system

Abstract

The system according to this embodiment aims to efficiently provide meals tailored to the individual nutritional requirements of each patient. [Solution] The system according to the embodiment comprises an analysis unit, a generation unit, a data analysis unit, a customization unit, and an optimization unit. The analysis unit analyzes the patient's nutritional indicator data. The generation unit generates meal recipes based on the data analyzed by the analysis unit. The data analysis unit analyzes the nutritional value of ingredients. The customization unit customizes meal plans based on the data analyzed by the data analysis unit. The optimization unit optimizes the cooking process based on available ingredients, cooking equipment, and staff schedules.

Description

Technical Field

[0003]

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there are problems that it is difficult to provide a diet that accurately meets the nutritional requirements of each patient, and it is difficult to efficiently allocate cooking resources and time.

[0005] The system according to the embodiment aims to efficiently provide a diet according to the nutritional requirements of each patient.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a generation unit, a data analysis unit, a customization unit, and an optimization unit. The analysis unit analyzes the patient's nutritional indicator data. The generation unit generates meal recipes based on the data analyzed by the analysis unit. The data analysis unit analyzes the nutritional value of ingredients. The customization unit customizes meal plans based on the data analyzed by the data analysis unit. The optimization unit optimizes the cooking process based on available ingredients, cooking equipment, and staff schedules. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently provide meals tailored to the individual nutritional requirements of each patient. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The nutrition management system according to an embodiment of the present invention is a system that utilizes generative AI to provide meals tailored to the individual nutritional requirements of each patient and to achieve efficient allocation of cooking resources and time. This nutrition management system analyzes the patient's nutritional indicator data and generates meal recipes optimized for salt and calories. The nutrition management system uses data analysis tools to analyze the nutritional value of ingredients and customize meal plans. The nutrition management system uses machine learning to optimize the cooking process based on available ingredients, cooking equipment, and staff schedules. The nutrition management system links with ingredient and recipe databases to provide cooking suggestions using accessible ingredients. The nutrition management system builds a feedback loop in which the AI ​​learns from patient feedback and uses it to improve future processes. As a result, the nutrition management system can streamline the provision of meals that meet individual nutritional needs, reduce time and costs by optimizing the cooking process, and improve patient satisfaction and meal quality. For example, the nutrition management system receives patient nutritional indicator data as input, and the generative AI analyzes that data to generate an optimal meal recipe. The generative AI receives prompts, for example, to optimize the patient's salt intake and calorie intake and generates meal recipes. Next, the nutrition management system uses data analysis tools to analyze the nutritional value of ingredients and customize meal plans. For example, data analysis tools analyze the vitamin and mineral content of ingredients and propose meal plans tailored to the patient's nutritional needs. Furthermore, nutrition management systems optimize cooking processes using machine learning. For instance, machine learning algorithms suggest optimal cooking processes based on available ingredients, cooking equipment, and staff schedules. This enables efficient allocation of cooking resources. Nutrition management systems also integrate with ingredient and recipe databases to suggest cooking methods using available ingredients. For example, a nutrition management system retrieves ingredient information from a database and suggests recipes that can be prepared with currently available ingredients. In addition, nutrition management systems build a feedback loop that learns from patient feedback and uses it to improve future plans. For example, a nutrition management system collects feedback from patients and uses that data to improve the next meal plan.This allows the nutrition management system to streamline the delivery of meals that meet patients' nutritional needs, reduce time and costs through optimized cooking processes, and improve patient satisfaction and meal quality.

[0029] The nutrition management system according to this embodiment comprises an analysis unit, a generation unit, a data analysis unit, a customization unit, and an optimization unit. The analysis unit analyzes the patient's nutritional indicator data. For example, the analysis unit analyzes the patient's nutritional indicator data such as vitamins, minerals, and calories. The analysis unit can analyze the patient's nutritional indicator data using a generation AI. The generation unit generates meal recipes based on the data analyzed by the analysis unit. For example, the generation unit generates meal recipes optimized for salt and calories using a generation AI. The generation unit inputs prompts to the generation AI, and the generation AI generates meal recipes based on those prompts. The data analysis unit analyzes the nutritional value of ingredients. For example, the data analysis unit analyzes the vitamin and mineral content of ingredients. The data analysis unit can analyze the nutritional value of ingredients using AI. The customization unit customizes meal plans based on the data analyzed by the data analysis unit. For example, the customization unit adjusts meal plans according to the patient's nutritional needs. The customization unit can customize meal plans using AI. The optimization unit optimizes the cooking process based on available ingredients, cooking equipment, and staff schedules. The optimization unit optimizes the cooking process, for example, using machine learning. The optimization unit can optimize the cooking process using AI. As a result, the nutrition management system according to the embodiment efficiently provides meals that meet individual nutritional needs by analyzing patient nutritional indicator data, generating meal recipes, analyzing the nutritional value of ingredients, customizing meal plans, and optimizing the cooking process. For example, the analysis unit inputs patient nutritional indicator data into the generation AI, which then analyzes the data. The generation unit generates meal recipes based on the data analyzed by the analysis unit. The data analysis unit uses AI to analyze the nutritional value of ingredients. The customization unit uses AI to customize meal plans based on the data analyzed by the data analysis unit. The optimization unit uses AI to optimize the cooking process based on available ingredients, cooking equipment, and staff schedules.As a result, the nutrition management system according to this embodiment analyzes the patient's nutritional indicator data, generates meal recipes, analyzes the nutritional value of ingredients, customizes meal plans, and optimizes cooking processes, thereby streamlining the provision of meals that meet individual nutritional needs.

[0030] The analysis unit analyzes the patient's nutritional indicator data. Specifically, it collects patient blood test results, dietary records, and anthropometric data, and analyzes this data in detail. For example, it identifies vitamin and mineral deficiencies or excesses from blood test results, and evaluates daily calorie intake and nutritional balance from dietary records. It calculates BMI and body fat percentage from anthropometric data to comprehensively understand the patient's health status. The analysis unit inputs this data into a generating AI, which then analyzes the data. Based on past data and statistical information, the generating AI evaluates the patient's nutritional status and identifies necessary nutrients and areas for improvement. For example, the generating AI can detect a patient's vitamin D deficiency and identify causes such as lack of sunlight or dietary content. This allows the analysis unit to accurately understand the patient's nutritional status and provide basic data for appropriate nutritional management.

[0031] The generation unit generates meal recipes based on data analyzed by the analysis unit. Specifically, prompts are input to the generation AI, which then generates meal recipes based on those prompts. These prompts include information such as the patient's nutritional needs, ingredient restrictions, and cooking methods. For example, if the user inputs "Generate a low-calorie breakfast recipe rich in vitamin D" to the generation AI, it will generate a low-calorie breakfast recipe using ingredients rich in vitamin D based on that instruction. The generation AI considers the nutritional value of the ingredients and cooking methods to propose a recipe that best suits the patient's nutritional needs. For example, the generation AI might suggest a recipe using fish or eggs, which are rich in vitamin D, and select healthy cooking methods such as baking or steaming. This allows the generation unit to efficiently generate and provide meal recipes that meet the patient's nutritional needs.

[0032] The Data Analysis Department analyzes the nutritional value of food ingredients. Specifically, it analyzes the nutritional value of food ingredients in detail, including vitamin content, mineral content, and calories. The Data Analysis Department can use AI to analyze the nutritional value of food ingredients. The AI ​​refers to a nutritional database of food ingredients and accurately understands the nutritional value of each ingredient. For example, the AI ​​analyzes the vitamin A content and calcium content of spinach and evaluates its nutritional value. Furthermore, the AI ​​compares the nutritional value of food ingredients and assists in selecting the most suitable ingredients. For example, the AI ​​can compare food ingredients that are rich in vitamin C and select the most suitable ingredient. As a result, the Data Analysis Department can accurately analyze the nutritional value of food ingredients and provide the information necessary for creating meal plans.

