system

Abstract

The system according to this embodiment aims to provide real-time and personalized nutritional advice based on individual meal records. [Solution] The system according to this embodiment comprises a reception unit, an evaluation unit, and an advice unit. The reception unit receives input of meal records. The evaluation unit evaluates the nutritional status based on the meal records received by the reception unit. The advice unit provides personalized nutritional advice based on the nutritional status 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, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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] [[ID=2I]]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it has not been sufficiently done to provide real-time and personalized nutritional advice based on individual meal records, and there is room for improvement.

[0005] The system according to the embodiment aims to provide real-time and personalized nutritional advice based on individual meal records.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an evaluation unit, and an advice unit. The reception unit receives input of meal records. The evaluation unit evaluates the nutritional status based on the meal records received by the reception unit. The advice unit provides personalized nutritional advice based on the nutritional status evaluated by the evaluation unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide real-time and personalized nutritional advice based on individual meal records. [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, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 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) An AI-driven personalized nutrition coach according to an embodiment of the present invention is a system that utilizes generative AI to provide real-time and personalized nutritional advice based on an individual's meal records. The AI-driven personalized nutrition coach works by having the user input their daily meal records into the system. Based on these inputs, the generative AI evaluates the user's nutritional status in real time and provides personalized nutritional advice based on the user's health status and goals. This system functions as a 24 / 7 AI coach, allowing users to receive nutritional advice anytime, anywhere. This supports users in maintaining their health and improving their fitness performance, aiming for long-term quality of life improvement and extension of healthy lifespan. For example, the user inputs their daily meal records into the system. For instance, they input detailed information such as what they ate for breakfast, lunch, dinner, and snacks. This information is analyzed by the generative AI. Next, the generative AI evaluates the user's nutritional status in real time based on the input meal records. For example, it analyzes the balance of nutrients such as calories, protein, fat, carbohydrates, vitamins, and minerals consumed. This allows the AI ​​to understand the user's nutritional status. Furthermore, the generative AI provides personalized nutritional advice based on the user's health status and goals. For example, users who want to lose weight can receive advice recommending calorie restriction or the intake of specific nutrients. Similarly, users who want to build muscle can receive advice on increasing protein intake. In this way, the system can provide an optimal nutrition plan tailored to individual goals. This system functions as a 24 / 7 AI coach, allowing users to receive nutrition advice anytime, anywhere. For instance, when choosing from a menu while dining out, the generating AI can suggest the best option in real time. It can also check the balance of nutrients consumed after a meal and advise on nutrients that should be supplemented in the next meal. Through this system, users can be supported in maintaining their health and improving their fitness performance, aiming for a long-term improvement in quality of life and an extension of healthy lifespan.For example, personalized nutritional advice based on daily meal records allows users to constantly monitor their health and manage their nutrition appropriately. Furthermore, the AI-generated analysis provides specific areas for dietary improvement, enabling more effective health management. In this way, AI-driven personalized nutrition coaches can support users in maintaining their health and improving their fitness performance, leading to improved long-term quality of life and extended healthy lifespans.

[0029] The AI-driven personalized nutrition coach according to this embodiment comprises a reception unit, an evaluation unit, and an advice unit. The reception unit receives daily meal records from the user. The meal records entered by the user include detailed information such as breakfast, lunch, dinner, and snacks. The reception unit can receive meal records, for example, through a smartphone app or a web application. The reception unit can also accept input via voice input or photo capture. For example, a user can enter a meal record by taking a photo of their meal and uploading it to the system. The evaluation unit uses a generation AI to evaluate the user's nutritional status based on the meal records received by the reception unit. The evaluation unit analyzes the balance of nutrients such as calories, protein, fat, carbohydrates, vitamins, and minerals consumed. The generation AI receives the user's meal records as input and evaluates the balance of nutrients. For example, the generation AI calculates the amount of calories and nutrients consumed by the user and evaluates how well it matches the recommended intake. The advice unit provides personalized nutrition advice based on the nutritional status evaluated by the evaluation unit. The advice unit provides advice to users who want to lose weight, for example, recommending calorie restriction or the intake of specific nutrients. It also provides advice to users who want to increase muscle mass, recommending increased protein intake. The advice unit uses a generative AI to generate an optimal nutrition plan based on the user's health status and goals. For example, the generative AI takes the user's goals and current nutritional status as input and outputs an optimal nutrition plan. As a result, the AI-driven personalized nutrition coach according to this embodiment can support the user in maintaining their health and improving their fitness performance, thereby achieving long-term quality of life improvement and extending healthy lifespan.

[0030] The reception system accepts users to enter their daily meal records. These records include detailed information such as breakfast, lunch, dinner, and snacks. The reception system can accept meal records via a smartphone app or web application. Specifically, users can open the application and enter information such as the type of meal, the name of the food consumed, the quantity, and the time of consumption. The reception system can also accept input via voice and photos. For example, users can take a photo of their meal and upload it to the system to enter their meal record. With voice input, users verbally describe their meal, and this voice data is converted into text and recorded. Furthermore, the reception system includes a feature that allows users to refer to their previously entered meal records, making it easy to select the same food again when re-entering it. This allows users to enter meal records quickly and easily. The reception system also saves users' meal records to a cloud server, enabling data backup and access from other devices. This allows users to check and update their meal records anytime, anywhere. In addition, the reception system protects user privacy by encrypting data and controlling access to prevent unauthorized access by third parties. This allows the reception desk to provide an environment where users can confidently enter their meal records.

[0031] The evaluation unit uses a generation AI to assess the user's nutritional status based on meal records received by the reception unit. The evaluation unit analyzes the balance of nutrients such as calories, protein, fat, carbohydrates, vitamins, and minerals consumed. The generation AI receives the user's meal records as input and evaluates the balance of nutrients. Specifically, the generation AI analyzes the meal records entered by the user and calculates the amount of nutrients contained in each food. For example, the generation AI refers to a food database to identify the nutritional components of each food and calculates the user's total intake based on this. Next, the generation AI considers the user's personal information such as age, gender, weight, and activity level, and compares it to recommended nutrient intakes. This allows the evaluation to assess how well the user's nutrient intake matches the recommended amount. Furthermore, the evaluation unit can analyze changes and trends in the user's nutritional status based on past meal records and health data. For example, if a user has been excessively consuming a particular nutrient over the past few weeks, the evaluation unit can detect this trend and provide appropriate advice. The evaluation unit can also detect abnormal eating patterns and nutritional imbalances and issue early warnings. This allows the evaluation unit to accurately and comprehensively assess the user's nutritional status and provide appropriate support for maintaining health and achieving goals.

[0032] The advice unit provides personalized nutritional advice based on the nutritional status assessed by the evaluation unit. For example, the advice unit provides advice recommending calorie restriction or intake of specific nutrients to users who want to lose weight. It also provides advice to increase protein intake to users who want to increase muscle mass. Specifically, the generation AI generates an optimal nutrition plan based on the user's health status and goals. For example, the generation AI takes the user's goals and current nutritional status as input and outputs an optimal nutrition plan. The generation AI analyzes the user's meal records and health data to provide specific meal suggestions tailored to individual needs. For example, it suggests a low-calorie, nutritionally balanced meal menu for users aiming to lose weight, and a high-protein, energy-replenishing meal menu for users aiming to build muscle. The advice unit also considers the user's eating habits and preferences to provide actionable advice. For example, it suggests alternative foods that avoid allergens to users with specific food allergies, and provides a plant-based nutrition plan for vegetarians and vegans. Furthermore, the advice unit can collect user feedback and continuously improve the accuracy and effectiveness of its advice. For example, the user reports the results of following the advice provided, and the AI ​​then adjusts the advice based on that data. This allows the advice unit to support the user in maintaining their health and improving their fitness performance, thereby achieving long-term improvements in quality of life and extending healthy life expectancy.

