System

Abstract

An object of a system according to an embodiment is to efficiently perform a meal proposal and feedback thereof in consideration of individual health conditions.SOLUTION: A system includes a meal proposal part, a calorie calculation part, a data collection part, and a feedback part. The meal suggestion unit suggests a meal in consideration of individual health conditions such as allergy and carbohydrate restriction of the user. The calorie calculator calculates calories based on the meal suggested by the meal suggestion unit, and keeps a record thereof. The data collection unit measures the weight and the amount of exercise using a smart weight scale or a smart ring. The feedback unit utilizes the data collected by the data collection unit as feedback to provide a better menu.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has the drawback of making it difficult to efficiently provide dietary suggestions that take into account individual health conditions and subsequent feedback.

[0005] The system according to the embodiment aims to efficiently suggest meals that take into account individual health conditions and provide feedback on those meals. [Means for solving the problem]

[0006] The system according to the embodiment includes a meal suggestion unit, a calorie calculation unit, a data collection unit, and a feedback unit. The meal suggestion unit suggests meals taking into consideration individual health conditions such as allergies and carbohydrate restrictions of the user. The calorie calculation unit calculates calories based on the meals suggested by the meal suggestion unit and keeps a record of the calculations. The data collection unit measures weight and exercise volume using a smart scale or smart ring. The feedback unit uses the data collected by the data collection unit as feedback to provide better menus. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently make meal suggestions and provide feedback thereto, taking into consideration individual health conditions. [Brief explanation of the drawings]

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

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A meal suggestion system according to an embodiment of the present invention is a system that suggests balanced meals in consideration of individual health conditions of a user, such as allergies and carbohydrate restrictions, and supports health management through calorie calculation and data collection. As a result, the meal suggestion system can suggest balanced meals based on the user's health condition and support health management through calorie calculation and data collection.

[0029] A meal suggestion system according to an embodiment includes a meal suggestion unit, a calorie calculation unit, a data collection unit, and a feedback unit. The meal suggestion unit suggests meals taking into account the user's individual health conditions, such as allergies and carbohydrate restrictions. For example, the generation AI receives input information, such as the user's allergy information and health conditions, and suggests balanced meals based on the input information. For example, the generation AI suggests nut-free recipes to a user with a nut allergy. Furthermore, the generation AI suggests low-carb meals to a user on a carbohydrate restriction diet. The generation AI receives input prompts containing the user's health information and dietary preferences, and suggests appropriate meals based on the prompts. The calorie calculation unit calculates calories based on the meals suggested by the meal suggestion unit and keeps a record of the calories. For example, if the generation AI suggests "chicken curry" and the user eats that meal, it calculates the calories (e.g., 500 kcal) and adds them to the user's calorie record. The calorie calculation unit has a function to calculate and record the calories of the suggested meals. The data collection unit measures weight and exercise volume using a smart scale or smart ring. For example, when a user steps on a smart scale, weight data is sent to the generation AI. Also, when a user wears a smart ring, daily exercise data is sent to the generation AI. The data collection unit collects and analyzes this data. The feedback unit uses the data collected by the data collection unit as feedback to provide better menus. For example, the generation AI analyzes the collected weight and exercise data and suggests better menus based on the user's health condition. As a result, the meal suggestion system according to the embodiment can suggest balanced meals based on the user's health condition and support health management through calorie calculation and data collection.

[0030] The meal suggestion unit can add the user's genetic information as input information and make meal suggestions based on genetic risk. The meal suggestion unit, for example, analyzes the user's genetic information and makes meal suggestions based on specific genetic risks (e.g., risk of heart disease or diabetes). For example, a low-fat, high-fiber diet is suggested for a user at high risk of heart disease. Furthermore, based on the genetic information, an appropriate diet is suggested for a user who needs to consume specific nutrients. For example, recipes using ingredients rich in vitamin D are suggested for a user at risk of vitamin D deficiency. Furthermore, the user's genetic information is used to evaluate the risk of allergies to specific ingredients and make meal suggestions that avoid that risk. For example, recipes that do not include nuts are suggested for a user who is genetically at high risk of nut allergies. This makes it possible to make meal suggestions based on genetic risk.

[0031] The meal suggestion unit analyzes the user's past meal history, learns the user's meal preferences and patterns, and can make more personalized suggestions. For example, the meal suggestion unit stores the user's past meal history in a database and analyzes that data to learn the user's meal preferences and patterns. For example, it identifies the ingredients and cooking trends that the user frequently chooses. It also learns the user's preferred seasonings and cooking methods based on the past meal history and suggests recipes based on that. For example, it suggests recipes that use a lot of spices to a user who likes spicy food. It also analyzes the user's meal history to identify the intake trends of specific nutrients. For example, it suggests recipes that include a lot of vegetables to a user who does not eat many vegetables. This makes it possible to suggest meals based on the user's preferences and patterns.