[0033] The customization department customizes meal plans based on data analyzed by the data analysis department. Specifically, it adjusts meal plans considering the patient's nutritional needs, preferences, and allergy information. The customization department can also customize meal plans using AI. The AI ​​proposes the optimal meal plan based on the patient's nutritional indicator data and the nutritional value of ingredients. For example, for a patient with a vitamin D deficiency, the AI ​​will propose a meal plan using ingredients rich in vitamin D. It also adjusts ingredient selection and cooking methods considering the patient's preferences and allergy information. For example, for a patient with a dairy allergy, it will propose a recipe that does not use dairy products. In this way, the customization department can provide meal plans tailored to the individual needs of each patient, making nutritional management more efficient.

[0034] The optimization department optimizes the cooking process based on available ingredients, cooking equipment, and staff schedules. Specifically, it uses machine learning to optimize the cooking process. The optimization department can optimize the cooking process using AI. The AI ​​considers available ingredients, cooking equipment, and staff schedules to propose the optimal cooking process. For example, the AI ​​calculates the optimal cooking order and cooking time based on available ingredients and proposes an efficient cooking process. It also considers staff schedules and distributes and adjusts cooking tasks. As a result, the optimization department can streamline the cooking process and improve the speed and quality of meal delivery. Furthermore, the optimization department can accumulate data on the cooking process and continuously make improvements. For example, it can analyze past cooking data to identify bottlenecks and areas for improvement and optimize the cooking process. As a result, the optimization department can always maintain the optimal cooking process and improve the efficiency and quality of meal delivery.

[0035] The optimization unit can link with ingredient and recipe databases to suggest cooking methods using accessible ingredients. For example, the optimization unit can retrieve ingredient information from ingredient and recipe databases and suggest recipes that can be cooked with currently available ingredients. The optimization unit can also suggest the optimal cooking method based on the ingredient information retrieved from the database. By linking with ingredient and recipe databases, the optimization unit can suggest cooking methods using accessible ingredients. For example, the optimization unit retrieves ingredient information from the database and makes cooking suggestions based on that information. The optimization unit suggests the optimal cooking method based on the ingredient information retrieved from the database. In this way, the optimization unit can link with ingredient and recipe databases and suggest cooking methods using accessible ingredients.

[0036] The optimization unit can optimize the cooking process using machine learning. For example, the optimization unit uses a machine learning algorithm to propose the optimal cooking process based on available ingredients, cooking equipment, and staff schedules. The optimization unit can optimize the cooking process by using machine learning. For example, the optimization unit uses a machine learning algorithm to propose the optimal cooking process based on available ingredients, cooking equipment, and staff schedules. The optimization unit optimizes the cooking process using machine learning. Thus, the optimization unit can optimize the cooking process using machine learning.

[0037] The generation unit can generate meal recipes optimized for salt and calories using a generation AI. For example, the generation unit uses the generation AI to generate meal recipes optimized for salt and calories. The generation unit inputs prompts to the generation AI, and the generation AI generates meal recipes based on those prompts. By using the generation AI, the generation unit can generate meal recipes optimized for salt and calories. For example, the generation unit uses the generation AI to generate meal recipes optimized for salt and calories. The generation unit inputs prompts to the generation AI, and the generation AI generates meal recipes based on those prompts. This allows the generation unit to generate meal recipes optimized for salt and calories using a generation AI.

[0038] The customization department can analyze the nutritional value of ingredients using data analysis tools and customize meal plans. For example, the customization department can use data analysis tools to analyze the vitamin and mineral content of ingredients and propose meal plans tailored to the patient's nutritional needs. By using data analysis tools, the customization department can analyze the nutritional value of ingredients and customize meal plans. For example, the customization department can use data analysis tools to analyze the vitamin and mineral content of ingredients and propose meal plans tailored to the patient's nutritional needs. The customization department uses data analysis tools to analyze the nutritional value of ingredients and customize meal plans. This allows the customization department to analyze the nutritional value of ingredients using data analysis tools and customize meal plans.

[0039] The optimization unit can learn from patient feedback and build a feedback loop to improve future plans. For example, the optimization unit collects feedback from patients and uses that data to improve the next meal plan. By building a feedback loop, the optimization unit can learn from patient feedback and use that data to improve future plans. For example, the optimization unit collects feedback from patients and uses that data to improve the next meal plan. The optimization unit builds a feedback loop. This allows the optimization unit to learn from patient feedback and build a feedback loop to improve future plans.

[0040] The analysis unit can analyze a patient's past health data and select the optimal analysis algorithm. For example, the analysis unit can use its generating AI to select the optimal nutrition indicator analysis algorithm based on the patient's past blood test data. The analysis unit can also analyze a patient's past dietary history and use its generating AI to select the optimal nutrition indicator analysis algorithm. The analysis unit can also refer to a patient's past exercise data and use its generating AI to select the optimal nutrition indicator analysis algorithm. In this way, the analysis unit can analyze a patient's past health data and select the optimal analysis algorithm.

[0041] The analysis unit can filter nutritional indicator data based on the patient's lifestyle and dietary history. For example, the analysis unit can use its generating AI to exclude specific foods from the nutritional indicator data based on the patient's dietary history. The analysis unit can also filter nutritional indicator data by considering the patient's lifestyle (smoking, drinking, etc.) and using its generating AI. The analysis unit can also use its generating AI to exclude allergens from the nutritional indicator data based on the patient's allergy information. This allows the analysis unit to filter nutritional indicator data based on the patient's lifestyle and dietary history during analysis.

[0042] The analysis unit can prioritize the analysis of highly relevant data by considering the patient's geographical location when analyzing nutritional indicator data. For example, the analysis unit can prioritize the analysis of highly relevant nutritional indicator data by considering the food culture of the area where the patient lives. The analysis unit can also prioritize the analysis of highly relevant nutritional indicator data by considering the climate of the area where the patient lives. The analysis unit can also prioritize the analysis of highly relevant nutritional indicator data by considering the availability of ingredients in the area where the patient lives. As a result, the analysis unit can prioritize the analysis of highly relevant data by considering the patient's geographical location when analyzing nutritional indicator data.

[0043] The analysis unit can analyze patients' social media activity and related data when analyzing nutritional indicator data. For example, the analysis unit can analyze the content of patients' social media posts, and the generating AI can analyze related nutritional indicator data. The analysis unit can also analyze the dietary trends of patients' social media followers, and the generating AI can analyze related nutritional indicator data. The analysis unit can also analyze patients' "likes" and comments on social media, and the generating AI can analyze related nutritional indicator data. As a result, the analysis unit can analyze patients' social media activity and related data when analyzing nutritional indicator data.

[0044] The generation unit can adjust the level of detail in a recipe based on the importance of nutrients when generating a meal recipe. For example, if there are many important nutrients, the generation AI will generate a recipe that includes detailed cooking instructions. If there are few important nutrients, the generation AI can also generate a simplified recipe. If there is a deficiency in a particular nutrient, the generation AI can also generate a detailed recipe to supplement that nutrient. In this way, the generation unit can adjust the level of detail in a recipe based on the importance of nutrients when generating a meal recipe.

[0045] The generation unit can apply different generation algorithms to meal recipes depending on the patient's dietary restrictions and allergy information. For example, if a patient is allergic to a specific ingredient, the generation AI will generate a recipe that excludes that ingredient. If a patient has specific dietary restrictions (such as low salt or low sugar), the generation AI can also generate a recipe that accommodates those restrictions. If a patient needs to consume a specific nutrient, the generation AI can also generate a recipe that includes that nutrient. This allows the generation unit to apply different generation algorithms to meal recipes depending on the patient's dietary restrictions and allergy information.

[0046] The generation unit can determine recipe priorities based on the patient's dietary history when generating meal recipes. For example, the generation unit's AI can determine recipe priorities based on dishes the patient has enjoyed eating in the past. The generation unit can also determine recipe priorities based on dishes the patient has avoided in the past. The generation unit can also prioritize suggesting dishes containing specific nutrients based on the patient's dietary history. As a result, the generation unit can determine recipe priorities based on the patient's dietary history when generating meal recipes.

