system

Abstract

The system according to this embodiment aims to evaluate individual health status and aging risk based on dietary data and to provide advice on optimal nutritional balance and dietary habits. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, an evaluation unit, and a provision unit. The reception unit receives input of meal data. The analysis unit analyzes the data received by the reception unit. The evaluation unit evaluates the risk of AGE accumulation based on the results obtained by the analysis unit. The provision unit provides optimal nutritional balance and dietary advice based on the results evaluated by the evaluation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it has not been sufficiently carried out to evaluate individual health conditions and aging risks based on dietary data and provide advice on appropriate nutritional balance and diet.

[0005] The system according to the embodiment aims to evaluate individual health conditions and aging risks based on dietary data and provide advice on optimal nutritional balance and diet.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, an evaluation unit, and a provision unit. The reception unit receives input of meal data. The analysis unit analyzes the data received by the reception unit. The evaluation unit evaluates the risk of AGE accumulation based on the results obtained by the analysis unit. The provision unit provides advice on optimal nutritional balance and dietary habits based on the results evaluated by the evaluation unit. [Effects of the Invention]

[0007] The system according to this embodiment can evaluate an individual's health status and aging risk based on dietary data, and provide advice on optimal nutritional balance and dietary habits. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The health assessment system according to an embodiment of the present invention is a system that uses AI to evaluate an individual's health status and aging risk based on dietary data. The health assessment system allows users to input daily dietary data, which the AI ​​then analyzes to assess the risk of AGE (Advanced Glycation End-products) accumulation due to high-temperature cooking. Furthermore, the AI ​​provides advice on optimal nutritional balance and dietary habits. This system helps users adopt a healthy lifestyle suited to their health status and slow the progression of glycation. It also provides guidance on a healthy aging process by clarifying the relationship between diet and aging. This service aims to help each user develop healthy lifestyle habits, understand the aging process, and contribute to the creation of a sustainable healthy society. For example, a user inputs daily dietary data, such as the contents and cooking methods of breakfast, lunch, and dinner. This information is input into the AI. Next, the AI ​​analyzes the input dietary data. The AI ​​evaluates the nutrients and cooking methods of each meal and determines the risk of AGE accumulation due to high-temperature cooking. For example, if there is a lot of high-temperature cooking such as frying or grilling, the risk of AGE accumulation is assessed as high. Furthermore, the AI ​​provides advice on optimal nutritional balance and dietary habits. For example, if a user has a high risk of AGE accumulation, the system advises increasing low-temperature cooking and raw food consumption. It also suggests consuming foods rich in specific nutrients if those nutrients are deficient. This system helps users adopt a healthy lifestyle suited to their individual health status. For instance, by improving their diet according to the advice, users can slow down the progression of glycation. Furthermore, by clarifying the relationship between diet and aging, users can understand their own aging process and aim for healthy aging. This service aims to contribute to the creation of a sustainable and healthy society by helping each user develop healthy lifestyle habits and understand the aging process. For example, by maintaining a healthy diet, users can expect to prevent lifestyle-related diseases and extend their healthy lifespan. This allows the health assessment system to efficiently analyze users' dietary data, evaluate the risk of AGE accumulation, and provide optimal nutritional balance and dietary advice.

[0029] The health assessment system according to this embodiment comprises a reception unit, an analysis unit, an evaluation unit, and a provision unit. The reception unit receives daily meal data from the user. This daily meal data may include, but is not limited to, the contents and cooking methods of breakfast, lunch, and dinner. For example, the user can manually input meal details into the reception unit. The reception unit can also receive meal data using a smartphone or personal computer. Furthermore, the reception unit can receive meal data using voice input or image recognition technology. For example, the reception unit can receive meal details by voice input from the user and convert them into text data using voice recognition technology. The reception unit can also receive photos of meals taken by the user and analyze the meal details using image recognition technology. The analysis unit analyzes the data received by the reception unit. The analysis unit evaluates, for example, the nutrients and cooking methods of each meal. The analysis unit can use AI to analyze meal data and evaluate the nutrients and cooking methods of each meal. For example, the analysis unit receives meal data as input and calculates the nutrient content using an AI model. The analysis unit can also evaluate the risk of AGE accumulation based on the cooking method. For example, the analysis unit assesses that the risk of AGE accumulation is high if there is a lot of high-temperature cooking, such as deep-frying or grilling. The evaluation unit assesses the risk of AGE accumulation based on the results obtained by the analysis unit. The evaluation unit quantifies the risk of AGE accumulation and provides it to the user. The evaluation unit can use AI to assess the risk of AGE accumulation. For example, the evaluation unit receives data on nutrients and cooking methods obtained by the analysis unit as input and calculates the risk of AGE accumulation. The provision unit provides advice on the optimal nutritional balance and diet based on the results evaluated by the evaluation unit. For example, if the risk of AGE accumulation is high, the provision unit advises increasing low-temperature cooking and raw food consumption. The provision unit can use AI to provide advice on the optimal nutritional balance and diet. For example, the provision unit receives the risk of AGE accumulation obtained by the evaluation unit as input and advises increasing low-temperature cooking and raw food consumption. The provision unit can also suggest consuming foods rich in specific nutrients if there is a deficiency in those nutrients.For example, if the user is deficient in vitamins or minerals, the system may suggest consuming foods rich in those nutrients. This allows the health assessment system according to the embodiment to efficiently analyze the user's dietary data, assess the risk of AGE accumulation, and provide advice on optimal nutritional balance and dietary habits.

[0030] The reception desk allows users to input their daily meal data. This data may include, but is not limited to, the contents and cooking methods of breakfast, lunch, and dinner. For example, users can manually input meal details. The reception desk can also input meal data using a smartphone or personal computer. Furthermore, the reception desk can input meal data using voice input or image recognition technology. For example, the reception desk can receive meal details via voice input from the user and convert it into text data using voice recognition technology. The reception desk can also receive meals via photos taken by the user and analyze the contents using image recognition technology. Specifically, voice recognition technology converts what the user says into text in real time, accurately recording the details of the meal. For example, a voice input such as "I had toast and scrambled eggs for breakfast" is immediately saved as text data. Image recognition technology analyzes photos of meals taken by the user and automatically recognizes the ingredients and types of dishes. For example, it can identify ingredients such as bread, eggs, and vegetables from a photograph and calculate the nutrients for each. This allows users to input detailed meal data without any effort. Furthermore, the input system can learn from the user's past input data to improve input accuracy. For example, it can remember the meals the user frequently eats and their preferred cooking methods, and provide an auto-completion function the next time the user enters information. This allows the user to input meal data more quickly and accurately.

[0031] The analysis unit analyzes the data received by the reception unit. For example, the analysis unit evaluates the nutrients and cooking methods of each meal. The analysis unit can use AI to analyze meal data and evaluate the nutrients and cooking methods of each meal. For example, the analysis unit receives meal data as input and uses an AI model to calculate the nutrient content. The analysis unit can also evaluate the risk of AGE accumulation based on cooking methods. For example, the analysis unit evaluates that there is a high risk of AGE accumulation if high-temperature cooking methods such as frying and grilling are frequent. Specifically, the AI ​​model extracts major nutrients such as protein, lipids, carbohydrates, vitamins, and minerals from meal data and calculates their respective contents. Furthermore, it analyzes data on cooking methods and evaluates the risk of AGE generation due to high-temperature cooking. For example, it indicates that the risk of AGE generation increases if fried or grilled foods are cooked frequently. The analysis unit can also analyze long-term nutritional balance and dietary trends based on the user's past meal data. This generates basic data to identify areas for improvement and risk factors in the user's diet and provide more effective advice. Furthermore, the analysis unit can improve the accuracy of its analysis results by utilizing external nutrition databases and expert knowledge. This allows the analysis unit to analyze users' dietary data in detail and accurately, providing information that forms the basis of health assessments.

