System

Abstract

An object of a system according to an embodiment is to efficiently provide a custom food according to individual nutritional requirements and dietary preferences.SOLUTION: A system according to an embodiment includes a nutrition analysis unit, a recipe generation unit, and a biofabrication unit. The nutrition analysis unit analyzes data relating to nutrition requirements or food preferences provided by the user. The recipe generation unit generates a recipe for the custom food based on the data analyzed by the nutrition analysis unit. The biofabrication unit manufactures a food based on the recipe generated by the recipe generation 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 an instruction sentence related to a description of 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have had the problem of making it difficult to efficiently provide customized foods that meet individual nutritional requirements and food preferences.

[0005] The system according to the embodiment aims to efficiently provide custom food products according to individual nutritional requirements and food preferences. [Means for solving the problem]

[0006] According to an embodiment, the system includes a nutritional analysis unit, a recipe generation unit, and a biofabrication unit. The nutritional analysis unit analyzes data related to nutritional requirements or food preferences provided by a user. The recipe generation unit generates a recipe for a custom food product based on the data analyzed by the nutritional analysis unit. The biofabrication unit manufactures the food product based on the recipe generated by the recipe generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently provide customized food products according to individual nutritional requirements and food preferences. [Brief explanation 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. [Figure 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. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 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. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 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 INVENTION

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

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

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single 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 coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, 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 symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and 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. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "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, RAM 30, and 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 (for example, 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., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with 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 CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

[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 user's emotion using the emotion identification model 59 and perform identification processing using the user's 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 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 device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A custom food creation system according to an embodiment of the present invention is a system that uses generative AI to create custom foods that meet individual nutritional requirements and preferences. As a result, the custom food creation system can provide custom foods that meet individual nutritional requirements and preferences, thereby achieving sustainable resource use and improved agricultural productivity.

[0029] A customized food creation system according to an embodiment includes a nutritional analysis unit, a recipe generation unit, and a biofabrication unit. The nutritional analysis unit analyzes data related to a user's nutritional requirements and food preferences. For example, the nutritional analysis unit analyzes the user's nutritional requirements, such as calories, vitamins, and minerals. The nutritional analysis unit can also analyze the user's taste preferences and allergy information. For example, if a user inputs a request such as "I want a high-protein, low-fat food," the nutritional analysis unit analyzes data based on the request. The recipe generation unit generates a customized food recipe based on the data analyzed by the nutritional analysis unit. For example, the recipe generation unit uses a generation AI to generate a food recipe based on the user's nutritional requirements and preferences. The recipe generation unit can also select an optimal recipe from multiple recipes proposed by the generation AI. For example, the generation AI generates a recipe for creating a meat substitute by culturing plant cells in response to a user's request. The biofabrication unit manufactures food based on the recipe generated by the recipe generation unit. For example, the biofabrication unit manufactures food using biofabrication technology based on a recipe designed by the generation AI. The biofabrication department can also suggest food production processes that utilize renewable energy. For example, the biofabrication department can suggest production methods that minimize waste. This allows the custom food creation system according to embodiments to provide custom foods that meet individual nutritional requirements and preferences. For example, this allows users to easily access healthy and environmentally friendly foods, and reduces food waste.

[0030] The nutritional analysis unit can analyze the user's past dietary history and make suggestions to optimize nutritional balance. For example, the generation AI analyzes the user's dietary history over the past year and evaluates the balance of nutrients ingested. For example, it identifies vitamin and mineral deficiencies and suggests foods to supplement them. The nutritional analysis unit also makes suggestions to optimize nutritional balance based on the user's dietary history. For example, the generation AI analyzes the user's dietary history and, if there is a deficiency of a specific nutrient, suggests foods that contain a lot of that nutrient. The nutritional analysis unit can also suggest a meal plan to optimize nutritional balance based on the user's dietary history. This makes it possible to optimize the user's nutritional balance.

[0031] The nutritional analysis unit can analyze the user's genetic information and design foods containing genetically appropriate nutrients. For example, the generation AI in the nutritional analysis unit analyzes the user's genetic information and evaluates the user's ability to metabolize specific nutrients. For example, for a user with a slow metabolism of vitamin D, foods high in vitamin D are suggested. The nutritional analysis unit also designs foods containing genetically appropriate nutrients based on the user's genetic information. For example, the generation AI analyzes the user's genetic information and designs foods containing nutrients that correspond to specific gene mutations. The nutritional analysis unit can also suggest a meal plan containing genetically appropriate nutrients based on the user's genetic information. This makes it possible to provide appropriate nutrients based on the user's genetic information.

[0032] The nutritional analysis unit can analyze the user's exercise data and suggest post-exercise recovery foods. For example, the generation AI analyzes the user's exercise data and suggests foods suitable for muscle recovery after exercise. For example, it suggests high-protein foods to be consumed within 30 minutes after exercise. The nutritional analysis unit can also suggest post-exercise recovery foods based on the user's exercise data. For example, the generation AI analyzes the user's exercise data and suggests foods suitable for replenishing energy after exercise. The nutritional analysis unit can also suggest a meal plan that includes post-exercise recovery foods based on the user's exercise data. This makes it possible to provide the user with post-exercise recovery foods.

[0033] The nutritional analysis unit can design custom allergen-free foods taking into account the user's allergy information. For example, the generation AI in the nutritional analysis unit designs allergen-free foods based on the user's allergy information. For example, it can suggest nut-free foods to a user with a nut allergy. The nutritional analysis unit also designs allergen-free foods based on the user's allergy information. For example, the generation AI analyzes the user's allergy information and designs foods that do not contain specific allergens. The nutritional analysis unit can also suggest allergen-free meal plans based on the user's allergy information. This makes it possible to provide safe foods based on the user's allergy information.