[0047] The generation unit can adjust the order of recipes based on the patient's preferences when generating meal recipes. For example, the generation unit's AI can adjust the order of recipes based on ingredients the patient likes. The generation unit can also adjust the order of recipes based on ingredients the patient avoids. The generation unit can also prioritize suggesting the most appealing recipes based on the patient's preferences. As a result, the generation unit can adjust the order of recipes based on the patient's preferences when generating meal recipes.

[0048] The data analysis unit can optimize its analysis algorithm by referring to past nutritional data when analyzing the nutritional value of food ingredients. For example, the data analysis unit can use AI to select the optimal nutritional value analysis algorithm based on past nutritional data. The data analysis unit can also analyze past nutritional data, and the AI ​​can improve the accuracy of the nutritional value analysis. The data analysis unit can also use AI to optimize the analysis method for specific nutrients by referring to past nutritional data. As a result, the data analysis unit can optimize its analysis algorithm by referring to past nutritional data when analyzing the nutritional value of food ingredients.

[0049] The data analysis unit can perform nutritional value analysis of food ingredients based on their origin and production methods. For example, the data analysis unit can use AI to analyze nutritional value based on the food ingredients' origin information. The data analysis unit can also use AI to analyze nutritional value while considering the food ingredients' production methods (organic farming, pesticide-free, etc.). The data analysis unit can also use AI to analyze nutritional value while considering the food ingredients' transportation methods. As a result, the data analysis unit can perform nutritional value analysis of food ingredients based on their origin and production methods.

[0050] The data analysis unit can perform nutritional value analysis of food ingredients while considering their storage conditions. For example, the data analysis unit can use AI to analyze nutritional value while considering the storage temperature of the food ingredients. The data analysis unit can also use AI to analyze nutritional value while considering the storage period of the food ingredients. The data analysis unit can also use AI to analyze nutritional value while considering the storage method of the food ingredients (freezing, refrigeration, etc.). As a result, the data analysis unit can perform nutritional value analysis of food ingredients while considering their storage conditions.

[0051] The data analysis unit can improve the accuracy of its analysis by referring to relevant literature data when analyzing the nutritional value of food ingredients. For example, the data analysis unit uses AI to analyze nutritional value by referring to the latest nutritional research. The data analysis unit can also optimize the nutritional value analysis algorithm based on past nutritional literature. Furthermore, the data analysis unit can improve the accuracy of its nutritional value analysis by referring to relevant nutritional databases. This allows the data analysis unit to improve the accuracy of its analysis by referring to relevant literature data when analyzing the nutritional value of food ingredients.

[0052] The customization function can select the optimal meal plan by referring to the patient's past eating history when customizing a meal plan. For example, the customization function can use AI to propose the optimal meal plan based on the patient's past eating history. The customization function can also prioritize suggesting meal plans containing specific nutrients based on the patient's past eating history. The customization function can also analyze the patient's past eating history and use AI to select the most effective meal plan. As a result, the customization function can select the optimal meal plan by referring to the patient's past eating history when customizing a meal plan.

[0053] The customization function can customize meal plans based on the patient's current health condition. For example, the AI ​​can suggest an optimal meal plan based on the patient's current health status (blood pressure, blood sugar levels, etc.). The AI ​​can also customize meal plans considering the patient's current physical condition (fatigue, stress, etc.). The AI ​​can also adjust meal plans according to the patient's current health goals (weight loss, muscle building, etc.). As a result, the customization function can customize meal plans based on the patient's current health condition.

[0054] The customization function can select the optimal meal plan by considering the patient's geographical location when customizing meal plans. For example, the customization function can use AI to suggest the optimal meal plan by considering the food culture of the area where the patient lives. The customization function can also use AI to suggest the optimal meal plan by considering the climate of the area where the patient lives. The customization function can also use AI to suggest the optimal meal plan by considering the availability of ingredients in the area where the patient lives. As a result, the customization function can select the optimal meal plan by considering the patient's geographical location when customizing meal plans.

[0055] The customization department can analyze a patient's social media activity and propose a meal plan when customizing it. For example, the customization department can analyze the content of a patient's social media posts and have the AI ​​suggest a relevant meal plan. The customization department can also analyze the eating habits of the patient's social media followers and have the AI ​​suggest a relevant meal plan. The customization department can also analyze the patient's "likes" and comments on social media and have the AI ​​suggest a relevant meal plan. In this way, the customization department can analyze a patient's social media activity and propose a meal plan when customizing it.

[0056] The optimization unit can optimize its optimization algorithm by referring to past cooking data when optimizing the cooking process. For example, the optimization unit can use AI to propose the optimal cooking process based on past cooking data. The optimization unit can also analyze past cooking data and have the AI ​​improve the efficiency of the cooking process. The optimization unit can also use AI to select an optimization method for a specific cooking process by referring to past cooking data. As a result, the optimization unit can optimize its optimization algorithm by referring to past cooking data when optimizing the cooking process.

[0057] The optimization unit can perform optimization based on the performance and usage of cooking equipment during the cooking process optimization. For example, the AI ​​optimizes the cooking process by considering the performance of the cooking equipment (heating speed, capacity, etc.). The AI ​​can also optimize the cooking process by considering the usage of the cooking equipment (frequency of use, maintenance status, etc.). The AI ​​can also suggest the optimal cooking process by considering the type of cooking equipment (oven, frying pan, etc.). As a result, the optimization unit can perform optimization based on the performance and usage of cooking equipment during the cooking process optimization.

[0058] The optimization unit can optimize the cooking process while considering the schedules of the cooking staff. For example, the optimization unit can use AI to propose the optimal cooking process while considering the working hours of the cooking staff. The optimization unit can also use AI to optimize the cooking process while considering the skill level of the cooking staff. The optimization unit can also use AI to adjust the cooking process while considering the break times of the cooking staff. As a result, the optimization unit can optimize the cooking process while considering the schedules of the cooking staff.

[0059] The optimization unit can improve the accuracy of the optimization process by referring to relevant cooking techniques. For example, the AI ​​can optimize the cooking process by referring to the latest cooking techniques. The AI ​​can also improve the efficiency of the cooking process based on past cooking techniques. The AI ​​can also select an optimization method for the cooking process by referring to a database of relevant cooking techniques. As a result, the optimization unit can improve the accuracy of the optimization process by referring to relevant cooking techniques.

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

[0061] The analysis unit can analyze a patient's past health data and select the optimal analysis algorithm. For example, based on the patient's past blood test data, the generating AI can select the optimal nutrition indicator analysis algorithm. The generating AI can also analyze the patient's past dietary history and select the optimal nutrition indicator analysis algorithm. The generating AI can also refer to the patient's past exercise data and select the optimal nutrition indicator analysis algorithm. In this way, the analysis unit can analyze a patient's past health data and select the optimal analysis algorithm.

[0062] The generation unit can apply different generation algorithms to meal recipes depending on the patient's dietary restrictions and allergy information. For example, if a patient is allergic to a specific ingredient, the generation AI will generate a recipe that excludes that ingredient. If a patient has specific dietary restrictions (such as low salt or low sugar), the generation AI can also generate a recipe that accommodates those restrictions. If a patient needs to consume a specific nutrient, the generation AI can also generate a recipe that includes that nutrient. In this way, the generation unit can apply different generation algorithms to meal recipes depending on the patient's dietary restrictions and allergy information.

[0063] The data analysis department can perform nutritional value analysis of food ingredients based on their origin and production methods. For example, the AI ​​can analyze nutritional value based on the food ingredients' origin information. The AI ​​can also analyze nutritional value considering the food ingredients' production methods (organic farming, pesticide-free, etc.). The AI ​​can also analyze nutritional value considering the food ingredients' transportation methods. As a result, the data analysis department can perform nutritional value analysis of food ingredients based on their origin and production methods.

