system

Abstract

The system according to this embodiment aims to provide a personalized meal plan based on data such as the patient's medical condition, intake, and energy expenditure. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a creation unit, and a provision unit. The collection unit collects data such as the patient's medical condition, sugar and salt intake, and energy consumption. The analysis unit analyzes the data collected by the collection unit in real time. The creation unit creates a personalized meal plan based on the analysis results obtained by the analysis unit. The provision unit provides the meal plan created by the creation 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, the method including 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 as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, a personalized diet plan based on data such as a patient's medical condition, intake, and energy consumption has not been sufficiently provided, and there is room for improvement.

[0005] The system according to the embodiment aims to provide a personalized diet plan based on data such as a patient's medical condition, intake, and energy consumption.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a creation unit, and a provision unit. The data collection unit collects data such as the patient's medical condition, sugar and salt intake, and energy expenditure. The analysis unit analyzes the data collected by the data collection unit in real time. The creation unit creates a personalized meal plan based on the analysis results obtained by the analysis unit. The provision unit provides the meal plan created by the creation unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide a personalized meal plan based on data such as the patient's medical condition, intake, and energy expenditure. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards 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) The dietary guidance system according to an embodiment of the present invention is a system that provides patient-appropriate dietary guidance using generative AI in conjunction with an online medical consultation service. The dietary guidance system collects data such as the patient's medical condition, sugar and salt intake, and energy expenditure. The generative AI analyzes this data in real time and creates a personalized meal plan based on previous patient cases and the latest medical research. This meal plan is adjusted to maximize the effects of the medications the patient is taking. This supports the improvement of the patient's health and maximizes medical benefits. For example, the dietary guidance system collects data such as the patient's medical condition, sugar and salt intake, and energy expenditure. At this time, it collects detailed data such as the content of the meals the patient eats on a daily basis and the amount of exercise the patient does. For example, by entering the contents of meals into an app, the patient can record their sugar and salt intake. It is also possible to monitor energy expenditure using a device such as a smartwatch. Next, the generative AI analyzes the collected data in real time. Based on previous patient cases and the latest medical research, the generative AI creates a meal plan that is optimal for the patient's health condition. For example, the system can suggest a meal plan that reduces sugar intake for diabetic patients and a meal plan that reduces salt intake for hypertensive patients. Furthermore, the generating AI also considers the interaction between food and medication and adjusts the meal plan accordingly. For instance, for patients taking a specific medication, it can suggest a diet rich in specific nutrients to maximize the effects of that medication. This supports the improvement of the patient's health and maximizes medical benefits. This system, in conjunction with online medical consultation services, can provide personalized dietary guidance tailored to each individual patient. Patients can continue to receive health management even after consultations and receive more effective treatment. For example, patients can input their meal details through an app, and the generating AI analyzes that data to suggest a meal plan, allowing patients to manage their health on a daily basis. In addition, because the generating AI analyzes data in real time, it can provide optimal meal plans based on the latest medical research.This allows the dietary guidance system to support improvements in patients' health and maximize medical benefits.

[0029] The dietary guidance system according to this embodiment comprises a data collection unit, an analysis unit, a data creation unit, and a data provision unit. The data collection unit collects data such as the patient's medical condition, sugar and salt intake, and energy expenditure. The data collection unit collects detailed data such as the content of meals and exercise levels that the patient consumes on a daily basis. For example, the patient can record their sugar and salt intake by entering the contents of their meals into an app. The data collection unit can also monitor energy expenditure using a device such as a smartwatch. For example, a smartwatch measures the patient's heart rate and exercise level and calculates energy expenditure. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. The analysis unit analyzes the data collected by the data collection unit in real time. The analysis unit analyzes the data in real time based on previous patient cases or the latest medical research, for example. For example, a generating AI refers to past patient data and performs analysis by comparing it with current patient data. The analysis unit can also analyze the data based on the latest medical research. For example, it performs analysis by referring to the latest research papers and medical guidelines. Some or all of the above-mentioned processes in the analysis unit are performed using generative AI. The creation unit creates a personalized meal plan based on the analysis results obtained by the analysis unit. For example, the creation unit creates a meal plan that is optimal for the patient's health condition. For example, it can suggest a meal plan that reduces sugar intake for diabetic patients and a meal plan that reduces salt intake for hypertensive patients. The creation unit can also adjust the meal plan considering the interaction between food and medication. For example, for patients taking a specific medication, it can suggest a meal that contains a lot of specific nutrients to maximize the effect of that medication. Some or all of the above-mentioned processes in the creation unit are performed using generative AI. The delivery unit provides the meal plan created by the creation unit. For example, the delivery unit provides the created meal plan to the patient. For example, the meal plan can be provided via email or app notification. The delivery unit can also provide it in paper form. For example, the meal plan can be printed and mailed to the patient.Some or all of the processing described above in the serving section may be performed using AI or not. This allows the dietary guidance system according to the embodiment to support the improvement of the patient's health condition and maximize medical benefits.