[0034] The biofabrication department can optimize the metabolic pathways of microorganisms and propose methods for highly efficient production of specific nutrients. For example, the generation AI in the biofabrication department analyzes the metabolic pathways of microorganisms and proposes genetic modifications for highly efficient production of specific nutrients. For example, the generation AI designs microorganisms that produce vitamin B12 with high efficiency. The biofabrication department can also optimize the metabolic pathways of microorganisms and propose methods for highly efficient production of specific nutrients. For example, the generation AI analyzes the metabolic pathways of microorganisms and optimizes specific enzyme reactions. The biofabrication department can also propose production processes for highly efficient production of specific nutrients based on the metabolic pathways of microorganisms. This enables highly efficient production of specific nutrients.

[0035] The biofabrication department can perform quality control in real time to ensure product consistency. For example, the generation AI can monitor the biofabrication process in real time and perform quality control. For example, it can monitor the growth status of microorganisms being cultured and respond immediately if an abnormality occurs. The biofabrication department can also perform quality control in real time to ensure product consistency. For example, the generation AI can monitor the biofabrication process and evaluate the quality of the product. The biofabrication department can also perform quality control in real time and propose processes to ensure product consistency. This can ensure product consistency.

[0036] The biofabrication department can combine different biomaterials to create foods with new textures and flavors. For example, the biofabrication department uses generative AI to combine different biomaterials to create foods with new textures. For example, plant cells and animal cells can be combined to produce foods with unique textures. The biofabrication department can also combine different biomaterials to create foods with new flavors. For example, the generative AI can combine different biomaterials to produce foods with new flavors. The biofabrication department can also propose a process for creating foods with new textures and flavors based on different biomaterials. This makes it possible to provide foods with new textures and flavors.

[0037] The biofabrication department can manufacture medical nutritional supplements. For example, the biofabrication department uses biofabrication technology based on recipes designed by the generative AI to manufacture nutritional supplements that are useful in treating specific diseases. For example, it manufactures low-sugar foods for diabetic patients. The biofabrication department can also manufacture medical nutritional supplements. For example, it manufactures nutritional supplements that are high in specific nutrients based on recipes designed by the generative AI. The biofabrication department can also propose a process for manufacturing medical nutritional supplements. This makes it possible to provide medical nutritional supplements.

[0038] Generative AI can suggest ways to reuse waste and minimize waste in the food manufacturing process. For example, generative AI can analyze waste generated in the food manufacturing process and suggest ways in which it can be reused. For example, vegetable peelings can be reused as fertilizer. Generative AI can also suggest ways in which waste can be reused and minimize waste in the food manufacturing process. For example, generative AI can analyze the food manufacturing process and suggest ways in which waste can be reused. Generative AI can also suggest processes to minimize waste in the food manufacturing process based on ways in which waste can be reused. This can minimize waste.

[0039] Generative AI can propose efficient ways to use water resources and reduce water usage in food production. For example, generative AI can analyze the amount of water used in the food production process and propose efficient ways to use it. For example, it can design a reusable water circulation system. Generative AI can also propose efficient ways to use water resources and reduce water usage in food production. For example, generative AI can analyze the food production process and propose ways to reduce water usage. Generative AI can also propose a process to reduce water usage in food production based on efficient ways to use water resources. This can reduce water usage.

[0040] Generative AI can propose energy-efficient manufacturing processes and promote the use of renewable energy. For example, generative AI can analyze energy consumption in food manufacturing processes and propose energy-efficient manufacturing processes. For example, it can use machines that operate on low energy. Generative AI can also propose energy-efficient manufacturing processes and promote the use of renewable energy. For example, generative AI can analyze food manufacturing processes and propose ways to use renewable energy. Generative AI can also propose processes that promote the use of renewable energy based on energy-efficient manufacturing processes. This can increase energy efficiency and promote the use of renewable energy.

[0041] The generative AI can suggest ways to prioritize the use of local agricultural products, thereby reducing transportation costs and the environmental impact. For example, the generative AI can suggest ways to prioritize the use of local agricultural products, thereby reducing transportation costs and the environmental impact. For example, partnering with local farmers to procure fresh ingredients. The generative AI can also suggest ways to prioritize the use of local agricultural products, thereby reducing transportation costs. For example, the generative AI can suggest ways to prioritize the use of local agricultural products, thereby reducing transportation costs. The generative AI can also suggest processes to reduce transportation costs and the environmental impact based on ways to prioritize the use of local agricultural products. This can reduce transportation costs and the environmental impact.

[0042] The generative AI can analyze the microbial community in the soil and suggest the optimal fertilizer and cultivation method. For example, the generative AI can analyze the microbial community in the soil and suggest the optimal fertilizer. For example, it can suggest a fertilizer containing microorganisms suitable for a specific crop. The generative AI can also analyze the microbial community in the soil and suggest the optimal cultivation method. For example, the generative AI can analyze the microbial community in the soil and suggest the optimal cultivation method for a specific crop. The generative AI can also propose a process for suggesting the optimal fertilizer and cultivation method based on the microbial community in the soil. This makes it possible to suggest the optimal fertilizer and cultivation method.

[0043] Generative AI can monitor crop growth data in real time and suggest the optimal harvest time. Generative AI, for example, monitors crop growth data in real time and suggests the optimal harvest time. For example, it determines the harvest time based on the growth rate and health of the crop. Generative AI can also monitor crop growth data in real time and suggest the optimal harvest time. For example, generative AI can analyze crop growth data and suggest the optimal harvest time. Generative AI can also propose a process for suggesting the optimal harvest time based on the crop growth data. This makes it possible to suggest the optimal harvest time.

