system

Abstract

The system according to this embodiment aims to suggest an optimal diet based on an individual's health condition, target weight, and preferences. [Solution] The system according to the embodiment comprises a collection unit, a suggestion unit, and a calculation unit. The collection unit collects information such as health status, target weight, and preferences. The suggestion unit suggests an optimal meal based on the information collected by the collection unit. The calculation unit calculates the nutrients from the meal actually eaten based on the meal suggested by the suggestion unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it has not been sufficiently done to propose an optimal diet based on individual health conditions, target weight, and preferences, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal diet based on individual health conditions, target weight, and preferences.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, a proposal unit, and a calculation unit. The collection unit collects information such as health conditions, target weight, and preferences. The proposal unit proposes an optimal diet based on the information collected by the collection unit. The calculation unit calculates nutrients from the diet actually eaten as proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can suggest an optimal meal based on an individual's health condition, target weight, and preferences. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The meal suggestion system according to an embodiment of the present invention is a system in which AI provides the optimal meal for each meal based on the user's health status, target weight, preferences, etc. The meal suggestion system allows the user to set goals such as wanting to lose weight or become healthier, and to provide information such as preferences, cooking skills, and restaurants within their range of activity. Based on this information, the AI ​​suggests the optimal meal menu for each meal and calculates nutrients from what is actually eaten. It functions as a goal-based hierarchical agent, searching for information from multiple conditions to support the user's diet. For example, if a health checkup reveals that the user is at risk of metabolic syndrome, improvement of visceral fat, blood fat, and cholesterol is necessary. The AI ​​suggests the optimal meal while considering the user's preferences. For example, for a user who likes ramen, it may instruct them to leave some of the broth and suggest adding spinach as a topping or adding vegetable juice to supplement vitamins. It may also instruct them to choose Japanese sweets for dessert. This system is divided into upper and lower agents, with the upper agent integrating the whole system and the lower agents handling specific tasks. For example, there may be a nutrition calculation agent, a shopping agent, a menu planning agent, etc., and each works in cooperation to provide the optimal meal. This system targets men and women of all ages who have health and lifestyle-related issues. These individuals need to diet due to lifestyle-related diseases or other health concerns, but struggle with managing their meals. The AI ​​agent helps them solve these problems by providing optimal meal recommendations, including nutritional information. The meal suggestion system can propose the best meals and calculate nutritional content based on the user's health status, target weight, preferences, and other information.

[0029] The meal suggestion system according to this embodiment comprises a collection unit, a suggestion unit, and a calculation unit. The collection unit collects information such as health status, target weight, and preferences. For example, the collection unit can collect the user's health checkup results and self-reported health status. The collection unit can also collect information such as the user's target weight, weight loss goals, and weight gain goals. Furthermore, the collection unit can collect information such as the user's preferences, allergy information, favorite foods, and disliked foods. For example, the collection unit stores the health checkup results entered by the user in a database, and the AI ​​analyzes them. The collection unit suggests an appropriate meal plan based on the target weight set by the user. The collection unit makes meal suggestions considering the user's preferences and allergy information. The suggestion unit suggests the optimal meal based on the information collected by the collection unit. For example, the suggestion unit can suggest a meal plan tailored to the user's health status and target weight. Furthermore, the suggestion unit can suggest a nutritionally balanced meal while considering the user's preferences. Furthermore, the suggestion unit can suggest places to purchase ingredients based on information about stores within the user's area of ​​activity. For example, the suggestion unit proposes a meal plan to reduce visceral fat and blood lipids based on the user's health check results. The suggestion unit proposes a meal plan that includes the user's favorite foods, taking into account the user's preferences. The suggestion unit proposes places to purchase ingredients based on information about supermarkets and restaurants within the user's usual range of activity. The calculation unit calculates the nutrients from the meals actually eaten, as suggested by the suggestion unit. The calculation unit can, for example, calculate the nutrients of the meals the user ate and evaluate calorie intake and nutritional balance. The calculation unit can also monitor the user's nutrient intake towards their target weight. Furthermore, the calculation unit can propose a meal plan to supplement necessary nutrients according to the user's health condition. For example, the calculation unit calculates the calories and nutrients of the meals the user ate and manages calorie intake. The calculation unit monitors the user's nutrient intake towards their target weight and adjusts the meal plan as needed. The calculation unit proposes a meal plan to supplement necessary nutrients such as vitamins and minerals according to the user's health condition.As a result, the meal suggestion system according to this embodiment can suggest an optimal meal and calculate nutrients based on information such as the user's health condition, target weight, and preferences.

[0030] The data collection unit collects information such as the user's health status, target weight, and preferences. Specifically, it can collect the user's health checkup results and self-reported health status. Health checkup results include detailed health data such as blood pressure, blood sugar levels, cholesterol levels, and BMI. This data can be obtained by the user entering the results of tests received at a medical institution or by linking with a health management application. Self-reported health status is collected when the user enters their daily physical condition and symptoms. For example, by entering information such as fatigue, changes in appetite, and sleep quality, a more detailed understanding of the user's health status can be obtained. The data collection unit also collects information such as the user's target weight, weight loss goals, and weight gain goals. The user can enter their current weight and target weight and set the timeframe for achieving it. This allows the system to propose a meal plan tailored to the user's goals. Furthermore, the data collection unit collects information such as the user's preferences, allergy information, favorite foods, and disliked foods. By entering whether or not the user has allergies and preferences for specific ingredients, this information can be reflected in the meal plan proposed by the system. For example, if the user has a dairy allergy, the data collection unit will propose a dairy-free meal plan based on that information. Furthermore, if a user has a preference for a particular ingredient, the system can prioritize suggesting recipes that include that ingredient. The data collection unit stores this information in a database, which is then analyzed by AI. Based on the collected data, the AI ​​analyzes the user's health status and preferences, providing the foundational data for suggesting the optimal meal plan. This allows the data collection unit to efficiently collect diverse user information, improving the overall accuracy and effectiveness of the system.