[0032] The evaluation unit assesses the risk of AGE accumulation based on the results obtained by the analysis unit. For example, the evaluation unit quantifies the risk of AGE accumulation and provides it to the user. The evaluation unit can use AI to assess the risk of AGE accumulation. For example, the evaluation unit receives data on nutrients and cooking methods obtained by the analysis unit as input and calculates the risk of AGE accumulation. Specifically, the evaluation unit uses an AI model to quantify the risk of AGE formation based on the nutrient balance and cooking method of each meal. For example, if fried or grilled foods are consumed frequently, the risk of AGE formation is assessed as high. In addition, the evaluation unit can perform a more personalized risk assessment by considering individual factors such as the user's age, gender, and health status. This allows users to receive a specific risk assessment tailored to their own health status. Furthermore, the evaluation unit can analyze long-term risk fluctuations based on past data and evaluate the effectiveness of improvements to the user's diet. For example, it can analyze dietary data from the past few months and evaluate how the risk of AGE formation has changed. This allows users to confirm the effectiveness of improvements to their diet and motivate them to make further improvements. The evaluation unit displays these evaluation results in a visually easy-to-understand format, allowing users to intuitively understand their own health status. For example, risk assessments can be displayed using graphs and charts, enabling users to grasp the level and fluctuations of risk at a glance. This allows the evaluation unit to provide users with concrete and easy-to-understand risk assessments, supporting their health management.

[0033] The service provider provides optimal nutritional balance and dietary advice based on the results evaluated by the evaluation department. For example, if the user has a high risk of AGE accumulation, the service provider will advise increasing low-temperature cooking and raw food consumption. The service provider can use AI to provide optimal nutritional balance and dietary advice. For example, the service provider will receive the AGE accumulation risk obtained by the evaluation department as input and advise increasing low-temperature cooking and raw food consumption. The service provider can also suggest consuming foods rich in specific nutrients if there is a deficiency in those nutrients. For example, if there is a deficiency in vitamins or minerals, the service provider will suggest consuming foods rich in those nutrients. Specifically, the service provider generates individual advice based on the user's dietary data and risk assessment results. For example, for a user with a high risk of AGE accumulation, it will recommend low-temperature cooking such as steaming and simmering, and advise reducing the frequency of fried and grilled foods. Also, if there is a deficiency in a specific nutrient, it will provide a list of foods rich in that nutrient and suggest specific ways of consuming them. For example, if there is a vitamin C deficiency, it will suggest consuming foods such as citrus fruits and broccoli. Furthermore, the service provider can offer flexible advice tailored to users' preferences and lifestyles. For example, it can suggest easy-to-prepare recipes or dining-out options for busy users. The service provider can also collect user feedback and continuously improve the accuracy and effectiveness of its advice. This allows the service provider to provide users with specific and practical advice, supporting them in achieving a healthy diet.

[0034] The analysis unit can evaluate the nutrients and cooking methods of each meal. For example, the analysis unit can evaluate the nutrients of each meal. For example, the analysis unit can evaluate nutrients such as vitamins, minerals, proteins, lipids, and carbohydrates. The analysis unit can also evaluate the cooking methods of each meal. For example, the analysis unit can evaluate cooking methods such as baking, boiling, steaming, and frying. By evaluating the nutrients and cooking methods of each meal, more accurate analysis results can be obtained. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input meal data into a generating AI and have the generating AI perform the evaluation of nutrients and cooking methods.

[0035] The evaluation unit can determine the risk of AGE accumulation due to high-temperature cooking. For example, the evaluation unit quantifies the risk of AGE accumulation due to high-temperature cooking and provides it to the user. For example, the evaluation unit determines that the risk of AGE accumulation is high if there is a lot of high-temperature cooking, such as oven cooking at 180 degrees or higher or stir-frying in a frying pan. In this way, by determining the risk of AGE accumulation due to high-temperature cooking, it is possible to provide the user with an appropriate risk assessment. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input cooking method data into a generating AI and have the generating AI perform the determination of the AGE accumulation risk.

[0036] The supply department can advise increasing low-temperature cooking and raw food consumption. For example, if there is a high risk of AGE accumulation, the supply department will advise increasing low-temperature cooking and raw food consumption. For example, the supply department will recommend low-temperature cooking methods such as slow cooking at 60 degrees Celsius or below, or sous vide cooking. The supply department can also advise increasing raw food consumption, such as raw vegetables and raw fish. By advising on increased low-temperature cooking and raw food consumption, the risk of AGE accumulation can be reduced. Some or all of the above processing in the supply department may be performed using AI, for example, or not using AI. For example, the supply department can input data on the risk of AGE accumulation into a generating AI and have the generating AI issue advice on low-temperature cooking and raw food consumption.

[0037] The service provider can suggest consuming foods rich in specific nutrients if those nutrients are deficient. For example, if a person is deficient in certain nutrients such as vitamin D, iron, or calcium, the service provider can suggest consuming foods rich in those nutrients. For instance, if a person is deficient in vitamin D, the service provider can suggest consuming foods rich in vitamin D, such as fish or mushrooms. Similarly, if a person is deficient in iron, the service provider can suggest consuming foods rich in iron, such as lean meat or spinach. This allows for improved nutritional balance by suggesting the consumption of foods rich in specific nutrients when those nutrients are deficient. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input nutrient data into a generating AI and have the generating AI suggest foods rich in those nutrients.

[0038] The service provider can assist users in adopting a healthy lifestyle. For example, the service provider can provide advice to help users adopt a healthy lifestyle. For example, the service provider can provide advice to support a healthy lifestyle, such as exercise habits, sleep habits, and stress management. This can promote improvements in health by assisting users in adopting a healthy lifestyle. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's health data into a generating AI and have the generating AI generate advice on a healthy lifestyle.

[0039] The reception desk can analyze the user's past meal data entry history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods that the user has frequently used in the past (such as voice input or text input). The reception desk can also use AI to analyze the user's past meal data entry history and select the optimal input method. For example, the reception desk can analyze patterns in the meal data entered by the user in the past and provide an easy-to-use format. The reception desk can also send reminders to users during times when they tend to forget to enter data in the past. By analyzing the user's past input history, the reception desk can select the optimal input method and improve input efficiency. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the past meal data entry history into a generating AI and have the generating AI select the optimal input method.

[0040] The reception unit can filter the input of meal data based on the user's current health status and lifestyle. For example, if the user has a specific health condition (e.g., diabetes), the reception unit can prompt the user to input meal data appropriate for that condition. The reception unit can use AI to filter data based on the user's current health status and lifestyle. For example, the reception unit can prompt the user to input appropriate meal data based on their lifestyle (e.g., vegetarian). The reception unit can also filter the input meal data based on the user's current health status (e.g., weight, blood pressure). This allows for the input of more appropriate meal data by filtering the data based on the user's health status and lifestyle. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input data on the user's health status and lifestyle into a generating AI and have the generating AI perform the data filtering.

