System

Abstract

An object of the system according to the embodiment is to provide a diet plan by analyzing a picture of a meal and automatically calculating calories and nutrients.SOLUTION: A system according to an embodiment includes a photograph upload unit, an analysis unit, a visualization unit, and a plan providing unit. The photograph upload unit allows a user to upload a photograph of a meal. The analysis unit analyzes the photograph uploaded by the photograph upload unit. The visualization unit visually displays the result analyzed by the analysis unit. The plan providing unit provides a diet plan based on the result obtained by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology requires users to manually calculate the calories and nutrients in their meals, which is time-consuming.

[0005] The system according to the embodiment aims to analyze photos of meals, automatically calculate calories and nutrients, and provide diet plans. [Means for solving the problem]

[0006] The system according to the embodiment includes a photo upload unit, an analysis unit, a visualization unit, and a plan provision unit. The photo upload unit allows a user to upload photos of their meals. The analysis unit analyzes the photos uploaded by the photo upload unit. The visualization unit visually displays the results of the analysis by the analysis unit. The plan provision unit provides a diet plan based on the results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze photos of meals, automatically calculate calories and nutrients, and provide diet plans. [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 pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The diet analysis system according to an embodiment of the present invention is a system in which a user simply uploads a photo of each meal, and a generation AI analyzes and visualizes the calories and nutrient deficiencies of that meal. This allows the diet analysis system to help the user understand the calorie and nutrient balance of their meals and provide an optimal diet plan.

[0029] The diet analysis system according to the embodiment includes a photo uploading unit, an analysis unit, a visualization unit, and a plan providing unit. The photo uploading unit allows a user to upload photos of meals. For example, photos taken with a smartphone camera are uploaded to a dedicated app. The photo uploading unit can also upload photos via a website. For example, a user uploads photos of meals from a computer. The photo uploading unit can also upload photos using a cloud storage service. For example, a user uploads photos stored in Google Drive or Dropbox. The analysis unit analyzes the photos uploaded by the photo uploading unit. For example, the generation AI can identify ingredients in the photos using image recognition technology. The generation AI can also analyze the types and quantities of ingredients using machine learning algorithms. The generation AI can also calculate the calories and nutrient content of ingredients. For example, the generation AI can calculate the calories of vegetables and meat in the photos. The visualization unit visually displays the results of the analysis by the analysis unit. For example, the generation AI can display the ingested calories and nutrient amounts using a pie chart or bar graph. The generation AI can also highlight nutrient deficiencies and excess nutrient intakes. The generation AI can also create infographics that allow users to understand the balance of their diet at a glance. For example, the generation AI displays the user's dietary information in a color-coded manner. The plan provider provides a diet plan based on the results obtained by the analysis unit. For example, the generation AI can suggest ingredients and dishes to supplement missing nutrients. The generation AI can also provide specific dietary ideas for reducing calories. The generation AI can also generate a diet plan based on the user's goals and current dietary information. For example, the generation AI can suggest a meal plan based on the user's weight and target weight. In this way, the diet analysis system according to the embodiment allows users to visually grasp the analysis results of calories and nutrients and obtain an optimal diet plan simply by uploading photos of their meals. For example, users can easily manage their daily dietary information and maintain a healthy diet.Furthermore, the user can learn specific points for improving their diet, and can achieve an effective diet.

[0030] The analysis unit can analyze the background information of meal photos and identify the location and time of the meal. For example, the analysis unit uses a generative AI to analyze the background information of meal photos and identify the location and time of the meal. For example, the analysis unit extracts the location and time of the photo from the metadata of the photo and identifies eating patterns. The analysis unit can also analyze the background characteristics of the photo to estimate the location of the meal. For example, it analyzes the interior of a restaurant or the characteristics of a home kitchen. The analysis unit can also analyze the information of the clock or calendar in the photo to identify the time of the meal. For example, it analyzes the position of the clock hands or the date on the calendar. This makes it easier to understand eating patterns by identifying the location and time of the meal. For example, users can understand their eating habits and find areas for improvement. Users can also evaluate the impact of eating at specific locations and times.

[0031] The analysis unit can analyze the color information of a meal photo and estimate the freshness of ingredients and cooking method. For example, the generation AI analyzes the color information of a meal photo and estimates the freshness of ingredients. For example, freshness is determined from the hue of vegetables or the color of meat. The analysis unit can also analyze changes in the color of ingredients and estimate cooking methods. For example, it analyzes the color of grilled meat or the color of boiled vegetables. The analysis unit can also analyze the shades of the color of ingredients and estimate cooking times. For example, it determines the degree of doneness and simmering time. This allows for more accurate analysis of calories and nutrients by estimating the freshness of ingredients and cooking method. For example, users can evaluate the quality of a meal based on the freshness of ingredients and cooking method. Users can also find areas for improvement in their cooking methods.

[0032] The photo uploading unit can also use voice input to improve the accuracy of analysis by allowing the user to verbally describe the meal contents. For example, when uploading a meal photo, the photo uploading unit adds a voice input function that allows the user to verbally describe the meal contents. For example, the names of ingredients and cooking methods can be verbally explained. The photo uploading unit can also use voice recognition technology to convert the user's voice into text data. For example, voice recognition software can automatically analyze the voice and save it as text. The photo uploading unit can also improve the accuracy of analysis by combining voice input and photo information. For example, information about ingredients described in the voice can be added to the photo analysis results. This improves the accuracy of meal content analysis by using voice input in combination. For example, by providing detailed information about the meal, the user can obtain more accurate calorie and nutrient analysis results. Furthermore, the user can specifically identify areas for improvement in their diet, leading to an effective diet.

[0033] The analysis unit can analyze the types of tableware and cutlery shown in a meal photo to identify the cultural background of the meal. For example, the generative AI can analyze the types of tableware shown in a meal photo to identify the cultural background of the meal. For example, it can identify Japanese or Western tableware. The analysis unit can also analyze the type of cutlery to identify the cultural background of the meal. For example, it can analyze the types of chopsticks, forks, and knives. The analysis unit can also analyze the design of the tableware and cutlery to identify the cultural background of the meal. For example, it can analyze traditional or modern designs. In this way, analyzing the type of tableware and cutlery can identify the cultural background of the meal. For example, the user can understand the cultural background of their meal. The user can also obtain tips on how to enjoy meals from different cultures.

[0034] The analysis unit can analyze the cooking method of a meal and calculate the variation in calories and nutrients based on that. For example, the analysis unit uses a generative AI to analyze the cooking method of a meal and calculate the variation in calories and nutrients based on that analysis. For example, it calculates the difference in calories between baking and boiling. The analysis unit can also analyze the variation in nutrients due to different cooking methods. For example, it analyzes the difference in nutrients between fried and steamed foods. The analysis unit can also analyze the variation in nutrients due to cooking time and temperature. For example, it analyzes the difference in nutrients between cooking for a long time and cooking for a short time. This allows for more accurate nutritional analysis by calculating the variation in calories and nutrients based on the cooking method. For example, users can improve the quality of their meals by obtaining information on calories and nutrients based on the cooking method. Users can also find areas to improve their cooking methods and achieve effective dieting.