[0032] The meal suggestion unit can take the user's lifestyle habits into consideration and suggest optimal meal timings. The meal suggestion unit, for example, analyzes the user's sleep patterns and suggests optimal meal timings. For example, for a user who works the night shift, it suggests meals that can replenish energy at night. It also takes the user's work schedule into consideration and suggests meals that can be easily prepared during busy times. For example, it suggests recipes that can be cooked in a short time or meals that are easy to carry. It also adjusts meal timings based on the user's lifestyle habits. For example, it suggests meals that are suitable after exercise or a nutritious breakfast for users who tend to skip breakfast. This makes it possible to suggest meal timings based on the user's lifestyle habits.

[0033] The meal suggestion unit can make meal suggestions according to the season and weather, and provide recipes that use seasonal ingredients. The meal suggestion unit, for example, suggests recipes that use seasonal ingredients. For example, in spring, it suggests recipes that use fresh asparagus or strawberries. It also makes meal suggestions according to the weather. For example, it suggests recipes for hot soups or stews on cold days, and suggests recipes for cold salads or smoothies on hot days. It also suggests meals that are suitable for changing seasons. For example, it suggests recipes that use ingredients that boost immunity in autumn, and suggests recipes that use ingredients that are high in vitamin C in winter. This makes it possible to make meal suggestions according to the season and weather.

[0034] The calorie calculation unit can perform more accurate calorie calculations by taking into account the cooking method and cooking time of the ingredients. The calorie calculation unit, for example, calculates calories by taking into account the cooking method of the ingredients. For example, it can handle cases where calories change depending on the cooking method, such as when the food is fried or grilled. It also calculates calories by taking cooking time into account. For example, it accurately calculates the calories of dishes that are simmered for a long time or cooked in a short time. It also improves the accuracy of calorie calculations based on the cooking method and cooking time. For example, the increase or decrease in calories for each cooking method is registered in a database, and calculations are performed based on that. This makes it possible to perform accurate calorie calculations that take into account the cooking method and cooking time.

[0035] The calorie calculation unit can perform individually customized calorie calculations taking into account the user's metabolic rate and amount of exercise. The calorie calculation unit, for example, calculates the user's basal metabolic rate and calculates calories based on that. For example, the basal metabolic rate is calculated based on age, gender, weight, and height. The calorie calculation unit also takes into account the user's amount of exercise. For example, the calorie consumption is calculated based on daily exercise data, and the total calorie intake is adjusted. The calorie calculation is also individually customized based on the user's metabolic rate and amount of exercise. For example, a user who exercises a lot may be suggested to increase their calorie intake. This makes it possible to calculate calories based on the user's metabolic rate and amount of exercise.

[0036] The calorie calculation unit can visualize and display the results of the calorie calculation, allowing the user to intuitively understand. The calorie calculation unit, for example, displays the results of the calorie calculation in a graph or chart, allowing the user to intuitively understand. For example, the daily calorie intake can be shown in a line graph. The results of the calorie calculation can also be visualized, allowing the user to easily check them. For example, the calorie intake can be displayed in a pie chart or bar graph. The visualized results of the calorie calculation can also be displayed on the user's smartphone or tablet, allowing the user to check them at any time. For example, the calorie intake can be managed using a dedicated app. In this way, the results of the calorie calculation can be visualized so that they can be intuitively understood.

[0037] The calorie calculation unit can integrate the results of the calorie calculation with other health data to provide comprehensive health management. The calorie calculation unit can integrate the results of the calorie calculation with other health data, such as blood glucose levels and blood pressure, to provide comprehensive health management. For example, the calorie intake can be adjusted according to fluctuations in blood glucose levels. The results of the calorie calculation can also be linked to other health data to comprehensively evaluate the user's health condition. For example, an appropriate calorie intake can be suggested based on blood pressure and heart rate data. The results of the calorie calculation can also be integrated with other health data to support the user's health management. For example, improvements to the diet can be suggested based on blood glucose and blood pressure data. This allows the results of the calorie calculation to be integrated with other health data to provide comprehensive health management.