[0031] The suggestion department proposes optimal meals based on information collected by the data collection department. Specifically, it can propose meal plans tailored to the user's health condition and target weight. For example, based on the user's health checkup results, it can propose a meal plan to reduce visceral fat and blood lipids. This includes recipes using low-calorie, high-nutrient ingredients. Depending on the user's target weight, meal plans to support weight loss or gain are also proposed. For users aiming to lose weight, meal plans incorporating calorie restriction or carbohydrate restriction are proposed, while for users aiming to gain weight, meal plans high in protein and calories are proposed. The suggestion department can also propose nutritionally balanced meals while considering the user's preferences. For example, it can propose recipes that include the user's favorite foods, ensuring that the enjoyment of meals is not diminished. Furthermore, the suggestion department can also suggest places to purchase ingredients based on information about stores within the user's usual range of activity. It collects information on supermarkets and restaurants that the user frequently visits and proposes recipes using ingredients available at those locations. This allows users to easily obtain ingredients and implement the proposed meal plan. The suggestion department uses AI to analyze this information and provide the user with the most suitable meal plan. The AI ​​analyzes the user's health status and preferences based on collected data and generates an optimal meal plan. This allows the recommendation department to provide meal plans tailored to the user's health status, target weight, and preferences, supporting the user's health management.

[0032] The calculation unit calculates nutrients from the meals actually eaten, based on the meals suggested by the suggestion unit. Specifically, it can calculate the nutrients in the meals the user eats and evaluate calorie intake and nutritional balance. Users can input the contents of their meals or upload photos of their meals, and the calculation unit will automatically analyze the nutrients. The AI ​​uses image recognition technology to analyze the photos of the meals and identify the ingredients and their quantities. This allows for accurate calculation of the calories and nutrients in the meals the user eats. The calculation unit can also monitor the user's nutrient intake towards their target weight. It compares the current nutrient intake against the user's set target weight and evaluates progress toward achieving the goal. For example, if the user is aiming to lose weight, it checks whether calorie intake exceeds the target value and adjusts the meal plan as needed. Furthermore, the calculation unit can suggest meal plans to supplement necessary nutrients according to the user's health condition. For example, if the user is deficient in vitamins or minerals, it will suggest recipes using ingredients rich in those nutrients. This allows the user to practice a balanced diet and maintain good health. Through these functions, the calculation unit analyzes the user's diet in detail and supports health management. By utilizing AI, it is possible to accurately understand the user's diet and provide appropriate nutritional management. This allows the calculation unit to provide nutritional management tailored to the user's health condition and target weight, thereby supporting the user's health maintenance.

[0033] The data collection unit can collect information such as the user's preferences, cooking skills, and the types of shops within their range of activity. For example, the data collection unit can collect information such as the user's favorite and disliked foods, and allergy information. The data collection unit can also collect information such as the types of dishes the user is good at cooking, dishes they are not good at cooking, and their cooking skills. For example, the data collection unit can collect information about shops within the user's range of activity, including information such as the distance from their home and shops along their commute route. This allows the data collection unit to collect more appropriate information based on the user's preferences and range of activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input information about the user's preferences and range of activity into a generating AI, which can then analyze and collect the information.

[0034] The suggestion unit can propose an optimal meal plan tailored to the user's health condition and target weight based on the collected information. For example, the suggestion unit can propose a nutritionally balanced meal plan based on the user's health check results and target weight. For example, the suggestion unit can also propose a meal plan that emphasizes specific nutrients depending on the user's health condition. For example, the suggestion unit can propose a weight-loss or weight-gain meal plan aimed at the user's target weight. In this way, the suggestion unit can propose an optimal meal plan tailored to the user's health condition and target weight. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input information about the user's health condition and target weight into a generating AI, which can then propose an optimal meal plan.

[0035] The calculation unit can calculate nutrients from what the user actually eats and support the user in achieving their goals. For example, the calculation unit can calculate the nutrients in the meals the user eats and evaluate the calorie intake and nutritional balance. For example, the calculation unit can monitor the user's nutrient intake towards their target weight and adjust the meal plan as needed. For example, the calculation unit can suggest a meal plan to supplement necessary nutrients according to the user's health condition. In this way, the calculation unit can calculate nutrients from what the user actually eats and support the user in achieving their goals. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input information about the meals the user ate into a generating AI, which can then calculate the nutrients.

[0036] The suggestion unit can propose the optimal meal while taking into account the user's preferences. For example, the suggestion unit can propose a meal plan considering the user's favorite and disliked foods, allergy information, etc. The suggestion unit can also propose a meal plan that includes specific ingredients to match the user's preferences. The suggestion unit can also propose increasing the variety of meals, taking into account the user's preferences. In this way, the suggestion unit can propose the optimal meal that takes the user's preferences into account. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input information about the user's preferences into a generating AI, and the generating AI can propose the optimal meal plan.

[0037] The suggestion unit can instruct users who like ramen to leave some of the broth and suggest adding spinach as a topping or vegetable juice to supplement their vitamin intake. The suggestion unit can, for example, instruct users who like ramen to leave some of the broth. The suggestion unit can, for example, suggest adding spinach as a topping or vegetable juice to supplement their vitamin intake. The suggestion unit can also, for example, suggest ways to balance the nutrition by adjusting the ramen toppings or side dishes. In this way, the suggestion unit can provide ramen-loving users with health-conscious meal suggestions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input information about ramen-loving users into a generating AI, and the generating AI can make health-conscious meal suggestions.

[0038] The suggestion unit can instruct the system to choose Japanese sweets for dessert. The suggestion unit can, for example, instruct the system to choose Japanese sweets for dessert. The suggestion unit can also, for example, suggest an appropriate dessert by considering the type of Japanese sweets, calories, and sugar content. The suggestion unit can also, for example, suggest a healthy dessert by considering the nutritional value of Japanese sweets. In this way, the suggestion unit can make a healthy meal suggestion by instructing the system to choose Japanese sweets for dessert. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input information about desserts into a generating AI, and the generating AI can make a healthy dessert suggestion.

[0039] The data collection unit can analyze the user's past eating history and select the optimal information collection method. For example, the data collection unit can analyze preferences based on the dishes the user has enjoyed eating in the past. For example, the data collection unit can identify disliked foods based on the foods the user has avoided in the past. For example, the data collection unit can analyze the frequency and patterns of meals from the user's eating history. In this way, the data collection unit can select the optimal information collection method by analyzing the user's past eating history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past eating history into a generating AI, which can then select the optimal information collection method.

[0040] The data collection unit can filter information based on the user's current health status and lifestyle. For example, the data collection unit can prioritize collecting foods containing necessary nutrients based on the user's health checkup results. The data collection unit can also collect appropriate dietary information by considering the user's lifestyle (exercise level, sleep duration, etc.). The data collection unit can also exclude foods that cause allergies based on the user's allergy information. This allows the data collection unit to collect more appropriate information by filtering it based on the user's current health status and lifestyle. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input information about the user's health checkup results and lifestyle into a generating AI, which can then filter the information.