[0044] Generative AI can analyze consumer purchasing data, make demand forecasts, and prevent overproduction. For example, generative AI can analyze consumer purchasing data in the past and make demand forecasts. For example, it can predict demand during specific seasons or events and prevent overproduction. Generative AI can also analyze consumer purchasing data and make demand forecasts, and prevent overproduction. For example, generative AI can analyze consumer purchasing data and make demand forecasts. Generative AI can also make demand forecasts based on consumer purchasing data and propose processes to prevent overproduction. This can prevent overproduction.

[0045] Generative AI can optimize food storage methods and make suggestions to prevent deterioration. For example, generative AI can analyze food storage methods and suggest optimal storage conditions. For example, optimizing temperature and humidity can prevent food deterioration. Generative AI can also optimize food storage methods and make suggestions to prevent deterioration. For example, generative AI can analyze food storage methods and suggest ways to prevent deterioration. Generative AI can also suggest processes to prevent deterioration based on food storage methods. This can prevent food deterioration.

[0046] Generative AI can optimize food distribution routes and prevent deterioration during transport. For example, generative AI can analyze food distribution routes and propose the optimal transportation method. For example, it can propose refrigerated transportation for foods that require temperature control. Generative AI can also optimize food distribution routes and prevent deterioration during transport. For example, generative AI can analyze food distribution routes and propose methods to prevent deterioration. Generative AI can also propose processes to prevent deterioration during transport based on the food distribution route. This makes it possible to prevent deterioration during transport.

[0047] Generative AI can suggest ways to repackage food to extend its expiration date. For example, generative AI can analyze how food is repackaged and make suggestions to extend its expiration date. For example, using special packaging materials that block oxygen. Generative AI can also suggest ways to repackage food to extend its expiration date. For example, generative AI can analyze how food is repackaged and suggest ways to extend its expiration date. Generative AI can also suggest a process to extend the expiration date based on how the food is repackaged. This can extend the expiration date.

[0048] Generative AI can propose crossbreeding of different crops and develop new varieties. For example, generative AI can analyze the genetic information of different crops and propose optimal crossbreeding combinations. For example, it can develop new varieties that combine disease resistance and high yields. Generative AI can also propose crossbreeding of different crops and develop new varieties. For example, generative AI can analyze the genetic information of different crops and propose methods for developing new varieties. Generative AI can also propose a process for developing new varieties based on the crossbreeding of different crops. This makes it possible to develop new varieties.

[0049] Generative AI can propose the optimization of urban agriculture and promote food production in urban areas. Generative AI can, for example, propose the optimization of urban agriculture and promote food production in urban areas. For example, it can propose rooftop farms and vertical farming. Generative AI can also propose the optimization of urban agriculture and promote food production in urban areas. For example, generative AI can propose the optimization of urban agriculture and propose methods to promote food production in urban areas. Generative AI can also propose processes to promote food production in urban areas based on the optimization of urban agriculture. This can promote food production in urban areas.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The custom food creation system may further include an environment analysis unit that analyzes the user's dining environment. The environment analysis unit may, for example, analyze the temperature, humidity, and lighting conditions of the user's dining area and suggest the optimal dining environment. For example, if the temperature is high, it may suggest cold foods, and if the lighting is dim, it may suggest visually appealing foods. The environment analysis unit may also analyze the user's dining environment and make suggestions to improve meal satisfaction. For example, it may suggest dining environments that incorporate music and aromas. This allows the user to have a more comfortable dining experience.

[0052] The custom food creation system may further include a health monitoring unit that monitors the user's health condition. The health monitoring unit monitors the user's health data, such as blood pressure, blood sugar level, and heart rate, in real time and suggests appropriate foods. For example, if the user's blood sugar level is high, it suggests low-sugar foods, and if the user's blood pressure is high, it suggests low-salt foods. The health monitoring unit may also suggest a meal plan to maintain or improve the user's health based on the user's health condition. This allows the user to select foods that suit their health condition.

[0053] The custom food creation system can further include a timing analysis unit that analyzes the timing of a user's meals. The timing analysis unit, for example, analyzes the time periods and frequency of the user's meals and suggests optimal meal timings. For example, a user who tends to skip breakfast can be suggested a quick breakfast, and a user who eats late at night can be suggested foods that are easy to digest. The timing analysis unit can also make suggestions to optimize meal timings based on the user's lifestyle rhythm. This allows the user to eat meals that suit their own lifestyle rhythm.

[0054] The custom food creation system can further include a frequency analysis unit that analyzes the user's meal frequency. The frequency analysis unit, for example, analyzes the user's meal frequency and suggests an optimal meal frequency. For example, for a user who eats multiple meals per day, it suggests meals at appropriate intervals, and for a user who eats infrequently, it suggests meals that take nutritional balance into consideration. The frequency analysis unit can also make suggestions to optimize meal frequency based on the user's lifestyle. This allows the user to maintain a meal frequency that suits their own lifestyle.

[0055] The custom food creation system may further include a quality analysis unit that analyzes the quality of the user's diet. The quality analysis unit may, for example, analyze the quality of the user's diet and suggest nutritionally balanced meals. For example, the quality analysis unit may suggest nutritious foods and make suggestions for improving an unbalanced diet. The quality analysis unit may also suggest a meal plan for maintaining or improving health based on the quality of the user's diet. This allows the user to improve the quality of their own diet.