[0038] The data collection unit can analyze data from the smart scale or smart ring in real time and provide instant feedback. The data collection unit, for example, analyzes data from the smart scale or smart ring in real time and provides instant feedback to the user. For example, it gives advice on diet and exercise based on data on weight fluctuations and exercise volume. It also evaluates the user's health status based on the data collected in real time and provides instant feedback. For example, it suggests appropriate rest and nutrition based on data on weight fluctuations and heart rate after exercise. It also analyzes data from the smart scale or smart ring in real time and provides feedback according to the user's health status. For example, if weight is increasing, it advises the user to reduce their calorie intake. This makes it possible to analyze data in real time and provide instant feedback.

[0039] The data collection unit can analyze the user's exercise patterns and sleep data to evaluate their overall health. The data collection unit can analyze the user's exercise patterns based on data from the smart ring, for example, to evaluate their overall health. For example, it can point out lack of exercise based on the number of steps taken each day and the amount of time spent exercising. It can also analyze the user's weight fluctuations based on data from the smart scale to evaluate their overall health. For example, it can suggest improvements to diet and exercise based on weight gain or loss. It can also analyze the sleep data from the smart ring to evaluate the user's sleep quality. For example, it can suggest improvements to sleep based on the time spent in deep sleep and the number of sleep interruptions. This makes it possible to evaluate the user's overall health based on their exercise patterns and sleep data.

[0040] The data collection unit can link data from the smart scale or smart ring with other health devices to perform comprehensive health management. The data collection unit, for example, links data from the smart scale or smart ring with a smart watch to perform comprehensive health management. For example, it integrates data on heart rate and exercise volume to evaluate health status. The data from the smart scale or smart ring can also be linked with a blood pressure monitor to perform comprehensive health management. For example, it can analyze the relationship between blood pressure fluctuations and weight to evaluate health status. The data from the smart scale or smart ring can also be linked with other health devices to perform comprehensive health management. For example, it can integrate data on blood sugar levels and oxygen saturation to evaluate health status. In this way, the data from the smart scale or smart ring can be linked with other health devices to perform comprehensive health management.

[0041] The data collection unit can store data from the smart scale or smart ring in the cloud and analyze long-term health data trends. The data collection unit, for example, stores data from the smart scale or smart ring in the cloud and analyzes long-term health data trends. For example, it displays weight fluctuations and changes in exercise volume in graphs. It also analyzes long-term changes in the user's health condition based on the data stored in the cloud. For example, it suggests ways to improve the health condition based on data from the past few months. It also analyzes long-term health data trends and provides feedback to the user. For example, if there is a tendency for weight to decrease, it provides advice on appropriate diet and exercise. In this way, it is possible to store data from the smart scale or smart ring in the cloud and analyze long-term health data trends.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The meal suggestion unit can suggest alternative ingredients taking into account the user's food preferences and allergy information. For example, for a user with a nut allergy, it can suggest recipes using seeds or legumes instead of nuts. For a user with a dairy allergy, it can suggest recipes using plant-based milk or yogurt instead of dairy products. Furthermore, if a specific ingredient is unavailable, it can suggest other ingredients with similar nutritional value. This enables flexible meal suggestions based on the user's food preferences and allergy information.

[0044] The calorie calculation unit can calculate calories taking into account the user's eating speed. For example, since a user who eats quickly tends to have a high calorie intake, the calorie calculation takes this into account. Also, for a user who eats slowly, the calorie intake can be set lower because they tend to feel full more easily. Furthermore, by suggesting an appropriate calorie intake based on the eating speed, it is possible to support the user's health management. This makes it possible to calculate calories taking into account the eating speed.

[0045] The data collection unit can make optimal meal suggestions taking into account the user's living environment and residential conditions. For example, if the user lives in an urban area, it can suggest recipes using easily available ingredients. If the user lives in a rural area, it can suggest recipes using fresh, locally sourced ingredients. It can also suggest easy-to-prepare recipes taking into account the kitchen facilities and cooking utensils in the user's home. This makes it possible to make meal suggestions based on the user's living environment and residential conditions.

[0046] The data collection unit can suggest optimal recipes taking into account the user's meal preparation time and cooking skills. For example, it can suggest simple recipes that can be prepared in a short time to a busy user, and it can suggest challenging recipes or new cooking methods to a user with advanced cooking skills. Furthermore, by suggesting appropriate recipes according to the user's meal preparation time and cooking skills, meal preparation can be carried out smoothly. This makes it possible to suggest recipes based on the user's meal preparation time and cooking skills.