[0035] The analysis unit can analyze the temperature information of the meal and perform an analysis that takes into account the variation in nutrients due to temperature. For example, the analysis unit uses a generation AI to analyze the temperature information of the meal and perform an analysis that takes into account the variation in nutrients due to temperature. For example, the analysis unit analyzes the difference in nutrients between cold and hot meals. The analysis unit can also evaluate the variation in nutrients due to the temperature of the meal. For example, the analysis unit analyzes the difference in nutrients between hot soup and cold salad. The analysis unit can also analyze the effect of the temperature of the meal on nutrient absorption. For example, the analysis unit evaluates the effect of hot meals on digestion and absorption. This allows for more accurate nutrient analysis by taking the temperature information of the meal into account. For example, a user can improve the quality of their diet by obtaining nutrient information based on the temperature of the meal. Furthermore, a user can achieve an effective diet by implementing a meal plan according to the temperature.

[0036] The analysis unit can analyze the aroma information of a meal and analyze nutrients based on the aroma components. For example, the analysis unit uses a generative AI to analyze the aroma information of a meal and analyze nutrients based on the aroma components. For example, it analyzes the nutrients of ingredients that contain specific aroma components. The analysis unit can also evaluate variations in nutrients due to differences in aroma components. For example, it analyzes the nutrients of spices and herbs with strong aromas. The analysis unit can also analyze the effects of aroma components on appetite and digestion. For example, it evaluates the effect of aromas on stimulating appetite. This enables more accurate nutrient analysis by taking into account the aroma information of a meal. For example, a user can improve the quality of their diet by obtaining nutrient information based on the aroma. Furthermore, a user can achieve an effective diet by implementing a meal plan that corresponds to the aroma.

[0037] The visualization unit can provide customizable graphs and charts tailored to the user's visual preferences. For example, the visualization unit provides customizable graphs and charts tailored to the user's visual preferences when visualizing nutrients using a generation AI. For example, the visualization unit allows the user to select colors and designs. The visualization unit can also allow the user to customize the format of the graph or chart. For example, the visualization unit can select formats such as pie charts, bar graphs, and infographics. The visualization unit can also adjust the way data is displayed based on the user's visual preferences. For example, the visualization unit can customize the order in which data is displayed and the way it is highlighted. This makes it possible to visualize nutrients more easily by providing customizable graphs and charts tailored to the user's visual preferences. For example, by using graphs and charts tailored to the user's preferences, the user can intuitively understand nutrient information. Furthermore, the user can enjoy diet management by selecting a visually appealing design.

[0038] The visualization unit can display a trend analysis compared with past data. For example, when the generation AI visualizes nutrients, the visualization unit displays a trend analysis compared with past data. For example, it displays a graph showing fluctuations in nutrient intake over the past month. The visualization unit can also display long-term trends based on the user's dietary history. For example, it analyzes fluctuations in calorie intake over the past year. The visualization unit can also display intake trends for specific nutrients. For example, it displays a graph showing fluctuations in vitamin and mineral intake. This makes it easier to understand the user's nutrient intake trends by displaying a trend analysis compared with past data. For example, the user can visually check changes in their diet and identify areas for improvement. Furthermore, by understanding long-term trends, the user can take measures to maintain a balanced diet.

[0039] The visualization unit can perform a three-dimensional display using 3D graphics. For example, the generation AI in the visualization unit performs a three-dimensional display using 3D graphics when visualizing nutrients. For example, the amount of nutrient intake is displayed using a 3D bar graph. The visualization unit can also use 3D graphics to three-dimensionally display the balance of nutrients. For example, the proportion of nutrients is displayed using a 3D pie chart. The visualization unit can also use 3D graphics to three-dimensionally display the fluctuations in nutrients. For example, the fluctuations in nutrients over time are displayed using a 3D line graph. This three-dimensional display using 3D graphics makes it easier to visually understand nutrient intake trends. For example, by using a three-dimensional graph, a user can intuitively grasp nutrient information. Furthermore, users can enjoy the display method using 3D graphics, making it easier to continue managing their diet.

[0040] The plan providing unit can analyze the user's exercise data and provide a diet plan that takes into account the balance between diet and exercise. For example, the plan providing unit uses a generation AI to analyze the user's exercise data and provide a diet plan that takes into account the balance between diet and exercise. For example, the plan providing unit can suggest a calorie intake amount based on the amount of exercise. The plan providing unit can also recommend the intake of specific nutrients based on the user's exercise data. For example, the plan providing unit can suggest the intake of protein, which is necessary for muscle recovery. The plan providing unit can also provide a meal plan based on the user's exercise goals. For example, a meal plan suitable for energy replenishment can be suggested for a user training for a marathon. In this way, by analyzing the user's exercise data, a diet plan that takes into account the balance between diet and exercise can be provided. For example, the user can achieve an effective diet by obtaining information on calories and nutrients based on the amount of exercise. The user can also maintain a healthy lifestyle by optimizing the balance between exercise and diet.

[0041] The plan providing unit can analyze the user's sleep data and provide a diet plan that takes into account the relationship between sleep quality and diet. For example, the plan providing unit uses a generation AI to analyze the user's sleep data and provide a diet plan that takes into account the relationship between sleep quality and diet. For example, it can suggest meals that improve sleep quality. The plan providing unit can also recommend the intake of specific nutrients based on the user's sleep data. For example, it can suggest ingredients that promote melatonin production. The plan providing unit can also provide a meal plan based on the user's sleep pattern. For example, it can suggest a nighttime meal plan for a user who works night shifts. In this way, by analyzing the user's sleep data, it is possible to provide a diet plan that takes into account the relationship between sleep quality and diet. For example, by obtaining information on meals that improve sleep quality, the user can maintain a healthy lifestyle. Furthermore, by optimizing the balance between sleep and diet, the user can achieve an effective diet.

[0042] The plan providing unit can analyze the user's lifestyle data and provide a comprehensive diet plan that takes into account factors other than diet. For example, the plan providing unit uses a generation AI to analyze the user's lifestyle data and provide a comprehensive diet plan that takes into account factors other than diet. For example, exercise habits and sleep patterns are taken into account. The plan providing unit can also recommend specific actions based on the user's lifestyle data. For example, it can suggest regular exercise and sufficient sleep. The plan providing unit can also provide a meal plan based on the user's lifestyle. For example, it can suggest a simple meal plan that fits into a busy daily life. In this way, by analyzing the user's lifestyle data, a comprehensive diet plan that takes into account factors other than diet can be provided. For example, by implementing a diet plan based on lifestyle habits, the user can maintain a healthy lifestyle. Furthermore, the user can achieve an effective diet by optimizing the balance between diet and lifestyle.