[0056] The custom food creation system may further include a cost analysis unit that analyzes the cost of a user's meals. The cost analysis unit may, for example, analyze the cost of a user's meals and suggest cost-effective foods. For example, the cost analysis unit may suggest foods that are nutritious and inexpensive. The cost analysis unit may also suggest meal plans based on the user's budget. This allows the user to select meals that fit their budget.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The nutritional analysis unit analyzes data related to the nutritional requirements and food preferences provided by the user. For example, it analyzes the nutritional requirements such as calories, vitamins, and minerals entered by the user, as well as taste preferences and allergy information. If the user inputs a request such as "I want foods that are high in protein and low in fat," the data is analyzed based on that request. Step 2: The recipe generation unit generates custom food recipes based on the data analyzed by the nutritional analysis unit. For example, the generation AI can be used to generate food recipes based on the user's nutritional requirements and preferences, and select the optimal one from multiple recipes. The generation AI can also generate recipes for cultivating plant cells to create meat substitutes according to the user's request. Step 3: The biofabrication unit produces food based on the recipes generated by the recipe generation unit. For example, the biofabrication unit produces food based on recipes designed by the generative AI using biofabrication technology. It can also suggest food production processes that use renewable energy and production methods that minimize waste.

[0059] (Example 2) A custom food creation system according to an embodiment of the present invention is a system that uses generative AI to create custom foods that meet individual nutritional requirements and preferences. As a result, the custom food creation system can provide custom foods that meet individual nutritional requirements and preferences, thereby achieving sustainable resource use and improved agricultural productivity.

[0060] A customized food creation system according to an embodiment includes a nutritional analysis unit, a recipe generation unit, and a biofabrication unit. The nutritional analysis unit analyzes data related to a user's nutritional requirements and food preferences. For example, the nutritional analysis unit analyzes the user's nutritional requirements, such as calories, vitamins, and minerals. The nutritional analysis unit can also analyze the user's taste preferences and allergy information. For example, if a user inputs a request such as "I want a high-protein, low-fat food," the nutritional analysis unit analyzes data based on the request. The recipe generation unit generates a customized food recipe based on the data analyzed by the nutritional analysis unit. For example, the recipe generation unit uses a generation AI to generate a food recipe based on the user's nutritional requirements and preferences. The recipe generation unit can also select an optimal recipe from multiple recipes proposed by the generation AI. For example, the generation AI generates a recipe for creating a meat substitute by culturing plant cells in response to a user's request. The biofabrication unit manufactures food based on the recipe generated by the recipe generation unit. For example, the biofabrication unit manufactures food using biofabrication technology based on a recipe designed by the generation AI. The biofabrication department can also suggest food production processes that utilize renewable energy. For example, the biofabrication department can suggest production methods that minimize waste. This allows the custom food creation system according to embodiments to provide custom foods that meet individual nutritional requirements and preferences. For example, this allows users to easily access healthy and environmentally friendly foods, and reduces food waste.

[0061] The nutritional analysis unit can analyze the user's past dietary history and make suggestions to optimize nutritional balance. For example, the generation AI analyzes the user's dietary history over the past year and evaluates the balance of nutrients ingested. For example, it identifies vitamin and mineral deficiencies and suggests foods to supplement them. The nutritional analysis unit also makes suggestions to optimize nutritional balance based on the user's dietary history. For example, the generation AI analyzes the user's dietary history and, if there is a deficiency of a specific nutrient, suggests foods that contain a lot of that nutrient. The nutritional analysis unit can also suggest a meal plan to optimize nutritional balance based on the user's dietary history. This makes it possible to optimize the user's nutritional balance.

[0062] The nutritional analysis unit can analyze the user's genetic information and design foods containing genetically appropriate nutrients. For example, the generation AI in the nutritional analysis unit analyzes the user's genetic information and evaluates the user's ability to metabolize specific nutrients. For example, for a user with a slow metabolism of vitamin D, foods high in vitamin D are suggested. The nutritional analysis unit also designs foods containing genetically appropriate nutrients based on the user's genetic information. For example, the generation AI analyzes the user's genetic information and designs foods containing nutrients that correspond to specific gene mutations. The nutritional analysis unit can also suggest a meal plan containing genetically appropriate nutrients based on the user's genetic information. This makes it possible to provide appropriate nutrients based on the user's genetic information.

[0063] The nutrition analysis unit can use the emotion estimation function to analyze the user's emotional state and suggest foods that reduce stress or improve mood. For example, the nutrition analysis unit can use the emotion estimation function to analyze the user's emotional state in real time and suggest foods that have a relaxing effect when stress is high. For example, chamomile tea or dark chocolate can be suggested. The nutrition analysis unit can also analyze the user's emotional state and suggest foods that improve mood. For example, the emotion estimation function can be used to analyze the user's emotional state and suggest foods that elicit positive emotions. The nutrition analysis unit can also suggest a meal plan that reduces stress or improves mood based on the user's emotional state. This makes it possible to provide foods that match the user's emotional state.

[0064] The nutritional analysis unit can analyze the user's exercise data and suggest post-exercise recovery foods. For example, the generation AI analyzes the user's exercise data and suggests foods suitable for muscle recovery after exercise. For example, it suggests high-protein foods to be consumed within 30 minutes after exercise. The nutritional analysis unit can also suggest post-exercise recovery foods based on the user's exercise data. For example, the generation AI analyzes the user's exercise data and suggests foods suitable for replenishing energy after exercise. The nutritional analysis unit can also suggest a meal plan that includes post-exercise recovery foods based on the user's exercise data. This makes it possible to provide the user with post-exercise recovery foods.

[0065] The nutritional analysis unit can design custom allergen-free foods taking into account the user's allergy information. For example, the generation AI in the nutritional analysis unit designs allergen-free foods based on the user's allergy information. For example, it can suggest nut-free foods to a user with a nut allergy. The nutritional analysis unit also designs allergen-free foods based on the user's allergy information. For example, the generation AI analyzes the user's allergy information and designs foods that do not contain specific allergens. The nutritional analysis unit can also suggest allergen-free meal plans based on the user's allergy information. This makes it possible to provide safe foods based on the user's allergy information.