[0047] The calorie calculation unit can calculate calories taking into account the frequency of a user's meals. For example, for a user who eats multiple meals per day, the calories for each meal can be calculated separately. Also, for a user who eats one meal per day, the calories for that meal can be set higher. Furthermore, by suggesting an appropriate calorie intake based on the frequency of meals, it is possible to support the user's health management. This makes it possible to calculate calories taking into account the frequency of meals.

[0048] The data collection unit can make optimal meal suggestions taking into account the user's place of eating and environment. For example, for a user who eats at the office, it can suggest recipes that are easy to carry and eat. For a user who eats at home, it can suggest recipes that the whole family can enjoy. Furthermore, by making appropriate meal suggestions based on the user's place of eating and environment, it is possible to improve the satisfaction and health benefits of meals. This makes it possible to make meal suggestions based on the user's place of eating and environment.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The meal suggestion unit suggests meals taking into account the user's individual health conditions, such as allergies and carbohydrate restrictions. For example, the generation AI receives the user's allergy information and health conditions, such as carbohydrate restrictions, as input information and suggests balanced meals based on that. For example, the generation AI might suggest nut-free recipes to a user with a nut allergy, or suggest low-carb meals to a user on a carbohydrate restriction diet. The input to the generation AI is a prompt that includes the user's health information and dietary preferences, and the generation AI suggests appropriate meals based on that prompt. Step 2: The calorie calculation unit calculates calories based on the meal suggested by the meal suggestion unit and keeps a record of the calories. For example, if the generation AI suggests "chicken curry" and the user eats that meal, it calculates the calories (e.g., 500 kcal) and adds them to the user's calorie record. The calorie calculation unit has the function of calculating and recording the calories of the suggested meal. Step 3: The data collection unit measures weight and exercise volume using a smart scale or smart ring. For example, when a user steps on a smart scale, weight data is sent to the generation AI. Also, when the user wears a smart ring, daily exercise volume data is sent to the generation AI. The data collection unit collects and analyzes this data. Step 4: The feedback unit uses the data collected by the data collection unit as feedback to provide better menus. For example, the generation AI analyzes the collected weight and exercise data and suggests better menus based on the user's health condition.

[0051] (Example 2) A meal suggestion system according to an embodiment of the present invention is a system that suggests balanced meals in consideration of individual health conditions of a user, such as allergies and carbohydrate restrictions, and supports health management through calorie calculation and data collection. As a result, the meal suggestion system can suggest balanced meals based on the user's health condition and support health management through calorie calculation and data collection.

[0052] A meal suggestion system according to an embodiment includes a meal suggestion unit, a calorie calculation unit, a data collection unit, and a feedback unit. The meal suggestion unit suggests meals taking into account the user's individual health conditions, such as allergies and carbohydrate restrictions. For example, the generation AI receives input information, such as the user's allergy information and health conditions, and suggests balanced meals based on the input information. For example, the generation AI suggests nut-free recipes to a user with a nut allergy. Furthermore, the generation AI suggests low-carb meals to a user on a carbohydrate restriction diet. The generation AI receives input prompts containing the user's health information and dietary preferences, and suggests appropriate meals based on the prompts. The calorie calculation unit calculates calories based on the meals suggested by the meal suggestion unit and keeps a record of the calories. For example, if the generation AI suggests "chicken curry" and the user eats that meal, it calculates the calories (e.g., 500 kcal) and adds them to the user's calorie record. The calorie calculation unit has a function to calculate and record the calories of the suggested meals. The data collection unit measures weight and exercise volume using a smart scale or smart ring. For example, when a user steps on a smart scale, weight data is sent to the generation AI. Also, when a user wears a smart ring, daily exercise data is sent to the generation AI. The data collection unit collects and analyzes this data. The feedback unit uses the data collected by the data collection unit as feedback to provide better menus. For example, the generation AI analyzes the collected weight and exercise data and suggests better menus based on the user's health condition. As a result, the meal suggestion system according to the embodiment can suggest balanced meals based on the user's health condition and support health management through calorie calculation and data collection.

[0053] The meal suggestion unit can add the user's genetic information as input information and make meal suggestions based on genetic risk. The meal suggestion unit, for example, analyzes the user's genetic information and makes meal suggestions based on specific genetic risks (e.g., risk of heart disease or diabetes). For example, a low-fat, high-fiber diet is suggested for a user at high risk of heart disease. Furthermore, based on the genetic information, an appropriate diet is suggested for a user who needs to consume specific nutrients. For example, recipes using ingredients rich in vitamin D are suggested for a user at risk of vitamin D deficiency. Furthermore, the user's genetic information is used to evaluate the risk of allergies to specific ingredients and make meal suggestions that avoid that risk. For example, recipes that do not include nuts are suggested for a user who is genetically at high risk of nut allergies. This makes it possible to make meal suggestions based on genetic risk.