[0041] The reception desk can prioritize inputting highly relevant data based on the user's geographical location when inputting meal data. For example, if the user is in a specific region, the reception desk may prompt them to prioritize inputting meal data based on the local food culture. The reception desk can also use AI to prioritize inputting highly relevant data based on the user's geographical location. For example, if the user is traveling, the reception desk may prompt them to prioritize inputting meal data from their travel destination. Furthermore, if the user is at home, the reception desk may prompt them to prioritize inputting their usual meal data. This allows for more appropriate data input by prioritizing the input of highly relevant data based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location information into a generating AI and have the generating AI prioritize the input of highly relevant data.

[0042] The reception unit can analyze the user's social media activity and input relevant data when inputting meal data. For example, the reception unit can automatically input photos of meals shared by the user on social media as meal data. The reception unit can use AI to analyze the user's social media activity and input relevant data. For example, the reception unit can prompt the user to input meal data based on the meals mentioned by the user on social media. The reception unit can also prompt the user to input relevant meal data based on the food trends the user follows on social media. This allows for efficient input of relevant data by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input data on the user's social media activity into a generating AI and have the generating AI input the relevant data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of each meal. For example, the analysis unit performs a detailed analysis for important meals (e.g., breakfast, dinner). The analysis unit can use AI to adjust the level of detail of the analysis based on the importance of each meal. For example, the analysis unit performs a simplified analysis for snacks or light meals. The analysis unit can also focus on a specific nutrient if that nutrient is important. By adjusting the level of detail of the analysis based on the importance of each meal, efficient analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input meal data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of the meal during analysis. For example, the analysis unit can apply different analysis algorithms for each category such as staple food, main dish, and side dish. The analysis unit can also use AI to apply different analysis algorithms depending on the category of the meal. For example, the analysis unit can apply different analysis algorithms for each cooking method such as high-temperature cooking, low-temperature cooking, and raw consumption. Furthermore, the analysis unit can also apply analysis algorithms that focus on specific nutrients (e.g., vitamins, minerals). By applying different analysis algorithms depending on the category of the meal, more accurate analysis results can be obtained. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input meal data into a generating AI and have the generating AI execute the application of analysis algorithms.

[0045] The analysis unit can determine the priority of analysis based on the timing of meal submission during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent meal data. The analysis unit can also use AI to determine the priority of analysis based on the timing of meal submission. For example, the analysis unit may prioritize the analysis of meal data from specific time periods (e.g., breakfast, dinner). The analysis unit can also prioritize the analysis of meal data within a period specified by the user. This enables efficient analysis by determining the priority of analysis based on the timing of meal submission. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input meal data into a generating AI and have the generating AI determine the priority of analysis.

[0046] The analysis unit can adjust the order of analysis based on the relationships between meals during the analysis process. For example, the analysis unit can analyze meal data using the same ingredients together. The analysis unit can use AI to adjust the order of analysis based on the relationships between meals. For example, the analysis unit can analyze meal data using the same cooking method together. The analysis unit can also analyze meal data containing the same nutrients together. This allows for more efficient analysis by adjusting the order of analysis based on the relationships between meals. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input meal data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0047] The evaluation unit can improve the accuracy of its evaluations based on the interrelationships between meals. For example, the evaluation unit considers the interrelationships of meals using the same ingredients when performing evaluations. The evaluation unit can use AI to improve the accuracy of its evaluations by considering the interrelationships between meals. For example, the evaluation unit considers the interrelationships of meals using the same cooking method when performing evaluations. The evaluation unit can also consider the interrelationships of meals containing the same nutrients when performing evaluations. This improves the accuracy of the evaluations by considering the interrelationships between meals. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input meal data into a generating AI and have the generating AI perform the evaluation accuracy improvement.

[0048] The evaluation unit can perform evaluations while considering the attribute information of the person submitting the meal. For example, the evaluation unit may perform evaluations based on the submitter's age. The evaluation unit may also use AI to perform evaluations while considering the submitter's attribute information. For example, the evaluation unit may perform evaluations based on the submitter's gender. Furthermore, the evaluation unit may also perform evaluations based on the submitter's health status. This allows for more individualized evaluations by considering the submitter's attribute information. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit may input the submitter's attribute information into a generating AI and have the generating AI perform the evaluation.

[0049] The evaluation unit can perform evaluations while considering the geographical distribution of meals. For example, the evaluation unit can perform evaluations while considering the meals common in a particular region. The evaluation unit can use AI to perform evaluations while considering the geographical distribution of meals. For example, the evaluation unit can perform evaluations while considering ingredients that are readily available in a particular region. The evaluation unit can also perform evaluations while considering the food culture of a particular region. This makes it possible to perform evaluations based on region-specific eating habits by considering the geographical distribution of meals. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input meal data into a generating AI and have the generating AI perform the evaluation.

[0050] The evaluation unit can improve the accuracy of its evaluation by referring to relevant literature on diet during the evaluation process. For example, the evaluation unit performs evaluations by referring to the latest nutritional research. The evaluation unit can use AI to improve the accuracy of its evaluations by referring to relevant literature on diet. For example, the evaluation unit performs evaluations by referring to relevant literature on diet and health. The evaluation unit can also perform evaluations by referring to relevant literature on diet and aging. This improves the accuracy of the evaluation by referring to relevant literature. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input data from relevant literature into a generating AI and have the generating AI perform the evaluation accuracy improvement.

[0051] The service provider can adjust the level of detail in the advice based on the importance of the meal when providing advice. For example, the service provider will provide detailed advice for important meals (e.g., breakfast, dinner). The service provider can use AI to adjust the level of detail in the advice based on the importance of the meal. For example, the service provider will provide simple advice for snacks or light meals. The service provider can also provide advice focusing on specific nutrients if those nutrients are important. This allows for more efficient advice by adjusting the level of detail based on the importance of the meal. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input meal data into a generating AI and have the generating AI adjust the level of detail in the advice.

[0052] The service provider can apply different advice algorithms depending on the meal category when providing advice. For example, the service provider can apply different advice algorithms for each category, such as staple foods, main dishes, and side dishes. The service provider can use AI to apply different advice algorithms depending on the meal category. For example, the service provider can apply different advice algorithms for each cooking method, such as high-temperature cooking, low-temperature cooking, and raw consumption. The service provider can also apply advice algorithms that focus on specific nutrients (e.g., vitamins, minerals). By applying different advice algorithms depending on the meal category, more accurate advice can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input meal data into a generating AI and have the generating AI execute the application of the advice algorithm.

[0053] The service provider can prioritize advice based on the timing of meal submissions. For example, the service provider can provide advice based on the most recent meal data. The service provider can use AI to prioritize advice based on the timing of meal submissions. For example, the service provider can provide advice based on meal data for specific time periods (e.g., breakfast, dinner). The service provider can also provide advice based on meal data within a period specified by the user. This enables efficient advice by prioritizing advice based on the timing of meal submissions. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input meal data into a generating AI and have the generating AI determine the priority of advice.