[0066] The nutrition analysis unit can use the emotion estimation function to analyze the user's emotional state and suggest foods that the user prefers when feeling a specific emotion. For example, the nutrition analysis unit can use the emotion estimation function to suggest foods that the user prefers when feeling joy. For example, the suggestion can be based on foods that the user ate when feeling joy in the past. The nutrition analysis unit can also analyze the user's emotional state and suggest foods that the user prefers when feeling a specific emotion. For example, the emotion estimation function can be used to suggest foods that the user prefers when feeling sad. The nutrition analysis unit can also suggest a meal plan that the user prefers when feeling a specific emotion based on the user's emotional state. This makes it possible to provide foods that match the user's emotions.

[0067] The biofabrication department can optimize the metabolic pathways of microorganisms and propose methods for highly efficient production of specific nutrients. For example, the generation AI in the biofabrication department analyzes the metabolic pathways of microorganisms and proposes genetic modifications for highly efficient production of specific nutrients. For example, the generation AI designs microorganisms that produce vitamin B12 with high efficiency. The biofabrication department can also optimize the metabolic pathways of microorganisms and propose methods for highly efficient production of specific nutrients. For example, the generation AI analyzes the metabolic pathways of microorganisms and optimizes specific enzyme reactions. The biofabrication department can also propose production processes for highly efficient production of specific nutrients based on the metabolic pathways of microorganisms. This enables highly efficient production of specific nutrients.

[0068] The biofabrication department can perform quality control in real time to ensure product consistency. For example, the generation AI can monitor the biofabrication process in real time and perform quality control. For example, it can monitor the growth status of microorganisms being cultured and respond immediately if an abnormality occurs. The biofabrication department can also perform quality control in real time to ensure product consistency. For example, the generation AI can monitor the biofabrication process and evaluate the quality of the product. The biofabrication department can also perform quality control in real time and propose processes to ensure product consistency. This can ensure product consistency.

[0069] The bio-fabrication unit can use the emotion estimation function to manufacture food with a flavor and texture that corresponds to the user's emotions. For example, when the user feels like relaxing, the bio-fabrication unit uses the emotion estimation function to manufacture food with a flavor that has a relaxing effect. For example, the bio-fabrication unit manufactures food with a lavender scent. The bio-fabrication unit also uses the emotion estimation function to manufacture food with a flavor and texture that corresponds to the user's emotions. For example, the bio-fabrication unit uses the emotion estimation function to manufacture food with a flavor that the user prefers when they are feeling happy. The bio-fabrication unit can also suggest a process for manufacturing food with a flavor and texture that corresponds to the user's emotions, based on the emotion estimation function. This makes it possible to provide food with a flavor and texture that corresponds to the user's emotions.

[0070] The biofabrication department can combine different biomaterials to create foods with new textures and flavors. For example, the biofabrication department uses generative AI to combine different biomaterials to create foods with new textures. For example, plant cells and animal cells can be combined to produce foods with unique textures. The biofabrication department can also combine different biomaterials to create foods with new flavors. For example, the generative AI can combine different biomaterials to produce foods with new flavors. The biofabrication department can also propose a process for creating foods with new textures and flavors based on different biomaterials. This makes it possible to provide foods with new textures and flavors.

[0071] The biofabrication department can manufacture medical nutritional supplements. For example, the biofabrication department uses biofabrication technology based on recipes designed by the generative AI to manufacture nutritional supplements that are useful in treating specific diseases. For example, it manufactures low-sugar foods for diabetic patients. The biofabrication department can also manufacture medical nutritional supplements. For example, it manufactures nutritional supplements that are high in specific nutrients based on recipes designed by the generative AI. The biofabrication department can also propose a process for manufacturing medical nutritional supplements. This makes it possible to provide medical nutritional supplements.

[0072] The bio-fabrication unit can use the emotion estimation function to manufacture food that has a texture and flavor that the user prefers when feeling a specific emotion. For example, the bio-fabrication unit uses the emotion estimation function to manufacture food that has a texture that the user prefers when feeling joy. For example, the bio-fabrication unit manufactures food that has a crispy texture. The bio-fabrication unit can also use the emotion estimation function to manufacture food that has a flavor that the user prefers when feeling a specific emotion. For example, the bio-fabrication unit can use the emotion estimation function to manufacture food that has a flavor that the user prefers when feeling sad. The bio-fabrication unit can also suggest a process for manufacturing food that has a texture and flavor that the user prefers when feeling a specific emotion, based on the emotion estimation function. This makes it possible to provide food that has a texture and flavor that corresponds to the user's specific emotion.

[0073] Generative AI can suggest ways to reuse waste and minimize waste in the food manufacturing process. For example, generative AI can analyze waste generated in the food manufacturing process and suggest ways in which it can be reused. For example, vegetable peelings can be reused as fertilizer. Generative AI can also suggest ways in which waste can be reused and minimize waste in the food manufacturing process. For example, generative AI can analyze the food manufacturing process and suggest ways in which waste can be reused. Generative AI can also suggest processes to minimize waste in the food manufacturing process based on ways in which waste can be reused. This can minimize waste.

[0074] Generative AI can propose efficient ways to use water resources and reduce water usage in food production. For example, generative AI can analyze the amount of water used in the food production process and propose efficient ways to use it. For example, it can design a reusable water circulation system. Generative AI can also propose efficient ways to use water resources and reduce water usage in food production. For example, generative AI can analyze the food production process and propose ways to reduce water usage. Generative AI can also propose a process to reduce water usage in food production based on efficient ways to use water resources. This can reduce water usage.