[0041] The data collection unit can prioritize collecting highly relevant information by considering the user's geographical location. For example, the data collection unit can prioritize collecting restaurant information near the user's current location. It can also prioritize collecting information on supermarkets and grocery stores within the user's range of activity. It can also prioritize collecting information on local specialties and seasonal ingredients in the user's area of ​​residence. This allows the data collection unit to collect more appropriate information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then prioritize collecting highly relevant information.

[0042] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can analyze photos of food shared by the user on social media to identify preferences. The data collection unit can also collect information on cooking accounts that the user follows. The data collection unit can also collect information on food-related groups and communities that the user participates in. In this way, the data collection unit can collect relevant information by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into a generating AI, which can then collect relevant information.

[0043] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the meal. For example, for important meals (such as breakfast), the suggestion unit will provide suggestions that include detailed nutritional information. For snacks or light meals, the suggestion unit may provide concise suggestions. For special event meals, the suggestion unit may provide elaborate suggestions. This allows the suggestion unit to provide more appropriate suggestions by adjusting the level of detail based on the importance of the meal. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input information about the importance of the meal into a generating AI, which can then adjust the level of detail in its suggestions.

[0044] The suggestion unit can apply different suggestion algorithms depending on the meal category when making suggestions. For example, the suggestion unit can apply a suggestion algorithm suitable for energy replenishment to breakfast. For example, the suggestion unit can also apply a suggestion algorithm that emphasizes nutritional balance to lunch. For example, the suggestion unit can also apply a suggestion algorithm that emphasizes easily digestible ingredients to dinner. In this way, the suggestion unit can make more appropriate suggestions by applying different suggestion algorithms depending on the meal category. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input information about the meal category into a generating AI, and the generating AI can apply different suggestion algorithms.

[0045] The proposal unit can determine the priority of proposals based on the timing of meal delivery. For example, the proposal unit may make breakfast proposals the night before. For example, the proposal unit may make lunch proposals the morning of the day. For example, the proposal unit may make dinner proposals at noon on the day of the day. This allows the proposal unit to make more appropriate proposals by determining the priority of proposals based on the timing of meal delivery. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input information about the timing of meal delivery into a generating AI, which can then determine the priority of proposals.

[0046] The suggestion unit can adjust the order of suggestions based on the relevance of the meal. For example, the suggestion unit may suggest the main dish first, followed by the side dishes. It may also suggest dessert last. The suggestion unit can also customize the order of suggestions according to the user's preferences. This allows the suggestion unit to make more appropriate suggestions by adjusting the order of suggestions based on the relevance of the meal. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input information about the relevance of the meal into a generating AI, which can then adjust the order of suggestions.

[0047] The calculation unit can analyze the user's past dietary history to select the optimal calculation method when calculating nutrients. For example, the calculation unit can calculate current nutrients based on data of nutrients the user has consumed in the past. For example, the calculation unit can also analyze the user's dietary history to determine nutrient intake trends and select the optimal calculation method. For example, the calculation unit can perform calculations to correct for nutrient deficiencies or excesses by referring to the user's past dietary history. In this way, the calculation unit can select the optimal nutrient calculation method by analyzing the user's past dietary history. Some or all of the above processes in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input the user's past dietary history into a generating AI, which can then select the optimal calculation method.

[0048] The calculation unit can customize the calculation method based on the user's current health condition when calculating nutrients. For example, the calculation unit can prioritize the calculation of necessary nutrients based on the user's health checkup results. The calculation unit can also calculate nutrients while considering the user's current physical condition (fatigue level, stress level, etc.). The calculation unit can also perform calculations that emphasize specific nutrients according to the user's health condition. In this way, the calculation unit can perform more appropriate nutrient calculations by customizing the calculation method based on the user's current health condition. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input information about the user's health checkup results and physical condition into a generating AI, which can then customize the calculation method.

[0049] The calculation unit can select the optimal calculation method when calculating nutrients, taking into account the user's geographical location information. For example, the calculation unit can calculate nutrients by considering local specialties and seasonal ingredients in the area where the user lives. The calculation unit can also calculate nutrients based on ingredients available within the user's range of activity. For example, the calculation unit can calculate nutrients by considering the intake trends of region-specific nutrients based on the user's geographical location information. As a result, the calculation unit can perform more appropriate nutrient calculations by selecting the optimal nutrient calculation method while taking into account the user's geographical location information. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input the user's geographical location information into a generating AI, which can then select the optimal calculation method.

[0050] The calculation unit can analyze the user's social media activity and propose a calculation method when calculating nutrients. For example, the calculation unit can analyze photos of meals shared by the user on social media and calculate nutrients. The calculation unit can also calculate nutrients based on information from cooking accounts that the user follows. The calculation unit can also calculate nutrients based on information from food-related groups and communities that the user participates in. In this way, the calculation unit can propose a more appropriate nutrient calculation method by analyzing the user's social media activity. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input the user's social media activity into a generating AI, which can then propose a calculation method.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The suggestion function can provide recipes for specific dishes based on the user's dietary preferences. For example, if the user prefers Italian food, the suggestion function can suggest pasta and pizza recipes. If the user prefers Japanese food, it can suggest sushi and tempura recipes. Furthermore, if the user prefers a specific ingredient, it can suggest recipes using that ingredient. In this way, the suggestion function can improve the user's satisfaction with their meals by providing recipes for specific dishes based on their dietary preferences.

[0053] The data collection unit can collect information about the user's meal frequency and timing. For example, it can collect information such as how many times a day the user eats and what times of day they eat. It can also collect information if the user has a habit of eating at a specific time of day. Furthermore, it can collect information about the environment in which the user eats (e.g., at home, at work, eating out, etc.). By collecting information about the user's meal frequency and timing, the data collection unit can provide more appropriate meal suggestions.

[0054] The suggestion function can provide recipes for specific dishes based on the user's dietary preferences. For example, if the user prefers Italian food, the suggestion function can suggest pasta and pizza recipes. If the user prefers Japanese food, it can suggest sushi and tempura recipes. Furthermore, if the user prefers a specific ingredient, it can suggest recipes using that ingredient. In this way, the suggestion function can improve the user's satisfaction with their meals by providing recipes for specific dishes based on their dietary preferences.