[0054] The service provider can adjust the order of advice based on the relevance of meals when providing advice. For example, the service provider can provide advice based on meal data using the same ingredients. The service provider can use AI to adjust the order of advice based on the relevance of meals. For example, the service provider can provide advice based on meal data using the same cooking method. The service provider can also provide advice based on meal data containing the same nutrients. This allows for more efficient advice by adjusting the order of advice based on the relevance of meals. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input meal data into a generating AI and have the generating AI perform the adjustment of the order of advice.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The reception desk can analyze a user's past meal data entry history and select the optimal input method. For example, it can prioritize suggesting input methods that the user has frequently used in the past (such as voice input or text input). It can also send reminders for times when the user tends to forget to enter data in the past. In this way, by analyzing the user's past input history, the system can select the optimal input method and improve input efficiency.

[0057] The reception desk can filter meal data input based on the user's current health status and lifestyle. For example, if a user has a specific health condition (e.g., diabetes), it can prompt them to input meal data appropriate for that condition. It can also prompt users to input appropriate meal data based on their lifestyle (e.g., vegetarian). By filtering data based on the user's health status and lifestyle, more appropriate meal data can be entered.

[0058] The analysis unit can adjust the level of detail of the analysis based on the importance of each meal. For example, a detailed analysis is performed for important meals (e.g., breakfast, dinner). A simpler analysis can be performed for snacks or light meals. This allows for more efficient analysis by adjusting the level of detail based on the importance of each meal.

[0059] The evaluation unit can improve the accuracy of its evaluations by considering the interrelationships between meals. For example, it can evaluate meals that use the same ingredients, or meals that use the same cooking method. By considering the interrelationships between meals, the accuracy of the evaluation is improved.

[0060] The service provider can prioritize advice based on when meals are submitted. For example, they can provide advice based on the most recent meal data. They can also provide advice based on meal data for specific time periods (e.g., breakfast, dinner). This allows for more efficient advice by prioritizing it based on when meals are submitted.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The reception desk allows users to input their daily meal data. Users can manually input meal data such as the contents and cooking methods of breakfast, lunch, and dinner. They can also input meal data using a smartphone or personal computer. Furthermore, it is possible to input meal data using voice input or image recognition technology. For example, a user can input the details of their meal by voice, and this will be converted into text data using voice recognition technology. Alternatively, a user can take a picture of their meal, and the contents will be analyzed using image recognition technology. Step 2: The analysis unit analyzes the data received by the reception unit. The analysis unit evaluates the nutrients and cooking methods of each meal. It can use AI to analyze meal data and calculate the nutrient content. It also evaluates the risk of AGE accumulation based on the cooking method. For example, if there is a lot of high-temperature cooking such as frying or grilling, it is evaluated as having a high risk of AGE accumulation. Step 3: The evaluation unit assesses the risk of AGE accumulation based on the results obtained by the analysis unit. The evaluation unit quantifies the risk of AGE accumulation and provides it to the user. Using AI, it receives data on nutrients and cooking methods obtained by the analysis unit as input and calculates the risk of AGE accumulation. Step 4: The supply department provides advice on optimal nutritional balance and dietary habits based on the results evaluated by the evaluation department. For example, if there is a high risk of AGE accumulation, they will advise increasing low-temperature cooking and raw food consumption. If there is a deficiency in a specific nutrient, they will suggest consuming foods rich in that nutrient.

[0063] (Example of form 2) The health assessment system according to an embodiment of the present invention is a system that uses AI to evaluate an individual's health status and aging risk based on dietary data. The health assessment system allows users to input daily dietary data, which the AI ​​then analyzes to assess the risk of AGE (Advanced Glycation End-products) accumulation due to high-temperature cooking. Furthermore, the AI ​​provides advice on optimal nutritional balance and dietary habits. This system helps users adopt a healthy lifestyle suited to their health status and slow the progression of glycation. It also provides guidance on a healthy aging process by clarifying the relationship between diet and aging. This service aims to help each user develop healthy lifestyle habits, understand the aging process, and contribute to the creation of a sustainable healthy society. For example, a user inputs daily dietary data, such as the contents and cooking methods of breakfast, lunch, and dinner. This information is input into the AI. Next, the AI ​​analyzes the input dietary data. The AI ​​evaluates the nutrients and cooking methods of each meal and determines the risk of AGE accumulation due to high-temperature cooking. For example, if there is a lot of high-temperature cooking such as frying or grilling, the risk of AGE accumulation is assessed as high. Furthermore, the AI ​​provides advice on optimal nutritional balance and dietary habits. For example, if a user has a high risk of AGE accumulation, the system advises increasing low-temperature cooking and raw food consumption. It also suggests consuming foods rich in specific nutrients if those nutrients are deficient. This system helps users adopt a healthy lifestyle suited to their individual health status. For instance, by improving their diet according to the advice, users can slow down the progression of glycation. Furthermore, by clarifying the relationship between diet and aging, users can understand their own aging process and aim for healthy aging. This service aims to contribute to the creation of a sustainable and healthy society by helping each user develop healthy lifestyle habits and understand the aging process. For example, by maintaining a healthy diet, users can expect to prevent lifestyle-related diseases and extend their healthy lifespan. This allows the health assessment system to efficiently analyze users' dietary data, evaluate the risk of AGE accumulation, and provide optimal nutritional balance and dietary advice.

[0064] The health assessment system according to this embodiment comprises a reception unit, an analysis unit, an evaluation unit, and a provision unit. The reception unit receives daily meal data from the user. This daily meal data may include, but is not limited to, the contents and cooking methods of breakfast, lunch, and dinner. For example, the user can manually input meal details into the reception unit. The reception unit can also receive meal data using a smartphone or personal computer. Furthermore, the reception unit can receive meal data using voice input or image recognition technology. For example, the reception unit can receive meal details by voice input from the user and convert them into text data using voice recognition technology. The reception unit can also receive photos of meals taken by the user and analyze the meal details using image recognition technology. The analysis unit analyzes the data received by the reception unit. The analysis unit evaluates, for example, the nutrients and cooking methods of each meal. The analysis unit can use AI to analyze meal data and evaluate the nutrients and cooking methods of each meal. For example, the analysis unit receives meal data as input and calculates the nutrient content using an AI model. The analysis unit can also evaluate the risk of AGE accumulation based on the cooking method. For example, the analysis unit assesses that the risk of AGE accumulation is high if there is a lot of high-temperature cooking, such as deep-frying or grilling. The evaluation unit assesses the risk of AGE accumulation based on the results obtained by the analysis unit. The evaluation unit quantifies the risk of AGE accumulation and provides it to the user. The evaluation unit can use AI to assess the risk of AGE accumulation. For example, the evaluation unit receives data on nutrients and cooking methods obtained by the analysis unit as input and calculates the risk of AGE accumulation. The provision unit provides advice on the optimal nutritional balance and diet based on the results evaluated by the evaluation unit. For example, if the risk of AGE accumulation is high, the provision unit advises increasing low-temperature cooking and raw food consumption. The provision unit can use AI to provide advice on the optimal nutritional balance and diet. For example, the provision unit receives the risk of AGE accumulation obtained by the evaluation unit as input and advises increasing low-temperature cooking and raw food consumption. The provision unit can also suggest consuming foods rich in specific nutrients if there is a deficiency in those nutrients.For example, if the user is deficient in vitamins or minerals, the system may suggest consuming foods rich in those nutrients. This allows the health assessment system according to the embodiment to efficiently analyze the user's dietary data, assess the risk of AGE accumulation, and provide advice on optimal nutritional balance and dietary habits.