[0043] The plan providing unit can analyze the user's health checkup data and provide a diet plan that is optimal from a medical perspective. For example, the plan providing unit uses a generation AI to analyze the user's health checkup data and provide a diet plan that is optimal from a medical perspective. For example, it suggests meal contents based on blood test results. The plan providing unit can also recommend the intake of specific nutrients based on the user's health checkup data. For example, it can suggest ingredients for lowering cholesterol levels. The plan providing unit can also provide a meal plan based on the user's health condition. For example, it can suggest a meal plan suitable for managing diabetes. In this way, by analyzing the user's health checkup data, it is possible to provide a diet plan that is optimal from a medical perspective. For example, by following a meal plan based on the results of a health checkup, the user can maintain a healthy lifestyle. Furthermore, the user can achieve an effective diet by receiving advice from a medical perspective.

[0044] The analysis unit can analyze the user's dietary data over the long term, identify seasonal dietary trends, and provide seasonal feedback. For example, the analysis unit uses a generative AI to analyze the user's dietary data over the long term and identify seasonal dietary trends. For example, it analyzes the differences in dietary content between summer and winter. The analysis unit can also evaluate seasonal nutrient variations. For example, it can analyze vitamins that are consumed in large amounts in summer and minerals that are consumed in large amounts in winter. The analysis unit can also evaluate the frequency of seasonal food consumption. For example, it can analyze the intake of summer and winter vegetables. By analyzing the user's dietary data over the long term, seasonal dietary trends can be identified and seasonal feedback can be provided. For example, by obtaining seasonal dietary information, the user can optimize the balance of their diet. Furthermore, the user can understand seasonal nutrient variations and achieve effective dieting.

[0045] The analysis unit can analyze the user's dietary data, identify eating habits with family and friends, and provide feedback from a social perspective. For example, the analysis unit, which uses a generative AI to analyze the user's dietary data, can identify eating habits with family and friends. For example, it can analyze the frequency and content of meals eaten with family. The analysis unit can also evaluate eating habits with friends. For example, it can analyze the frequency of eating out with friends and the content of meals eaten. The analysis unit can also evaluate the impact of eating with family and friends on nutrient intake. For example, it can analyze whether eating with family improves nutrient balance. By analyzing the user's dietary data, it is possible to identify eating habits with family and friends and provide feedback from a social perspective. For example, by obtaining information about meals eaten with family and friends, the user can optimize the balance of their diet. Furthermore, by receiving feedback from a social perspective, the user can achieve an effective diet.

[0046] The analysis unit analyzes the user's dietary data, identifies dietary trends at the travel destination, and can provide feedback tailored to the destination. For example, the analysis unit uses a generation AI to analyze the user's dietary data and identify dietary trends at the travel destination. For example, it analyzes the content and frequency of meals eaten during the trip. The analysis unit can also evaluate nutrient variations at each travel destination. For example, it analyzes the nutrients in meals eaten in a specific region. The analysis unit can also evaluate the frequency of food use at the travel destination. For example, it analyzes ingredients commonly used at the travel destination. By analyzing the user's dietary data, it is possible to identify dietary trends at the travel destination and provide feedback tailored to the travel destination. For example, by obtaining information about meals eaten at the travel destination, the user can optimize the balance of their meals. Furthermore, the user can understand nutrient variations at each travel destination and achieve an effective diet.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The analysis unit can analyze the origin information of ingredients in meal photos and evaluate their quality and nutritional value. For example, the generation AI analyzes the origin information written on the labels or packaging of ingredients to evaluate the quality of the ingredients. The analysis unit can also evaluate the nutritional value of ingredients based on their origin. For example, it can analyze the nutritional value of vegetables and fruits produced in a specific region. The analysis unit can also recommend locally produced ingredients to users based on the origin information of ingredients. By analyzing the origin information of ingredients, users can understand the quality and nutritional value of ingredients and choose healthier meals. Furthermore, by using locally produced ingredients, users can contribute to revitalizing the local economy.

[0049] The analysis unit can analyze the organic cultivation information of ingredients in a meal photo and evaluate their health benefits. For example, the generation AI analyzes the organic cultivation information written on the label or package of an ingredient to evaluate its health benefits. The analysis unit can also evaluate nutritional value based on the organically grown ingredients. For example, it analyzes the nutritional value of organically grown vegetables and fruits. The analysis unit can also recommend healthy meals to users based on the organically grown ingredients. By analyzing the organic cultivation information, users can understand the health benefits of ingredients and choose healthier meals. Furthermore, by using organically grown ingredients, users can contribute to environmental protection.

[0050] The analysis unit can analyze the cultivation method of ingredients in a meal photo and evaluate the nutritional value and health benefits of the ingredients. For example, the generation AI can analyze the cultivation method listed on the ingredient's label or package to evaluate the ingredient's nutritional value. The analysis unit can also evaluate the health benefits of ingredients based on the cultivation method. For example, it can analyze the nutritional value of hydroponically and organically grown vegetables and fruits. The analysis unit can also recommend healthy meals to users based on the cultivation method. By analyzing the cultivation method, users can understand the nutritional value and health benefits of ingredients and choose healthier meals. Furthermore, by using ingredients based on their cultivation method, users can contribute to environmental protection.

[0051] The analysis unit can analyze the storage method of ingredients in meal photos and evaluate their freshness and nutritional value. For example, the generation AI can analyze the storage method written on the label or package of ingredients to evaluate their freshness. The analysis unit can also evaluate the nutritional value of ingredients based on the storage method. For example, it can analyze the nutritional value of vegetables and fruits stored frozen or refrigerated. The analysis unit can also recommend appropriate food storage methods to users based on the storage method. By analyzing storage methods, users can understand the freshness and nutritional value of ingredients and choose healthier meals. Furthermore, by using appropriate storage methods, users can reduce food waste and contribute to environmental protection.

[0052] The analysis unit can analyze the processing method of ingredients in a meal photo and evaluate the nutritional value and health benefits of the ingredients. For example, the generative AI analyzes the processing method written on the label or package of an ingredient to evaluate the nutritional value of the ingredient. The analysis unit can also evaluate the health benefits of ingredients based on the processing method. For example, it analyzes the nutritional value of frozen and dried vegetables and fruits. The analysis unit can also recommend healthy ingredient choices to users based on the processing method. By analyzing the processing method, users can understand the nutritional value and health benefits of ingredients and choose healthier meals. Furthermore, by using ingredients based on their processing method, users can reduce food waste and contribute to environmental protection.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The photo upload unit allows the user to upload a photo of their meal. For example, a photo taken with a smartphone camera can be uploaded to a dedicated app. The photo upload unit can also upload photos via a website. For example, the user can upload a photo of their meal from their computer. The photo upload unit can also upload photos using a cloud storage service. For example, the user can upload photos stored on Google Drive or Dropbox. Step 2: The analysis unit analyzes the photos uploaded by the photo upload unit. For example, the generation AI uses image recognition technology to identify the ingredients in the photo. The generation AI can also analyze the type and amount of ingredients using machine learning algorithms. The generation AI can also calculate the calories and nutrient content of the ingredients. For example, the generation AI calculates the calories of the vegetables and meat in the photo. Step 3: The visualization unit visually displays the results of the analysis by the analysis unit. For example, the generation AI displays the amount of calories and nutrients ingested in a pie chart or bar graph. The generation AI can also highlight nutrients that are lacking or being consumed in excess. The generation AI can also create infographics that allow the user to understand the balance of their diet at a glance. For example, the generation AI displays the user's dietary details in different colors. Step 4: The plan provider provides a diet plan based on the results obtained by the analyzer. For example, the generation AI suggests ingredients and dishes to supplement missing nutrients. The generation AI can also provide specific dietary ideas to reduce calories. The generation AI can also generate a diet plan based on the user's goals and current dietary content. For example, the generation AI suggests a meal plan based on the user's weight and target weight.