[0055] The calculation unit can calculate nutrients based on the user's meal frequency and timing. For example, it can calculate nutrients based on information such as how many times a day the user eats and when those meals take place. It can also calculate nutrients based on information if the user has a habit of eating at specific times. Furthermore, it can calculate nutrients based on the environment in which the user eats (e.g., at home, at work, eating out, etc.). This allows the calculation unit to perform more accurate nutrient calculations based on the user's meal frequency and timing.

[0056] The suggestion function can provide recipes for specific dishes based on the user's dietary preferences. For example, if the user prefers Italian food, the suggestion function can suggest pasta and pizza recipes. If the user prefers Japanese food, it can suggest sushi and tempura recipes. Furthermore, if the user prefers a specific ingredient, it can suggest recipes using that ingredient. In this way, the suggestion function can improve the user's satisfaction with their meals by providing recipes for specific dishes based on their dietary preferences.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The data collection unit collects information such as health status, target weight, and preferences. For example, it collects information such as the user's health checkup results, self-reported health status, target weight, weight loss goals, weight gain goals, preferences, allergy information, favorite foods, and disliked foods. The data collection unit stores the health checkup results entered by the user in a database, which is then analyzed by AI. It also collects information to suggest an appropriate meal plan based on the target weight set by the user. Furthermore, it collects information to suggest meals that take into account the user's preferences and allergy information. Step 2: The suggestion department proposes the optimal meal plan based on the information collected by the collection department. For example, it proposes a meal plan tailored to the user's health condition and target weight, and suggests a nutritionally balanced meal while considering the user's preferences. It can also suggest places to purchase ingredients based on information about stores within the user's usual range of activity. Specifically, it proposes a meal plan to reduce visceral fat and blood lipids based on the user's health checkup results, and proposes a meal plan that includes the user's favorite foods, taking their preferences into consideration. Furthermore, it suggests places to purchase ingredients based on information about supermarkets and restaurants within the user's usual range of activity. Step 3: The calculation unit calculates nutrients from the meals actually eaten, as suggested by the proposal unit. For example, it calculates the nutrients in the meals the user ate and evaluates calorie intake and nutritional balance. It also monitors the user's nutrient intake towards their target weight and adjusts the meal plan as needed. Furthermore, it proposes a meal plan to supplement necessary nutrients such as vitamins and minerals according to the user's health condition. Specifically, it calculates the calories and nutrients in the meals the user ate and manages calorie intake. It monitors the user's nutrient intake towards their target weight and adjusts the meal plan as needed. It proposes a meal plan to supplement necessary nutrients according to the user's health condition.

[0059] (Example of form 2) The meal suggestion system according to an embodiment of the present invention is a system in which AI provides the optimal meal for each meal based on the user's health status, target weight, preferences, etc. The meal suggestion system allows the user to set goals such as wanting to lose weight or become healthier, and to provide information such as preferences, cooking skills, and restaurants within their range of activity. Based on this information, the AI ​​suggests the optimal meal menu for each meal and calculates nutrients from what is actually eaten. It functions as a goal-based hierarchical agent, searching for information from multiple conditions to support the user's diet. For example, if a health checkup reveals that the user is at risk of metabolic syndrome, improvement of visceral fat, blood fat, and cholesterol is necessary. The AI ​​suggests the optimal meal while considering the user's preferences. For example, for a user who likes ramen, it may instruct them to leave some of the broth and suggest adding spinach as a topping or adding vegetable juice to supplement vitamins. It may also instruct them to choose Japanese sweets for dessert. This system is divided into upper and lower agents, with the upper agent integrating the whole system and the lower agents handling specific tasks. For example, there may be a nutrition calculation agent, a shopping agent, a menu planning agent, etc., and each works in cooperation to provide the optimal meal. This system targets men and women of all ages who have health and lifestyle-related issues. These individuals need to diet due to lifestyle-related diseases or other health concerns, but struggle with managing their meals. The AI ​​agent helps them solve these problems by providing optimal meal recommendations, including nutritional information. The meal suggestion system can propose the best meals and calculate nutritional content based on the user's health status, target weight, preferences, and other information.

[0060] The meal suggestion system according to this embodiment comprises a collection unit, a suggestion unit, and a calculation unit. The collection unit collects information such as health status, target weight, and preferences. For example, the collection unit can collect the user's health checkup results and self-reported health status. The collection unit can also collect information such as the user's target weight, weight loss goals, and weight gain goals. Furthermore, the collection unit can collect information such as the user's preferences, allergy information, favorite foods, and disliked foods. For example, the collection unit stores the health checkup results entered by the user in a database, and the AI ​​analyzes them. The collection unit suggests an appropriate meal plan based on the target weight set by the user. The collection unit makes meal suggestions considering the user's preferences and allergy information. The suggestion unit suggests the optimal meal based on the information collected by the collection unit. For example, the suggestion unit can suggest a meal plan tailored to the user's health status and target weight. Furthermore, the suggestion unit can suggest a nutritionally balanced meal while considering the user's preferences. Furthermore, the suggestion unit can suggest places to purchase ingredients based on information about stores within the user's area of ​​activity. For example, the suggestion unit proposes a meal plan to reduce visceral fat and blood lipids based on the user's health check results. The suggestion unit proposes a meal plan that includes the user's favorite foods, taking into account the user's preferences. The suggestion unit proposes places to purchase ingredients based on information about supermarkets and restaurants within the user's usual range of activity. The calculation unit calculates the nutrients from the meals actually eaten, as suggested by the suggestion unit. The calculation unit can, for example, calculate the nutrients of the meals the user ate and evaluate calorie intake and nutritional balance. The calculation unit can also monitor the user's nutrient intake towards their target weight. Furthermore, the calculation unit can propose a meal plan to supplement necessary nutrients according to the user's health condition. For example, the calculation unit calculates the calories and nutrients of the meals the user ate and manages calorie intake. The calculation unit monitors the user's nutrient intake towards their target weight and adjusts the meal plan as needed. The calculation unit proposes a meal plan to supplement necessary nutrients such as vitamins and minerals according to the user's health condition.As a result, the meal suggestion system according to this embodiment can suggest an optimal meal and calculate nutrients based on information such as the user's health condition, target weight, and preferences.