[0030] The data collection unit collects data on the patient's medical condition, sugar and salt intake, and energy expenditure. Specifically, it collects detailed data on the patient's daily diet and exercise levels. For example, patients can record their sugar and salt intake by entering their meal contents into a dedicated app. This app has functions to scan food barcodes and automatically analyze nutrients from photos of meals. The data collection unit can also monitor energy expenditure using devices such as smartwatches and fitness trackers. These devices measure the patient's heart rate, steps, exercise level, and sleep patterns to calculate energy expenditure. Furthermore, the data collection unit can acquire biometric data such as blood glucose levels and blood pressure from medical devices. This allows the data collection unit to comprehensively understand the patient's health status and collect data in real time. The collected data is stored on a cloud server and made accessible to the analysis and creation units. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis department analyzes data collected by the data collection department in real time. Specifically, it analyzes data in real time based on previous patient cases and the latest medical research. For example, a generative AI refers to past patient data and compares it with current patient data for analysis. The generative AI can learn from vast amounts of past patient data and extract patterns and trends. This allows it to identify abnormal values ​​and high-risk patterns by comparing them with current patient data. The analysis department can also analyze data based on the latest medical research. For example, it refers to the latest research papers and medical guidelines to evaluate patient data and propose optimal treatment and dietary plans. The generative AI uses natural language processing technology to automatically analyze the latest research papers and medical guidelines and extract relevant information. This allows the analysis department to analyze collected data quickly and accurately and understand patients' health status in real time. Furthermore, the analysis department can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, it can predict fluctuations in risk for specific patients based on historical data and formulate future countermeasures. This allows the analysis department to not only understand the situation in real time but also to handle long-term risk management, improving the reliability and safety of the entire system.

[0032] The creation unit creates personalized meal plans based on the analysis results obtained by the analysis unit. Specifically, it creates meal plans that are optimal for the patient's health condition. For example, it can suggest a meal plan that reduces sugar intake for diabetic patients and a meal plan that reduces salt intake for hypertensive patients. The generation AI calculates the optimal nutritional balance based on the patient's individual health data and proposes specific meal menus. For example, the generation AI can suggest ingredient selections and cooking methods considering the patient's preferences and allergy information. The creation unit can also adjust meal plans considering interactions between food and medication. For example, for patients taking a specific medication, it can suggest meals that are rich in specific nutrients to maximize the effect of that medication. The generation AI can refer to a drug database and evaluate drug-food interactions. This allows the creation unit to provide meal plans that are optimal for the patient's health condition and maximize treatment effectiveness. Furthermore, the creation unit can adjust meal plans considering seasonal and regional characteristics. For example, it can propose menus that utilize seasonal ingredients or plans that incorporate traditional local dishes. This allows the creation unit to provide attractive and easy-to-follow meal plans for patients and support the improvement of their health condition.