[0055] (Example 2) The diet analysis system according to an embodiment of the present invention is a system in which a user simply uploads a photo of each meal, and a generation AI analyzes and visualizes the calories and nutrient deficiencies of that meal. This allows the diet analysis system to help the user understand the calorie and nutrient balance of their meals and provide an optimal diet plan.

[0056] The diet analysis system according to the embodiment includes a photo uploading unit, an analysis unit, a visualization unit, and a plan providing unit. The photo uploading unit allows a user to upload photos of meals. For example, photos taken with a smartphone camera are uploaded to a dedicated app. The photo uploading unit can also upload photos via a website. For example, a user uploads photos of meals from a computer. The photo uploading unit can also upload photos using a cloud storage service. For example, a user uploads photos stored in Google Drive or Dropbox. The analysis unit analyzes the photos uploaded by the photo uploading unit. For example, the generation AI can identify ingredients in the photos using image recognition technology. The generation AI can also analyze the types and quantities of ingredients using machine learning algorithms. The generation AI can also calculate the calories and nutrient content of ingredients. For example, the generation AI can calculate the calories of vegetables and meat in the photos. The visualization unit visually displays the results of the analysis by the analysis unit. For example, the generation AI can display the ingested calories and nutrient amounts using a pie chart or bar graph. The generation AI can also highlight nutrient deficiencies and excess nutrient intakes. The generation AI can also create infographics that allow users to understand the balance of their diet at a glance. For example, the generation AI displays the user's dietary information in a color-coded manner. The plan provider provides a diet plan based on the results obtained by the analysis unit. For example, the generation AI can suggest ingredients and dishes to supplement missing nutrients. The generation AI can also provide specific dietary ideas for reducing calories. The generation AI can also generate a diet plan based on the user's goals and current dietary information. For example, the generation AI can suggest a meal plan based on the user's weight and target weight. In this way, the diet analysis system according to the embodiment allows users to visually grasp the analysis results of calories and nutrients and obtain an optimal diet plan simply by uploading photos of their meals. For example, users can easily manage their daily dietary information and maintain a healthy diet.Furthermore, the user can learn specific points for improving their diet, and can achieve an effective diet.

[0057] The analysis unit can analyze the background information of meal photos and identify the location and time of the meal. For example, the analysis unit uses a generative AI to analyze the background information of meal photos and identify the location and time of the meal. For example, the analysis unit extracts the location and time of the photo from the metadata of the photo and identifies eating patterns. The analysis unit can also analyze the background characteristics of the photo to estimate the location of the meal. For example, it analyzes the interior of a restaurant or the characteristics of a home kitchen. The analysis unit can also analyze the information of the clock or calendar in the photo to identify the time of the meal. For example, it analyzes the position of the clock hands or the date on the calendar. This makes it easier to understand eating patterns by identifying the location and time of the meal. For example, users can understand their eating habits and find areas for improvement. Users can also evaluate the impact of eating at specific locations and times.

[0058] The analysis unit can analyze the color information of a meal photo and estimate the freshness of ingredients and cooking method. For example, the generation AI analyzes the color information of a meal photo and estimates the freshness of ingredients. For example, freshness is determined from the hue of vegetables or the color of meat. The analysis unit can also analyze changes in the color of ingredients and estimate cooking methods. For example, it analyzes the color of grilled meat or the color of boiled vegetables. The analysis unit can also analyze the shades of the color of ingredients and estimate cooking times. For example, it determines the degree of doneness and simmering time. This allows for more accurate analysis of calories and nutrients by estimating the freshness of ingredients and cooking method. For example, users can evaluate the quality of a meal based on the freshness of ingredients and cooking method. Users can also find areas for improvement in their cooking methods.

[0059] The analysis unit can use the emotion estimation function to analyze the emotions of a user when uploading a meal photo and identify factors that influence meal selection. For example, the analysis unit can use the emotion estimation function to analyze the user's facial expression when uploading a meal photo and identify the emotion. For example, the emotion can be estimated from a smile or a serious expression. The analysis unit can also analyze the user's voice to identify the emotion. For example, the emotion can be estimated from the tone and speed of the voice. The analysis unit can also analyze the user's biometric data (heart rate and electrodermal activity) to identify the emotion. For example, the emotion can be estimated from heart rate fluctuations and electrodermal activity. By analyzing the user's emotions, factors that influence meal selection can be identified and a more appropriate diet plan can be provided. For example, the user can understand how their emotions affect their meal selection and find areas for improvement in their diet based on their emotions. The user can also achieve an effective diet by implementing a meal plan that corresponds to their emotions.

[0060] The photo uploading unit can also use voice input to improve the accuracy of analysis by allowing the user to verbally describe the meal contents. For example, when uploading a meal photo, the photo uploading unit adds a voice input function that allows the user to verbally describe the meal contents. For example, the names of ingredients and cooking methods can be verbally explained. The photo uploading unit can also use voice recognition technology to convert the user's voice into text data. For example, voice recognition software can automatically analyze the voice and save it as text. The photo uploading unit can also improve the accuracy of analysis by combining voice input and photo information. For example, information about ingredients described in the voice can be added to the photo analysis results. This improves the accuracy of meal content analysis by using voice input in combination. For example, by providing detailed information about the meal, the user can obtain more accurate calorie and nutrient analysis results. Furthermore, the user can specifically identify areas for improvement in their diet, leading to an effective diet.

[0061] The analysis unit can analyze the types of tableware and cutlery shown in a meal photo to identify the cultural background of the meal. For example, the generative AI can analyze the types of tableware shown in a meal photo to identify the cultural background of the meal. For example, it can identify Japanese or Western tableware. The analysis unit can also analyze the type of cutlery to identify the cultural background of the meal. For example, it can analyze the types of chopsticks, forks, and knives. The analysis unit can also analyze the design of the tableware and cutlery to identify the cultural background of the meal. For example, it can analyze traditional or modern designs. In this way, analyzing the type of tableware and cutlery can identify the cultural background of the meal. For example, the user can understand the cultural background of their meal. The user can also obtain tips on how to enjoy meals from different cultures.