[0075] Generative AI can propose energy-efficient manufacturing processes and promote the use of renewable energy. For example, generative AI can analyze energy consumption in food manufacturing processes and propose energy-efficient manufacturing processes. For example, it can use machines that operate on low energy. Generative AI can also propose energy-efficient manufacturing processes and promote the use of renewable energy. For example, generative AI can analyze food manufacturing processes and propose ways to use renewable energy. Generative AI can also propose processes that promote the use of renewable energy based on energy-efficient manufacturing processes. This can increase energy efficiency and promote the use of renewable energy.

[0076] The generative AI can suggest ways to prioritize the use of local agricultural products, thereby reducing transportation costs and the environmental impact. For example, the generative AI can suggest ways to prioritize the use of local agricultural products, thereby reducing transportation costs and the environmental impact. For example, partnering with local farmers to procure fresh ingredients. The generative AI can also suggest ways to prioritize the use of local agricultural products, thereby reducing transportation costs. For example, the generative AI can suggest ways to prioritize the use of local agricultural products, thereby reducing transportation costs. The generative AI can also suggest processes to reduce transportation costs and the environmental impact based on ways to prioritize the use of local agricultural products. This can reduce transportation costs and the environmental impact.

[0077] The generative AI can analyze the microbial community in the soil and suggest the optimal fertilizer and cultivation method. For example, the generative AI can analyze the microbial community in the soil and suggest the optimal fertilizer. For example, it can suggest a fertilizer containing microorganisms suitable for a specific crop. The generative AI can also analyze the microbial community in the soil and suggest the optimal cultivation method. For example, the generative AI can analyze the microbial community in the soil and suggest the optimal cultivation method for a specific crop. The generative AI can also propose a process for suggesting the optimal fertilizer and cultivation method based on the microbial community in the soil. This makes it possible to suggest the optimal fertilizer and cultivation method.

[0078] Generative AI can monitor crop growth data in real time and suggest the optimal harvest time. Generative AI, for example, monitors crop growth data in real time and suggests the optimal harvest time. For example, it determines the harvest time based on the growth rate and health of the crop. Generative AI can also monitor crop growth data in real time and suggest the optimal harvest time. For example, generative AI can analyze crop growth data and suggest the optimal harvest time. Generative AI can also propose a process for suggesting the optimal harvest time based on the crop growth data. This makes it possible to suggest the optimal harvest time.

[0079] Generative AI can analyze consumer purchasing data, make demand forecasts, and prevent overproduction. For example, generative AI can analyze consumer purchasing data in the past and make demand forecasts. For example, it can predict demand during specific seasons or events and prevent overproduction. Generative AI can also analyze consumer purchasing data and make demand forecasts, and prevent overproduction. For example, generative AI can analyze consumer purchasing data and make demand forecasts. Generative AI can also make demand forecasts based on consumer purchasing data and propose processes to prevent overproduction. This can prevent overproduction.

[0080] Generative AI can optimize food storage methods and make suggestions to prevent deterioration. For example, generative AI can analyze food storage methods and suggest optimal storage conditions. For example, optimizing temperature and humidity can prevent food deterioration. Generative AI can also optimize food storage methods and make suggestions to prevent deterioration. For example, generative AI can analyze food storage methods and suggest ways to prevent deterioration. Generative AI can also suggest processes to prevent deterioration based on food storage methods. This can prevent food deterioration.

[0081] Generative AI can optimize food distribution routes and prevent deterioration during transport. For example, generative AI can analyze food distribution routes and propose the optimal transportation method. For example, it can propose refrigerated transportation for foods that require temperature control. Generative AI can also optimize food distribution routes and prevent deterioration during transport. For example, generative AI can analyze food distribution routes and propose methods to prevent deterioration. Generative AI can also propose processes to prevent deterioration during transport based on the food distribution route. This makes it possible to prevent deterioration during transport.

[0082] Generative AI can suggest ways to repackage food to extend its expiration date. For example, generative AI can analyze how food is repackaged and make suggestions to extend its expiration date. For example, using special packaging materials that block oxygen. Generative AI can also suggest ways to repackage food to extend its expiration date. For example, generative AI can analyze how food is repackaged and suggest ways to extend its expiration date. Generative AI can also suggest a process to extend the expiration date based on how the food is repackaged. This can extend the expiration date.

[0083] Generative AI can make suggestions to increase consumer purchasing motivation and reduce food waste. For example, generative AI can use emotion estimation functions to make suggestions to increase consumer purchasing motivation. For example, it can suggest advertisements and promotions that elicit positive emotions. Generative AI can also make suggestions to increase consumer purchasing motivation and reduce food waste. For example, generative AI can analyze consumer emotions and suggest ways to increase purchasing motivation. Generative AI can also suggest processes to reduce food waste based on suggestions to increase consumer purchasing motivation. This can reduce food waste.

[0084] The generative AI can use its emotion estimation function to analyze the emotions of users when making environmentally conscious choices and promote sustainable choices. For example, the generative AI uses its emotion estimation function to analyze the emotions of users when making environmentally conscious choices in real time. For example, if the emotion is strong, it will recommend that choice. The generative AI can also use its emotion estimation function to analyze the emotions of users when making environmentally conscious choices and promote sustainable choices. For example, the generative AI can analyze the emotions of users when making environmentally conscious choices and propose methods to promote sustainable choices. The generative AI can also use its emotion estimation function to analyze the emotions of users when making environmentally conscious choices and propose a process to promote sustainable choices. This can promote sustainable choices.