[0033] The service provider provides the meal plans created by the development team. Specifically, they provide the created meal plans to patients. For example, meal plans can be provided via email or app notifications. Patients can check their meal plans anytime, anywhere using their smartphones or tablets. The service provider can also provide meal plans in paper form. For example, they can print out the meal plans and mail them to patients. This allows them to serve patients who do not use digital devices. Furthermore, the service provider can provide additional information and resources to support the implementation of the meal plans. For example, they can provide detailed explanations of recipes and cooking methods, shopping lists for ingredients, and advice on meal timing and quantity. The service provider can also collect feedback from patients and use it to improve meal plans. For example, by having patients input the results and their impressions of implementing the meal plan into the app, the service provider can collect this information and incorporate it into creating the next plan. This allows the service provider to continuously provide patients with optimal meal plans and support the improvement of their health. Furthermore, the service provider can collaborate with patients' families and medical staff to support the implementation of meal plans. For example, by sharing the contents of the meal plan with family members and requesting their cooperation, they can help patients implement the plan more effectively. This allows the service provider to comprehensively support the improvement of patients' health conditions and maximize medical benefits.

[0034] The data collection unit can collect detailed data on the patient's daily diet and exercise levels. For example, the unit can record sugar and salt intake by having the patient input their meal details into an app. The data collection unit can also monitor energy expenditure using devices such as smartwatches. For example, a smartwatch can measure the patient's heart rate and exercise level and calculate energy expenditure. The data collection unit can also provide a dedicated application to collect detailed data on the patient's daily diet and exercise levels. For example, the application can automatically record and collect data on the patient's diet and exercise levels. This allows for more accurate analysis by collecting detailed data on the patient's daily diet and exercise levels. Detailed data may include, but is not limited to, the type, quantity, and timing of meals, and the type, duration, and intensity of exercise. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the patient's entered data on diet and exercise levels into a generating AI, which can then collect and organize the data.

[0035] The analysis unit can analyze data in real time based on previous patient cases and the latest medical research. For example, the analysis unit can refer to previous patient cases and compare them with current patient data for analysis. For example, the generating AI can refer to past patient data and compare it with current patient data for analysis. The analysis unit can also analyze data based on the latest medical research. For example, it can refer to the latest research papers and medical guidelines for analysis. Furthermore, in order to analyze data in real time, the analysis unit can start analysis immediately as soon as data is collected. For example, it can analyze data sent from the collection unit in real time and output the analysis results immediately. This allows for more accurate analysis results by analyzing data in real time based on previous patient cases and the latest medical research. Previous patient cases include, but are not limited to, past medical records and treatment results. The latest medical research includes, but are not limited to, the latest papers and research data. Some or all of the above processing in the analysis unit is performed using the generating AI. For example, the analysis unit can input past patient data and the latest medical research into the generating AI, which can then analyze the data.

[0036] The creation unit can create a meal plan that is optimal for the patient's health condition. For example, it can propose a meal plan that limits sugar intake for diabetic patients and a meal plan that limits salt intake for hypertensive patients. The creation unit can also create meal plans that take into account the patient's preferences and allergy information. For example, if a patient is allergic to a particular food, it will create a meal plan that does not include that food. Furthermore, the creation unit can create meal plans that take into account the patient's nutritional balance. For example, to optimize the patient's nutritional balance, it will propose a meal plan that is rich in a particular nutrient. In this way, by creating a meal plan that is optimal for the patient's health condition, it is possible to support the improvement of the patient's health condition. An optimal meal plan includes, but is not limited to, nutritional balance, calorie restriction, and patient preferences. Some or all of the above processing in the creation unit is performed using a generation AI. For example, the creation unit inputs the patient's health condition, preferences, and allergy information into the generation AI, which can then create an optimal meal plan.

[0037] The planning unit can adjust meal plans considering interactions between food and medication. For example, for a patient taking a specific medication, the planning unit can suggest a diet rich in specific nutrients to maximize the effect of that medication. For example, for a patient taking antibiotics, it can suggest a diet rich in probiotics. The planning unit can also adjust meal plans to avoid foods that affect the absorption of medication. For example, for a patient taking a specific medication, it can suggest avoiding grapefruit. Furthermore, the planning unit can adjust meal plans to avoid foods that reduce the effect of medication. For example, for a patient taking anticoagulants, it can suggest avoiding foods rich in vitamin K. By adjusting meal plans to consider interactions between food and medication, the effect of medication can be maximized, and the medical benefits can be maximized. Interactions between food and medication include, but are not limited to, foods that affect the absorption of medication or foods that reduce the effect of medication. Some or all of the above processing in the planning unit is performed using a generating AI. For example, the planning unit inputs information about the medications the patient is taking into the generating AI, and the generating AI can adjust the meal plan.