[0064] The optimization unit can perform optimization based on the performance and usage of cooking equipment during the cooking process optimization. For example, the AI ​​optimizes the cooking process by considering the performance of the cooking equipment (heating speed, capacity, etc.). The AI ​​can also optimize the cooking process by considering the usage of the cooking equipment (frequency of use, maintenance status, etc.). The AI ​​can also suggest the optimal cooking process by considering the type of cooking equipment (oven, frying pan, etc.). In this way, the optimization unit can perform optimization based on the performance and usage of cooking equipment during the cooking process optimization.

[0065] The customization function allows for the customization of meal plans based on the patient's current health condition. For example, the AI ​​can suggest an optimal meal plan based on the patient's current health status (blood pressure, blood sugar levels, etc.). The AI ​​can also customize the meal plan considering the patient's current physical condition (fatigue, stress, etc.). The AI ​​can also adjust the meal plan according to the patient's current health goals (weight loss, muscle building, etc.). As a result, the customization function allows for the customization of meal plans based on the patient's current health condition.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The analysis unit analyzes the patient's nutritional indicator data. For example, it analyzes the patient's nutritional indicator data such as vitamins, minerals, and calories, and performs the analysis using a generating AI. Step 2: The generation unit generates meal recipes based on the data analyzed by the analysis unit. For example, it uses a generation AI to generate meal recipes optimized for salt and calories. Prompts are input to the generation AI, and meal recipes are generated based on those prompts. Step 3: The data analysis unit analyzes the nutritional value of the ingredients. For example, it analyzes the vitamin and mineral content of the ingredients and uses AI to analyze the nutritional value of the ingredients. Step 4: The customization unit customizes the meal plan based on the data analyzed by the data analysis unit. For example, it adjusts the meal plan according to the patient's nutritional needs and customizes the meal plan using AI. Step 5: The optimization unit optimizes the cooking process based on available ingredients, cooking equipment, and staff schedules. For example, it may use machine learning to optimize the cooking process, or use AI to optimize the cooking process.

[0068] (Example of form 2) The nutrition management system according to an embodiment of the present invention is a system that utilizes generative AI to provide meals tailored to the individual nutritional requirements of each patient and to achieve efficient allocation of cooking resources and time. This nutrition management system analyzes the patient's nutritional indicator data and generates meal recipes optimized for salt and calories. The nutrition management system uses data analysis tools to analyze the nutritional value of ingredients and customize meal plans. The nutrition management system uses machine learning to optimize the cooking process based on available ingredients, cooking equipment, and staff schedules. The nutrition management system links with ingredient and recipe databases to provide cooking suggestions using accessible ingredients. The nutrition management system builds a feedback loop in which the AI ​​learns from patient feedback and uses it to improve future processes. As a result, the nutrition management system can streamline the provision of meals that meet individual nutritional needs, reduce time and costs by optimizing the cooking process, and improve patient satisfaction and meal quality. For example, the nutrition management system receives patient nutritional indicator data as input, and the generative AI analyzes that data to generate an optimal meal recipe. The generative AI receives prompts, for example, to optimize the patient's salt intake and calorie intake and generates meal recipes. Next, the nutrition management system uses data analysis tools to analyze the nutritional value of ingredients and customize meal plans. For example, data analysis tools analyze the vitamin and mineral content of ingredients and propose meal plans tailored to the patient's nutritional needs. Furthermore, nutrition management systems optimize cooking processes using machine learning. For instance, machine learning algorithms suggest optimal cooking processes based on available ingredients, cooking equipment, and staff schedules. This enables efficient allocation of cooking resources. Nutrition management systems also integrate with ingredient and recipe databases to suggest cooking methods using available ingredients. For example, a nutrition management system retrieves ingredient information from a database and suggests recipes that can be prepared with currently available ingredients. In addition, nutrition management systems build a feedback loop that learns from patient feedback and uses it to improve future plans. For example, a nutrition management system collects feedback from patients and uses that data to improve the next meal plan.This allows the nutrition management system to streamline the delivery of meals that meet patients' nutritional needs, reduce time and costs through optimized cooking processes, and improve patient satisfaction and meal quality.

[0069] The nutrition management system according to this embodiment comprises an analysis unit, a generation unit, a data analysis unit, a customization unit, and an optimization unit. The analysis unit analyzes the patient's nutritional indicator data. For example, the analysis unit analyzes the patient's nutritional indicator data such as vitamins, minerals, and calories. The analysis unit can analyze the patient's nutritional indicator data using a generation AI. The generation unit generates meal recipes based on the data analyzed by the analysis unit. For example, the generation unit generates meal recipes optimized for salt and calories using a generation AI. The generation unit inputs prompts to the generation AI, and the generation AI generates meal recipes based on those prompts. The data analysis unit analyzes the nutritional value of ingredients. For example, the data analysis unit analyzes the vitamin and mineral content of ingredients. The data analysis unit can analyze the nutritional value of ingredients using AI. The customization unit customizes meal plans based on the data analyzed by the data analysis unit. For example, the customization unit adjusts meal plans according to the patient's nutritional needs. The customization unit can customize meal plans using AI. The optimization unit optimizes the cooking process based on available ingredients, cooking equipment, and staff schedules. The optimization unit optimizes the cooking process, for example, using machine learning. The optimization unit can optimize the cooking process using AI. As a result, the nutrition management system according to the embodiment efficiently provides meals that meet individual nutritional needs by analyzing patient nutritional indicator data, generating meal recipes, analyzing the nutritional value of ingredients, customizing meal plans, and optimizing the cooking process. For example, the analysis unit inputs patient nutritional indicator data into the generation AI, which then analyzes the data. The generation unit generates meal recipes based on the data analyzed by the analysis unit. The data analysis unit uses AI to analyze the nutritional value of ingredients. The customization unit uses AI to customize meal plans based on the data analyzed by the data analysis unit. The optimization unit uses AI to optimize the cooking process based on available ingredients, cooking equipment, and staff schedules.As a result, the nutrition management system according to this embodiment analyzes the patient's nutritional indicator data, generates meal recipes, analyzes the nutritional value of ingredients, customizes meal plans, and optimizes cooking processes, thereby streamlining the provision of meals that meet individual nutritional needs.

[0070] The analysis unit analyzes the patient's nutritional indicator data. Specifically, it collects patient blood test results, dietary records, and anthropometric data, and analyzes this data in detail. For example, it identifies vitamin and mineral deficiencies or excesses from blood test results, and evaluates daily calorie intake and nutritional balance from dietary records. It calculates BMI and body fat percentage from anthropometric data to comprehensively understand the patient's health status. The analysis unit inputs this data into a generating AI, which then analyzes the data. Based on past data and statistical information, the generating AI evaluates the patient's nutritional status and identifies necessary nutrients and areas for improvement. For example, the generating AI can detect a patient's vitamin D deficiency and identify causes such as lack of sunlight or dietary content. This allows the analysis unit to accurately understand the patient's nutritional status and provide basic data for appropriate nutritional management.

[0071] The generation unit generates meal recipes based on data analyzed by the analysis unit. Specifically, prompts are input to the generation AI, which then generates meal recipes based on those prompts. These prompts include information such as the patient's nutritional needs, ingredient restrictions, and cooking methods. For example, if the user inputs "Generate a low-calorie breakfast recipe rich in vitamin D" to the generation AI, it will generate a low-calorie breakfast recipe using ingredients rich in vitamin D based on that instruction. The generation AI considers the nutritional value of the ingredients and cooking methods to propose a recipe that best suits the patient's nutritional needs. For example, the generation AI might suggest a recipe using fish or eggs, which are rich in vitamin D, and select healthy cooking methods such as baking or steaming. This allows the generation unit to efficiently generate and provide meal recipes that meet the patient's nutritional needs.