[0054] The meal suggestion unit analyzes the user's past meal history, learns the user's meal preferences and patterns, and can make more personalized suggestions. For example, the meal suggestion unit stores the user's past meal history in a database and analyzes that data to learn the user's meal preferences and patterns. For example, it identifies the ingredients and cooking trends that the user frequently chooses. It also learns the user's preferred seasonings and cooking methods based on the past meal history and suggests recipes based on that. For example, it suggests recipes that use a lot of spices to a user who likes spicy food. It also analyzes the user's meal history to identify the intake trends of specific nutrients. For example, it suggests recipes that include a lot of vegetables to a user who does not eat many vegetables. This makes it possible to suggest meals based on the user's preferences and patterns.

[0055] The meal suggestion unit uses the emotion estimation function to suggest meals according to the user's current emotional state, thereby reducing stress and improving mood. The meal suggestion unit, for example, analyzes the user's emotional state in real time, and suggests recipes using ingredients with a relaxing effect when the user is under high stress. For example, it suggests recipes including chamomile tea and dark chocolate. The emotion estimation function also suggests meals suitable for replenishing energy when the user is tired. For example, it suggests a recipe for an energy bar using bananas and nuts. It also suggests meals that will improve mood based on the user's emotional state. For example, it suggests a salad recipe using colorful vegetables and fruits to elicit positive emotions. This makes it possible to suggest meals based on the user's emotional state.

[0056] The meal suggestion unit can take the user's lifestyle habits into consideration and suggest optimal meal timings. The meal suggestion unit, for example, analyzes the user's sleep patterns and suggests optimal meal timings. For example, for a user who works the night shift, it suggests meals that can replenish energy at night. It also takes the user's work schedule into consideration and suggests meals that can be easily prepared during busy times. For example, it suggests recipes that can be cooked in a short time or meals that are easy to carry. It also adjusts meal timings based on the user's lifestyle habits. For example, it suggests meals that are suitable after exercise or a nutritious breakfast for users who tend to skip breakfast. This makes it possible to suggest meal timings based on the user's lifestyle habits.

[0057] The meal suggestion unit can make meal suggestions according to the season and weather, and provide recipes that use seasonal ingredients. The meal suggestion unit, for example, suggests recipes that use seasonal ingredients. For example, in spring, it suggests recipes that use fresh asparagus or strawberries. It also makes meal suggestions according to the weather. For example, it suggests recipes for hot soups or stews on cold days, and suggests recipes for cold salads or smoothies on hot days. It also suggests meals that are suitable for changing seasons. For example, it suggests recipes that use ingredients that boost immunity in autumn, and suggests recipes that use ingredients that are high in vitamin C in winter. This makes it possible to make meal suggestions according to the season and weather.

[0058] The meal suggestion unit can use the emotion estimation function to suggest meals based on the user's emotions and select ingredients that correspond to a specific emotional state. For example, if the user is feeling stressed, the meal suggestion unit can use the emotion estimation function to suggest recipes using ingredients that have a relaxing effect. For example, a recipe including chamomile tea or dark chocolate can be suggested. Also, if the user is tired, a meal suitable for replenishing energy can be suggested. For example, a recipe for an energy bar using bananas and nuts can be suggested. Also, based on the user's emotional state, a meal that will improve mood can be suggested. For example, a salad recipe using colorful vegetables and fruits can be suggested to elicit positive emotions. This makes it possible to suggest meals based on the user's emotions.

[0059] The calorie calculation unit can perform more accurate calorie calculations by taking into account the cooking method and cooking time of the ingredients. The calorie calculation unit, for example, calculates calories by taking into account the cooking method of the ingredients. For example, it can handle cases where calories change depending on the cooking method, such as when the food is fried or grilled. It also calculates calories by taking cooking time into account. For example, it accurately calculates the calories of dishes that are simmered for a long time or cooked in a short time. It also improves the accuracy of calorie calculations based on the cooking method and cooking time. For example, the increase or decrease in calories for each cooking method is registered in a database, and calculations are performed based on that. This makes it possible to perform accurate calorie calculations that take into account the cooking method and cooking time.