[0065] The reception desk allows users to input their daily meal data. This data may include, but is not limited to, the contents and cooking methods of breakfast, lunch, and dinner. For example, users can manually input meal details. The reception desk can also input meal data using a smartphone or personal computer. Furthermore, the reception desk can input meal data using voice input or image recognition technology. For example, the reception desk can receive meal details via voice input from the user and convert it into text data using voice recognition technology. The reception desk can also receive meals via photos taken by the user and analyze the contents using image recognition technology. Specifically, voice recognition technology converts what the user says into text in real time, accurately recording the details of the meal. For example, a voice input such as "I had toast and scrambled eggs for breakfast" is immediately saved as text data. Image recognition technology analyzes photos of meals taken by the user and automatically recognizes the ingredients and types of dishes. For example, it can identify ingredients such as bread, eggs, and vegetables from a photograph and calculate the nutrients for each. This allows users to input detailed meal data without any effort. Furthermore, the input system can learn from the user's past input data to improve input accuracy. For example, it can remember the meals the user frequently eats and their preferred cooking methods, and provide an auto-completion function the next time the user enters information. This allows the user to input meal data more quickly and accurately.

[0066] The analysis unit analyzes the data received by the reception unit. For example, the analysis unit evaluates the nutrients and cooking methods of each meal. The analysis unit can use AI to analyze meal data and evaluate the nutrients and cooking methods of each meal. For example, the analysis unit receives meal data as input and uses an AI model to calculate the nutrient content. The analysis unit can also evaluate the risk of AGE accumulation based on cooking methods. For example, the analysis unit evaluates that there is a high risk of AGE accumulation if high-temperature cooking methods such as frying and grilling are frequent. Specifically, the AI ​​model extracts major nutrients such as protein, lipids, carbohydrates, vitamins, and minerals from meal data and calculates their respective contents. Furthermore, it analyzes data on cooking methods and evaluates the risk of AGE generation due to high-temperature cooking. For example, it indicates that the risk of AGE generation increases if fried or grilled foods are cooked frequently. The analysis unit can also analyze long-term nutritional balance and dietary trends based on the user's past meal data. This generates basic data to identify areas for improvement and risk factors in the user's diet and provide more effective advice. Furthermore, the analysis unit can improve the accuracy of its analysis results by utilizing external nutrition databases and expert knowledge. This allows the analysis unit to analyze users' dietary data in detail and accurately, providing information that forms the basis of health assessments.

[0067] The evaluation unit assesses the risk of AGE accumulation based on the results obtained by the analysis unit. For example, the evaluation unit quantifies the risk of AGE accumulation and provides it to the user. The evaluation unit can use AI to assess the risk of AGE accumulation. For example, the evaluation unit receives data on nutrients and cooking methods obtained by the analysis unit as input and calculates the risk of AGE accumulation. Specifically, the evaluation unit uses an AI model to quantify the risk of AGE formation based on the nutrient balance and cooking method of each meal. For example, if fried or grilled foods are consumed frequently, the risk of AGE formation is assessed as high. In addition, the evaluation unit can perform a more personalized risk assessment by considering individual factors such as the user's age, gender, and health status. This allows users to receive a specific risk assessment tailored to their own health status. Furthermore, the evaluation unit can analyze long-term risk fluctuations based on past data and evaluate the effectiveness of improvements to the user's diet. For example, it can analyze dietary data from the past few months and evaluate how the risk of AGE formation has changed. This allows users to confirm the effectiveness of improvements to their diet and motivate them to make further improvements. The evaluation unit displays these evaluation results in a visually easy-to-understand format, allowing users to intuitively understand their own health status. For example, risk assessments can be displayed using graphs and charts, enabling users to grasp the level and fluctuations of risk at a glance. This allows the evaluation unit to provide users with concrete and easy-to-understand risk assessments, supporting their health management.

[0068] The service provider provides optimal nutritional balance and dietary advice based on the results evaluated by the evaluation department. For example, if the user has a high risk of AGE accumulation, the service provider will advise increasing low-temperature cooking and raw food consumption. The service provider can use AI to provide optimal nutritional balance and dietary advice. For example, the service provider will receive the AGE accumulation risk obtained by the evaluation department as input and advise increasing low-temperature cooking and raw food consumption. The service provider can also suggest consuming foods rich in specific nutrients if there is a deficiency in those nutrients. For example, if there is a deficiency in vitamins or minerals, the service provider will suggest consuming foods rich in those nutrients. Specifically, the service provider generates individual advice based on the user's dietary data and risk assessment results. For example, for a user with a high risk of AGE accumulation, it will recommend low-temperature cooking such as steaming and simmering, and advise reducing the frequency of fried and grilled foods. Also, if there is a deficiency in a specific nutrient, it will provide a list of foods rich in that nutrient and suggest specific ways of consuming them. For example, if there is a vitamin C deficiency, it will suggest consuming foods such as citrus fruits and broccoli. Furthermore, the service provider can offer flexible advice tailored to users' preferences and lifestyles. For example, it can suggest easy-to-prepare recipes or dining-out options for busy users. The service provider can also collect user feedback and continuously improve the accuracy and effectiveness of its advice. This allows the service provider to provide users with specific and practical advice, supporting them in achieving a healthy diet.

[0069] The analysis unit can evaluate the nutrients and cooking methods of each meal. For example, the analysis unit can evaluate the nutrients of each meal. For example, the analysis unit can evaluate nutrients such as vitamins, minerals, proteins, lipids, and carbohydrates. The analysis unit can also evaluate the cooking methods of each meal. For example, the analysis unit can evaluate cooking methods such as baking, boiling, steaming, and frying. By evaluating the nutrients and cooking methods of each meal, more accurate analysis results can be obtained. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input meal data into a generating AI and have the generating AI perform the evaluation of nutrients and cooking methods.

[0070] The evaluation unit can determine the risk of AGE accumulation due to high-temperature cooking. For example, the evaluation unit quantifies the risk of AGE accumulation due to high-temperature cooking and provides it to the user. For example, the evaluation unit determines that the risk of AGE accumulation is high if there is a lot of high-temperature cooking, such as oven cooking at 180 degrees or higher or stir-frying in a frying pan. In this way, by determining the risk of AGE accumulation due to high-temperature cooking, it is possible to provide the user with an appropriate risk assessment. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input cooking method data into a generating AI and have the generating AI perform the determination of the AGE accumulation risk.

[0071] The supply department can advise increasing low-temperature cooking and raw food consumption. For example, if there is a high risk of AGE accumulation, the supply department will advise increasing low-temperature cooking and raw food consumption. For example, the supply department will recommend low-temperature cooking methods such as slow cooking at 60 degrees Celsius or below, or sous vide cooking. The supply department can also advise increasing raw food consumption, such as raw vegetables and raw fish. By advising on increased low-temperature cooking and raw food consumption, the risk of AGE accumulation can be reduced. Some or all of the above processing in the supply department may be performed using AI, for example, or not using AI. For example, the supply department can input data on the risk of AGE accumulation into a generating AI and have the generating AI issue advice on low-temperature cooking and raw food consumption.