[0072] The Data Analysis Department analyzes the nutritional value of food ingredients. Specifically, it analyzes the nutritional value of food ingredients in detail, including vitamin content, mineral content, and calories. The Data Analysis Department can use AI to analyze the nutritional value of food ingredients. The AI ​​refers to a nutritional database of food ingredients and accurately understands the nutritional value of each ingredient. For example, the AI ​​analyzes the vitamin A content and calcium content of spinach and evaluates its nutritional value. Furthermore, the AI ​​compares the nutritional value of food ingredients and assists in selecting the most suitable ingredients. For example, the AI ​​can compare food ingredients that are rich in vitamin C and select the most suitable ingredient. As a result, the Data Analysis Department can accurately analyze the nutritional value of food ingredients and provide the information necessary for creating meal plans.

[0073] The customization department customizes meal plans based on data analyzed by the data analysis department. Specifically, it adjusts meal plans considering the patient's nutritional needs, preferences, and allergy information. The customization department can also customize meal plans using AI. The AI ​​proposes the optimal meal plan based on the patient's nutritional indicator data and the nutritional value of ingredients. For example, for a patient with a vitamin D deficiency, the AI ​​will propose a meal plan using ingredients rich in vitamin D. It also adjusts ingredient selection and cooking methods considering the patient's preferences and allergy information. For example, for a patient with a dairy allergy, it will propose a recipe that does not use dairy products. In this way, the customization department can provide meal plans tailored to the individual needs of each patient, making nutritional management more efficient.

[0074] The optimization department optimizes the cooking process based on available ingredients, cooking equipment, and staff schedules. Specifically, it uses machine learning to optimize the cooking process. The optimization department can optimize the cooking process using AI. The AI ​​considers available ingredients, cooking equipment, and staff schedules to propose the optimal cooking process. For example, the AI ​​calculates the optimal cooking order and cooking time based on available ingredients and proposes an efficient cooking process. It also considers staff schedules and distributes and adjusts cooking tasks. As a result, the optimization department can streamline the cooking process and improve the speed and quality of meal delivery. Furthermore, the optimization department can accumulate data on the cooking process and continuously make improvements. For example, it can analyze past cooking data to identify bottlenecks and areas for improvement and optimize the cooking process. As a result, the optimization department can always maintain the optimal cooking process and improve the efficiency and quality of meal delivery.

[0075] The optimization unit can link with ingredient and recipe databases to suggest cooking methods using accessible ingredients. For example, the optimization unit can retrieve ingredient information from ingredient and recipe databases and suggest recipes that can be cooked with currently available ingredients. The optimization unit can also suggest the optimal cooking method based on the ingredient information retrieved from the database. By linking with ingredient and recipe databases, the optimization unit can suggest cooking methods using accessible ingredients. For example, the optimization unit retrieves ingredient information from the database and makes cooking suggestions based on that information. The optimization unit suggests the optimal cooking method based on the ingredient information retrieved from the database. In this way, the optimization unit can link with ingredient and recipe databases and suggest cooking methods using accessible ingredients.

[0076] The optimization unit can optimize the cooking process using machine learning. For example, the optimization unit uses a machine learning algorithm to propose the optimal cooking process based on available ingredients, cooking equipment, and staff schedules. The optimization unit can optimize the cooking process by using machine learning. For example, the optimization unit uses a machine learning algorithm to propose the optimal cooking process based on available ingredients, cooking equipment, and staff schedules. The optimization unit optimizes the cooking process using machine learning. Thus, the optimization unit can optimize the cooking process using machine learning.

[0077] The generation unit can generate meal recipes optimized for salt and calories using a generation AI. For example, the generation unit uses the generation AI to generate meal recipes optimized for salt and calories. The generation unit inputs prompts to the generation AI, and the generation AI generates meal recipes based on those prompts. By using the generation AI, the generation unit can generate meal recipes optimized for salt and calories. For example, the generation unit uses the generation AI to generate meal recipes optimized for salt and calories. The generation unit inputs prompts to the generation AI, and the generation AI generates meal recipes based on those prompts. This allows the generation unit to generate meal recipes optimized for salt and calories using a generation AI.

[0078] The customization department can analyze the nutritional value of ingredients using data analysis tools and customize meal plans. For example, the customization department can use data analysis tools to analyze the vitamin and mineral content of ingredients and propose meal plans tailored to the patient's nutritional needs. By using data analysis tools, the customization department can analyze the nutritional value of ingredients and customize meal plans. For example, the customization department can use data analysis tools to analyze the vitamin and mineral content of ingredients and propose meal plans tailored to the patient's nutritional needs. The customization department uses data analysis tools to analyze the nutritional value of ingredients and customize meal plans. This allows the customization department to analyze the nutritional value of ingredients using data analysis tools and customize meal plans.

[0079] The optimization unit can learn from patient feedback and build a feedback loop to improve future plans. For example, the optimization unit collects feedback from patients and uses that data to improve the next meal plan. By building a feedback loop, the optimization unit can learn from patient feedback and use that data to improve future plans. For example, the optimization unit collects feedback from patients and uses that data to improve the next meal plan. The optimization unit builds a feedback loop. This allows the optimization unit to learn from patient feedback and build a feedback loop to improve future plans.

[0080] The analysis unit can estimate the patient's emotions and adjust the analysis method of nutritional indicator data based on the estimated emotions. For example, if the patient is stressed, the generation AI will prioritize analyzing nutrients that are effective in reducing stress. If the patient is relaxed, the generation AI can also focus on analyzing nutrients that enhance relaxation. If the patient is tired, the generation AI can also analyze nutrients that are effective in relieving fatigue. In this way, the analysis unit can estimate the patient's emotions and adjust the analysis method of nutritional indicator data based on the estimated emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The analysis unit can analyze a patient's past health data and select the optimal analysis algorithm. For example, the analysis unit can use its generating AI to select the optimal nutrition indicator analysis algorithm based on the patient's past blood test data. The analysis unit can also analyze a patient's past dietary history and use its generating AI to select the optimal nutrition indicator analysis algorithm. The analysis unit can also refer to a patient's past exercise data and use its generating AI to select the optimal nutrition indicator analysis algorithm. In this way, the analysis unit can analyze a patient's past health data and select the optimal analysis algorithm.

[0082] The analysis unit can filter nutritional indicator data based on the patient's lifestyle and dietary history. For example, the analysis unit can use its generating AI to exclude specific foods from the nutritional indicator data based on the patient's dietary history. The analysis unit can also filter nutritional indicator data by considering the patient's lifestyle (smoking, drinking, etc.) and using its generating AI. The analysis unit can also use its generating AI to exclude allergens from the nutritional indicator data based on the patient's allergy information. This allows the analysis unit to filter nutritional indicator data based on the patient's lifestyle and dietary history during analysis.

[0083] The analysis unit can estimate the patient's emotions and prioritize the analysis results based on the estimated emotions. For example, if the patient is stressed, the generating AI will prioritize displaying nutrients that are effective in reducing stress as analysis results. If the patient is relaxed, the generating AI can also prioritize displaying nutrients that enhance relaxation as analysis results. If the patient is tired, the generating AI can also prioritize displaying nutrients that are effective in relieving fatigue as analysis results. In this way, the analysis unit can estimate the patient's emotions and prioritize the analysis results based on the estimated emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generating AI. The generating AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0084] The analysis unit can prioritize the analysis of highly relevant data by considering the patient's geographical location when analyzing nutritional indicator data. For example, the analysis unit can prioritize the analysis of highly relevant nutritional indicator data by considering the food culture of the area where the patient lives. The analysis unit can also prioritize the analysis of highly relevant nutritional indicator data by considering the climate of the area where the patient lives. The analysis unit can also prioritize the analysis of highly relevant nutritional indicator data by considering the availability of ingredients in the area where the patient lives. As a result, the analysis unit can prioritize the analysis of highly relevant data by considering the patient's geographical location when analyzing nutritional indicator data.