[0085] The generative AI can use its emotion estimation function to analyze the emotions a user feels when making a sustainable choice and promote environmentally friendly choices. For example, the generative AI uses its emotion estimation function to analyze the emotions a user feels when making a sustainable choice in real time. For example, if the emotion is strong, it can recommend that choice. The generative AI can also use its emotion estimation function to analyze the emotions a user feels when making a sustainable choice and promote environmentally friendly choices. For example, the generative AI can analyze the emotions a user feels when making a sustainable choice and propose a method to promote environmentally friendly choices. The generative AI can also use its emotion estimation function to analyze the emotions a user feels when making a sustainable choice and propose a process to promote environmentally friendly choices. This can promote environmentally friendly choices.

[0086] The generative AI can use the emotion estimation function to analyze the emotional state of farmers and make suggestions to improve work efficiency. For example, the generative AI can use the emotion estimation function to analyze the emotional state of farmers in real time and make suggestions to improve work efficiency. For example, it can suggest taking a break if stress is high. The generative AI can also use the emotion estimation function to analyze the emotional state of farmers and make suggestions to improve work efficiency. For example, the generative AI can analyze the emotions of farmers and suggest ways to improve work efficiency. The generative AI can also use the emotion estimation function to analyze the emotional state of farmers and suggest processes to improve work efficiency. This can improve the work efficiency of farmers.

[0087] Generative AI can propose crossbreeding of different crops and develop new varieties. For example, generative AI can analyze the genetic information of different crops and propose optimal crossbreeding combinations. For example, it can develop new varieties that combine disease resistance and high yields. Generative AI can also propose crossbreeding of different crops and develop new varieties. For example, generative AI can analyze the genetic information of different crops and propose methods for developing new varieties. Generative AI can also propose a process for developing new varieties based on the crossbreeding of different crops. This makes it possible to develop new varieties.

[0088] Generative AI can propose the optimization of urban agriculture and promote food production in urban areas. Generative AI can, for example, propose the optimization of urban agriculture and promote food production in urban areas. For example, it can propose rooftop farms and vertical farming. Generative AI can also propose the optimization of urban agriculture and promote food production in urban areas. For example, generative AI can propose the optimization of urban agriculture and propose methods to promote food production in urban areas. Generative AI can also propose processes to promote food production in urban areas based on the optimization of urban agriculture. This can promote food production in urban areas.

[0089] The generative AI can use the emotion estimation function to analyze the emotional state of farmers and make suggestions to improve work efficiency. For example, the generative AI can use the emotion estimation function to analyze the emotional state of farmers in real time and make suggestions to improve work efficiency. For example, it can suggest taking a break if stress is high. The generative AI can also use the emotion estimation function to analyze the emotional state of farmers and make suggestions to improve work efficiency. For example, the generative AI can analyze the emotions of farmers and suggest ways to improve work efficiency. The generative AI can also use the emotion estimation function to analyze the emotional state of farmers and suggest processes to improve work efficiency. This can improve the work efficiency of farmers.

[0090] Generative AI can use its emotion estimation function to make suggestions to increase consumer purchasing motivation, thereby reducing food waste. For example, generative AI can use its emotion estimation function to make suggestions to increase consumer purchasing motivation. For example, it can suggest advertisements and promotions that elicit positive emotions. Generative AI can also make suggestions to increase consumer purchasing motivation, thereby reducing food waste. For example, generative AI can analyze consumer emotions and suggest ways to increase purchasing motivation. Generative AI can also suggest processes to reduce food waste based on suggestions to increase consumer purchasing motivation. This can reduce food waste.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The custom food creation system may further include an environment analysis unit that analyzes the user's dining environment. The environment analysis unit may, for example, analyze the temperature, humidity, and lighting conditions of the user's dining area and suggest the optimal dining environment. For example, if the temperature is high, it may suggest cold foods, and if the lighting is dim, it may suggest visually appealing foods. The environment analysis unit may also analyze the user's dining environment and make suggestions to improve meal satisfaction. For example, it may suggest dining environments that incorporate music and aromas. This allows the user to have a more comfortable dining experience.

[0093] The custom food creation system may further include a health monitoring unit that monitors the user's health condition. The health monitoring unit monitors the user's health data, such as blood pressure, blood sugar level, and heart rate, in real time and suggests appropriate foods. For example, if the user's blood sugar level is high, it suggests low-sugar foods, and if the user's blood pressure is high, it suggests low-salt foods. The health monitoring unit may also suggest a meal plan to maintain or improve the user's health based on the user's health condition. This allows the user to select foods that suit their health condition.

[0094] The custom food creation system can further include a timing analysis unit that analyzes the timing of a user's meals. The timing analysis unit, for example, analyzes the time periods and frequency of the user's meals and suggests optimal meal timings. For example, a user who tends to skip breakfast can be suggested a quick breakfast, and a user who eats late at night can be suggested foods that are easy to digest. The timing analysis unit can also make suggestions to optimize meal timings based on the user's lifestyle rhythm. This allows the user to eat meals that suit their own lifestyle rhythm.

[0095] The custom food creation system can also estimate the user's emotions and suggest meal portions according to the emotions. For example, if the user is feeling stressed, the emotion estimation function can be used to suggest an appropriate amount of food that will help relieve stress. For example, if the user is feeling high stress, the system can suggest a small amount of food with a relaxing effect to prevent overeating. The emotion estimation function can also be used to suggest an appropriate amount of reward food if the user is feeling happy. This allows the user to eat an appropriate amount of food according to their emotions.

[0096] The custom food creation system can further estimate the user's emotions and suggest meal timings based on the emotions. For example, if the user feels tired, the emotion estimation function can be used to suggest a meal timing that is suitable for replenishing energy. For example, if the user feels very tired, the system can suggest a light meal that is suitable for replenishing energy. Furthermore, if the user feels relaxed, the emotion estimation function can be used to suggest a meal timing that will enhance the relaxation effect. This allows the user to eat meals at the optimal timing based on their emotions.