[0061] The data collection unit collects information such as the user's health status, target weight, and preferences. Specifically, it can collect the user's health checkup results and self-reported health status. Health checkup results include detailed health data such as blood pressure, blood sugar levels, cholesterol levels, and BMI. This data can be obtained by the user entering the results of tests received at a medical institution or by linking with a health management application. Self-reported health status is collected when the user enters their daily physical condition and symptoms. For example, by entering information such as fatigue, changes in appetite, and sleep quality, a more detailed understanding of the user's health status can be obtained. The data collection unit also collects information such as the user's target weight, weight loss goals, and weight gain goals. The user can enter their current weight and target weight and set the timeframe for achieving it. This allows the system to propose a meal plan tailored to the user's goals. Furthermore, the data collection unit collects information such as the user's preferences, allergy information, favorite foods, and disliked foods. By entering whether or not the user has allergies and preferences for specific ingredients, this information can be reflected in the meal plan proposed by the system. For example, if the user has a dairy allergy, the data collection unit will propose a dairy-free meal plan based on that information. Furthermore, if a user has a preference for a particular ingredient, the system can prioritize suggesting recipes that include that ingredient. The data collection unit stores this information in a database, which is then analyzed by AI. Based on the collected data, the AI ​​analyzes the user's health status and preferences, providing the foundational data for suggesting the optimal meal plan. This allows the data collection unit to efficiently collect diverse user information, improving the overall accuracy and effectiveness of the system.

[0062] The suggestion department proposes optimal meals based on information collected by the data collection department. Specifically, it can propose meal plans tailored to the user's health condition and target weight. For example, based on the user's health checkup results, it can propose a meal plan to reduce visceral fat and blood lipids. This includes recipes using low-calorie, high-nutrient ingredients. Depending on the user's target weight, meal plans to support weight loss or gain are also proposed. For users aiming to lose weight, meal plans incorporating calorie restriction or carbohydrate restriction are proposed, while for users aiming to gain weight, meal plans high in protein and calories are proposed. The suggestion department can also propose nutritionally balanced meals while considering the user's preferences. For example, it can propose recipes that include the user's favorite foods, ensuring that the enjoyment of meals is not diminished. Furthermore, the suggestion department can also suggest places to purchase ingredients based on information about stores within the user's usual range of activity. It collects information on supermarkets and restaurants that the user frequently visits and proposes recipes using ingredients available at those locations. This allows users to easily obtain ingredients and implement the proposed meal plan. The suggestion department uses AI to analyze this information and provide the user with the most suitable meal plan. The AI ​​analyzes the user's health status and preferences based on collected data and generates an optimal meal plan. This allows the recommendation department to provide meal plans tailored to the user's health status, target weight, and preferences, supporting the user's health management.

[0063] The calculation unit calculates nutrients from the meals actually eaten, based on the meals suggested by the suggestion unit. Specifically, it can calculate the nutrients in the meals the user eats and evaluate calorie intake and nutritional balance. Users can input the contents of their meals or upload photos of their meals, and the calculation unit will automatically analyze the nutrients. The AI ​​uses image recognition technology to analyze the photos of the meals and identify the ingredients and their quantities. This allows for accurate calculation of the calories and nutrients in the meals the user eats. The calculation unit can also monitor the user's nutrient intake towards their target weight. It compares the current nutrient intake against the user's set target weight and evaluates progress toward achieving the goal. For example, if the user is aiming to lose weight, it checks whether calorie intake exceeds the target value and adjusts the meal plan as needed. Furthermore, the calculation unit can suggest meal plans to supplement necessary nutrients according to the user's health condition. For example, if the user is deficient in vitamins or minerals, it will suggest recipes using ingredients rich in those nutrients. This allows the user to practice a balanced diet and maintain good health. Through these functions, the calculation unit analyzes the user's diet in detail and supports health management. By utilizing AI, it is possible to accurately understand the user's diet and provide appropriate nutritional management. This allows the calculation unit to provide nutritional management tailored to the user's health condition and target weight, thereby supporting the user's health maintenance.

[0064] The data collection unit can collect information such as the user's preferences, cooking skills, and the types of shops within their range of activity. For example, the data collection unit can collect information such as the user's favorite and disliked foods, and allergy information. The data collection unit can also collect information such as the types of dishes the user is good at cooking, dishes they are not good at cooking, and their cooking skills. For example, the data collection unit can collect information about shops within the user's range of activity, including information such as the distance from their home and shops along their commute route. This allows the data collection unit to collect more appropriate information based on the user's preferences and range of activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input information about the user's preferences and range of activity into a generating AI, which can then analyze and collect the information.

[0065] The suggestion unit can propose an optimal meal plan tailored to the user's health condition and target weight based on the collected information. For example, the suggestion unit can propose a nutritionally balanced meal plan based on the user's health check results and target weight. For example, the suggestion unit can also propose a meal plan that emphasizes specific nutrients depending on the user's health condition. For example, the suggestion unit can propose a weight-loss or weight-gain meal plan aimed at the user's target weight. In this way, the suggestion unit can propose an optimal meal plan tailored to the user's health condition and target weight. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input information about the user's health condition and target weight into a generating AI, which can then propose an optimal meal plan.

[0066] The calculation unit can calculate nutrients from what the user actually eats and support the user in achieving their goals. For example, the calculation unit can calculate the nutrients in the meals the user eats and evaluate the calorie intake and nutritional balance. For example, the calculation unit can monitor the user's nutrient intake towards their target weight and adjust the meal plan as needed. For example, the calculation unit can suggest a meal plan to supplement necessary nutrients according to the user's health condition. In this way, the calculation unit can calculate nutrients from what the user actually eats and support the user in achieving their goals. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input information about the meals the user ate into a generating AI, which can then calculate the nutrients.

[0067] The suggestion unit can propose the optimal meal while taking into account the user's preferences. For example, the suggestion unit can propose a meal plan considering the user's favorite and disliked foods, allergy information, etc. The suggestion unit can also propose a meal plan that includes specific ingredients to match the user's preferences. The suggestion unit can also propose increasing the variety of meals, taking into account the user's preferences. In this way, the suggestion unit can propose the optimal meal that takes the user's preferences into account. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input information about the user's preferences into a generating AI, and the generating AI can propose the optimal meal plan.

[0068] The suggestion unit can instruct users who like ramen to leave some of the broth and suggest adding spinach as a topping or vegetable juice to supplement their vitamin intake. The suggestion unit can, for example, instruct users who like ramen to leave some of the broth. The suggestion unit can, for example, suggest adding spinach as a topping or vegetable juice to supplement their vitamin intake. The suggestion unit can also, for example, suggest ways to balance the nutrition by adjusting the ramen toppings or side dishes. In this way, the suggestion unit can provide ramen-loving users with health-conscious meal suggestions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input information about ramen-loving users into a generating AI, and the generating AI can make health-conscious meal suggestions.