[0085] The analysis unit can analyze patients' social media activity and related data when analyzing nutritional indicator data. For example, the analysis unit can analyze the content of patients' social media posts, and the generating AI can analyze related nutritional indicator data. The analysis unit can also analyze the dietary trends of patients' social media followers, and the generating AI can analyze related nutritional indicator data. The analysis unit can also analyze patients' "likes" and comments on social media, and the generating AI can analyze related nutritional indicator data. As a result, the analysis unit can analyze patients' social media activity and related data when analyzing nutritional indicator data.

[0086] The generation unit can estimate the patient's emotions and adjust the way meal recipes are presented based on those estimated emotions. For example, if the patient is stressed, the generation AI will generate a simple and easy-to-understand recipe. If the patient is relaxed, the generation AI can also generate a recipe with detailed explanations. If the patient is tired, the generation AI can also generate a recipe that can be prepared in a short amount of time. In this way, the generation unit can estimate the patient's emotions and adjust the way meal recipes are presented based on those estimated emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The generation unit can adjust the level of detail in a recipe based on the importance of nutrients when generating a meal recipe. For example, if there are many important nutrients, the generation AI will generate a recipe that includes detailed cooking instructions. If there are few important nutrients, the generation AI can also generate a simplified recipe. If there is a deficiency in a particular nutrient, the generation AI can also generate a detailed recipe to supplement that nutrient. In this way, the generation unit can adjust the level of detail in a recipe based on the importance of nutrients when generating a meal recipe.

[0088] The generation unit can apply different generation algorithms to meal recipes depending on the patient's dietary restrictions and allergy information. For example, if a patient is allergic to a specific ingredient, the generation AI will generate a recipe that excludes that ingredient. If a patient has specific dietary restrictions (such as low salt or low sugar), the generation AI can also generate a recipe that accommodates those restrictions. If a patient needs to consume a specific nutrient, the generation AI can also generate a recipe that includes that nutrient. This allows the generation unit to apply different generation algorithms to meal recipes depending on the patient's dietary restrictions and allergy information.

[0089] The generation unit can estimate the patient's emotions and adjust the recipe length based on the estimated emotions. For example, if the patient is in a hurry, the generation AI can generate a short, concise recipe. If the patient is relaxed, the generation AI can also generate a longer recipe with more detailed explanations. If the patient is agitated, the generation AI can also generate a recipe with visually stimulating effects. This allows the generation unit to estimate the patient's emotions and adjust the recipe length based on the estimated emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The generation unit can determine recipe priorities based on the patient's dietary history when generating meal recipes. For example, the generation unit's AI can determine recipe priorities based on dishes the patient has enjoyed eating in the past. The generation unit can also determine recipe priorities based on dishes the patient has avoided in the past. The generation unit can also prioritize suggesting dishes containing specific nutrients based on the patient's dietary history. As a result, the generation unit can determine recipe priorities based on the patient's dietary history when generating meal recipes.

[0091] The generation unit can adjust the order of recipes based on the patient's preferences when generating meal recipes. For example, the generation unit's AI can adjust the order of recipes based on ingredients the patient likes. The generation unit can also adjust the order of recipes based on ingredients the patient avoids. The generation unit can also prioritize suggesting the most appealing recipes based on the patient's preferences. As a result, the generation unit can adjust the order of recipes based on the patient's preferences when generating meal recipes.

[0092] The data analysis unit can estimate the patient's emotions and adjust the method of analyzing the nutritional value of food ingredients based on the estimated emotions. For example, if the patient is stressed, the AI ​​will prioritize analyzing nutrients that are effective in reducing stress. If the patient is relaxed, the AI ​​can also focus on analyzing nutrients that enhance relaxation. If the patient is tired, the AI ​​can also analyze nutrients that are effective in relieving fatigue. In this way, the data analysis unit can estimate the patient's emotions and adjust the method of analyzing the nutritional value of food ingredients based on the estimated emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The data analysis unit can optimize its analysis algorithm by referring to past nutritional data when analyzing the nutritional value of food ingredients. For example, the data analysis unit can use AI to select the optimal nutritional value analysis algorithm based on past nutritional data. The data analysis unit can also analyze past nutritional data, and the AI ​​can improve the accuracy of the nutritional value analysis. The data analysis unit can also use AI to optimize the analysis method for specific nutrients by referring to past nutritional data. As a result, the data analysis unit can optimize its analysis algorithm by referring to past nutritional data when analyzing the nutritional value of food ingredients.

[0094] The data analysis unit can perform nutritional value analysis of food ingredients based on their origin and production methods. For example, the data analysis unit can use AI to analyze nutritional value based on the food ingredients' origin information. The data analysis unit can also use AI to analyze nutritional value while considering the food ingredients' production methods (organic farming, pesticide-free, etc.). The data analysis unit can also use AI to analyze nutritional value while considering the food ingredients' transportation methods. As a result, the data analysis unit can perform nutritional value analysis of food ingredients based on their origin and production methods.

[0095] The data analysis unit can estimate the patient's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the patient is anxious, the AI ​​can provide a simple and easy-to-read display method. If the patient is relaxed, the AI ​​can also provide a display method that includes detailed information. If the patient is in a hurry, the AI ​​can also provide a concise display method. Thus, the data analysis unit can estimate the patient's emotions and adjust the display method of the analysis results based on the estimated emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The data analysis unit can perform nutritional value analysis of food ingredients while considering their storage conditions. For example, the data analysis unit can use AI to analyze nutritional value while considering the storage temperature of the food ingredients. The data analysis unit can also use AI to analyze nutritional value while considering the storage period of the food ingredients. The data analysis unit can also use AI to analyze nutritional value while considering the storage method of the food ingredients (freezing, refrigeration, etc.). As a result, the data analysis unit can perform nutritional value analysis of food ingredients while considering their storage conditions.

[0097] The data analysis unit can improve the accuracy of its analysis by referring to relevant literature data when analyzing the nutritional value of food ingredients. For example, the data analysis unit uses AI to analyze nutritional value by referring to the latest nutritional research. The data analysis unit can also optimize the nutritional value analysis algorithm based on past nutritional literature. Furthermore, the data analysis unit can improve the accuracy of its nutritional value analysis by referring to relevant nutritional databases. This allows the data analysis unit to improve the accuracy of its analysis by referring to relevant literature data when analyzing the nutritional value of food ingredients.

[0098] The customization unit can estimate the patient's emotions and adjust the customization method of the meal plan based on the estimated emotions. For example, if the patient is stressed, the customization unit can suggest a meal plan that is effective in reducing stress using AI. If the patient is relaxed, the customization unit can also suggest a meal plan that enhances relaxation using AI. If the patient is tired, the customization unit can also suggest a meal plan that is effective in relieving fatigue using AI. In this way, the customization unit can estimate the patient's emotions and adjust the customization method of the meal plan based on the estimated emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The customization function can select the optimal meal plan by referring to the patient's past eating history when customizing a meal plan. For example, the customization function can use AI to propose the optimal meal plan based on the patient's past eating history. The customization function can also prioritize suggesting meal plans containing specific nutrients based on the patient's past eating history. The customization function can also analyze the patient's past eating history and use AI to select the most effective meal plan. As a result, the customization function can select the optimal meal plan by referring to the patient's past eating history when customizing a meal plan.

[0100] The customization function can customize meal plans based on the patient's current health condition. For example, the AI ​​can suggest an optimal meal plan based on the patient's current health status (blood pressure, blood sugar levels, etc.). The AI ​​can also customize meal plans considering the patient's current physical condition (fatigue, stress, etc.). The AI ​​can also adjust meal plans according to the patient's current health goals (weight loss, muscle building, etc.). As a result, the customization function can customize meal plans based on the patient's current health condition.