[0062] The analysis unit uses the emotion estimation function to analyze the emotions of a user when uploading a meal photo in real time and can make suggestions to elicit positive emotions. For example, the analysis unit uses the emotion estimation function to analyze facial expressions of a user when uploading a meal photo in real time. For example, the analysis unit calculates an emotion score based on changes in facial expressions and provides feedback. The analysis unit can also analyze the user's voice in real time to identify emotions. For example, the analysis unit analyzes the tone and speed of the voice to calculate an emotion score and provide feedback. The analysis unit can also analyze the user's biometric data (heart rate and electrodermal activity) in real time to identify emotions. For example, the analysis unit calculates an emotion score based on heart rate fluctuations and provides feedback. In this way, the analysis unit can analyze the user's emotions in real time and provide appropriate feedback to make suggestions to elicit positive emotions. For example, by having positive emotions while eating, a user can improve their satisfaction with the meal. Furthermore, by implementing a meal plan based on emotions, a user can achieve an effective diet.

[0063] The analysis unit can analyze the cooking method of a meal and calculate the variation in calories and nutrients based on that. For example, the analysis unit uses a generative AI to analyze the cooking method of a meal and calculate the variation in calories and nutrients based on that analysis. For example, it calculates the difference in calories between baking and boiling. The analysis unit can also analyze the variation in nutrients due to different cooking methods. For example, it analyzes the difference in nutrients between fried and steamed foods. The analysis unit can also analyze the variation in nutrients due to cooking time and temperature. For example, it analyzes the difference in nutrients between cooking for a long time and cooking for a short time. This allows for more accurate nutritional analysis by calculating the variation in calories and nutrients based on the cooking method. For example, users can improve the quality of their meals by obtaining information on calories and nutrients based on the cooking method. Users can also find areas to improve their cooking methods and achieve effective dieting.

[0064] The analysis unit can use the emotion estimation function to analyze the user's emotions regarding food and suggest a nutrient balance based on the emotions. The analysis unit, for example, uses the emotion estimation function to analyze the user's emotions regarding food and suggest a nutrient balance based on the emotions. For example, if the user is feeling stressed, nutrients with a relaxing effect are suggested. The analysis unit can also recommend the intake of specific nutrients based on the user's emotions. For example, if the user is feeling tired, nutrients suitable for replenishing energy are suggested. The analysis unit can also provide a meal plan based on the user's emotions. For example, ingredients and dishes that elicit positive emotions are suggested. In this way, a more appropriate meal plan can be provided by suggesting a nutrient balance based on the user's emotions. For example, a user can improve the quality of their diet by obtaining information on nutrients that correspond to their emotions. Furthermore, a user can achieve an effective diet by implementing a meal plan based on their emotions.

[0065] The analysis unit can analyze the temperature information of the meal and perform an analysis that takes into account the variation in nutrients due to temperature. For example, the analysis unit uses a generation AI to analyze the temperature information of the meal and perform an analysis that takes into account the variation in nutrients due to temperature. For example, the analysis unit analyzes the difference in nutrients between cold and hot meals. The analysis unit can also evaluate the variation in nutrients due to the temperature of the meal. For example, the analysis unit analyzes the difference in nutrients between hot soup and cold salad. The analysis unit can also analyze the effect of the temperature of the meal on nutrient absorption. For example, the analysis unit evaluates the effect of hot meals on digestion and absorption. This allows for more accurate nutrient analysis by taking the temperature information of the meal into account. For example, a user can improve the quality of their diet by obtaining nutrient information based on the temperature of the meal. Furthermore, a user can achieve an effective diet by implementing a meal plan according to the temperature.

[0066] The analysis unit can analyze the aroma information of a meal and analyze nutrients based on the aroma components. For example, the analysis unit uses a generative AI to analyze the aroma information of a meal and analyze nutrients based on the aroma components. For example, it analyzes the nutrients of ingredients that contain specific aroma components. The analysis unit can also evaluate variations in nutrients due to differences in aroma components. For example, it analyzes the nutrients of spices and herbs with strong aromas. The analysis unit can also analyze the effects of aroma components on appetite and digestion. For example, it evaluates the effect of aromas on stimulating appetite. This enables more accurate nutrient analysis by taking into account the aroma information of a meal. For example, a user can improve the quality of their diet by obtaining nutrient information based on the aroma. Furthermore, a user can achieve an effective diet by implementing a meal plan that corresponds to the aroma.

[0067] The analysis unit can use the emotion estimation function to analyze the user's emotions regarding food in real time and suggest calorie and nutrient balance based on the emotions. The analysis unit can, for example, use the emotion estimation function to analyze the user's emotions regarding food in real time and suggest calorie and nutrient balance based on the emotions. For example, if the user is feeling stressed, ingredients with a relaxing effect can be suggested. The analysis unit can also recommend the intake of specific nutrients based on the user's emotions. For example, if the user is feeling tired, nutrients suitable for energy replenishment can be suggested. The analysis unit can also provide a meal plan based on the user's emotions in real time. For example, ingredients and dishes that elicit positive emotions can be suggested. This allows for a more appropriate meal plan to be provided by suggesting calorie and nutrient balance based on the user's emotions. For example, a user can improve the quality of their meals by obtaining information on nutrients that correspond to their emotions. A user can also achieve an effective diet by implementing a meal plan based on their emotions.

[0068] The visualization unit can provide customizable graphs and charts tailored to the user's visual preferences. For example, the visualization unit provides customizable graphs and charts tailored to the user's visual preferences when visualizing nutrients using a generation AI. For example, the visualization unit allows the user to select colors and designs. The visualization unit can also allow the user to customize the format of the graph or chart. For example, the visualization unit can select formats such as pie charts, bar graphs, and infographics. The visualization unit can also adjust the way data is displayed based on the user's visual preferences. For example, the visualization unit can customize the order in which data is displayed and the way it is highlighted. This makes it possible to visualize nutrients more easily by providing customizable graphs and charts tailored to the user's visual preferences. For example, by using graphs and charts tailored to the user's preferences, the user can intuitively understand nutrient information. Furthermore, the user can enjoy diet management by selecting a visually appealing design.

[0069] The visualization unit can display a trend analysis compared with past data. For example, when the generation AI visualizes nutrients, the visualization unit displays a trend analysis compared with past data. For example, it displays a graph showing fluctuations in nutrient intake over the past month. The visualization unit can also display long-term trends based on the user's dietary history. For example, it analyzes fluctuations in calorie intake over the past year. The visualization unit can also display intake trends for specific nutrients. For example, it displays a graph showing fluctuations in vitamin and mineral intake. This makes it easier to understand the user's nutrient intake trends by displaying a trend analysis compared with past data. For example, the user can visually check changes in their diet and identify areas for improvement. Furthermore, by understanding long-term trends, the user can take measures to maintain a balanced diet.

[0070] The visualization unit can use the emotion estimation function to visualize nutrients based on the user's emotions and suggest a display method that elicits positive emotions. The visualization unit, for example, uses the emotion estimation function to visualize nutrients based on the user's emotions. For example, bright colors and designs are used to elicit positive emotions. The visualization unit can also suggest a data display method according to the user's emotions. For example, data is highlighted based on the emotions. The visualization unit can also adjust the data display order based on the user's emotions. For example, good data is displayed first to elicit positive emotions. In this way, by visualizing nutrients based on the user's emotions, a display method that elicits positive emotions can be suggested. For example, by using a data display method based on emotions, the user can more easily understand nutrient information. Furthermore, by having positive emotions, the user can enjoy managing their diet.