[0069] The suggestion unit can instruct the system to choose Japanese sweets for dessert. The suggestion unit can, for example, instruct the system to choose Japanese sweets for dessert. The suggestion unit can also, for example, suggest an appropriate dessert by considering the type of Japanese sweets, calories, and sugar content. The suggestion unit can also, for example, suggest a healthy dessert by considering the nutritional value of Japanese sweets. In this way, the suggestion unit can make a healthy meal suggestion by instructing the system to choose Japanese sweets for dessert. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input information about desserts into a generating AI, and the generating AI can make a healthy dessert suggestion.

[0070] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit will collect information during times when the user is relaxed. For example, if the user is busy, the data collection unit can collect information during times when the user is free. For example, if the user is relaxed, the data collection unit can collect detailed information. This allows the data collection unit to collect more appropriate information by adjusting the timing of information collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's emotion data into the generative AI, which can then adjust the timing of information collection.

[0071] The data collection unit can analyze the user's past eating history and select the optimal information collection method. For example, the data collection unit can analyze preferences based on the dishes the user has enjoyed eating in the past. For example, the data collection unit can identify disliked foods based on the foods the user has avoided in the past. For example, the data collection unit can analyze the frequency and patterns of meals from the user's eating history. In this way, the data collection unit can select the optimal information collection method by analyzing the user's past eating history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past eating history into a generating AI, which can then select the optimal information collection method.

[0072] The data collection unit can filter information based on the user's current health status and lifestyle. For example, the data collection unit can prioritize collecting foods containing necessary nutrients based on the user's health checkup results. The data collection unit can also collect appropriate dietary information by considering the user's lifestyle (exercise level, sleep duration, etc.). The data collection unit can also exclude foods that cause allergies based on the user's allergy information. This allows the data collection unit to collect more appropriate information by filtering it based on the user's current health status and lifestyle. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input information about the user's health checkup results and lifestyle into a generating AI, which can then filter the information.

[0073] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit may prioritize collecting information on foods with relaxing effects. For example, if the user is tired, the data collection unit may prioritize collecting information on foods suitable for energy replenishment. For example, if the user is health-conscious, the data collection unit may prioritize collecting information on foods with a good nutritional balance. In this way, the data collection unit can collect more appropriate information by determining the priority of information to collect according to the user's 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. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's emotion data into a generative AI and determine the priority of information to be collected by the generative AI.

[0074] The data collection unit can prioritize collecting highly relevant information by considering the user's geographical location. For example, the data collection unit can prioritize collecting restaurant information near the user's current location. It can also prioritize collecting information on supermarkets and grocery stores within the user's range of activity. It can also prioritize collecting information on local specialties and seasonal ingredients in the user's area of ​​residence. This allows the data collection unit to collect more appropriate information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then prioritize collecting highly relevant information.

[0075] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can analyze photos of food shared by the user on social media to identify preferences. The data collection unit can also collect information on cooking accounts that the user follows. The data collection unit can also collect information on food-related groups and communities that the user participates in. In this way, the data collection unit can collect relevant information by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into a generating AI, which can then collect relevant information.

[0076] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the user is feeling stressed, the suggestion unit might suggest a meal with relaxing effects. If the user is feeling tired, the suggestion unit might suggest a meal suitable for energy replenishment. If the user is health-conscious, the suggestion unit might suggest a nutritionally balanced meal. This allows the suggestion unit to make more appropriate suggestions by adjusting the way it presents its suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the suggestion unit may be performed using AI, or not using AI. For example, the suggestion unit can input user emotion data into a generative AI, which can then adjust the way it presents its suggestions.

[0077] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the meal. For example, for important meals (such as breakfast), the suggestion unit will provide suggestions that include detailed nutritional information. For snacks or light meals, the suggestion unit may provide concise suggestions. For special event meals, the suggestion unit may provide elaborate suggestions. This allows the suggestion unit to provide more appropriate suggestions by adjusting the level of detail based on the importance of the meal. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input information about the importance of the meal into a generating AI, which can then adjust the level of detail in its suggestions.

[0078] The suggestion unit can apply different suggestion algorithms depending on the meal category when making suggestions. For example, the suggestion unit can apply a suggestion algorithm suitable for energy replenishment to breakfast. For example, the suggestion unit can also apply a suggestion algorithm that emphasizes nutritional balance to lunch. For example, the suggestion unit can also apply a suggestion algorithm that emphasizes easily digestible ingredients to dinner. In this way, the suggestion unit can make more appropriate suggestions by applying different suggestion algorithms depending on the meal category. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input information about the meal category into a generating AI, and the generating AI can apply different suggestion algorithms.

[0079] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit will make short, to-the-point suggestions. If the user is relaxed, the suggestion unit may make longer suggestions that include detailed explanations. If the user is excited, the suggestion unit may make visually stimulating suggestions. This allows the suggestion unit to make more appropriate suggestions by adjusting the length of suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI, which can then adjust the length of the suggestions.

[0080] The proposal unit can determine the priority of proposals based on the timing of meal delivery. For example, the proposal unit may make breakfast proposals the night before. For example, the proposal unit may make lunch proposals the morning of the day. For example, the proposal unit may make dinner proposals at noon on the day of the day. This allows the proposal unit to make more appropriate proposals by determining the priority of proposals based on the timing of meal delivery. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input information about the timing of meal delivery into a generating AI, which can then determine the priority of proposals.

[0081] The suggestion unit can adjust the order of suggestions based on the relevance of the meal. For example, the suggestion unit may suggest the main dish first, followed by the side dishes. It may also suggest dessert last. The suggestion unit can also customize the order of suggestions according to the user's preferences. This allows the suggestion unit to make more appropriate suggestions by adjusting the order of suggestions based on the relevance of the meal. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input information about the relevance of the meal into a generating AI, which can then adjust the order of suggestions.

[0082] The calculation unit can estimate the user's emotions and adjust the nutrient calculation method based on the estimated emotions. For example, if the user is stressed, the calculation unit may prioritize calculating nutrients that help reduce stress. For example, if the user is tired, the calculation unit may prioritize calculating nutrients suitable for energy replenishment. For example, if the user is health-conscious, the calculation unit may perform calculations that emphasize nutritional balance. In this way, the calculation unit can perform more appropriate nutrient calculations by adjusting the nutrient calculation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input the user's emotion data into the generative AI, which can then adjust the nutrient calculation method.