[0060] The calorie calculation unit can perform individually customized calorie calculations taking into account the user's metabolic rate and amount of exercise. The calorie calculation unit, for example, calculates the user's basal metabolic rate and calculates calories based on that. For example, the basal metabolic rate is calculated based on age, gender, weight, and height. The calorie calculation unit also takes into account the user's amount of exercise. For example, the calorie consumption is calculated based on daily exercise data, and the total calorie intake is adjusted. The calorie calculation is also individually customized based on the user's metabolic rate and amount of exercise. For example, a user who exercises a lot may be suggested to increase their calorie intake. This makes it possible to calculate calories based on the user's metabolic rate and amount of exercise.

[0061] The calorie calculation unit can visualize and display the results of the calorie calculation, allowing the user to intuitively understand. The calorie calculation unit, for example, displays the results of the calorie calculation in a graph or chart, allowing the user to intuitively understand. For example, the daily calorie intake can be shown in a line graph. The results of the calorie calculation can also be visualized, allowing the user to easily check them. For example, the calorie intake can be displayed in a pie chart or bar graph. The visualized results of the calorie calculation can also be displayed on the user's smartphone or tablet, allowing the user to check them at any time. For example, the calorie intake can be managed using a dedicated app. In this way, the results of the calorie calculation can be visualized so that they can be intuitively understood.

[0062] The calorie calculation unit can integrate the results of the calorie calculation with other health data to provide comprehensive health management. The calorie calculation unit can integrate the results of the calorie calculation with other health data, such as blood glucose levels and blood pressure, to provide comprehensive health management. For example, the calorie intake can be adjusted according to fluctuations in blood glucose levels. The results of the calorie calculation can also be linked to other health data to comprehensively evaluate the user's health condition. For example, an appropriate calorie intake can be suggested based on blood pressure and heart rate data. The results of the calorie calculation can also be integrated with other health data to support the user's health management. For example, improvements to the diet can be suggested based on blood glucose and blood pressure data. This allows the results of the calorie calculation to be integrated with other health data to provide comprehensive health management.

[0063] The calorie calculation unit can use the emotion estimation function to give advice on calorie intake based on the user's emotions and provide a meal plan that corresponds to a specific emotional state. For example, the calorie calculation unit can use the emotion estimation function to advise the user to limit their calorie intake if they are feeling stressed. For example, it can suggest low-calorie snacks and drinks. Also, if the user is tired, it can advise on a calorie intake appropriate for energy replenishment. For example, it can suggest an energy bar made with bananas or nuts. Also, it can use the emotion estimation function to give advice on calorie intake based on the user's emotional state. For example, it can recommend a moderate calorie intake to elicit positive emotions. This makes it possible to provide calorie intake advice and a meal plan based on the user's emotions.

[0064] The data collection unit can analyze data from the smart scale or smart ring in real time and provide instant feedback. The data collection unit, for example, analyzes data from the smart scale or smart ring in real time and provides instant feedback to the user. For example, it gives advice on diet and exercise based on data on weight fluctuations and exercise volume. It also evaluates the user's health status based on the data collected in real time and provides instant feedback. For example, it suggests appropriate rest and nutrition based on data on weight fluctuations and heart rate after exercise. It also analyzes data from the smart scale or smart ring in real time and provides feedback according to the user's health status. For example, if weight is increasing, it advises the user to reduce their calorie intake. This makes it possible to analyze data in real time and provide instant feedback.

[0065] The data collection unit can analyze the user's exercise patterns and sleep data to evaluate their overall health. The data collection unit can analyze the user's exercise patterns based on data from the smart ring, for example, to evaluate their overall health. For example, it can point out lack of exercise based on the number of steps taken each day and the amount of time spent exercising. It can also analyze the user's weight fluctuations based on data from the smart scale to evaluate their overall health. For example, it can suggest improvements to diet and exercise based on weight gain or loss. It can also analyze the sleep data from the smart ring to evaluate the user's sleep quality. For example, it can suggest improvements to sleep based on the time spent in deep sleep and the number of sleep interruptions. This makes it possible to evaluate the user's overall health based on their exercise patterns and sleep data.

[0066] The data collection unit uses the emotion estimation function to provide feedback according to the user's emotional state, thereby maintaining motivation. For example, the data collection unit uses the emotion estimation function to provide feedback with a relaxing effect when the user is feeling stressed. For example, the data collection unit provides advice on deep breathing or meditation. Also, when the user is tired, the data collection unit provides feedback recommending energy replenishment and rest. For example, the data collection unit suggests light stretching or nutritious snacks. Also, the emotion estimation function is used to provide feedback according to the user's emotional state, thereby maintaining motivation. For example, encouraging messages or success stories are presented to elicit positive emotions. In this way, feedback based on the user's emotional state can be provided, thereby maintaining motivation.

