sentiment 2. The system of claim 1, (Appendix 35) The creation unit is Consider your customers' geographic location and use local ingredients 2. The system of claim 1, (Appendix 36) The creation unit is Analyze your customers' social media activity and create relevant dishes 2. The system of claim 1, [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a question section for obtaining a customer's answer; an analysis unit that analyzes the answer acquired by the questioning unit; a generation unit that generates an image of a dish based on the result of the analysis by the analysis unit; A command unit that issues a command to a 3D food printer based on the image generated by the generation unit; A preparation unit that prepares a dish based on the command issued by the command unit. A system characterized by:

2. The interrogation unit is Obtain customer preferences, allergy information, and food preferences 2. The system of claim 1 .

3. The analysis unit is Analyzing the answers acquired by the questioning unit to identify characteristics of the dish 2. The system of claim 1 .

4. The generation unit is Generate an image of a dish based on the characteristics identified by the analysis unit.

4. The system of claim 3.

5. The instruction unit includes: issue commands to a 3D food printer based on the image generated by the generating unit 2. The system of claim 1 .

6. The interrogation unit is Infer customer sentiment and adjust question content and order based on inferred customer sentiment 2. The system of claim 1 .

7. The interrogation unit is Analyze the customer's past response history and automatically generate appropriate questions 2. The system of claim 1 .

Images