[0072] The service provider can suggest consuming foods rich in specific nutrients if those nutrients are deficient. For example, if a person is deficient in certain nutrients such as vitamin D, iron, or calcium, the service provider can suggest consuming foods rich in those nutrients. For instance, if a person is deficient in vitamin D, the service provider can suggest consuming foods rich in vitamin D, such as fish or mushrooms. Similarly, if a person is deficient in iron, the service provider can suggest consuming foods rich in iron, such as lean meat or spinach. This allows for improved nutritional balance by suggesting the consumption of foods rich in specific nutrients when those nutrients are deficient. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input nutrient data into a generating AI and have the generating AI suggest foods rich in those nutrients.

[0073] The service provider can assist users in adopting a healthy lifestyle. For example, the service provider can provide advice to help users adopt a healthy lifestyle. For example, the service provider can provide advice to support a healthy lifestyle, such as exercise habits, sleep habits, and stress management. This can promote improvements in health by assisting users in adopting a healthy lifestyle. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's health data into a generating AI and have the generating AI generate advice on a healthy lifestyle.

[0074] The reception desk can estimate the user's emotions and adjust the timing of meal data entry based on the estimated emotions. For example, if the user is feeling stressed, the reception desk can send a notification prompting them to enter meal data during a time when they can relax. The reception desk can use AI to estimate the user's emotions and adjust the timing of meal data entry. For example, the reception desk can receive the user's facial expressions and voice data as input and estimate their emotions using an emotion estimation algorithm. The reception desk can also provide a simple input form that can be completed quickly if the user is busy. For example, if the reception desk is relaxed, it can send a notification prompting the user to enter detailed meal data. This allows for data entry at a more appropriate time by adjusting the timing of meal data entry according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input user facial expression data into a generating AI and have the AI ​​perform emotion estimation.

[0075] The reception desk can analyze the user's past meal data entry history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods that the user has frequently used in the past (such as voice input or text input). The reception desk can also use AI to analyze the user's past meal data entry history and select the optimal input method. For example, the reception desk can analyze patterns in the meal data entered by the user in the past and provide an easy-to-use format. The reception desk can also send reminders to users during times when they tend to forget to enter data in the past. By analyzing the user's past input history, the reception desk can select the optimal input method and improve input efficiency. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the past meal data entry history into a generating AI and have the generating AI select the optimal input method.

[0076] The reception unit can filter the input of meal data based on the user's current health status and lifestyle. For example, if the user has a specific health condition (e.g., diabetes), the reception unit can prompt the user to input meal data appropriate for that condition. The reception unit can use AI to filter data based on the user's current health status and lifestyle. For example, the reception unit can prompt the user to input appropriate meal data based on their lifestyle (e.g., vegetarian). The reception unit can also filter the input meal data based on the user's current health status (e.g., weight, blood pressure). This allows for the input of more appropriate meal data by filtering the data based on the user's health status and lifestyle. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input data on the user's health status and lifestyle into a generating AI and have the generating AI perform the data filtering.

[0077] The reception desk can estimate the user's emotions and prioritize the food data to be entered based on the estimated emotions. For example, if the user is feeling stressed, the reception desk may prompt them to prioritize entering easy-to-enter food data. The reception desk can use AI to estimate the user's emotions and prioritize the food data to be entered. For example, the reception desk can receive the user's facial expressions and voice data as input and estimate the emotions using an emotion estimation algorithm. The reception desk can also send a notification prompting the user to prioritize entering detailed food data if they are relaxed. This enables efficient data entry by prioritizing the data to be entered according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0078] The reception desk can prioritize inputting highly relevant data based on the user's geographical location when inputting meal data. For example, if the user is in a specific region, the reception desk may prompt them to prioritize inputting meal data based on the local food culture. The reception desk can also use AI to prioritize inputting highly relevant data based on the user's geographical location. For example, if the user is traveling, the reception desk may prompt them to prioritize inputting meal data from their travel destination. Furthermore, if the user is at home, the reception desk may prompt them to prioritize inputting their usual meal data. This allows for more appropriate data input by prioritizing the input of highly relevant data based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location information into a generating AI and have the generating AI prioritize the input of highly relevant data.

[0079] The reception unit can analyze the user's social media activity and input relevant data when inputting meal data. For example, the reception unit can automatically input photos of meals shared by the user on social media as meal data. The reception unit can use AI to analyze the user's social media activity and input relevant data. For example, the reception unit can prompt the user to input meal data based on the meals mentioned by the user on social media. The reception unit can also prompt the user to input relevant meal data based on the food trends the user follows on social media. This allows for efficient input of relevant data by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input data on the user's social media activity into a generating AI and have the generating AI input the relevant data.

[0080] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit provides a simple and visually easy-to-understand analysis result. The analysis unit can use AI to estimate the user's emotions and adjust the presentation of the analysis. For example, the analysis unit receives the user's facial expressions and voice data as input and estimates the emotions using an emotion estimation algorithm. The analysis unit can also provide detailed analysis results if the user is relaxed. By adjusting the presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are easier to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of each meal. For example, the analysis unit performs a detailed analysis for important meals (e.g., breakfast, dinner). The analysis unit can use AI to adjust the level of detail of the analysis based on the importance of each meal. For example, the analysis unit performs a simplified analysis for snacks or light meals. The analysis unit can also focus on a specific nutrient if that nutrient is important. By adjusting the level of detail of the analysis based on the importance of each meal, efficient analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input meal data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the category of the meal during analysis. For example, the analysis unit can apply different analysis algorithms for each category such as staple food, main dish, and side dish. The analysis unit can also use AI to apply different analysis algorithms depending on the category of the meal. For example, the analysis unit can apply different analysis algorithms for each cooking method such as high-temperature cooking, low-temperature cooking, and raw consumption. Furthermore, the analysis unit can also apply analysis algorithms that focus on specific nutrients (e.g., vitamins, minerals). By applying different analysis algorithms depending on the category of the meal, more accurate analysis results can be obtained. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input meal data into a generating AI and have the generating AI execute the application of analysis algorithms.

[0083] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. The analysis unit can use AI to estimate the user's emotions and adjust the length of the analysis. For example, the analysis unit can receive the user's facial expressions and voice data as input and estimate the emotions using an emotion estimation algorithm. The analysis unit can also provide detailed analysis results if the user is relaxed. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0084] The analysis unit can determine the priority of analysis based on the timing of meal submission during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent meal data. The analysis unit can also use AI to determine the priority of analysis based on the timing of meal submission. For example, the analysis unit may prioritize the analysis of meal data from specific time periods (e.g., breakfast, dinner). The analysis unit can also prioritize the analysis of meal data within a period specified by the user. This enables efficient analysis by determining the priority of analysis based on the timing of meal submission. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input meal data into a generating AI and have the generating AI determine the priority of analysis.

[0085] The analysis unit can adjust the order of analysis based on the relationships between meals during the analysis process. For example, the analysis unit can analyze meal data using the same ingredients together. The analysis unit can use AI to adjust the order of analysis based on the relationships between meals. For example, the analysis unit can analyze meal data using the same cooking method together. The analysis unit can also analyze meal data containing the same nutrients together. This allows for more efficient analysis by adjusting the order of analysis based on the relationships between meals. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input meal data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0086] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated emotions. For example, if the user is stressed, the evaluation unit may relax strict evaluation criteria. The evaluation unit can use AI to estimate the user's emotions and adjust the evaluation criteria. For example, the evaluation unit can receive the user's facial expressions and voice data as input and estimate the emotions using an emotion estimation algorithm. The evaluation unit can also apply detailed evaluation criteria if the user is relaxed. This allows for a more appropriate evaluation by adjusting the evaluation criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0087] The evaluation unit can improve the accuracy of its evaluations based on the interrelationships between meals. For example, the evaluation unit considers the interrelationships of meals using the same ingredients when performing evaluations. The evaluation unit can use AI to improve the accuracy of its evaluations by considering the interrelationships between meals. For example, the evaluation unit considers the interrelationships of meals using the same cooking method when performing evaluations. The evaluation unit can also consider the interrelationships of meals containing the same nutrients when performing evaluations. This improves the accuracy of the evaluations by considering the interrelationships between meals. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input meal data into a generating AI and have the generating AI perform the evaluation accuracy improvement.