[0033] The evaluation unit can analyze the balance of nutrients such as calories, protein, fat, carbohydrates, vitamins, and minerals consumed. For example, the evaluation unit calculates the amount of calories and nutrients consumed based on the meal record entered by the user. The evaluation unit uses a generating AI to evaluate the balance of each nutrient. For example, the generating AI receives the amount of calories and nutrients consumed by the user as input and evaluates how well it matches the recommended intake. The evaluation unit can also use the generating AI to evaluate the user's nutritional status in real time. For example, the generating AI evaluates the balance of nutrients in real time based on the meal record entered by the user and provides feedback to the user. This allows for a detailed evaluation of the user's nutritional status. 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 meal record into the generating AI and have the generating AI perform the evaluation of the balance of nutrients.

[0034] The advice unit can provide advice to users who want to lose weight, recommending calorie restriction or intake of specific nutrients. For example, if a user has a goal of losing weight, the advice unit will provide advice recommending calorie restriction or intake of specific nutrients. The advice unit uses a generative AI to generate an optimal nutrition plan based on the user's goal. For example, the generative AI takes the user's goal and current nutritional status as input and outputs advice recommending calorie restriction or intake of specific nutrients. The advice unit can also use the generative AI to provide advice recommending calorie restriction or intake of specific nutrients based on the user's meal records. For example, the generative AI generates advice recommending calorie restriction or intake of specific nutrients based on the meal records entered by the user. This allows for the provision of specific nutrition advice tailored to the user's goal. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's goal and current nutritional status into the generative AI and have the generative AI generate an optimal nutrition plan.

[0035] The advice unit can provide advice to users who want to increase muscle mass by suggesting they increase their protein intake. For example, if a user has the goal of increasing muscle mass, the advice unit will provide advice to increase protein intake. The advice unit uses a generative AI to generate an optimal nutrition plan based on the user's goals. For example, the generative AI takes the user's goals and current nutritional status as input and outputs advice to increase protein intake. The advice unit can also use the generative AI to provide advice to increase protein intake based on the user's meal records. For example, the generative AI generates advice to increase protein intake based on the meal records entered by the user. This allows for the provision of specific nutrition advice tailored to the user's goals. Some or all of the above-described processes in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's goals and current nutritional status into the generative AI and have the generative AI generate an optimal nutrition plan.

[0036] The advice unit can suggest the optimal choice in real time when a user is choosing from a menu while dining out. For example, when a user is choosing from a menu while dining out, the advice unit uses generative AI to suggest the optimal choice in real time. The advice unit uses generative AI to suggest the optimal menu based on the user's current nutritional status and goals. For example, the generative AI takes the user's current nutritional status and goals as input and suggests the optimal menu for dining out. The advice unit can also use generative AI to suggest menus that the user should choose when dining out in real time. For example, the generative AI takes menus that the user should choose when dining out as input and outputs the optimal choice. This allows the user to receive appropriate nutritional advice even when dining out. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's current nutritional status and goals into the generative AI and have the generative AI suggest the optimal menu for dining out.

[0037] The advice unit can check the balance of nutrients consumed after a meal and advise on the nutrients that should be supplemented in the next meal. For example, the advice unit checks the balance of nutrients consumed by the user after a meal and advises on the nutrients that should be supplemented in the next meal. The advice unit uses a generation AI to advise on the nutrients that should be supplemented in the next meal based on the user's meal record. For example, the generation AI takes the balance of nutrients consumed by the user as input and outputs the nutrients that should be supplemented in the next meal. The advice unit can also use the generation AI to evaluate the user's nutritional status in real time and advise on the nutrients that should be supplemented in the next meal. For example, the generation AI evaluates the balance of nutrients consumed by the user in real time and advises on the nutrients that should be supplemented in the next meal. This allows the user to know the nutrients that should be supplemented in the next meal. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's meal record into the generation AI and have the generation AI provide advice on the nutrients that should be supplemented in the next meal.

[0038] The reception unit can analyze the user's past meal records and select the optimal input method. For example, the reception unit can automatically display meal contents that the user has frequently entered in the past as suggestions. The reception unit uses generative AI to analyze the user's past meal records. For example, the generative AI receives the user's past meal records as input and selects the optimal input method. The reception unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the generative AI suggests the optimal input method based on the input methods the user has used in the past. The reception unit can also predict and suggest meal contents to be entered at specific time periods based on the user's past meal records. For example, the generative AI predicts and suggests meal contents to be entered at specific time periods based on the user's past meal records. This allows the reception unit to provide the optimal input method based on the user's past meal records. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's past meal records into the generative AI and have the generative AI select the optimal input method.

[0039] The reception unit can filter the food entries based on the user's current health status and goals. For example, if the user is on a diet, the reception unit will prioritize inputting low-calorie meals. The reception unit uses a generative AI to filter based on the user's current health status and goals. For example, the generative AI receives the user's current health status and goals as input and filters them when the food entry is made. The reception unit can also prioritize inputting high-protein meals if the user is aiming to build muscle. For example, the generative AI will prioritize inputting high-protein meals based on the user's goals. The reception unit can also prioritize inputting meals containing specific nutrients if the user needs to consume those nutrients. For example, the generative AI will filter meals based on the nutrients the user needs to consume. This makes it possible to input food entries that are tailored to the user's health status and goals. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's health status and goals into the generative AI and have the generative AI perform the filtering of the food entries.

[0040] The reception unit can prioritize inputting highly relevant meal records by considering the user's geographical location when entering meal records. For example, if the user is in a specific region, the reception unit will prioritize inputting meals using ingredients from that region. The reception unit uses a generation AI to input meal records while considering the user's geographical location. For example, the generation AI receives the user's geographical location as input and prioritizes inputting highly relevant meal records. The reception unit can also prioritize inputting meals that include local specialties of the travel destination if the user is traveling. For example, the generation AI will prioritize inputting meals that include local specialties of the travel destination if the user is traveling. The reception unit can also prioritize inputting meals that can be cooked at home if the user is at home. For example, the generation AI will prioritize inputting meals that can be cooked at home if the user is at home. This enables the input of appropriate meal records based on the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location information into the generating AI and have the AI ​​input highly relevant meal records.

[0041] The reception unit can analyze the user's social media activity and input relevant meal records when the user enters meal records. For example, the reception unit can automatically input meal details shared by the user on social media. The reception unit uses generative AI to analyze the user's social media activity. For example, the generative AI receives the user's social media activity as input and inputs relevant meal records. The reception unit can also input meal details based on the meal content of influencers the user follows. For example, the generative AI inputs relevant meal records based on the meal content of influencers the user follows. The reception unit can also input information based on the meal-related groups the user participates in. For example, the generative AI inputs relevant meal records based on the meal-related groups the user participates in. This enables the input of appropriate meal records based on 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 without AI. For example, the reception unit can input the user's social media activity into the generative AI and have the generative AI input relevant meal records.