[0038] The service provider can provide the created meal plan to the patient. For example, the service provider can provide the created meal plan to the patient via email or app notification. The service provider can also provide it in paper form. For example, the meal plan can be printed and mailed to the patient. Furthermore, the service provider can collect patient feedback when providing the meal plan. For example, the patient can provide feedback on the meal plan through an app. This ensures that the patient receives appropriate dietary guidance by providing them with the created meal plan. Delivery includes, but is not limited to, email, app notification, and paper form. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input the created meal plan into a generating AI, which can then determine how to provide it to the patient.

[0039] The data collection unit can analyze a patient's past health data and select the optimal data collection method. For example, the data collection unit can collect detailed data on the patient's diet based on their past eating history. For example, the data collection unit can collect data on energy expenditure based on the patient's exercise history. The data collection unit can also collect data on specific health indicators based on the patient's medical history. For example, the data collection unit can refer to the patient's past medical records to collect data on specific health indicators. By analyzing the patient's past health data, the optimal data collection method can be selected. The optimal data collection method includes, but is not limited to, data accuracy, ease of collection, and patient burden. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the patient's past health data into a generating AI, which can then select the optimal data collection method.

[0040] The data collection unit can filter data while considering the patient's lifestyle and environmental factors. For example, the data collection unit can collect dietary data based on the patient's lifestyle. For example, the data collection unit can collect energy consumption data based on the patient's living environment. The data collection unit can also collect stress level data based on the patient's occupation. For example, the data collection unit can collect stress level data based on the patient's occupation. By filtering the data while considering the patient's lifestyle and environmental factors, more relevant data can be collected. Lifestyle and environmental factors include, but are not limited to, the timing of meals, the frequency of exercise, and the living environment. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on the patient's lifestyle and environmental factors into a generating AI, which can then filter the data.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the patient's geographical location information during data collection. For example, if the patient lives in an urban area, the data collection unit can prioritize the collection of restaurant meal data. For example, if the patient lives in a rural area, the data collection unit can prioritize the collection of home-cooked meal data. Furthermore, if the patient is traveling, the data collection unit can prioritize the collection of meal data at their travel destination. For example, if the patient is traveling, the data collection unit can prioritize the collection of meal data at their travel destination. This allows for the collection of more appropriate data by prioritizing the collection of highly relevant data by considering the patient's geographical location information. Geographical location information includes, but is not limited to, GPS data and address information. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit can input the patient's geographical location information into a generating AI, which can then prioritize the collection of highly relevant data.

[0042] The data collection unit can analyze the patient's social media activity and collect relevant data during data collection. For example, the data collection unit can collect details of meals shared by the patient on social media. For example, the data collection unit can collect details of exercise mentioned by the patient on social media. The data collection unit can also collect details of health conditions mentioned by the patient on social media. For example, the data collection unit can collect details of health conditions mentioned by the patient on social media. This allows for the collection of relevant data by analyzing the patient's social media activity. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on the patient's social media activity into a generating AI, which can then collect relevant data.

[0043] The analysis unit can improve the accuracy of its analysis by considering the interrelationships between data. For example, the analysis unit can analyze by considering the interrelationships between dietary data and energy expenditure data. For example, the analysis unit can analyze by considering the interrelationships between sugar intake and blood glucose levels. The analysis unit can also analyze by considering the interrelationships between salt intake and blood pressure. For example, the analysis unit can analyze by considering the interrelationships between salt intake and blood pressure. This improves the accuracy of the analysis by considering the interrelationships between data. Interrelationships between data include, but are not limited to, correlation analysis and identification of causal relationships. Some or all of the above processing in the analysis unit is performed using generative AI. For example, the analysis unit can input the interrelationships between dietary data and energy expenditure data into the generative AI, which can then improve the accuracy of the analysis.