[0071] The visualization unit can perform a three-dimensional display using 3D graphics. For example, the generation AI in the visualization unit performs a three-dimensional display using 3D graphics when visualizing nutrients. For example, the amount of nutrient intake is displayed using a 3D bar graph. The visualization unit can also use 3D graphics to three-dimensionally display the balance of nutrients. For example, the proportion of nutrients is displayed using a 3D pie chart. The visualization unit can also use 3D graphics to three-dimensionally display the fluctuations in nutrients. For example, the fluctuations in nutrients over time are displayed using a 3D line graph. This three-dimensional display using 3D graphics makes it easier to visually understand nutrient intake trends. For example, by using a three-dimensional graph, a user can intuitively grasp nutrient information. Furthermore, users can enjoy the display method using 3D graphics, making it easier to continue managing their diet.

[0072] The visualization unit uses the emotion estimation function to visualize nutrients based on the user's emotions in real time, and can suggest a display method that elicits positive emotions. The visualization unit, for example, uses the emotion estimation function to visualize nutrients based on the user's emotions in real time. For example, the display method is changed depending on the user's emotions. The visualization unit can also suggest a data display method based on emotions in real time. For example, data is highlighted based on emotions. The visualization unit can also adjust the data display order in real time based on the user's emotions. For example, good data is displayed first to elicit positive emotions. In this way, by visualizing nutrients based on the user's emotions in real time, a display method that elicits positive emotions can be suggested. For example, by using the data display method based on emotions, the user can more easily understand nutrient information. Furthermore, by having positive emotions, the user can enjoy diet management.

[0073] The plan providing unit can analyze the user's exercise data and provide a diet plan that takes into account the balance between diet and exercise. For example, the plan providing unit uses a generation AI to analyze the user's exercise data and provide a diet plan that takes into account the balance between diet and exercise. For example, the plan providing unit can suggest a calorie intake amount based on the amount of exercise. The plan providing unit can also recommend the intake of specific nutrients based on the user's exercise data. For example, the plan providing unit can suggest the intake of protein, which is necessary for muscle recovery. The plan providing unit can also provide a meal plan based on the user's exercise goals. For example, a meal plan suitable for energy replenishment can be suggested for a user training for a marathon. In this way, by analyzing the user's exercise data, a diet plan that takes into account the balance between diet and exercise can be provided. For example, the user can achieve an effective diet by obtaining information on calories and nutrients based on the amount of exercise. The user can also maintain a healthy lifestyle by optimizing the balance between exercise and diet.

[0074] The plan providing unit can analyze the user's sleep data and provide a diet plan that takes into account the relationship between sleep quality and diet. For example, the plan providing unit uses a generation AI to analyze the user's sleep data and provide a diet plan that takes into account the relationship between sleep quality and diet. For example, it can suggest meals that improve sleep quality. The plan providing unit can also recommend the intake of specific nutrients based on the user's sleep data. For example, it can suggest ingredients that promote melatonin production. The plan providing unit can also provide a meal plan based on the user's sleep pattern. For example, it can suggest a nighttime meal plan for a user who works night shifts. In this way, by analyzing the user's sleep data, it is possible to provide a diet plan that takes into account the relationship between sleep quality and diet. For example, by obtaining information on meals that improve sleep quality, the user can maintain a healthy lifestyle. Furthermore, by optimizing the balance between sleep and diet, the user can achieve an effective diet.

[0075] The plan providing unit can use the emotion estimation function to provide a diet plan based on the user's emotions and make suggestions that elicit positive emotions. The plan providing unit, for example, uses the emotion estimation function to provide a diet plan based on the user's emotions. For example, it can suggest meal contents that elicit positive emotions. The plan providing unit can also recommend the intake of specific nutrients based on the user's emotions. For example, it can suggest ingredients that are effective in reducing stress. The plan providing unit can also provide a meal plan according to the user's emotions. For example, it can suggest ingredients and dishes that will lift your mood. In this way, by providing a diet plan based on the user's emotions, it becomes possible to make suggestions that elicit positive emotions. For example, by following a meal plan based on emotions, the user can improve their satisfaction with their meals. Furthermore, by having positive emotions, the user can achieve an effective diet.

[0076] The plan providing unit can analyze the user's lifestyle data and provide a comprehensive diet plan that takes into account factors other than diet. For example, the plan providing unit uses a generation AI to analyze the user's lifestyle data and provide a comprehensive diet plan that takes into account factors other than diet. For example, exercise habits and sleep patterns are taken into account. The plan providing unit can also recommend specific actions based on the user's lifestyle data. For example, it can suggest regular exercise and sufficient sleep. The plan providing unit can also provide a meal plan based on the user's lifestyle. For example, it can suggest a simple meal plan that fits into a busy daily life. In this way, by analyzing the user's lifestyle data, a comprehensive diet plan that takes into account factors other than diet can be provided. For example, by implementing a diet plan based on lifestyle habits, the user can maintain a healthy lifestyle. Furthermore, the user can achieve an effective diet by optimizing the balance between diet and lifestyle.

[0077] The plan providing unit can analyze the user's health checkup data and provide a diet plan that is optimal from a medical perspective. For example, the plan providing unit uses a generation AI to analyze the user's health checkup data and provide a diet plan that is optimal from a medical perspective. For example, it suggests meal contents based on blood test results. The plan providing unit can also recommend the intake of specific nutrients based on the user's health checkup data. For example, it can suggest ingredients for lowering cholesterol levels. The plan providing unit can also provide a meal plan based on the user's health condition. For example, it can suggest a meal plan suitable for managing diabetes. In this way, by analyzing the user's health checkup data, it is possible to provide a diet plan that is optimal from a medical perspective. For example, by following a meal plan based on the results of a health checkup, the user can maintain a healthy lifestyle. Furthermore, the user can achieve an effective diet by receiving advice from a medical perspective.

[0078] The plan providing unit can use the emotion estimation function to provide a diet plan based on the user's emotions in real time and make suggestions that elicit positive emotions. The plan providing unit, for example, uses the emotion estimation function to provide a diet plan based on the user's emotions in real time. For example, it can suggest meal contents that elicit positive emotions. The plan providing unit can also recommend the intake of specific nutrients in real time based on the user's emotions. For example, it can suggest ingredients that are effective in reducing stress. The plan providing unit can also provide a meal plan based on the user's emotions in real time. For example, it can suggest ingredients and dishes that will lift your mood. In this way, by providing a diet plan based on the user's emotions in real time, it becomes possible to make suggestions that elicit positive emotions. For example, by following a meal plan based on emotions, the user can improve their satisfaction with their meals. Furthermore, by having positive emotions, the user can achieve an effective diet.