[0088] The evaluation unit can perform evaluations while considering the attribute information of the person submitting the meal. For example, the evaluation unit may perform evaluations based on the submitter's age. The evaluation unit may also use AI to perform evaluations while considering the submitter's attribute information. For example, the evaluation unit may perform evaluations based on the submitter's gender. Furthermore, the evaluation unit may also perform evaluations based on the submitter's health status. This allows for more individualized evaluations by considering the submitter's attribute information. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit may input the submitter's attribute information into a generating AI and have the generating AI perform the evaluation.

[0089] The evaluation unit can estimate the user's emotions and adjust the order in which evaluation results are displayed based on the estimated emotions. For example, if the user is feeling stressed, the evaluation unit will prioritize displaying positive evaluation results. The evaluation unit can use AI to estimate the user's emotions and adjust the order in which evaluation results are displayed. For example, the evaluation unit can receive the user's facial expressions and voice data as input and estimate emotions using an emotion estimation algorithm. The evaluation unit can also display detailed evaluation results if the user is relaxed. This allows for the provision of more appropriate information by adjusting the display order of evaluation results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0090] The evaluation unit can perform evaluations while considering the geographical distribution of meals. For example, the evaluation unit can perform evaluations while considering the meals common in a particular region. The evaluation unit can use AI to perform evaluations while considering the geographical distribution of meals. For example, the evaluation unit can perform evaluations while considering ingredients that are readily available in a particular region. The evaluation unit can also perform evaluations while considering the food culture of a particular region. This makes it possible to perform evaluations based on region-specific eating habits by considering the geographical distribution of meals. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input meal data into a generating AI and have the generating AI perform the evaluation.

[0091] The evaluation unit can improve the accuracy of its evaluation by referring to relevant literature on diet during the evaluation process. For example, the evaluation unit performs evaluations by referring to the latest nutritional research. The evaluation unit can use AI to improve the accuracy of its evaluations by referring to relevant literature on diet. For example, the evaluation unit performs evaluations by referring to relevant literature on diet and health. The evaluation unit can also perform evaluations by referring to relevant literature on diet and aging. This improves the accuracy of the evaluation by referring to relevant literature. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input data from relevant literature into a generating AI and have the generating AI perform the evaluation accuracy improvement.

[0092] The service provider can estimate the user's emotions and adjust the way advice is expressed based on the estimated emotions. For example, if the user is feeling stressed, the service provider will offer advice in gentle language. The service provider can use AI to estimate the user's emotions and adjust the way advice is expressed. For example, the service provider can receive the user's facial expressions and voice data as input and estimate the emotions using an emotion estimation algorithm. The service provider can also provide detailed advice if the user is relaxed. This allows for more appropriate advice to be provided by adjusting the way advice is expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0093] The service provider can adjust the level of detail in the advice based on the importance of the meal when providing advice. For example, the service provider will provide detailed advice for important meals (e.g., breakfast, dinner). The service provider can use AI to adjust the level of detail in the advice based on the importance of the meal. For example, the service provider will provide simple advice for snacks or light meals. The service provider can also provide advice focusing on specific nutrients if those nutrients are important. This allows for more efficient advice by adjusting the level of detail based on the importance of the meal. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input meal data into a generating AI and have the generating AI adjust the level of detail in the advice.

[0094] The service provider can apply different advice algorithms depending on the meal category when providing advice. For example, the service provider can apply different advice algorithms for each category, such as staple foods, main dishes, and side dishes. The service provider can use AI to apply different advice algorithms depending on the meal category. For example, the service provider can apply different advice algorithms for each cooking method, such as high-temperature cooking, low-temperature cooking, and raw consumption. The service provider can also apply advice algorithms that focus on specific nutrients (e.g., vitamins, minerals). By applying different advice algorithms depending on the meal category, more accurate advice can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input meal data into a generating AI and have the generating AI execute the application of the advice algorithm.

[0095] The service provider can estimate the user's emotions and adjust the length of the advice based on the estimated emotions. For example, if the user is in a hurry, the service provider will provide short, concise advice. The service provider can use AI to estimate the user's emotions and adjust the length of the advice. For example, the service provider can receive the user's facial expressions and voice data as input and estimate the emotions using an emotion estimation algorithm. The service provider can also provide detailed advice if the user is relaxed. This allows for more appropriate advice to be provided by adjusting the length of the advice according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0096] The service provider can prioritize advice based on the timing of meal submissions. For example, the service provider can provide advice based on the most recent meal data. The service provider can use AI to prioritize advice based on the timing of meal submissions. For example, the service provider can provide advice based on meal data for specific time periods (e.g., breakfast, dinner). The service provider can also provide advice based on meal data within a period specified by the user. This enables efficient advice by prioritizing advice based on the timing of meal submissions. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input meal data into a generating AI and have the generating AI determine the priority of advice.

[0097] The service provider can adjust the order of advice based on the relevance of meals when providing advice. For example, the service provider can provide advice based on meal data using the same ingredients. The service provider can use AI to adjust the order of advice based on the relevance of meals. For example, the service provider can provide advice based on meal data using the same cooking method. The service provider can also provide advice based on meal data containing the same nutrients. This allows for more efficient advice by adjusting the order of advice based on the relevance of meals. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input meal data into a generating AI and have the generating AI perform the adjustment of the order of advice.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The reception desk can estimate the user's emotions and adjust the timing of meal data entry based on those emotions. For example, if a user is feeling stressed, it can send a notification prompting them to enter meal data during a time when they can relax. If a user is busy, it can also provide a simple input form that can be completed quickly. This allows for more appropriate data entry by adjusting the timing of meal data entry according to the user's emotions.

[0100] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, if the user is stressed, it can provide simple and visually easy-to-understand analysis results. Conversely, if the user is relaxed, it can provide detailed analysis results. By adjusting the presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are easier to understand.

[0101] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on those emotions. For example, if the user is feeling stressed, strict evaluation criteria can be relaxed. Conversely, if the user is relaxed, detailed evaluation criteria can be applied. This allows for more accurate evaluations by adjusting the evaluation criteria according to the user's emotions.

[0102] The service provider can estimate the user's emotions and adjust the way advice is presented based on those emotions. For example, if the user is feeling stressed, it will provide advice in gentle language. Conversely, if the user is relaxed, it can provide more detailed advice. By adjusting the way advice is presented according to the user's emotions, it can provide more appropriate advice.

[0103] The service provider can estimate the user's emotions and adjust the length of the advice based on those emotions. For example, if the user is in a hurry, it can provide short, concise advice. Conversely, if the user is relaxed, it can provide more detailed advice. By adjusting the length of the advice according to the user's emotions, it can provide more appropriate advice.