[0044] The analysis unit can perform analysis while considering patient attribute information. For example, the analysis unit can perform analysis while considering the patient's age. For example, the analysis unit can perform analysis while considering the patient's gender. Furthermore, the analysis unit can also perform analysis while considering the patient's medical history. For example, the analysis unit can perform analysis while considering the patient's medical history. This allows for more individualized analysis by considering patient attribute information. Patient attribute information includes, but is not limited to, age, gender, and medical history. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input patient attribute information into the generation AI, and the generation AI can perform the analysis.

[0045] The analysis unit can perform analyses while considering the geographical distribution of the data. For example, the analysis unit can perform analyses while considering the dietary habits of the area where the patient lives. For example, the analysis unit can perform analyses while considering the exercise habits of the area where the patient lives. Furthermore, the analysis unit can also perform analyses while considering the medical resources of the area where the patient lives. For example, the analysis unit can perform analyses while considering the medical resources of the area where the patient lives. This allows for more region-appropriate analysis by considering the geographical distribution of the data. Geographical distribution includes, but is not limited to, differences in data from different regions and region-specific factors. Some or all of the above processing in the analysis unit is performed using generative AI. For example, the analysis unit can input data from the area where the patient lives into the generative AI, and the generative AI can perform the analysis.

[0046] The analysis department can improve the accuracy of its analysis by referring to relevant literature during the analysis process. For example, the analysis department can perform analysis by referring to the latest medical research. For example, the analysis department can perform analysis by referring to previous patient cases. The analysis department can also perform analysis by referring to medical guidelines. For example, the analysis department can perform analysis by referring to medical guidelines. This improves the accuracy of the analysis by referring to relevant literature. Relevant literature includes, but is not limited to, specific journals and research papers. Some or all of the above processing in the analysis department is performed using generative AI. For example, the analysis department can input relevant literature into the generative AI, which can then improve the accuracy of the analysis.

[0047] The creation unit can analyze a patient's past eating history to create an optimal meal plan. For example, the creation unit can create a plan that limits sugar intake based on the patient's past eating history. For example, the creation unit can create a plan that limits salt intake based on the patient's past eating history. The creation unit can also create a balanced plan based on the patient's past eating history. For example, the creation unit can create a balanced plan based on the patient's past eating history. This allows for the creation of a more appropriate meal plan by analyzing the patient's past eating history. Past eating history includes, but is not limited to, the type, quantity, and timing of meals. Some or all of the above processing in the creation unit is performed using a generation AI. For example, the creation unit can input the patient's past eating history into the generation AI, which can then create an optimal plan.

[0048] The creation unit can customize meal plans based on the patient's current health condition. For example, if the patient has diabetes, the creation unit can create a plan that limits sugar intake. For example, if the patient has high blood pressure, the creation unit can create a plan that limits salt intake. Furthermore, if the patient is obese, the creation unit can create a plan that includes calorie restriction. For example, if the patient is obese, the creation unit can create a plan that includes calorie restriction. This allows for the provision of more appropriate meal plans by customizing them based on the patient's current health condition. Current health conditions include, but are not limited to, blood glucose levels, blood pressure, and weight. Some or all of the above processing in the creation unit is performed using a generating AI. For example, the creation unit inputs the patient's current health condition into the generating AI, which can then customize the plan.

[0049] The creation unit can create an optimal meal plan by considering the patient's geographical location information. For example, if the patient lives in an urban area, the creation unit can create a plan that takes into account eating out. For example, if the patient lives in a rural area, the creation unit can create a plan that takes into account home-cooked meals. Furthermore, if the patient is traveling, the creation unit can create a plan that takes into account meals at the travel destination. For example, if the patient is traveling, the creation unit can create a plan that takes into account meals at the travel destination. This allows for the creation of a more appropriate meal plan by considering the patient's geographical location information. Geographical location information includes, but is not limited to, GPS data and address information. Some or all of the above processing in the creation unit is performed using a generating AI. For example, the creation unit can input the patient's geographical location information into the generating AI, which can then create an optimal plan.