[0042] The evaluation unit can improve the accuracy of its evaluation by considering the interrelationships of meal records when assessing nutritional status. For example, the evaluation unit can assess the overall nutritional status by considering the nutritional balance between breakfast and lunch. The evaluation unit uses a generative AI to analyze the interrelationships of meal records. For example, the generative AI receives the user's meal records as input and analyzes the interrelationships. The evaluation unit can also analyze meal records on a weekly basis to assess long-term nutritional balance. For example, the generative AI analyzes the user's meal records on a weekly basis to assess long-term nutritional balance. The evaluation unit can also consider the impact of the intake of a specific nutrient on other nutrients. For example, the generative AI analyzes and evaluates the impact of the intake of a specific nutrient on other nutrients. This improves the accuracy of the evaluation by considering the interrelationships of meal records. 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 meal records into the generative AI and have the generative AI perform the analysis of interrelationships.

[0043] The evaluation unit can perform nutritional status assessments while considering the user's health history. For example, the evaluation unit can assess nutritional status based on the user's past health checkup results. The evaluation unit uses a generative AI to analyze the user's health history. For example, the generative AI receives the user's past health checkup results as input and evaluates the nutritional status. The evaluation unit can also evaluate the intake of specific nutrients while considering the user's past medical history. For example, the generative AI evaluates the intake of specific nutrients based on the user's past medical history. The evaluation unit can also assess nutritional status while considering energy expenditure based on the user's past exercise history. For example, the generative AI evaluates nutritional status while considering energy expenditure based on the user's past exercise history. This makes it possible to evaluate nutritional status appropriately based on the user's health history. 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 health history into the generative AI and have the generative AI perform the nutritional status evaluation.

[0044] The evaluation unit can perform nutritional status assessments while considering the user's geographical distribution. For example, the evaluation unit can assess nutritional status while considering the food culture of the area where the user lives. The evaluation unit uses generative AI to analyze the user's geographical distribution. For example, the generative AI can receive the user's geographical distribution as input and evaluate the nutritional status. The evaluation unit can also assess nutritional status while considering the food available at the travel destination if the user is traveling. For example, the generative AI can assess nutritional status based on the food available at the travel destination if the user is traveling. The evaluation unit can also assess nutritional status based on the ingredients of a specific region if the user is in that region. For example, the generative AI can assess nutritional status based on the ingredients of a specific region if the user is in that region. This makes it possible to perform an appropriate nutritional status assessment based on the user's geographical distribution. 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 geographical distribution into the generative AI and have the generative AI perform the nutritional status assessment.

[0045] The evaluation unit can improve the accuracy of its nutritional status assessments by referring to relevant literature. For example, the evaluation unit can update its evaluation criteria by referring to the latest nutritional research. The evaluation unit uses generative AI to analyze the relevant literature. For example, the generative AI can receive relevant literature as input and update the evaluation criteria. The evaluation unit can also adjust the evaluation criteria for specific nutrients based on relevant medical literature. For example, the generative AI adjusts the evaluation criteria for specific nutrients based on relevant medical literature. The evaluation unit can also improve the accuracy of its assessments by referring to literature on the user's health status. For example, the generative AI improves the accuracy of its assessments based on literature on the user's health status. Thus, the accuracy of the assessment is improved by referring to relevant literature. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input relevant literature into the generative AI and have the generative AI update the evaluation criteria.

[0046] The advice unit can analyze the user's past meal records to select the most appropriate advice when providing it. For example, the advice unit can advise on nutrients that should be supplemented in the next meal based on the balance of nutrients the user has consumed in the past. The advice unit uses a generative AI to analyze the user's past meal records. For example, the generative AI takes the user's past meal records as input and selects the most appropriate advice. The advice unit can also suggest similar meals based on the meals the user has enjoyed eating in the past. For example, the generative AI suggests similar meals based on the meals the user has enjoyed eating in the past. The advice unit can also provide advice to increase the intake of specific nutrients based on the user's past meal records. For example, the generative AI provides advice to increase the intake of specific nutrients based on the user's past meal records. This allows the advice unit to provide the most appropriate advice based on the user's past meal records. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's past meal records into the generative AI and have the generative AI select the most appropriate advice.

[0047] The advice unit can customize the content of advice based on the user's current health condition when providing advice. For example, if the user is feeling unwell, the advice unit may suggest easily digestible meals. The advice unit uses a generative AI to analyze the user's current health condition. For example, the generative AI receives the user's current health condition as input and customizes the content of the advice. The advice unit can also suggest meals suitable for energy replenishment if the user has just exercised. For example, the generative AI suggests meals suitable for energy replenishment if the user has just exercised. The advice unit can also suggest low-calorie meals if the user is on a diet. For example, the generative AI suggests low-calorie meals if the user is on a diet. This allows the advice unit to provide appropriate advice according to the user's current health condition. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's current health condition into the generative AI and have the generative AI customize the content of the advice.

[0048] The advice unit can select the most appropriate advice by considering the user's geographical location when providing advice. For example, if the user is in a specific region, the advice unit can suggest meals using ingredients from that region. The advice unit uses generative AI to analyze the user's geographical location. For example, the generative AI receives the user's geographical location as input and selects the most appropriate advice. The advice unit can also suggest meals that include local specialties of the travel destination if the user is traveling. For example, the generative AI suggests meals that include local specialties of the travel destination if the user is traveling. The advice unit can also suggest meals that can be cooked at home if the user is at home. For example, the generative AI suggests meals that can be cooked at home if the user is at home. This allows the advice unit to provide appropriate advice based on the user's geographical location. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's geographical location into the generative AI and have the generative AI select the most appropriate advice.

[0049] The advice unit can analyze the user's social media activity and propose advice when providing it. For example, the advice unit can provide advice based on the meals the user has shared on social media. The advice unit uses generative AI to analyze the user's social media activity. For example, the generative AI takes the user's social media activity as input and proposes advice. The advice unit can also provide advice based on the meals of influencers the user follows. For example, the generative AI proposes advice based on the meals of influencers the user follows. The advice unit can also provide advice based on information about food-related groups the user participates in. For example, the generative AI proposes advice based on information about food-related groups the user participates in. This allows the advice unit to provide appropriate advice based on the user's social media activity. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's social media activity into the generative AI and have the generative AI propose advice.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The reception desk can analyze the user's past meal records and select the optimal input method. For example, it can automatically display meal contents that the user has frequently entered in the past as suggestions. Generative AI is used to analyze the user's past meal records. For example, the generative AI receives the user's past meal records as input and selects the optimal input method. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). This allows the system to provide the optimal input method based on the user's past meal records.

[0052] The reception system can filter meal entries based on the user's current health status and goals. For example, if a user is on a diet, low-calorie meals will be prioritized. Generative AI is used to filter entries based on the user's current health status and goals. For instance, the generative AI receives the user's current health status and goals as input and filters them during meal entry. Furthermore, if a user is aiming to build muscle, high-protein meals can be prioritized. This allows for meal entries tailored to the user's health status and goals.

[0053] The evaluation unit can improve the accuracy of its assessments of nutritional status by considering the interrelationships of food records. For example, it can assess overall nutritional status by considering the nutritional balance between breakfast and lunch. It uses a generation AI to analyze the interrelationships of food records. For example, the generation AI receives the user's food records as input and analyzes the interrelationships. It can also analyze food records on a weekly basis to assess long-term nutritional balance. This improves the accuracy of the assessment by considering the interrelationships of food records.