[0083] The calculation unit can analyze the user's past dietary history to select the optimal calculation method when calculating nutrients. For example, the calculation unit can calculate current nutrients based on data of nutrients the user has consumed in the past. For example, the calculation unit can also analyze the user's dietary history to determine nutrient intake trends and select the optimal calculation method. For example, the calculation unit can perform calculations to correct for nutrient deficiencies or excesses by referring to the user's past dietary history. In this way, the calculation unit can select the optimal nutrient calculation method by analyzing the user's past dietary history. Some or all of the above processes in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input the user's past dietary history into a generating AI, which can then select the optimal calculation method.

[0084] The calculation unit can customize the calculation method based on the user's current health condition when calculating nutrients. For example, the calculation unit can prioritize the calculation of necessary nutrients based on the user's health checkup results. The calculation unit can also calculate nutrients while considering the user's current physical condition (fatigue level, stress level, etc.). The calculation unit can also perform calculations that emphasize specific nutrients according to the user's health condition. In this way, the calculation unit can perform more appropriate nutrient calculations by customizing the calculation method based on the user's current health condition. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input information about the user's health checkup results and physical condition into a generating AI, which can then customize the calculation method.

[0085] The calculation unit can estimate the user's emotions and determine the priority of nutrient calculations based on the estimated emotions. For example, if the user is stressed, the calculation unit will prioritize calculating nutrients that help reduce stress. For example, if the user is tired, the calculation unit can also prioritize calculating nutrients suitable for energy replenishment. For example, if the user is health-conscious, the calculation unit can perform calculations that emphasize nutritional balance. In this way, the calculation unit can perform more appropriate nutrient calculations by determining the priority of nutrient calculations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input the user's emotion data into a generative AI, and the generative AI can determine the priority of nutrient calculations.

[0086] The calculation unit can select the optimal calculation method when calculating nutrients, taking into account the user's geographical location information. For example, the calculation unit can calculate nutrients by considering local specialties and seasonal ingredients in the area where the user lives. The calculation unit can also calculate nutrients based on ingredients available within the user's range of activity. For example, the calculation unit can calculate nutrients by considering the intake trends of region-specific nutrients based on the user's geographical location information. As a result, the calculation unit can perform more appropriate nutrient calculations by selecting the optimal nutrient calculation method while taking into account the user's geographical location information. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input the user's geographical location information into a generating AI, which can then select the optimal calculation method.

[0087] The calculation unit can analyze the user's social media activity and propose a calculation method when calculating nutrients. For example, the calculation unit can analyze photos of meals shared by the user on social media and calculate nutrients. The calculation unit can also calculate nutrients based on information from cooking accounts that the user follows. The calculation unit can also calculate nutrients based on information from food-related groups and communities that the user participates in. In this way, the calculation unit can propose a more appropriate nutrient calculation method by analyzing the user's social media activity. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input the user's social media activity into a generating AI, which can then propose a calculation method.

[0088] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0089] The suggestion function can provide recipes for specific dishes based on the user's dietary preferences. For example, if the user prefers Italian food, the suggestion function can suggest pasta and pizza recipes. If the user prefers Japanese food, it can suggest sushi and tempura recipes. Furthermore, if the user prefers a specific ingredient, it can suggest recipes using that ingredient. In this way, the suggestion function can improve the user's satisfaction with their meals by providing recipes for specific dishes based on their dietary preferences.

[0090] The data collection unit can collect information about the user's meal frequency and timing. For example, it can collect information such as how many times a day the user eats and what times of day they eat. It can also collect information if the user has a habit of eating at a specific time of day. Furthermore, it can collect information about the environment in which the user eats (e.g., at home, at work, eating out, etc.). By collecting information about the user's meal frequency and timing, the data collection unit can provide more appropriate meal suggestions.

[0091] The suggestion function can provide recipes for specific dishes based on the user's dietary preferences. For example, if the user prefers Italian food, the suggestion function can suggest pasta and pizza recipes. If the user prefers Japanese food, it can suggest sushi and tempura recipes. Furthermore, if the user prefers a specific ingredient, it can suggest recipes using that ingredient. In this way, the suggestion function can improve the user's satisfaction with their meals by providing recipes for specific dishes based on their dietary preferences.

[0092] The calculation unit can calculate nutrients based on the user's meal frequency and timing. For example, it can calculate nutrients based on information such as how many times a day the user eats and when those meals take place. It can also calculate nutrients based on information if the user has a habit of eating at specific times. Furthermore, it can calculate nutrients based on the environment in which the user eats (e.g., at home, at work, eating out, etc.). This allows the calculation unit to perform more accurate nutrient calculations based on the user's meal frequency and timing.

[0093] The suggestion function can provide recipes for specific dishes based on the user's dietary preferences. For example, if the user prefers Italian food, the suggestion function can suggest pasta and pizza recipes. If the user prefers Japanese food, it can suggest sushi and tempura recipes. Furthermore, if the user prefers a specific ingredient, it can suggest recipes using that ingredient. In this way, the suggestion function can improve the user's satisfaction with their meals by providing recipes for specific dishes based on their dietary preferences.

[0094] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on those emotions. For example, if the user is stressed, information can be collected during times when they are relaxed. If the user is busy, information can be collected during times when they are free. If the user is relaxed, detailed information can be collected. In this way, the data collection unit can adjust the timing of information collection according to the user's emotions, enabling more appropriate information collection.

[0095] The suggestion function can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is feeling stressed, it can suggest a meal with relaxing effects. If the user is tired, it can suggest a meal suitable for replenishing energy. If the user is health-conscious, it can suggest a nutritionally balanced meal. In this way, the suggestion function can provide more appropriate suggestions by adjusting the way suggestions are presented according to the user's emotions.

[0096] The calculation unit can estimate the user's emotions and adjust the nutrient calculation method based on those emotions. For example, if the user is stressed, it can prioritize calculating nutrients that help reduce stress. If the user is tired, it can prioritize calculating nutrients suitable for energy replenishment. If the user is health-conscious, it can perform calculations that emphasize nutritional balance. In this way, the calculation unit can adjust the nutrient calculation method according to the user's emotions, enabling more appropriate nutrient calculations.

[0097] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on those emotions. For example, if the user is stressed, it can prioritize collecting information on foods with relaxing effects. If the user is tired, it can prioritize collecting information on foods suitable for energy replenishment. If the user is health-conscious, it can prioritize collecting information on foods with a good nutritional balance. In this way, the data collection unit can collect more appropriate information by determining the priority of information to collect according to the user's emotions.