[0050] The creation unit can analyze a patient's social media activity when creating a meal plan and propose a plan based on that. For example, the creation unit can create a plan based on the meal content the patient has shared on social media. For example, the creation unit can create a plan based on health goals the patient has mentioned on social media. The creation unit can also create a plan based on the food preferences the patient has mentioned on social media. For example, the creation unit can create a plan based on food preferences the patient has mentioned on social media. This allows the creation unit to propose a more appropriate meal plan by analyzing the patient's social media activity. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. Some or all of the above processing in the creation unit is performed using a generative AI. For example, the creation unit can input data on the patient's social media activity into the generative AI, which can then propose a plan.

[0051] The service provider can select the optimal service method when providing a meal plan by referring to the patient's past feedback. For example, the service provider can select the optimal service method based on the service method the patient has preferred in the past. For example, the service provider can select a service method that reflects improvements based on the patient's past feedback. The service provider can also select a customized service method based on the patient's past feedback. For example, the service provider can select a customized service method based on the patient's past feedback. This allows the plan to be provided in a more appropriate way by referring to the patient's past feedback. Past feedback includes, but is not limited to, patient evaluations and areas for improvement. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the patient's past feedback into a generating AI, which can then select the optimal service method.

[0052] The service provider can select the optimal service method when providing a meal plan, taking into account the patient's device information. For example, if the patient is using a smartphone, the service provider can select a service method that matches the screen size. For example, if the patient is using a tablet, the service provider can select a service method optimized for a larger screen. Furthermore, if the patient is using a smartwatch, the service provider can select a concise and highly visible service method. For example, if the patient is using a smartwatch, the service provider can select a concise and highly visible service method. This allows the service provider to deliver the plan in a more appropriate way by considering the patient's device information. Device information includes, but is not limited to, smartphones, tablets, and personal computers. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the patient's device information into a generating AI, which can then select the optimal service method.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The data collection unit can analyze the patient's past health data and select the optimal data collection method. For example, it can collect detailed data on the patient's diet based on their past eating history. It can also collect data on energy expenditure based on the patient's exercise history. By analyzing the patient's past health data, the optimal data collection method can be selected. Some or all of the above processing in the data collection unit may be performed using AI, or it may be performed without using AI.

[0055] The analysis unit can improve the accuracy of its analysis by considering the interrelationships between data. For example, it can analyze the relationship between dietary data and energy expenditure data. It can also analyze the relationship between sugar intake and blood glucose levels. By considering the interrelationships between data, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit is performed using generative AI.

[0056] The creation unit can analyze a patient's past eating history to create an optimal meal plan. For example, it can create a plan that limits sugar intake based on the patient's past eating history. It can also create a plan that limits salt intake based on the patient's past eating history. In this way, a more appropriate meal plan can be created by analyzing the patient's past eating history. Some or all of the above processing in the creation unit is performed using generation AI.

[0057] The service provider can select the optimal service method when providing a meal plan by referring to the patient's past feedback. For example, it can select the optimal service method based on the service method the patient has preferred in the past. It can also select a service method that reflects improvements based on the patient's past feedback. This allows the plan to be provided in a more appropriate way by referring to the patient's past feedback. Some or all of the above processing in the service provider may be performed using AI or not.

[0058] The service delivery unit can select the optimal delivery method when providing a meal plan, taking into account the patient's device information. For example, if the patient is using a smartphone, the service delivery unit can select a delivery method that matches the screen size. Similarly, if the patient is using a tablet, the service delivery unit can select a delivery method optimized for a larger screen. This allows for the delivery of the plan in a more appropriate manner by considering the patient's device information. Some or all of the above processing in the service delivery unit may be performed using AI, or it may be performed without AI.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The data collection unit collects data such as the patient's medical condition, sugar and salt intake, and energy expenditure. For example, it collects detailed data such as the patient's daily diet and exercise levels. By entering the contents of their meals into the app, patients can record their sugar and salt intake. Energy expenditure can also be monitored using devices such as smartwatches. For example, a smartwatch can measure the patient's heart rate and exercise level and calculate energy expenditure. Step 2: The analysis unit analyzes the data collected by the collection unit in real time. For example, it analyzes the data in real time based on previous patient cases and the latest medical research. The generating AI refers to past patient data and compares it with current patient data for analysis. It also performs analysis by referring to the latest research papers and medical guidelines. Step 3: The creation department creates a personalized meal plan based on the analysis results obtained by the analysis department. For example, it can create a meal plan that is optimal for the patient's health condition. For example, it can suggest a meal plan that reduces sugar intake for diabetic patients and a meal plan that reduces salt intake for hypertensive patients. It can also adjust the meal plan to take into account interactions between food and medication. For patients taking specific medications, it can suggest a diet that is rich in specific nutrients to maximize the effects of those medications. Step 4: The delivery department provides the meal plan created by the creation department. For example, the meal plan can be provided via email or app notification. It can also be provided in paper form, and the meal plan can be printed and mailed to the patient.