[0054] The evaluation unit can perform nutritional status assessments while considering the user's health history. For example, it can assess nutritional status based on the user's past health checkup results. It uses a generation AI to analyze the user's health history. For example, the generation AI receives the user's past health checkup results as input and evaluates the nutritional status. It can also evaluate the intake of specific nutrients while considering the user's past medical history. This makes it possible to assess nutritional status appropriately based on the user's health history.

[0055] The advice section can analyze the user's past meal records to select the most appropriate advice when providing it. For example, it can advise on nutrients that should be supplemented in the next meal based on the balance of nutrients the user has consumed in the past. Generative AI is used to analyze the user's past meal records. For example, the generative AI takes the user's past meal records as input and selects the most appropriate advice. It can also suggest similar meals based on the types of meals the user has enjoyed eating in the past. This allows the system to provide optimal advice based on the user's past meal records.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The reception desk accepts the user's daily meal log. The meal log includes detailed information such as breakfast, lunch, dinner, and snacks. The reception desk can accept meal logs via, for example, a smartphone app or web application. The reception desk can also accept input via voice or photo. For example, a user can take a photo of their meal and upload it to the system to enter their meal log. Step 2: The evaluation unit uses a generation AI to assess the user's nutritional status based on the meal records received by the reception unit. The evaluation unit analyzes, for example, the balance of nutrients such as calories, protein, fat, carbohydrates, vitamins, and minerals consumed. The generation AI receives the user's meal records as input and evaluates the balance of nutrients. For example, the generation AI calculates the amount of calories and nutrients consumed by the user and evaluates how well it matches the recommended intake. Step 3: The advice unit provides personalized nutritional advice based on the nutritional status assessed by the evaluation unit. For example, the advice unit provides advice recommending calorie restriction or intake of specific nutrients to users who want to lose weight. It also provides advice to increase protein intake to users who want to increase muscle mass. The advice unit uses a generation AI to generate an optimal nutrition plan based on the user's health status and goals. For example, the generation AI takes the user's goals and current nutritional status as input and outputs an optimal nutrition plan.

[0058] (Example of form 2) An AI-driven personalized nutrition coach according to an embodiment of the present invention is a system that utilizes generative AI to provide real-time and personalized nutritional advice based on an individual's meal records. The AI-driven personalized nutrition coach works by having the user input their daily meal records into the system. Based on these inputs, the generative AI evaluates the user's nutritional status in real time and provides personalized nutritional advice based on the user's health status and goals. This system functions as a 24 / 7 AI coach, allowing users to receive nutritional advice anytime, anywhere. This supports users in maintaining their health and improving their fitness performance, aiming for long-term quality of life improvement and extension of healthy lifespan. For example, the user inputs their daily meal records into the system. For instance, they input detailed information such as what they ate for breakfast, lunch, dinner, and snacks. This information is analyzed by the generative AI. Next, the generative AI evaluates the user's nutritional status in real time based on the input meal records. For example, it analyzes the balance of nutrients such as calories, protein, fat, carbohydrates, vitamins, and minerals consumed. This allows the AI ​​to understand the user's nutritional status. Furthermore, the generative AI provides personalized nutritional advice based on the user's health status and goals. For example, users who want to lose weight can receive advice recommending calorie restriction or the intake of specific nutrients. Similarly, users who want to build muscle can receive advice on increasing protein intake. In this way, the system can provide an optimal nutrition plan tailored to individual goals. This system functions as a 24 / 7 AI coach, allowing users to receive nutrition advice anytime, anywhere. For instance, when choosing from a menu while dining out, the generating AI can suggest the best option in real time. It can also check the balance of nutrients consumed after a meal and advise on nutrients that should be supplemented in the next meal. Through this system, users can be supported in maintaining their health and improving their fitness performance, aiming for a long-term improvement in quality of life and an extension of healthy lifespan.For example, personalized nutritional advice based on daily meal records allows users to constantly monitor their health and manage their nutrition appropriately. Furthermore, the AI-generated analysis provides specific areas for dietary improvement, enabling more effective health management. In this way, AI-driven personalized nutrition coaches can support users in maintaining their health and improving their fitness performance, leading to improved long-term quality of life and extended healthy lifespans.

[0059] The AI-driven personalized nutrition coach according to this embodiment comprises a reception unit, an evaluation unit, and an advice unit. The reception unit receives daily meal records from the user. The meal records entered by the user include detailed information such as breakfast, lunch, dinner, and snacks. The reception unit can receive meal records, for example, through a smartphone app or a web application. The reception unit can also accept input via voice input or photo capture. For example, a user can enter a meal record by taking a photo of their meal and uploading it to the system. The evaluation unit uses a generation AI to evaluate the user's nutritional status based on the meal records received by the reception unit. The evaluation unit analyzes the balance of nutrients such as calories, protein, fat, carbohydrates, vitamins, and minerals consumed. The generation AI receives the user's meal records as input and evaluates the balance of nutrients. For example, the generation AI calculates the amount of calories and nutrients consumed by the user and evaluates how well it matches the recommended intake. The advice unit provides personalized nutrition advice based on the nutritional status evaluated by the evaluation unit. The advice unit provides advice to users who want to lose weight, for example, recommending calorie restriction or the intake of specific nutrients. It also provides advice to users who want to increase muscle mass, recommending increased protein intake. The advice unit uses a generative AI to generate an optimal nutrition plan based on the user's health status and goals. For example, the generative AI takes the user's goals and current nutritional status as input and outputs an optimal nutrition plan. As a result, the AI-driven personalized nutrition coach according to this embodiment can support the user in maintaining their health and improving their fitness performance, thereby achieving long-term quality of life improvement and extending healthy lifespan.

[0060] The reception system accepts users to enter their daily meal records. These records include detailed information such as breakfast, lunch, dinner, and snacks. The reception system can accept meal records via a smartphone app or web application. Specifically, users can open the application and enter information such as the type of meal, the name of the food consumed, the quantity, and the time of consumption. The reception system can also accept input via voice and photos. For example, users can take a photo of their meal and upload it to the system to enter their meal record. With voice input, users verbally describe their meal, and this voice data is converted into text and recorded. Furthermore, the reception system includes a feature that allows users to refer to their previously entered meal records, making it easy to select the same food again when re-entering it. This allows users to enter meal records quickly and easily. The reception system also saves users' meal records to a cloud server, enabling data backup and access from other devices. This allows users to check and update their meal records anytime, anywhere. In addition, the reception system protects user privacy by encrypting data and controlling access to prevent unauthorized access by third parties. This allows the reception desk to provide an environment where users can confidently enter their meal records.