[0101] The customization unit can estimate the patient's emotions and determine the priority of customizations based on the estimated emotions. For example, if the patient is stressed, the customization unit will prioritize customizations that are effective in reducing stress. If the patient is relaxed, the customization unit can also prioritize customizations that enhance relaxation. If the patient is tired, the customization unit can also prioritize customizations that are effective in relieving fatigue. In this way, the customization unit can estimate the patient's emotions and determine the priority of customizations based on the estimated emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The customization function can select the optimal meal plan by considering the patient's geographical location when customizing meal plans. For example, the customization function can use AI to suggest the optimal meal plan by considering the food culture of the area where the patient lives. The customization function can also use AI to suggest the optimal meal plan by considering the climate of the area where the patient lives. The customization function can also use AI to suggest the optimal meal plan by considering the availability of ingredients in the area where the patient lives. As a result, the customization function can select the optimal meal plan by considering the patient's geographical location when customizing meal plans.

[0103] The customization department can analyze a patient's social media activity and propose a meal plan when customizing it. For example, the customization department can analyze the content of a patient's social media posts and have the AI ​​suggest a relevant meal plan. The customization department can also analyze the eating habits of the patient's social media followers and have the AI ​​suggest a relevant meal plan. The customization department can also analyze the patient's "likes" and comments on social media and have the AI ​​suggest a relevant meal plan. In this way, the customization department can analyze a patient's social media activity and propose a meal plan when customizing it.

[0104] The optimization unit can estimate the patient's emotions and adjust the cooking process optimization method based on the estimated emotions. For example, if the patient is stressed, the AI ​​will suggest a cooking process that is effective in reducing stress. If the patient is relaxed, the AI ​​can also suggest a cooking process that enhances relaxation. If the patient is tired, the AI ​​can also suggest a cooking process that is effective in relieving fatigue. Thus, the optimization unit can estimate the patient's emotions and adjust the cooking process optimization method based on the estimated emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The optimization unit can optimize its optimization algorithm by referring to past cooking data when optimizing the cooking process. For example, the optimization unit can use AI to propose the optimal cooking process based on past cooking data. The optimization unit can also analyze past cooking data and have the AI ​​improve the efficiency of the cooking process. The optimization unit can also use AI to select an optimization method for a specific cooking process by referring to past cooking data. As a result, the optimization unit can optimize its optimization algorithm by referring to past cooking data when optimizing the cooking process.

[0106] The optimization unit can perform optimization based on the performance and usage of cooking equipment during the cooking process optimization. For example, the AI ​​optimizes the cooking process by considering the performance of the cooking equipment (heating speed, capacity, etc.). The AI ​​can also optimize the cooking process by considering the usage of the cooking equipment (frequency of use, maintenance status, etc.). The AI ​​can also suggest the optimal cooking process by considering the type of cooking equipment (oven, frying pan, etc.). As a result, the optimization unit can perform optimization based on the performance and usage of cooking equipment during the cooking process optimization.

[0107] The optimization unit can estimate the patient's emotions and determine optimization priorities based on the estimated emotions. For example, if the patient is stressed, the AI ​​will prioritize optimizations that are effective in reducing stress. If the patient is relaxed, the AI ​​can also prioritize optimizations that enhance relaxation. If the patient is tired, the AI ​​can also prioritize optimizations that are effective in relieving fatigue. Thus, the optimization unit can estimate the patient's emotions and determine optimization priorities based on the estimated emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0108] The optimization unit can optimize the cooking process while considering the schedules of the cooking staff. For example, the optimization unit can use AI to propose the optimal cooking process while considering the working hours of the cooking staff. The optimization unit can also use AI to optimize the cooking process while considering the skill level of the cooking staff. The optimization unit can also use AI to adjust the cooking process while considering the break times of the cooking staff. As a result, the optimization unit can optimize the cooking process while considering the schedules of the cooking staff.

[0109] The optimization unit can improve the accuracy of the optimization process by referring to relevant cooking techniques. For example, the AI ​​can optimize the cooking process by referring to the latest cooking techniques. The AI ​​can also improve the efficiency of the cooking process based on past cooking techniques. The AI ​​can also select an optimization method for the cooking process by referring to a database of relevant cooking techniques. As a result, the optimization unit can improve the accuracy of the optimization process by referring to relevant cooking techniques.

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

[0111] The analysis unit can estimate the patient's emotions and adjust the analysis method of nutritional indicator data based on the estimated emotions. For example, if the patient is stressed, the generating AI will prioritize analyzing nutrients that are effective in reducing stress. If the patient is relaxed, the generating AI can also focus on analyzing nutrients that enhance relaxation. If the patient is tired, the generating AI can also analyze nutrients that are effective in relieving fatigue. In this way, the analysis unit can estimate the patient's emotions and adjust the analysis method of nutritional indicator data based on the estimated emotions.

[0112] The generation unit can estimate the patient's emotions and adjust the way the meal recipe is presented based on those emotions. For example, if the patient is stressed, the generation AI will generate a simple and easy-to-understand recipe. If the patient is relaxed, the generation AI can also generate a recipe with detailed explanations. If the patient is tired, the generation AI can also generate a recipe that can be prepared in a short amount of time. In this way, the generation unit can estimate the patient's emotions and adjust the way the meal recipe is presented based on those emotions.

[0113] The customization unit can estimate the patient's emotions and adjust the meal plan customization method based on those estimated emotions. For example, if the patient is stressed, the AI ​​will suggest a meal plan that is effective in reducing stress. If the patient is relaxed, the AI ​​can also suggest a meal plan that enhances relaxation. If the patient is tired, the AI ​​can also suggest a meal plan that is effective in relieving fatigue. In this way, the customization unit can estimate the patient's emotions and adjust the meal plan customization method based on those estimated emotions.

[0114] The optimization unit can estimate the patient's emotions and adjust the cooking process optimization method based on the estimated emotions. For example, if the patient is stressed, the AI ​​can suggest a cooking process that is effective in reducing stress. If the patient is relaxed, the AI ​​can also suggest a cooking process that enhances relaxation. If the patient is tired, the AI ​​can also suggest a cooking process that is effective in relieving fatigue. In this way, the optimization unit can estimate the patient's emotions and adjust the cooking process optimization method based on the estimated emotions.

[0115] The data analysis unit can estimate the patient's emotions and adjust the method of analyzing the nutritional value of food ingredients based on those estimated emotions. For example, if the patient is stressed, the AI ​​will prioritize analyzing nutrients that are effective in reducing stress. If the patient is relaxed, the AI ​​can focus on analyzing nutrients that enhance relaxation. If the patient is tired, the AI ​​can analyze nutrients that are effective in relieving fatigue. In this way, the data analysis unit can estimate the patient's emotions and adjust the method of analyzing the nutritional value of food ingredients based on those estimated emotions.

[0116] The analysis unit can analyze a patient's past health data and select the optimal analysis algorithm. For example, based on the patient's past blood test data, the generating AI can select the optimal nutrition indicator analysis algorithm. The generating AI can also analyze the patient's past dietary history and select the optimal nutrition indicator analysis algorithm. The generating AI can also refer to the patient's past exercise data and select the optimal nutrition indicator analysis algorithm. In this way, the analysis unit can analyze a patient's past health data and select the optimal analysis algorithm.

[0117] The generation unit can apply different generation algorithms to meal recipes depending on the patient's dietary restrictions and allergy information. For example, if a patient is allergic to a specific ingredient, the generation AI will generate a recipe that excludes that ingredient. If a patient has specific dietary restrictions (such as low salt or low sugar), the generation AI can also generate a recipe that accommodates those restrictions. If a patient needs to consume a specific nutrient, the generation AI can also generate a recipe that includes that nutrient. In this way, the generation unit can apply different generation algorithms to meal recipes depending on the patient's dietary restrictions and allergy information.

[0118] The data analysis department can perform nutritional value analysis of food ingredients based on their origin and production methods. For example, the AI ​​can analyze nutritional value based on the food ingredients' origin information. The AI ​​can also analyze nutritional value considering the food ingredients' production methods (organic farming, pesticide-free, etc.). The AI ​​can also analyze nutritional value considering the food ingredients' transportation methods. As a result, the data analysis department can perform nutritional value analysis of food ingredients based on their origin and production methods.