[0098] The suggestion function can estimate the user's emotions and adjust the length of the suggestion based on that estimation. For example, if the user is in a hurry, it can provide a short, to-the-point suggestion. If the user is relaxed, it can provide a longer suggestion with more detailed explanations. If the user is excited, it can provide a visually stimulating suggestion. By adjusting the length of the suggestion according to the user's emotions, the suggestion function can provide more appropriate suggestions.

[0099] The following briefly describes the processing flow for example form 2.

[0100] Step 1: The data collection unit collects information such as health status, target weight, and preferences. For example, it collects information such as the user's health checkup results, self-reported health status, target weight, weight loss goals, weight gain goals, preferences, allergy information, favorite foods, and disliked foods. The data collection unit stores the health checkup results entered by the user in a database, which is then analyzed by AI. It also collects information to suggest an appropriate meal plan based on the target weight set by the user. Furthermore, it collects information to suggest meals that take into account the user's preferences and allergy information. Step 2: The suggestion department proposes the optimal meal plan based on the information collected by the collection department. For example, it proposes a meal plan tailored to the user's health condition and target weight, and suggests a nutritionally balanced meal while considering the user's preferences. It can also suggest places to purchase ingredients based on information about stores within the user's usual range of activity. Specifically, it proposes a meal plan to reduce visceral fat and blood lipids based on the user's health checkup results, and proposes a meal plan that includes the user's favorite foods, taking their preferences into consideration. Furthermore, it suggests places to purchase ingredients based on information about supermarkets and restaurants within the user's usual range of activity. Step 3: The calculation unit calculates nutrients from the meals actually eaten, as suggested by the proposal unit. For example, it calculates the nutrients in the meals the user ate and evaluates calorie intake and nutritional balance. It also monitors the user's nutrient intake towards their target weight and adjusts the meal plan as needed. Furthermore, it proposes a meal plan to supplement necessary nutrients such as vitamins and minerals according to the user's health condition. Specifically, it calculates the calories and nutrients in the meals the user ate and manages calorie intake. It monitors the user's nutrient intake towards their target weight and adjusts the meal plan as needed. It proposes a meal plan to supplement necessary nutrients according to the user's health condition.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] Each of the multiple elements described above, including the collection unit, proposal unit, and calculation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects the user's health check results and self-reported health status. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a meal plan tailored to the user's health status and target weight. The calculation unit is implemented by the specific processing unit 290 of the data processing unit 12 and calculates the nutrients in the meals the user has eaten and evaluates the calorie intake and nutritional balance. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0105] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] Each of the multiple elements described above, including the data collection unit, proposal unit, and calculation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart glasses 214 and collects the user's health check results and self-reported health status. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a meal plan tailored to the user's health status and target weight. The calculation unit is implemented by the specific processing unit 290 of the data processing unit 12 and calculates the nutrients in the meals the user has eaten and evaluates the calorie intake and nutritional balance. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0121] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements described above, including the collection unit, proposal unit, and calculation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects the user's health check results and self-reported health status. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a meal plan tailored to the user's health status and target weight. The calculation unit is implemented by the specific processing unit 290 of the data processing unit 12 and calculates the nutrients in the meals the user has eaten and evaluates the calorie intake and nutritional balance. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

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

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.).

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0151] 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.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0153] Each of the multiple elements described above, including the collection unit, proposal unit, and calculation unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects the user's health check results and self-reported health status. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a meal plan tailored to the user's health status and target weight. The calculation unit is implemented by the specific processing unit 290 of the data processing unit 12 and calculates the nutrients in the meals the user has eaten and evaluates the calorie intake and nutritional balance. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] (Note 1) A collection unit that collects information such as health status, target weight, and preferences, A suggestion unit proposes the optimal meal based on the information collected by the aforementioned collection unit, The system includes a calculation unit that calculates nutrients from the actual meal consumed, based on the meal proposed by the proposal unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is The system collects information about the user's preferences, cooking skills, and the types of restaurants within their usual range of activity. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, Based on the collected information, we suggest the optimal diet tailored to the user's health condition and target weight. The system described in Appendix 1, characterized by the features described herein. (Note 4) The calculation unit, We calculate the nutrients from what you actually eat and support you in achieving your goals. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We suggest the optimal meal while taking user preferences into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, For users who like ramen, the system instructs them to leave some of the broth and suggests adding spinach as a topping or vegetable juice to supplement their vitamin intake. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned proposal section is, I instructed them to choose Japanese sweets for dessert. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze the user's past meal history and select the optimal method for collecting information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting information, filtering is performed based on the user's current health status and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the meal. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the meal category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When making proposals, prioritize them based on the timing of meal delivery. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on their relevance to the meals. The system described in Appendix 1, characterized by the features described herein. (Note 20) The calculation unit, The system estimates the user's emotions and adjusts the nutrient calculation method based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The calculation unit, When calculating nutrients, the system analyzes the user's past dietary history to select the optimal calculation method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The calculation unit, When calculating nutrients, the calculation method is customized based on the user's current health status. The system described in Appendix 1, characterized by the features described herein. (Note 23) The calculation unit, The system estimates the user's emotions and determines the priority of nutrient calculations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The calculation unit, When calculating nutrients, the system selects the optimal calculation method by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The calculation unit, When calculating nutrients, we analyze the user's social media activity and suggest calculation methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects information such as health status, target weight, and preferences, A suggestion unit proposes the optimal meal based on the information collected by the aforementioned collection unit, The system includes a calculation unit that calculates nutrients from the actual meal consumed, based on the meal proposed by the proposal unit. A system characterized by the following features.

2. The aforementioned collection unit is The system collects information about the user's preferences, cooking skills, and the types of restaurants within their usual range of activity. The system according to feature 1.

3. The aforementioned proposal section is, Based on the collected information, we suggest the optimal diet tailored to the user's health condition and target weight. The system according to feature 1.

4. The calculation unit, We calculate the nutrients from what you actually eat and support you in achieving your goals. The system according to feature 1.

5. The aforementioned proposal section is, We suggest the optimal meal while taking user preferences into consideration. The system according to feature 1.

6. The aforementioned proposal section is, For users who like ramen, the system instructs them to leave some of the broth and suggests adding spinach as a topping or vegetable juice to supplement their vitamin intake. The system according to feature 1.

7. The aforementioned proposal section is, I instructed them to choose Japanese sweets for dessert. The system according to feature 1.

8. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system according to feature 1.

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