[0061] The evaluation unit uses a generation AI to assess the user's nutritional status based on meal records received by the reception unit. The evaluation unit analyzes the balance of nutrients such as calories, protein, fat, carbohydrates, vitamins, and minerals consumed. The generation AI receives the user's meal records as input and evaluates the balance of nutrients. Specifically, the generation AI analyzes the meal records entered by the user and calculates the amount of nutrients contained in each food. For example, the generation AI refers to a food database to identify the nutritional components of each food and calculates the user's total intake based on this. Next, the generation AI considers the user's personal information such as age, gender, weight, and activity level, and compares it to recommended nutrient intakes. This allows the evaluation to assess how well the user's nutrient intake matches the recommended amount. Furthermore, the evaluation unit can analyze changes and trends in the user's nutritional status based on past meal records and health data. For example, if a user has been excessively consuming a particular nutrient over the past few weeks, the evaluation unit can detect this trend and provide appropriate advice. The evaluation unit can also detect abnormal eating patterns and nutritional imbalances and issue early warnings. This allows the evaluation unit to accurately and comprehensively assess the user's nutritional status and provide appropriate support for maintaining health and achieving goals.

[0062] The advice unit provides personalized nutritional advice based on the nutritional status assessed by the evaluation unit. For example, the advice unit provides advice recommending calorie restriction or intake of specific nutrients to users who want to lose weight. It also provides advice to increase protein intake to users who want to increase muscle mass. Specifically, the generation AI generates an optimal nutrition plan based on the user's health status and goals. For example, the generation AI takes the user's goals and current nutritional status as input and outputs an optimal nutrition plan. The generation AI analyzes the user's meal records and health data to provide specific meal suggestions tailored to individual needs. For example, it suggests a low-calorie, nutritionally balanced meal menu for users aiming to lose weight, and a high-protein, energy-replenishing meal menu for users aiming to build muscle. The advice unit also considers the user's eating habits and preferences to provide actionable advice. For example, it suggests alternative foods that avoid allergens to users with specific food allergies, and provides a plant-based nutrition plan for vegetarians and vegans. Furthermore, the advice unit can collect user feedback and continuously improve the accuracy and effectiveness of its advice. For example, the user reports the results of following the advice provided, and the AI ​​then adjusts the advice based on that data. This allows the advice unit to support the user in maintaining their health and improving their fitness performance, thereby achieving long-term improvements in quality of life and extending healthy life expectancy.

[0063] The evaluation unit can analyze the balance of nutrients such as calories, protein, fat, carbohydrates, vitamins, and minerals consumed. For example, the evaluation unit calculates the amount of calories and nutrients consumed based on the meal record entered by the user. The evaluation unit uses a generating AI to evaluate the balance of each nutrient. For example, the generating AI receives the amount of calories and nutrients consumed by the user as input and evaluates how well it matches the recommended intake. The evaluation unit can also use the generating AI to evaluate the user's nutritional status in real time. For example, the generating AI evaluates the balance of nutrients in real time based on the meal record entered by the user and provides feedback to the user. This allows for a detailed evaluation of the user's nutritional status. 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 meal record into the generating AI and have the generating AI perform the evaluation of the balance of nutrients.

[0064] The advice unit can provide advice to users who want to lose weight, recommending calorie restriction or intake of specific nutrients. For example, if a user has a goal of losing weight, the advice unit will provide advice recommending calorie restriction or intake of specific nutrients. The advice unit uses a generative AI to generate an optimal nutrition plan based on the user's goal. For example, the generative AI takes the user's goal and current nutritional status as input and outputs advice recommending calorie restriction or intake of specific nutrients. The advice unit can also use the generative AI to provide advice recommending calorie restriction or intake of specific nutrients based on the user's meal records. For example, the generative AI generates advice recommending calorie restriction or intake of specific nutrients based on the meal records entered by the user. This allows for the provision of specific nutrition advice tailored to the user's goal. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's goal and current nutritional status into the generative AI and have the generative AI generate an optimal nutrition plan.

[0065] The advice unit can provide advice to users who want to increase muscle mass by suggesting they increase their protein intake. For example, if a user has the goal of increasing muscle mass, the advice unit will provide advice to increase protein intake. The advice unit uses a generative AI to generate an optimal nutrition plan based on the user's goals. For example, the generative AI takes the user's goals and current nutritional status as input and outputs advice to increase protein intake. The advice unit can also use the generative AI to provide advice to increase protein intake based on the user's meal records. For example, the generative AI generates advice to increase protein intake based on the meal records entered by the user. This allows for the provision of specific nutrition advice tailored to the user's goals. Some or all of the above-described processes in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's goals and current nutritional status into the generative AI and have the generative AI generate an optimal nutrition plan.

[0066] The advice unit can suggest the optimal choice in real time when a user is choosing from a menu while dining out. For example, when a user is choosing from a menu while dining out, the advice unit uses generative AI to suggest the optimal choice in real time. The advice unit uses generative AI to suggest the optimal menu based on the user's current nutritional status and goals. For example, the generative AI takes the user's current nutritional status and goals as input and suggests the optimal menu for dining out. The advice unit can also use generative AI to suggest menus that the user should choose when dining out in real time. For example, the generative AI takes menus that the user should choose when dining out as input and outputs the optimal choice. This allows the user to receive appropriate nutritional advice even when dining out. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's current nutritional status and goals into the generative AI and have the generative AI suggest the optimal menu for dining out.

[0067] The advice unit can check the balance of nutrients consumed after a meal and advise on the nutrients that should be supplemented in the next meal. For example, the advice unit checks the balance of nutrients consumed by the user after a meal and advises on the nutrients that should be supplemented in the next meal. The advice unit uses a generation AI to advise on the nutrients that should be supplemented in the next meal based on the user's meal record. For example, the generation AI takes the balance of nutrients consumed by the user as input and outputs the nutrients that should be supplemented in the next meal. The advice unit can also use the generation AI to evaluate the user's nutritional status in real time and advise on the nutrients that should be supplemented in the next meal. For example, the generation AI evaluates the balance of nutrients consumed by the user in real time and advises on the nutrients that should be supplemented in the next meal. This allows the user to know the nutrients that should be supplemented in the next meal. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's meal record into the generation AI and have the generation AI provide advice on the nutrients that should be supplemented in the next meal.

[0068] The reception desk can estimate the user's emotions and adjust the timing of meal record entry based on the estimated emotions. For example, if the user is feeling stressed, the reception desk will prompt them to enter their meal record during a time when they can relax. The reception desk estimates the user's emotions using an emotion estimation algorithm. For example, it may use facial recognition technology or voice analysis technology to estimate the user's emotions. The reception desk can also provide a simple interface that allows for quick entry when the user is busy. For example, during busy times, it may present simple options to allow for quick entry of meal records. The reception desk can also encourage detailed entry when the user is relaxed, improving the accuracy of the meal record. For example, during relaxed times, it may provide an interface that prompts for detailed meal content entry. This allows for meal records to be entered at the appropriate time 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes at the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0069] The reception unit can analyze the user's past meal records and select the optimal input method. For example, the reception unit can automatically display meal contents that the user has frequently entered in the past as suggestions. The reception unit uses generative AI to analyze the user's past meal records. For example, the generative AI receives the user's past meal records as input and selects the optimal input method. The reception unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the generative AI suggests the optimal input method based on the input methods the user has used in the past. The reception unit can also predict and suggest meal contents to be entered at specific time periods based on the user's past meal records. For example, the generative AI predicts and suggests meal contents to be entered at specific time periods based on the user's past meal records. This allows the reception unit to provide the optimal input method based on the user's past meal records. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's past meal records into the generative AI and have the generative AI select the optimal input method.