[0079] The analysis unit can analyze the user's dietary data over the long term, identify seasonal dietary trends, and provide seasonal feedback. For example, the analysis unit uses a generative AI to analyze the user's dietary data over the long term and identify seasonal dietary trends. For example, it analyzes the differences in dietary content between summer and winter. The analysis unit can also evaluate seasonal nutrient variations. For example, it can analyze vitamins that are consumed in large amounts in summer and minerals that are consumed in large amounts in winter. The analysis unit can also evaluate the frequency of seasonal food consumption. For example, it can analyze the intake of summer and winter vegetables. By analyzing the user's dietary data over the long term, seasonal dietary trends can be identified and seasonal feedback can be provided. For example, by obtaining seasonal dietary information, the user can optimize the balance of their diet. Furthermore, the user can understand seasonal nutrient variations and achieve effective dieting.

[0080] The analysis unit can use the emotion estimation function to provide feedback based on the user's emotions and make suggestions to elicit positive emotions. The analysis unit, for example, uses the emotion estimation function to provide feedback based on the user's emotions. For example, it can send an encouraging message to elicit positive emotions. The analysis unit can also recommend specific actions based on the user's emotions. For example, it can suggest relaxation methods to reduce stress. The analysis unit can also provide a meal plan based on the user's emotions. For example, it can suggest ingredients and dishes to improve mood. In this way, providing feedback based on the user's emotions makes it possible to make suggestions to elicit positive emotions. For example, by receiving emotion-based feedback, the user can improve their satisfaction with meals. Furthermore, by having positive emotions, the user can achieve an effective diet.

[0081] The analysis unit can analyze the user's dietary data, identify eating habits with family and friends, and provide feedback from a social perspective. For example, the analysis unit, which uses a generative AI to analyze the user's dietary data, can identify eating habits with family and friends. For example, it can analyze the frequency and content of meals eaten with family. The analysis unit can also evaluate eating habits with friends. For example, it can analyze the frequency of eating out with friends and the content of meals eaten. The analysis unit can also evaluate the impact of eating with family and friends on nutrient intake. For example, it can analyze whether eating with family improves nutrient balance. By analyzing the user's dietary data, it is possible to identify eating habits with family and friends and provide feedback from a social perspective. For example, by obtaining information about meals eaten with family and friends, the user can optimize the balance of their diet. Furthermore, by receiving feedback from a social perspective, the user can achieve an effective diet.

[0082] The analysis unit analyzes the user's dietary data, identifies dietary trends at the travel destination, and can provide feedback tailored to the destination. For example, the analysis unit uses a generation AI to analyze the user's dietary data and identify dietary trends at the travel destination. For example, it analyzes the content and frequency of meals eaten during the trip. The analysis unit can also evaluate nutrient variations at each travel destination. For example, it analyzes the nutrients in meals eaten in a specific region. The analysis unit can also evaluate the frequency of food use at the travel destination. For example, it analyzes ingredients commonly used at the travel destination. By analyzing the user's dietary data, it is possible to identify dietary trends at the travel destination and provide feedback tailored to the travel destination. For example, by obtaining information about meals eaten at the travel destination, the user can optimize the balance of their meals. Furthermore, the user can understand nutrient variations at each travel destination and achieve an effective diet.

[0083] The analysis unit can use the emotion estimation function to provide feedback based on the user's emotions in real time and make suggestions to elicit positive emotions. The analysis unit, for example, uses the emotion estimation function to provide feedback based on the user's emotions in real time. For example, it can send an encouraging message to elicit positive emotions. The analysis unit can also recommend specific actions in real time based on the user's emotions. For example, it can suggest relaxation methods to reduce stress. The analysis unit can also provide a meal plan in real time based on the user's emotions. For example, it can suggest ingredients and dishes to improve mood. In this way, by providing feedback based on the user's emotions in real time, it becomes possible to make suggestions to elicit positive emotions. For example, by receiving feedback based on emotions, the user can improve their satisfaction with their meals. Furthermore, by having positive emotions, the user can achieve an effective diet.

[0084] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0085] The analysis unit can analyze the origin information of ingredients in meal photos and evaluate their quality and nutritional value. For example, the generation AI analyzes the origin information written on the labels or packaging of ingredients to evaluate the quality of the ingredients. The analysis unit can also evaluate the nutritional value of ingredients based on their origin. For example, it can analyze the nutritional value of vegetables and fruits produced in a specific region. The analysis unit can also recommend locally produced ingredients to users based on the origin information of ingredients. By analyzing the origin information of ingredients, users can understand the quality and nutritional value of ingredients and choose healthier meals. Furthermore, by using locally produced ingredients, users can contribute to revitalizing the local economy.

[0086] The analysis unit can use the emotion estimation function to analyze the emotions of a user when uploading a meal photo and identify factors that influence meal selection. For example, the emotion estimation function can be used to analyze the user's facial expression when uploading a meal photo to identify the emotion. For example, the emotion can be estimated from a smile or a serious expression. The analysis unit can also analyze the user's voice to identify the emotion. For example, the emotion can be estimated from the tone and speed of the voice. The analysis unit can also analyze the user's biometric data (heart rate and electrodermal activity) to identify the emotion. For example, the emotion can be estimated from heart rate fluctuations and electrodermal activity. By analyzing the user's emotions, factors that influence meal selection can be identified and a more appropriate diet plan can be provided. For example, the user can understand how their emotions affect their meal selection and find areas for improvement in their diet based on their emotions. The user can also achieve an effective diet by implementing a meal plan that corresponds to their emotions.

[0087] The analysis unit can analyze the organic cultivation information of ingredients in a meal photo and evaluate their health benefits. For example, the generation AI analyzes the organic cultivation information written on the label or package of an ingredient to evaluate its health benefits. The analysis unit can also evaluate nutritional value based on the organically grown ingredients. For example, it analyzes the nutritional value of organically grown vegetables and fruits. The analysis unit can also recommend healthy meals to users based on the organically grown ingredients. By analyzing the organic cultivation information, users can understand the health benefits of ingredients and choose healthier meals. Furthermore, by using organically grown ingredients, users can contribute to environmental protection.

[0088] The analysis unit uses the emotion estimation function to analyze the emotions of a user when uploading a meal photo in real time and make suggestions to elicit positive emotions. For example, the emotion estimation function is used to analyze the facial expressions of a user when uploading a meal photo in real time. For example, the emotion estimation function is used to calculate an emotion score based on changes in facial expressions and provide feedback. The analysis unit can also analyze the user's voice in real time to identify emotions. For example, the tone and speed of the voice can be analyzed to calculate an emotion score and provide feedback. The analysis unit can also analyze the user's biometric data (heart rate and electrodermal activity) in real time to identify emotions. For example, the emotion score can be calculated based on fluctuations in heart rate and provide feedback. In this way, the analysis unit can analyze the user's emotions in real time and provide appropriate feedback to make suggestions to elicit positive emotions. For example, by having positive emotions while eating, a user can improve their satisfaction with the meal. Furthermore, by implementing a meal plan based on emotions, a user can achieve an effective diet.