[0119] The optimization unit can perform optimization based on the performance and usage of cooking equipment during the cooking process optimization. For example, the AI ​​optimizes the cooking process by considering the performance of the cooking equipment (heating speed, capacity, etc.). The AI ​​can also optimize the cooking process by considering the usage of the cooking equipment (frequency of use, maintenance status, etc.). The AI ​​can also suggest the optimal cooking process by considering the type of cooking equipment (oven, frying pan, etc.). In this way, the optimization unit can perform optimization based on the performance and usage of cooking equipment during the cooking process optimization.

[0120] The customization function allows for the customization of meal plans based on the patient's current health condition. For example, the AI ​​can suggest an optimal meal plan based on the patient's current health status (blood pressure, blood sugar levels, etc.). The AI ​​can also customize the meal plan considering the patient's current physical condition (fatigue, stress, etc.). The AI ​​can also adjust the meal plan according to the patient's current health goals (weight loss, muscle building, etc.). As a result, the customization function allows for the customization of meal plans based on the patient's current health condition.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The analysis unit analyzes the patient's nutritional indicator data. For example, it analyzes the patient's nutritional indicator data such as vitamins, minerals, and calories, and performs the analysis using a generating AI. Step 2: The generation unit generates meal recipes based on the data analyzed by the analysis unit. For example, it uses a generation AI to generate meal recipes optimized for salt and calories. Prompts are input to the generation AI, and meal recipes are generated based on those prompts. Step 3: The data analysis unit analyzes the nutritional value of the ingredients. For example, it analyzes the vitamin and mineral content of the ingredients and uses AI to analyze the nutritional value of the ingredients. Step 4: The customization unit customizes the meal plan based on the data analyzed by the data analysis unit. For example, it adjusts the meal plan according to the patient's nutritional needs and customizes the meal plan using AI. Step 5: The optimization unit optimizes the cooking process based on available ingredients, cooking equipment, and staff schedules. For example, it may use machine learning to optimize the cooking process, or use AI to optimize the cooking process.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the analysis unit, generation unit, data analysis unit, customization unit, and optimization unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12. The data analysis unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The customization unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The optimization unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

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

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

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

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the analysis unit, generation unit, data analysis unit, customization unit, and optimization unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12. The data analysis unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The customization unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The optimization unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

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

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

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

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

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

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the analysis unit, generation unit, data analysis unit, customization unit, and optimization unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12. The data analysis unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The customization unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The optimization unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

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

[0160] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

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

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0166] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0168] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the analysis unit, generation unit, data analysis unit, customization unit, and optimization unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12. The data analysis unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The customization unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The optimization unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

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

[0177] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0178] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0179] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0180] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

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

[0185] 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.

[0186] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0187] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0188] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0189] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0193] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0194] (Note 1) An analysis unit that analyzes patient nutritional indicator data, A generation unit that generates meal recipes based on the data analyzed by the analysis unit, The data analysis department analyzes the nutritional value of food ingredients, A customization unit customizes the meal plan based on the data analyzed by the aforementioned data analysis unit, It includes an optimization unit that optimizes the cooking process based on available ingredients, cooking equipment, and staff schedules. A system characterized by the following features. (Note 2) The optimization unit, It integrates with ingredient and recipe databases to provide cooking suggestions using available ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 3) The optimization unit, Optimizing the cooking process using machine learning The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is The AI ​​generates meal recipes optimized for salt and calories. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned customization unit is Use data analysis tools to analyze the nutritional value of ingredients and customize meal plans. The system described in Appendix 1, characterized by the features described herein. (Note 6) The optimization unit, We build a feedback loop that learns from patient feedback and uses it to improve future sessions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, We estimate the patient's emotions and adjust the analysis method of nutritional indicator data based on the estimated patient emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, Analyze the patient's past health data and select the optimal analysis algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, When analyzing nutritional indicator data, filtering is performed based on the patient's lifestyle and dietary history. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, The system estimates the patient's emotions and prioritizes the analysis results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, When analyzing nutritional indicator data, the analysis prioritizes highly relevant data, taking into account the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, When analyzing nutritional indicator data, we analyze patients' social media activity and related data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is The system estimates the patient's emotions and adjusts the way meal recipes are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating meal recipes, adjust the level of detail in the recipes based on the importance of the nutrients. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating meal recipes, different generation algorithms are applied depending on the patient's dietary restrictions and allergy information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is The system estimates the patient's emotions and adjusts the recipe length based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating meal recipes, the system prioritizes recipes based on the patient's dietary history. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating meal recipes, the order of the recipes is adjusted based on the patient's preferences. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned data analysis unit, The system estimates the patient's emotions and adjusts the nutritional value analysis method of the food ingredients based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned data analysis unit, When analyzing the nutritional value of food ingredients, the analysis algorithm is optimized by referring to past nutritional data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned data analysis unit, When analyzing the nutritional value of food ingredients, the analysis is performed based on the origin and production method of the ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned data analysis unit, The system estimates the patient's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned data analysis unit, When analyzing the nutritional value of food ingredients, the storage conditions of the ingredients should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned data analysis unit, When analyzing the nutritional value of food ingredients, we improve the accuracy of the analysis by referring to relevant literature data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned customization unit is The system estimates the patient's emotions and adjusts how the meal plan is customized based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned customization unit is When customizing a meal plan, the optimal plan is selected by referring to the patient's past eating history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned customization unit is When customizing a meal plan, the plan is customized based on the patient's current health condition. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned customization unit is It estimates the patient's emotions and determines customization priorities based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned customization unit is When customizing meal plans, the optimal plan is selected by considering the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned customization unit is When customizing meal plans, we analyze the patient's social media activity to propose a plan. The system described in Appendix 1, characterized by the features described herein. (Note 31) The optimization unit, The system estimates the patient's emotions and adjusts the cooking process optimization method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The optimization unit, When optimizing the cooking process, the optimization algorithm is optimized by referring to past cooking data. The system described in Appendix 1, characterized by the features described herein. (Note 33) The optimization unit, When optimizing the cooking process, the optimization is performed based on the performance and usage of the cooking equipment. The system described in Appendix 1, characterized by the features described herein. (Note 34) The optimization unit, The system estimates the patient's emotions and determines optimization priorities based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The optimization unit, When optimizing the cooking process, the schedule of the cooking staff should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 36) The optimization unit, When optimizing the cooking process, referencing relevant cooking techniques improves the accuracy of the optimization. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

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

Claims

1. An analysis unit that analyzes patient nutritional indicator data, A generation unit that generates meal recipes based on the data analyzed by the analysis unit, The data analysis department analyzes the nutritional value of food ingredients, A customization unit customizes the meal plan based on the data analyzed by the aforementioned data analysis unit, It includes an optimization unit that optimizes the cooking process based on available ingredients, cooking equipment, and staff schedules. A system characterized by the following features.

2. The optimization unit, It integrates with ingredient and recipe databases to provide cooking suggestions using available ingredients. The system according to feature 1.

3. The optimization unit, Optimizing the cooking process using machine learning The system according to feature 1.

4. The generating unit is The AI ​​generates meal recipes optimized for salt and calories. The system according to feature 1.

5. The aforementioned customization unit is Use data analysis tools to analyze the nutritional value of ingredients and customize meal plans. The system according to feature 1.

6. The optimization unit, We build a feedback loop that learns from patient feedback and uses it to improve future sessions. The system according to feature 1.

7. The aforementioned analysis unit, We estimate the patient's emotions and adjust the analysis method of nutritional indicator data based on the estimated patient emotions. The system according to feature 1.

8. The aforementioned analysis unit, Analyze the patient's past health data and select the optimal analysis algorithm. The system according to feature 1.

9. The aforementioned analysis unit, When analyzing nutritional indicator data, filtering is performed based on the patient's lifestyle and dietary history. The system according to feature 1.

10. The aforementioned analysis unit, The system estimates the patient's emotions and prioritizes the analysis results based on the estimated emotions. The system according to feature 1.

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