[0067] The data collection unit can link data from the smart scale or smart ring with other health devices to perform comprehensive health management. The data collection unit, for example, links data from the smart scale or smart ring with a smart watch to perform comprehensive health management. For example, it integrates data on heart rate and exercise volume to evaluate health status. The data from the smart scale or smart ring can also be linked with a blood pressure monitor to perform comprehensive health management. For example, it can analyze the relationship between blood pressure fluctuations and weight to evaluate health status. The data from the smart scale or smart ring can also be linked with other health devices to perform comprehensive health management. For example, it can integrate data on blood sugar levels and oxygen saturation to evaluate health status. In this way, the data from the smart scale or smart ring can be linked with other health devices to perform comprehensive health management.

[0068] The data collection unit can store data from the smart scale or smart ring in the cloud and analyze long-term health data trends. The data collection unit, for example, stores data from the smart scale or smart ring in the cloud and analyzes long-term health data trends. For example, it displays weight fluctuations and changes in exercise volume in graphs. It also analyzes long-term changes in the user's health condition based on the data stored in the cloud. For example, it suggests ways to improve the health condition based on data from the past few months. It also analyzes long-term health data trends and provides feedback to the user. For example, if there is a tendency for weight to decrease, it provides advice on appropriate diet and exercise. In this way, it is possible to store data from the smart scale or smart ring in the cloud and analyze long-term health data trends.

[0069] The data collection unit can use the emotion estimation function to provide feedback based on the user's emotions and provide health advice corresponding to a specific emotional state. For example, the data collection unit can use the emotion estimation function to provide feedback with a relaxing effect if the user is feeling stressed. For example, the data collection unit can provide advice on deep breathing or meditation. Also, if the user is tired, the data collection unit can provide feedback recommending energy replenishment and rest. For example, the data collection unit can suggest light stretching or nutritious snacks. The data collection unit can also use the emotion estimation function to provide feedback according to the user's emotional state to maintain motivation. For example, encouraging messages or success stories can be presented to elicit positive emotions. This makes it possible to provide feedback and health advice based on the user's emotions.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The meal suggestion unit can suggest alternative ingredients taking into account the user's food preferences and allergy information. For example, for a user with a nut allergy, it can suggest recipes using seeds or legumes instead of nuts. For a user with a dairy allergy, it can suggest recipes using plant-based milk or yogurt instead of dairy products. Furthermore, if a specific ingredient is unavailable, it can suggest other ingredients with similar nutritional value. This enables flexible meal suggestions based on the user's food preferences and allergy information.

[0072] The meal suggestion unit can suggest meal presentation and plating methods based on the user's emotional state. For example, if the user is feeling stressed, the unit can suggest plating methods using colors and arrangements that have a relaxing effect. If the user is tired, the unit can suggest plating methods that are visually invigorating to replenish energy. Furthermore, by devising meal presentations according to the user's emotional state, it is possible to increase the enjoyment and satisfaction of the meal. This makes it possible to present meals based on the user's emotional state.

[0073] The calorie calculation unit can calculate calories taking into account the user's eating speed. For example, since a user who eats quickly tends to have a high calorie intake, the calorie calculation takes this into account. Also, for a user who eats slowly, the calorie intake can be set lower because they tend to feel full more easily. Furthermore, by suggesting an appropriate calorie intake based on the eating speed, it is possible to support the user's health management. This makes it possible to calculate calories taking into account the eating speed.

[0074] The data collection unit can suggest the type and intensity of exercise based on the user's emotional state. For example, if the user is feeling stressed, it can suggest relaxing yoga or stretching. If the user is tired, it can suggest light walking or relaxation exercise. Furthermore, by adjusting the type and intensity of exercise according to the user's emotional state, it is possible to maximize the effect of exercise. This makes it possible to suggest exercises based on the user's emotional state.

[0075] The data collection unit can make optimal meal suggestions taking into account the user's living environment and residential conditions. For example, if the user lives in an urban area, it can suggest recipes using easily available ingredients. If the user lives in a rural area, it can suggest recipes using fresh, locally sourced ingredients. It can also suggest easy-to-prepare recipes taking into account the kitchen facilities and cooking utensils in the user's home. This makes it possible to make meal suggestions based on the user's living environment and residential conditions.

[0076] The meal suggestion unit can adjust the amount of food and portion size based on the user's emotional state. For example, if the user is feeling stressed, the unit can suggest a meal that is small but satisfying. If the user is tired, the unit can suggest a meal of a moderate amount suitable for replenishing energy. Furthermore, adjusting the amount of food and portion size according to the user's emotional state can improve the satisfaction and health benefits of the meal. This makes it possible to adjust the amount of food and portion size based on the user's emotional state.