[0097] The custom food creation system can also estimate the user's emotions and suggest a dining environment that matches the user's emotions. For example, if the user is feeling stressed, the emotion estimation function can be used to suggest a dining environment that has a relaxing effect. For example, a quiet place or a place where the sounds of nature can be heard can be suggested. Also, if the user is feeling happy, the emotion estimation function can be used to suggest a dining environment in a pleasant atmosphere. This allows the user to enjoy a meal in an optimal environment that matches their emotions.

[0098] The custom food creation system can further estimate the user's emotions and suggest types of food according to the emotions. For example, if the user is feeling sad, the emotion estimation function can suggest foods that have a mood-boosting effect, such as chocolate or fruit. Also, if the user is excited, the emotion estimation function can suggest foods that have a relaxing effect. This allows the user to select the optimal food according to their emotions.

[0099] The custom food creation system can further include a frequency analysis unit that analyzes the user's meal frequency. The frequency analysis unit, for example, analyzes the user's meal frequency and suggests an optimal meal frequency. For example, for a user who eats multiple meals per day, it suggests meals at appropriate intervals, and for a user who eats infrequently, it suggests meals that take nutritional balance into consideration. The frequency analysis unit can also make suggestions to optimize meal frequency based on the user's lifestyle. This allows the user to maintain a meal frequency that suits their own lifestyle.

[0100] The custom food creation system may further include a quality analysis unit that analyzes the quality of the user's diet. The quality analysis unit may, for example, analyze the quality of the user's diet and suggest nutritionally balanced meals. For example, the quality analysis unit may suggest nutritious foods and make suggestions for improving an unbalanced diet. The quality analysis unit may also suggest a meal plan for maintaining or improving health based on the quality of the user's diet. This allows the user to improve the quality of their own diet.

[0101] The custom food creation system may further include a cost analysis unit that analyzes the cost of a user's meals. The cost analysis unit may, for example, analyze the cost of a user's meals and suggest cost-effective foods. For example, the cost analysis unit may suggest foods that are nutritious and inexpensive. The cost analysis unit may also suggest meal plans based on the user's budget. This allows the user to select meals that fit their budget.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The nutritional analysis unit analyzes data related to the nutritional requirements and food preferences provided by the user. For example, it analyzes the nutritional requirements such as calories, vitamins, and minerals entered by the user, as well as taste preferences and allergy information. If the user inputs a request such as "I want foods that are high in protein and low in fat," the data is analyzed based on that request. Step 2: The recipe generation unit generates custom food recipes based on the data analyzed by the nutritional analysis unit. For example, the generation AI can be used to generate food recipes based on the user's nutritional requirements and preferences, and select the optimal one from multiple recipes. The generation AI can also generate recipes for cultivating plant cells to create meat substitutes according to the user's request. Step 3: The biofabrication unit produces food based on the recipes generated by the recipe generation unit. For example, the biofabrication unit produces food based on recipes designed by the generative AI using biofabrication technology. It can also suggest food production processes that use renewable energy and production methods that minimize waste.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] Furthermore, the processing by the data processing system 10 described above 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 necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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, RAM 30, and 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.

[0110] 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.

[0111] 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 audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] 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 carried out in a secure state.

[0114] Fig. 4 shows an example of the 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.

[0115] 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.

[0116] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0118] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0120] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, 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, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 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.

[0124] 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, RAM 30, and 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.

[0125] 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.

[0126] 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 audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] 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 carried out in a secure state.

[0129] 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.

[0130] 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.

[0131] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0133] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] 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 audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0135] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] 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 also 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. Furthermore, 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, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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, RAM 30, and 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.

[0140] 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.

[0141] 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 audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] 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 carried out in a secure state.

[0144] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the 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.

[0145] 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.

[0146] 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.

[0147] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0148] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0149] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] 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 voice indicating a user input regarding 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.

[0151] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, 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.

[0153] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] FIG. 9 illustrates 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. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0155] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically 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.

[0156] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0157] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of 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, "relieved," "calm," and "reassuring," have similar emotion values.

[0160] 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.

[0161] 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 USB (Universal Serial Bus) 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.

[0162] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0163] 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.

[0164] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0165] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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.

[0166] As an example of a system configured with a single 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 system that uses a processor that realizes the functions of an 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.

[0167] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0168] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0169] The above-described description and illustrations are a detailed explanation 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, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects 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 replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0170] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0171] 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 nutritional analysis unit that analyzes data provided by a user regarding nutritional requirements or food preferences; a recipe creation unit that creates recipes for customized food products based on the data analyzed by the nutritional analysis unit; a bio-fabrication unit that produces food products based on the recipes generated by the recipe generation unit. A system characterized by:

2. The nutritional analysis unit Analyzing the user's genetic information and designing foods containing genetically appropriate nutrients 2. The system of claim 1.

3. The nutritional analysis unit Analyze the user's exercise data and suggest post-exercise recovery foods 2. The system of claim 1.

4. The biofabrication unit includes: Proposing methods to optimize metabolic pathways in microorganisms and produce specific nutrients with high efficiency 2. The system of claim 1.

5. The generated AI is Propose ways to reuse waste and minimize said waste in the food production process 2. The system of claim 1.

6. The generated AI is Analyzing soil microorganisms and proposing optimal fertilizers and cultivation methods 2. The system of claim 1.

7. The generated AI is Analyzing consumer purchasing data, forecasting demand, and preventing overproduction 2. The system of claim 1.

8. The nutritional analysis unit Analyzing the user's emotional state and suggesting foods that reduce stress or improve mood 2. The system of claim 1.

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