[0070] The reception unit can filter the food entries based on the user's current health status and goals. For example, if the user is on a diet, the reception unit will prioritize inputting low-calorie meals. The reception unit uses a generative AI to filter based on the user's current health status and goals. For example, the generative AI receives the user's current health status and goals as input and filters them when the food entry is made. The reception unit can also prioritize inputting high-protein meals if the user is aiming to build muscle. For example, the generative AI will prioritize inputting high-protein meals based on the user's goals. The reception unit can also prioritize inputting meals containing specific nutrients if the user needs to consume those nutrients. For example, the generative AI will filter meals based on the nutrients the user needs to consume. This makes it possible to input food entries that are tailored to the user's health status and goals. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's health status and goals into the generative AI and have the generative AI perform the filtering of the food entries.

[0071] The reception unit can estimate the user's emotions and determine the priority of meal entries based on the estimated emotions. For example, if the user is stressed, the reception unit will prioritize meals that help reduce stress. The reception unit estimates the user's emotions using an emotion estimation algorithm. For example, it may use facial recognition technology or voice analysis technology to estimate the user's emotions. The reception unit can also prioritize meals that are balanced if the user is relaxed. For example, a generative AI will prioritize meals that are balanced if the user is relaxed. The reception unit can also prioritize meals that are suitable for energy replenishment if the user is tired. For example, a generative AI will prioritize meals that are suitable for energy replenishment if the user is tired. This enables the input of appropriate meal records 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-described processes at the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0072] The reception unit can prioritize inputting highly relevant meal records by considering the user's geographical location when entering meal records. For example, if the user is in a specific region, the reception unit will prioritize inputting meals using ingredients from that region. The reception unit uses a generation AI to input meal records while considering the user's geographical location. For example, the generation AI receives the user's geographical location as input and prioritizes inputting highly relevant meal records. The reception unit can also prioritize inputting meals that include local specialties of the travel destination if the user is traveling. For example, the generation AI will prioritize inputting meals that include local specialties of the travel destination if the user is traveling. The reception unit can also prioritize inputting meals that can be cooked at home if the user is at home. For example, the generation AI will prioritize inputting meals that can be cooked at home if the user is at home. This enables the input of appropriate meal records based on the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location information into the generating AI and have the AI ​​input highly relevant meal records.

[0073] The reception unit can analyze the user's social media activity and input relevant meal records when the user enters meal records. For example, the reception unit can automatically input meal details shared by the user on social media. The reception unit uses generative AI to analyze the user's social media activity. For example, the generative AI receives the user's social media activity as input and inputs relevant meal records. The reception unit can also input meal details based on the meal content of influencers the user follows. For example, the generative AI inputs relevant meal records based on the meal content of influencers the user follows. The reception unit can also input information based on the meal-related groups the user participates in. For example, the generative AI inputs relevant meal records based on the meal-related groups the user participates in. This enables the input of appropriate meal records based on 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 without AI. For example, the reception unit can input the user's social media activity into the generative AI and have the generative AI input relevant meal records.

[0074] The evaluation unit can estimate the user's emotions and adjust the nutritional status evaluation criteria based on the estimated emotions. For example, if the user is stressed, the evaluation unit will prioritize evaluation criteria for nutrients that help reduce stress. The evaluation unit estimates the user's emotions using an emotion estimation algorithm. For example, it may use facial recognition technology or voice analysis technology to estimate the user's emotions. The evaluation unit can also prioritize evaluation criteria for balanced nutrients if the user is relaxed. For example, the generating AI will prioritize evaluation criteria for balanced nutrients if the user is relaxed. The evaluation unit can also prioritize evaluation criteria for nutrients suitable for energy replenishment if the user is tired. For example, the generating AI will prioritize evaluation criteria for nutrients suitable for energy replenishment if the user is tired. This makes it possible to evaluate the nutritional status appropriately according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generating AI. The generating AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0075] The evaluation unit can improve the accuracy of its evaluation by considering the interrelationships of meal records when assessing nutritional status. For example, the evaluation unit can assess the overall nutritional status by considering the nutritional balance between breakfast and lunch. The evaluation unit uses a generative AI to analyze the interrelationships of meal records. For example, the generative AI receives the user's meal records as input and analyzes the interrelationships. The evaluation unit can also analyze meal records on a weekly basis to assess long-term nutritional balance. For example, the generative AI analyzes the user's meal records on a weekly basis to assess long-term nutritional balance. The evaluation unit can also consider the impact of the intake of a specific nutrient on other nutrients. For example, the generative AI analyzes and evaluates the impact of the intake of a specific nutrient on other nutrients. This improves the accuracy of the evaluation by considering the interrelationships of meal records. 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 meal records into the generative AI and have the generative AI perform the analysis of interrelationships.

[0076] The evaluation unit can perform nutritional status assessments while considering the user's health history. For example, the evaluation unit can assess nutritional status based on the user's past health checkup results. The evaluation unit uses a generative AI to analyze the user's health history. For example, the generative AI receives the user's past health checkup results as input and evaluates the nutritional status. The evaluation unit can also evaluate the intake of specific nutrients while considering the user's past medical history. For example, the generative AI evaluates the intake of specific nutrients based on the user's past medical history. The evaluation unit can also assess nutritional status while considering energy expenditure based on the user's past exercise history. For example, the generative AI evaluates nutritional status while considering energy expenditure based on the user's past exercise history. This makes it possible to evaluate nutritional status appropriately based on the user's health history. 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 health history into the generative AI and have the generative AI perform the nutritional status evaluation.

[0077] The evaluation unit can estimate the user's emotions and adjust the display order of evaluation results based on the estimated user emotions. For example, if the user is feeling stressed, the evaluation unit will first display the evaluation results for nutrients that help reduce stress. The evaluation unit estimates the user's emotions using an emotion estimation algorithm. For example, it may use facial recognition technology or voice analysis technology to estimate the user's emotions. The evaluation unit can also display the evaluation results for balanced nutrients first if the user is relaxed. For example, the generating AI will display the evaluation results for balanced nutrients first if the user is relaxed. The evaluation unit can also display the evaluation results for nutrients suitable for energy replenishment first if the user is tired. For example, the generating AI will display the evaluation results for nutrients suitable for energy replenishment first if the user is tired. This allows evaluation results to be displayed in an appropriate order according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generating AI. The generating AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0078] The evaluation unit can perform nutritional status assessments while considering the user's geographical distribution. For example, the evaluation unit can assess nutritional status while considering the food culture of the area where the user lives. The evaluation unit uses generative AI to analyze the user's geographical distribution. For example, the generative AI can receive the user's geographical distribution as input and evaluate the nutritional status. The evaluation unit can also assess nutritional status while considering the food available at the travel destination if the user is traveling. For example, the generative AI can assess nutritional status based on the food available at the travel destination if the user is traveling. The evaluation unit can also assess nutritional status based on the ingredients of a specific region if the user is in that region. For example, the generative AI can assess nutritional status based on the ingredients of a specific region if the user is in that region. This makes it possible to perform an appropriate nutritional status assessment based on the user's geographical distribution. 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 geographical distribution into the generative AI and have the generative AI perform the nutritional status assessment.