[0104] The reception desk can analyze a user's past meal data entry history and select the optimal input method. For example, it can prioritize suggesting input methods that the user has frequently used in the past (such as voice input or text input). It can also send reminders for times when the user tends to forget to enter data in the past. In this way, by analyzing the user's past input history, the system can select the optimal input method and improve input efficiency.

[0105] The reception desk can filter meal data input based on the user's current health status and lifestyle. For example, if a user has a specific health condition (e.g., diabetes), it can prompt them to input meal data appropriate for that condition. It can also prompt users to input appropriate meal data based on their lifestyle (e.g., vegetarian). By filtering data based on the user's health status and lifestyle, more appropriate meal data can be entered.

[0106] The analysis unit can adjust the level of detail of the analysis based on the importance of each meal. For example, a detailed analysis is performed for important meals (e.g., breakfast, dinner). A simpler analysis can be performed for snacks or light meals. This allows for more efficient analysis by adjusting the level of detail based on the importance of each meal.

[0107] The evaluation unit can improve the accuracy of its evaluations by considering the interrelationships between meals. For example, it can evaluate meals that use the same ingredients, or meals that use the same cooking method. By considering the interrelationships between meals, the accuracy of the evaluation is improved.

[0108] The service provider can prioritize advice based on when meals are submitted. For example, they can provide advice based on the most recent meal data. They can also provide advice based on meal data for specific time periods (e.g., breakfast, dinner). This allows for more efficient advice by prioritizing it based on when meals are submitted.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The reception desk allows users to input their daily meal data. Users can manually input meal data such as the contents and cooking methods of breakfast, lunch, and dinner. They can also input meal data using a smartphone or personal computer. Furthermore, it is possible to input meal data using voice input or image recognition technology. For example, a user can input the details of their meal by voice, and this will be converted into text data using voice recognition technology. Alternatively, a user can take a picture of their meal, and the contents will be analyzed using image recognition technology. Step 2: The analysis unit analyzes the data received by the reception unit. The analysis unit evaluates the nutrients and cooking methods of each meal. It can use AI to analyze meal data and calculate the nutrient content. It also evaluates the risk of AGE accumulation based on the cooking method. For example, if there is a lot of high-temperature cooking such as frying or grilling, it is evaluated as having a high risk of AGE accumulation. Step 3: The evaluation unit assesses the risk of AGE accumulation based on the results obtained by the analysis unit. The evaluation unit quantifies the risk of AGE accumulation and provides it to the user. Using AI, it receives data on nutrients and cooking methods obtained by the analysis unit as input and calculates the risk of AGE accumulation. Step 4: The supply department provides advice on optimal nutritional balance and dietary habits based on the results evaluated by the evaluation department. For example, if there is a high risk of AGE accumulation, they will advise increasing low-temperature cooking and raw food consumption. If there is a deficiency in a specific nutrient, they will suggest consuming foods rich in that nutrient.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements described above, including the reception unit, analysis unit, evaluation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, where the user inputs daily meal data. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, where the meal data is analyzed and the nutrients and cooking methods of each meal are evaluated. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12, where the risk of AGE accumulation is evaluated. The provision unit is implemented by the output device 40 of the smart device 14, where optimal nutritional balance and dietary advice are provided. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the reception unit, analysis unit, evaluation unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, where the user inputs daily meal data by voice. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the meal data and evaluates the nutrients and cooking methods of each meal. The evaluation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which evaluates the risk of AGE accumulation. The provision unit is implemented, for example, by the speaker 240 of the smart glasses 214, which provides advice on optimal nutritional balance and dietary habits. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the reception unit, analysis unit, evaluation unit, and provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, where the user inputs daily meal data by voice. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the meal data and evaluates the nutrients and cooking methods of each meal. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which evaluates the risk of AGE accumulation. The provision unit is implemented by, for example, the speaker 240 of the headset terminal 314, which provides advice on optimal nutritional balance and dietary habits. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the reception unit, analysis unit, evaluation unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414, where the user inputs daily meal data by voice. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the meal data and evaluates the nutrients and cooking methods of each meal. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which evaluates the risk of AGE accumulation. The provision unit is implemented by, for example, the speaker 240 of the robot 414, which provides advice on optimal nutritional balance and dietary habits. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0164] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] 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.

[0174] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] (Note 1) A reception area for receiving meal data, An analysis unit that analyzes the data received by the reception unit, An evaluation unit that evaluates the risk of AGE accumulation based on the results obtained by the analysis unit, The system includes a provisioning unit that provides advice on optimal nutritional balance and dietary habits based on the results evaluated by the aforementioned evaluation unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Evaluate the nutrients and cooking methods of each meal. The system described in Appendix 1, characterized by the features described herein. (Note 3) The evaluation unit, Determining the risk of AGE accumulation due to high-temperature cooking. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Advise people to increase their use of low-temperature cooking and raw food consumption. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, If there is a deficiency in a particular nutrient, we suggest consuming foods that are rich in that nutrient. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Helping users adopt a healthy lifestyle The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of meal data entry based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is The system analyzes the user's past meal data entry history and selects the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering meal data, filtering is performed based on the user's current health status and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the user's emotions and prioritizes the food data to be entered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering meal data, the system prioritizes inputting data that is highly relevant based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When entering meal data, the system analyzes the user's social media activity and inputs relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of each meal. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the meal category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis will be determined based on when the meals were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of meals. The system described in Appendix 1, characterized by the features described herein. (Note 19) The evaluation unit, It estimates the user's emotions and adjusts the evaluation criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The evaluation unit, During evaluation, improve the accuracy of the evaluation based on the interrelationships of meals. The system described in Appendix 1, characterized by the features described herein. (Note 21) The evaluation unit, During the evaluation, the attribute information of the person who submitted the meal will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The evaluation unit, It estimates the user's emotions and adjusts the order in which evaluation results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The evaluation unit, During the evaluation, the geographical distribution of the food will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The evaluation unit, During the evaluation, we refer to relevant literature on diet to improve the accuracy of the assessment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing advice, adjust the level of detail based on the importance of the diet. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing advice, different advice algorithms are applied depending on the category of the meal. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the advice based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing advice, we prioritize the advice based on when the meals were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing advice, adjust the order of advice based on its relevance to the diet. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area for receiving meal data, An analysis unit that analyzes the data received by the reception unit, An evaluation unit that evaluates the risk of AGE accumulation based on the results obtained by the analysis unit, The system includes a provisioning unit that provides advice on optimal nutritional balance and dietary habits based on the results evaluated by the aforementioned evaluation unit. A system characterized by the following features.

2. The aforementioned analysis unit, Evaluate the nutrients and cooking methods of each meal. The system according to feature 1.

3. The evaluation unit described above, Determining the risk of AGE accumulation due to high-temperature cooking. The system according to feature 1.

4. The aforementioned supply unit is, Advise people to increase their use of low-temperature cooking and raw food consumption. The system according to feature 1.

5. The aforementioned supply unit is, If there is a deficiency in a particular nutrient, we suggest consuming foods that are rich in that nutrient. The system according to feature 1.

6. The aforementioned supply unit is, Helping users adopt a healthy lifestyle The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of meal data entry based on the estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is The system analyzes the user's past meal data entry history and selects the optimal input method. The system according to feature 1.

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