[0077] The data collection unit can suggest optimal recipes taking into account the user's meal preparation time and cooking skills. For example, it can suggest simple recipes that can be prepared in a short time to a busy user, and it can suggest challenging recipes or new cooking methods to a user with advanced cooking skills. Furthermore, by suggesting appropriate recipes according to the user's meal preparation time and cooking skills, meal preparation can be carried out smoothly. This makes it possible to suggest recipes based on the user's meal preparation time and cooking skills.

[0078] The calorie calculation unit can calculate calories taking into account the frequency of a user's meals. For example, for a user who eats multiple meals per day, the calories for each meal can be calculated separately. Also, for a user who eats one meal per day, the calories for that meal can be set higher. Furthermore, by suggesting an appropriate calorie intake based on the frequency of meals, it is possible to support the user's health management. This makes it possible to calculate calories taking into account the frequency of meals.

[0079] The data collection unit can suggest meal timings and snacks based on the user's emotional state. For example, if the user is feeling stressed, snacks that have a relaxing effect are suggested. Also, if the user is tired, snacks that are suitable for replenishing energy are suggested. Furthermore, by suggesting meal timings and snacks according to the user's emotional state, it is possible to improve the satisfaction and health benefits of meals. This makes it possible to suggest meal timings and snacks based on the user's emotional state.

[0080] The data collection unit can make optimal meal suggestions taking into account the user's place of eating and environment. For example, for a user who eats at the office, it can suggest recipes that are easy to carry and eat. For a user who eats at home, it can suggest recipes that the whole family can enjoy. Furthermore, by making appropriate meal suggestions based on the user's place of eating and environment, it is possible to improve the satisfaction and health benefits of meals. This makes it possible to make meal suggestions based on the user's place of eating and environment.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The meal suggestion unit suggests meals taking into account the user's individual health conditions, such as allergies and carbohydrate restrictions. For example, the generation AI receives the user's allergy information and health conditions, such as carbohydrate restrictions, as input information and suggests balanced meals based on that. For example, the generation AI might suggest nut-free recipes to a user with a nut allergy, or suggest low-carb meals to a user on a carbohydrate restriction diet. The input to the generation AI is a prompt that includes the user's health information and dietary preferences, and the generation AI suggests appropriate meals based on that prompt. Step 2: The calorie calculation unit calculates calories based on the meal suggested by the meal suggestion unit and keeps a record of the calories. For example, if the generation AI suggests "chicken curry" and the user eats that meal, it calculates the calories (e.g., 500 kcal) and adds them to the user's calorie record. The calorie calculation unit has the function of calculating and recording the calories of the suggested meal. Step 3: The data collection unit measures weight and exercise volume using a smart scale or smart ring. For example, when a user steps on a smart scale, weight data is sent to the generation AI. Also, when the user wears a smart ring, daily exercise volume data is sent to the generation AI. The data collection unit collects and analyzes this data. Step 4: The feedback unit uses the data collected by the data collection unit as feedback to provide better menus. For example, the generation AI analyzes the collected weight and exercise data and suggests better menus based on the user's health condition.

[0083] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0089] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0090] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0091] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0092] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0093] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0094] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0097] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0103] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0104] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0105] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0107] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0108] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0109] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0111] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0112] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0113] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0124] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0133] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0134] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0135] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0136] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0137] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0138] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0139] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0140] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0141] 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.

[0142] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0143] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0144] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0145] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0146] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0147] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0148] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0149] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0150] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a meal suggestion section that suggests meals taking into consideration individual health conditions such as allergies and carbohydrate restrictions of the user; a calorie calculation unit that calculates calories based on the meals suggested by the meal suggestion unit and keeps a record of the calculated calories; A data collection unit that measures weight and exercise volume using a smart scale and a smart ring; A feedback unit that utilizes the data collected by the data collection unit as feedback to provide a better menu. A system characterized by:

2. The meal suggestion unit The user's genetic information is added as input information, and dietary suggestions are made based on genetic risk.

2. The system of claim 1.

3. The meal suggestion unit Analyzing the user's past eating history and learning their eating preferences and patterns to provide more personalized suggestions 2. The system of claim 1.

4. The meal suggestion unit Providing dietary suggestions based on the user's current emotional state to reduce stress and improve mood 2. The system of claim 1.

5. The meal suggestion unit Taking into account the user's lifestyle habits, the system suggests optimal meal timings 2. The system of claim 1.

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