[0079] The evaluation unit can improve the accuracy of its nutritional status assessments by referring to relevant literature. For example, the evaluation unit can update its evaluation criteria by referring to the latest nutritional research. The evaluation unit uses generative AI to analyze the relevant literature. For example, the generative AI can receive relevant literature as input and update the evaluation criteria. The evaluation unit can also adjust the evaluation criteria for specific nutrients based on relevant medical literature. For example, the generative AI adjusts the evaluation criteria for specific nutrients based on relevant medical literature. The evaluation unit can also improve the accuracy of its assessments by referring to literature on the user's health status. For example, the generative AI improves the accuracy of its assessments based on literature on the user's health status. Thus, the accuracy of the assessment is improved by referring to relevant literature. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input relevant literature into the generative AI and have the generative AI update the evaluation criteria.

[0080] The advice unit can estimate the user's emotions and adjust the way it expresses advice based on those emotions. For example, if the user is stressed, the advice unit will provide advice in gentle language. The advice unit estimates the user's emotions using an emotion estimation algorithm. For example, it may use facial recognition technology or voice analysis technology to estimate the user's emotions. The advice unit can also provide detailed advice if the user is relaxed. For example, a generative AI will provide detailed advice if the user is relaxed. The advice unit can also provide concise and to-the-point advice if the user is in a hurry. For example, a generative AI will provide concise and to-the-point advice if the user is in a hurry. This allows the advice unit to provide advice in an appropriate expression depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0081] The advice unit can analyze the user's past meal records to select the most appropriate advice when providing it. For example, the advice unit can advise on nutrients that should be supplemented in the next meal based on the balance of nutrients the user has consumed in the past. The advice unit uses a generative AI to analyze the user's past meal records. For example, the generative AI takes the user's past meal records as input and selects the most appropriate advice. The advice unit can also suggest similar meals based on the meals the user has enjoyed eating in the past. For example, the generative AI suggests similar meals based on the meals the user has enjoyed eating in the past. The advice unit can also provide advice to increase the intake of specific nutrients based on the user's past meal records. For example, the generative AI provides advice to increase the intake of specific nutrients based on the user's past meal records. This allows the advice unit to provide the most appropriate advice based on the user's past meal records. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's past meal records into the generative AI and have the generative AI select the most appropriate advice.

[0082] The advice unit can customize the content of advice based on the user's current health condition when providing advice. For example, if the user is feeling unwell, the advice unit may suggest easily digestible meals. The advice unit uses a generative AI to analyze the user's current health condition. For example, the generative AI receives the user's current health condition as input and customizes the content of the advice. The advice unit can also suggest meals suitable for energy replenishment if the user has just exercised. For example, the generative AI suggests meals suitable for energy replenishment if the user has just exercised. The advice unit can also suggest low-calorie meals if the user is on a diet. For example, the generative AI suggests low-calorie meals if the user is on a diet. This allows the advice unit to provide appropriate advice according to the user's current health condition. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's current health condition into the generative AI and have the generative AI customize the content of the advice.

[0083] The advice unit can estimate the user's emotions and prioritize advice based on those emotions. For example, if the user is stressed, the advice unit will prioritize providing advice that helps reduce stress. The advice unit estimates the user's emotions using emotion estimation algorithms. For example, it may use facial recognition technology or voice analysis technology to estimate the user's emotions. The advice unit can also prioritize providing balanced advice if the user is relaxed. For example, a generative AI will prioritize providing balanced advice if the user is relaxed. The advice unit can also prioritize providing advice suitable for energy replenishment if the user is tired. For example, a generative AI will prioritize providing advice suitable for energy replenishment if the user is tired. This allows for the provision of advice with appropriate priorities according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0084] The advice unit can select the most appropriate advice by considering the user's geographical location when providing advice. For example, if the user is in a specific region, the advice unit can suggest meals using ingredients from that region. The advice unit uses generative AI to analyze the user's geographical location. For example, the generative AI receives the user's geographical location as input and selects the most appropriate advice. The advice unit can also suggest meals that include local specialties of the travel destination if the user is traveling. For example, the generative AI suggests meals that include local specialties of the travel destination if the user is traveling. The advice unit can also suggest meals that can be cooked at home if the user is at home. For example, the generative AI suggests meals that can be cooked at home if the user is at home. This allows the advice unit to provide appropriate advice based on the user's geographical location. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's geographical location into the generative AI and have the generative AI select the most appropriate advice.

[0085] The advice unit can analyze the user's social media activity and propose advice when providing it. For example, the advice unit can provide advice based on the meals the user has shared on social media. The advice unit uses generative AI to analyze the user's social media activity. For example, the generative AI takes the user's social media activity as input and proposes advice. The advice unit can also provide advice based on the meals of influencers the user follows. For example, the generative AI proposes advice based on the meals of influencers the user follows. The advice unit can also provide advice based on information about food-related groups the user participates in. For example, the generative AI proposes advice based on information about food-related groups the user participates in. This allows the advice unit to provide appropriate advice based on the user's social media activity. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's social media activity into the generative AI and have the generative AI propose advice.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The reception desk can estimate the user's emotions and adjust the timing of meal record entry based on those estimates. For example, if the user is feeling stressed, it will prompt them to enter their meal record during a time when they can relax. An emotion estimation algorithm is used, along with facial recognition and voice analysis technologies, to estimate the user's emotions. Furthermore, if the user is busy, a simplified interface that allows for quick entry can be provided. For example, during busy periods, simple options can be presented to enable quick meal record entry. This allows users to enter their meal records at the appropriate time according to their emotions.

[0088] The evaluation unit can estimate the user's emotions and adjust the nutritional status evaluation criteria based on those emotions. For example, if the user is stressed, the evaluation criteria for nutrients that help reduce stress will be emphasized. The system uses an emotion estimation algorithm, facial recognition technology, and voice analysis technology to estimate the user's emotions. Conversely, if the user is relaxed, the evaluation criteria for balanced nutrients can also be emphasized. This makes it possible to evaluate nutritional status appropriately according to the user's emotions.

[0089] The advice unit 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. It uses an emotion estimation algorithm, facial recognition technology, and voice analysis technology to estimate the user's emotions. Furthermore, if the user is relaxed, it can provide more detailed advice. This allows for the provision of advice in an appropriate manner depending on the user's emotions.

[0090] The advice section can estimate the user's emotions and prioritize advice based on those emotions. For example, if the user is stressed, it will prioritize advice that helps reduce stress. It uses an emotion estimation algorithm, along with facial recognition and voice analysis technologies, to estimate the user's emotions. Furthermore, if the user is relaxed, it can prioritize providing balanced advice. This allows for the provision of advice with appropriate priorities according to the user's emotions.

[0091] The evaluation unit can estimate the user's emotions and adjust the display order of evaluation results based on the estimated emotions. For example, if the user is feeling stressed, the evaluation results for nutrients that help reduce stress will be displayed first. The system uses an emotion estimation algorithm, facial recognition technology, and voice analysis technology to estimate the user's emotions. Conversely, if the user is relaxed, the evaluation results for balanced nutrients can be displayed first. This allows the evaluation results to be displayed in an appropriate order according to the user's emotions.

[0092] The reception desk can analyze the user's past meal records and select the optimal input method. For example, it can automatically display meal contents that the user has frequently entered in the past as suggestions. Generative AI is used to analyze the user's past meal records. For example, the generative AI receives the user's past meal records as input and selects the optimal input method. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). This allows the system to provide the optimal input method based on the user's past meal records.