[0089] The analysis unit can analyze the cultivation method of ingredients in a meal photo and evaluate the nutritional value and health benefits of the ingredients. For example, the generation AI can analyze the cultivation method listed on the ingredient's label or package to evaluate the ingredient's nutritional value. The analysis unit can also evaluate the health benefits of ingredients based on the cultivation method. For example, it can analyze the nutritional value of hydroponically and organically grown vegetables and fruits. The analysis unit can also recommend healthy meals to users based on the cultivation method. By analyzing the cultivation method, users can understand the nutritional value and health benefits of ingredients and choose healthier meals. Furthermore, by using ingredients based on their cultivation method, users can contribute to environmental protection.

[0090] The analysis unit can use the emotion estimation function to analyze the user's emotions regarding food and suggest a nutrient balance based on the emotions. For example, the emotion estimation function can be used to analyze the user's emotions regarding food and suggest a nutrient balance based on the emotions. For example, if the user is feeling stressed, nutrients with a relaxing effect can be suggested. The analysis unit can also recommend the intake of specific nutrients based on the user's emotions. For example, if the user is feeling tired, nutrients suitable for replenishing energy can be suggested. The analysis unit can also provide a meal plan based on the user's emotions. For example, ingredients and dishes that elicit positive emotions can be suggested. In this way, a more appropriate meal plan can be provided by suggesting a nutrient balance based on the user's emotions. For example, a user can improve the quality of their diet by obtaining information on nutrients that correspond to their emotions. Furthermore, a user can achieve an effective diet by implementing a meal plan based on their emotions.

[0091] The analysis unit can analyze the storage method of ingredients in meal photos and evaluate their freshness and nutritional value. For example, the generation AI can analyze the storage method written on the label or package of ingredients to evaluate their freshness. The analysis unit can also evaluate the nutritional value of ingredients based on the storage method. For example, it can analyze the nutritional value of vegetables and fruits stored frozen or refrigerated. The analysis unit can also recommend appropriate food storage methods to users based on the storage method. By analyzing storage methods, users can understand the freshness and nutritional value of ingredients and choose healthier meals. Furthermore, by using appropriate storage methods, users can reduce food waste and contribute to environmental protection.

[0092] The analysis unit can use the emotion estimation function to analyze the user's emotions regarding food in real time and suggest calorie and nutrient balance based on the emotions. For example, the emotion estimation function can be used to analyze the user's emotions regarding food in real time and suggest calorie and nutrient balance based on the emotions. For example, if the user is feeling stressed, ingredients with a relaxing effect can be suggested. The analysis unit can also recommend the intake of specific nutrients based on the user's emotions. For example, if the user is feeling tired, nutrients suitable for replenishing energy can be suggested. The analysis unit can also provide a meal plan based on the user's emotions in real time. For example, ingredients and dishes that elicit positive emotions can be suggested. This allows for a more appropriate meal plan to be provided by suggesting calorie and nutrient balance based on the user's emotions. For example, a user can improve the quality of their diet by obtaining information on nutrients that correspond to their emotions. A user can also achieve an effective diet by implementing a meal plan based on their emotions.

[0093] The analysis unit can analyze the processing method of ingredients in a meal photo and evaluate the nutritional value and health benefits of the ingredients. For example, the generative AI analyzes the processing method written on the label or package of an ingredient to evaluate the nutritional value of the ingredient. The analysis unit can also evaluate the health benefits of ingredients based on the processing method. For example, it analyzes the nutritional value of frozen and dried vegetables and fruits. The analysis unit can also recommend healthy ingredient choices to users based on the processing method. By analyzing the processing method, users can understand the nutritional value and health benefits of ingredients and choose healthier meals. Furthermore, by using ingredients based on their processing method, users can reduce food waste and contribute to environmental protection.

[0094] The analysis unit can use the emotion estimation function to analyze the user's emotions regarding food in real time and suggest calorie and nutrient balance based on the emotions. For example, the emotion estimation function can be used to analyze the user's emotions regarding food in real time and suggest calorie and nutrient balance based on the emotions. For example, if the user is feeling stressed, ingredients with a relaxing effect can be suggested. The analysis unit can also recommend the intake of specific nutrients based on the user's emotions. For example, if the user is feeling tired, nutrients suitable for replenishing energy can be suggested. The analysis unit can also provide a meal plan based on the user's emotions in real time. For example, ingredients and dishes that elicit positive emotions can be suggested. This allows for a more appropriate meal plan to be provided by suggesting calorie and nutrient balance based on the user's emotions. For example, a user can improve the quality of their diet by obtaining information on nutrients that correspond to their emotions. A user can also achieve an effective diet by implementing a meal plan based on their emotions.

[0095] The processing flow of the second embodiment will be briefly explained below.

[0096] Step 1: The photo upload unit allows the user to upload a photo of their meal. For example, a photo taken with a smartphone camera can be uploaded to a dedicated app. The photo upload unit can also upload photos via a website. For example, the user can upload a photo of their meal from their computer. The photo upload unit can also upload photos using a cloud storage service. For example, the user can upload photos stored on Google Drive or Dropbox. Step 2: The analysis unit analyzes the photos uploaded by the photo upload unit. For example, the generation AI uses image recognition technology to identify the ingredients in the photo. The generation AI can also analyze the type and amount of ingredients using machine learning algorithms. The generation AI can also calculate the calories and nutrient content of the ingredients. For example, the generation AI calculates the calories of the vegetables and meat in the photo. Step 3: The visualization unit visually displays the results of the analysis by the analysis unit. For example, the generation AI displays the amount of calories and nutrients ingested in a pie chart or bar graph. The generation AI can also highlight nutrients that are lacking or being consumed in excess. The generation AI can also create infographics that allow the user to understand the balance of their diet at a glance. For example, the generation AI displays the user's dietary details in different colors. Step 4: The plan provider provides a diet plan based on the results obtained by the analyzer. For example, the generation AI suggests ingredients and dishes to supplement missing nutrients. The generation AI can also provide specific dietary ideas to reduce calories. The generation AI can also generate a diet plan based on the user's goals and current dietary content. For example, the generation AI suggests a meal plan based on the user's weight and target weight.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0101] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0110] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0111] 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.

[0112] 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.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] 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.

[0115] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0116] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0125] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0126] 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.

[0127] 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.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] 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.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0141] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0142] 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.

[0143] 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.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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]

[0164] 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 photo upload section where users upload photos of their meals; an analysis unit that analyzes the photos uploaded by the photo upload unit; a visualization unit that visually displays the results of the analysis performed by the analysis unit; a plan providing unit that provides a diet plan based on the results obtained by the analysis unit; A system characterized by:

2. The analysis unit Analyzing the color information of food photos to estimate the freshness of ingredients and cooking method 2. The system of claim 1.

3. The photo upload unit Improve analysis accuracy by using voice input and having the user verbally describe the contents of their meal 2. The system of claim 1.

4. The visualization unit Providing customizable graphs and charts tailored to the user's visual preferences 2. The system of claim 1.

5. The plan providing unit Analyzing the exercise data of the user and providing the diet plan that takes into consideration the balance between diet and exercise.

2. The system of claim 1.

6. The analysis unit Using an emotion estimation function, the emotion of the user when uploading a food photo is analyzed to identify factors that influence the food selection.

2. The system of claim 1.

Images