[0093] The reception system can filter meal entries based on the user's current health status and goals. For example, if a user is on a diet, low-calorie meals will be prioritized. Generative AI is used to filter entries based on the user's current health status and goals. For instance, the generative AI receives the user's current health status and goals as input and filters them during meal entry. Furthermore, if a user is aiming to build muscle, high-protein meals can be prioritized. This allows for meal entries tailored to the user's health status and goals.

[0094] The evaluation unit can improve the accuracy of its assessments of nutritional status by considering the interrelationships of food records. For example, it can assess overall nutritional status by considering the nutritional balance between breakfast and lunch. It uses a generation AI to analyze the interrelationships of food records. For example, the generation AI receives the user's food records as input and analyzes the interrelationships. It can also analyze food records on a weekly basis to assess long-term nutritional balance. This improves the accuracy of the assessment by considering the interrelationships of food records.

[0095] The evaluation unit can perform nutritional status assessments while considering the user's health history. For example, it can assess nutritional status based on the user's past health checkup results. It uses a generation AI to analyze the user's health history. For example, the generation AI receives the user's past health checkup results as input and evaluates the nutritional status. It can also evaluate the intake of specific nutrients while considering the user's past medical history. This makes it possible to assess nutritional status appropriately based on the user's health history.

[0096] The advice section can analyze the user's past meal records to select the most appropriate advice when providing it. For example, it can advise on nutrients that should be supplemented in the next meal based on the balance of nutrients the user has consumed in the past. Generative AI is used to analyze the user's past meal records. For example, the generative AI takes the user's past meal records as input and selects the most appropriate advice. It can also suggest similar meals based on the types of meals the user has enjoyed eating in the past. This allows the system to provide optimal advice based on the user's past meal records.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The reception desk accepts the user's daily meal log. The meal log includes detailed information such as breakfast, lunch, dinner, and snacks. The reception desk can accept meal logs via, for example, a smartphone app or web application. The reception desk can also accept input via voice or photo. For example, a user can take a photo of their meal and upload it to the system to enter their meal log. Step 2: The evaluation unit uses a generation AI to assess the user's nutritional status based on the meal records received by the reception unit. The evaluation unit analyzes, for example, the balance of nutrients such as calories, protein, fat, carbohydrates, vitamins, and minerals consumed. The generation AI receives the user's meal records as input and evaluates the balance of nutrients. For example, the generation AI calculates the amount of calories and nutrients consumed by the user and evaluates how well it matches the recommended intake. Step 3: The advice unit provides personalized nutritional advice based on the nutritional status assessed by the evaluation unit. For example, the advice unit provides advice recommending calorie restriction or intake of specific nutrients to users who want to lose weight. It also provides advice to increase protein intake to users who want to increase muscle mass. The advice unit uses a generation AI to generate an optimal nutrition plan based on the user's health status and goals. For example, the generation AI takes the user's goals and current nutritional status as input and outputs an optimal nutrition plan.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] Each of the multiple elements described above, including the reception unit, evaluation unit, and advice 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 control unit 46A of the smart device 14 and accepts the user's input of a meal record via a smartphone app or web application. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates the user's nutritional status based on the meal record using generating AI. The advice unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides personalized nutritional advice based on the evaluated nutritional status. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] Each of the multiple elements described above, including the reception unit, evaluation unit, and advice 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 control unit 46A of the smart glasses 214 and receives the user's input of a meal record through the smart glasses. The evaluation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and evaluates the user's nutritional status based on the meal record using generating AI. The advice unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and provides personalized nutritional advice based on the evaluated nutritional status. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the reception unit, evaluation unit, and advice unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives the user's input of a meal record through the headset. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates the user's nutritional status based on the meal record using generating AI. The advice unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides personalized nutritional advice based on the evaluated nutritional status. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] Each of the multiple elements described above, including the reception unit, evaluation unit, and advice unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives the user's input of a meal record through the robot. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates the user's nutritional status based on the meal record using generating AI. The advice unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides personalized nutritional advice based on the evaluated nutritional status. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] (Note 1) The reception area accepts entries for meal records, An evaluation unit that evaluates nutritional status based on the meal records received by the reception unit, The system includes an advice unit that provides personalized nutritional advice based on the nutritional status evaluated by the evaluation unit. A system characterized by the following features. (Note 2) The evaluation unit, Analyze the balance of nutrients such as calories, protein, fat, carbohydrates, vitamins, and minerals consumed. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned advice section, For users who want to lose weight, we provide advice recommending calorie restriction and intake of specific nutrients. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned advice section, For users who want to increase muscle mass, we advise them to increase their protein intake. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned advice section, When choosing from a menu while dining out, it suggests the best option in real time. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned advice section, We check the balance of nutrients consumed after a meal and advise on the nutrients that should be supplemented in the next meal. 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 log entries based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past meal records and select 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 records, filtering is performed based on the user's current health status and goals. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of meal entries 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 records, the system prioritizes entering meals that are highly relevant to 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 users enter their meal records, the system analyzes their social media activity and inputs relevant meal records. The system described in Appendix 1, characterized by the features described herein. (Note 13) The evaluation unit, The system estimates the user's emotions and adjusts the nutritional status assessment criteria based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The evaluation unit, When assessing nutritional status, consider the interrelationships between food records to improve the accuracy of the assessment. The system described in Appendix 1, characterized by the features described herein. (Note 15) The evaluation unit, When assessing nutritional status, the user's health history should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 16) The evaluation unit, The system estimates the user's emotions and adjusts the display order of evaluation results based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The evaluation unit, When evaluating nutritional status, the evaluation should take into account the geographical distribution of users. The system described in Appendix 1, characterized by the features described herein. (Note 18) The evaluation unit, When assessing nutritional status, refer to relevant literature to improve the accuracy of the assessment. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned advice section, 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 20) The aforementioned advice section, When providing advice, the system analyzes the user's past meal records to select the most appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned advice section, When providing advice, customize the advice based on the user's current health status. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned advice section, It estimates the user's emotions and prioritizes advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned advice section, When providing advice, the system selects the most appropriate advice by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned advice section, When providing advice, we analyze the user's social media activity to propose appropriate advice. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0171] 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. The reception area accepts entries for meal records, An evaluation unit that evaluates nutritional status based on the meal records received by the reception unit, The system includes an advice unit that provides personalized nutritional advice based on the nutritional status evaluated by the evaluation unit. A system characterized by the following features.

2. The evaluation unit, Analyze the balance of nutrients such as calories, protein, fat, carbohydrates, vitamins, and minerals consumed. The system according to feature 1.

3. The aforementioned advice section, For users who want to lose weight, we provide advice recommending calorie restriction and intake of specific nutrients. The system according to feature 1.

4. The aforementioned advice section, For users who want to increase muscle mass, we advise them to increase their protein intake. The system according to feature 1.

5. The aforementioned advice section, When choosing from a menu while dining out, it suggests the best option in real time. The system according to feature 1.

6. The aforementioned advice section, We check the balance of nutrients consumed after a meal and advise on the nutrients that should be supplemented in the next meal. 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 log entries based on those emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past meal records and select the optimal input method. The system according to feature 1.

9. The aforementioned reception unit is When entering meal records, filtering is performed based on the user's current health status and goals. The system according to feature 1.

10. The aforementioned reception unit is It estimates the user's emotions and determines the priority of meal entries based on those estimated emotions. The system according to feature 1.

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