[0061] (Example of form 2) The dietary guidance system according to an embodiment of the present invention is a system that provides patient-appropriate dietary guidance using generative AI in conjunction with an online medical consultation service. The dietary guidance system collects data such as the patient's medical condition, sugar and salt intake, and energy expenditure. The generative AI analyzes this data in real time and creates a personalized meal plan based on previous patient cases and the latest medical research. This meal plan is adjusted to maximize the effects of the medications the patient is taking. This supports the improvement of the patient's health and maximizes medical benefits. For example, the dietary guidance system collects data such as the patient's medical condition, sugar and salt intake, and energy expenditure. At this time, it collects detailed data such as the content of the meals the patient eats on a daily basis and the amount of exercise the patient does. For example, by entering the contents of meals into an app, the patient can record their sugar and salt intake. It is also possible to monitor energy expenditure using a device such as a smartwatch. Next, the generative AI analyzes the collected data in real time. Based on previous patient cases and the latest medical research, the generative AI creates a meal plan that is optimal for the patient's health condition. For example, the system can suggest a meal plan that reduces sugar intake for diabetic patients and a meal plan that reduces salt intake for hypertensive patients. Furthermore, the generating AI also considers the interaction between food and medication and adjusts the meal plan accordingly. For instance, for patients taking a specific medication, it can suggest a diet rich in specific nutrients to maximize the effects of that medication. This supports the improvement of the patient's health and maximizes medical benefits. This system, in conjunction with online medical consultation services, can provide personalized dietary guidance tailored to each individual patient. Patients can continue to receive health management even after consultations and receive more effective treatment. For example, patients can input their meal details through an app, and the generating AI analyzes that data to suggest a meal plan, allowing patients to manage their health on a daily basis. In addition, because the generating AI analyzes data in real time, it can provide optimal meal plans based on the latest medical research.This allows the dietary guidance system to support improvements in patients' health and maximize medical benefits.

[0062] The dietary guidance system according to this embodiment comprises a data collection unit, an analysis unit, a data creation unit, and a data provision unit. The data collection unit collects data such as the patient's medical condition, sugar and salt intake, and energy expenditure. The data collection unit collects detailed data such as the content of meals and exercise levels that the patient consumes on a daily basis. For example, the patient can record their sugar and salt intake by entering the contents of their meals into an app. The data collection unit can also monitor energy expenditure using a device such as a smartwatch. For example, a smartwatch measures the patient's heart rate and exercise level and calculates energy expenditure. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. The analysis unit analyzes the data collected by the data collection unit in real time. The analysis unit analyzes the data in real time based on previous patient cases or the latest medical research, for example. For example, a generating AI refers to past patient data and performs analysis by comparing it with current patient data. The analysis unit can also analyze the data based on the latest medical research. For example, it performs analysis by referring to the latest research papers and medical guidelines. Some or all of the above-mentioned processes in the analysis unit are performed using generative AI. The creation unit creates a personalized meal plan based on the analysis results obtained by the analysis unit. For example, the creation unit creates a meal plan that is optimal for the patient's health condition. For example, it can suggest a meal plan that reduces sugar intake for diabetic patients and a meal plan that reduces salt intake for hypertensive patients. The creation unit can also adjust the meal plan considering the interaction between food and medication. For example, for patients taking a specific medication, it can suggest a meal that contains a lot of specific nutrients to maximize the effect of that medication. Some or all of the above-mentioned processes in the creation unit are performed using generative AI. The delivery unit provides the meal plan created by the creation unit. For example, the delivery unit provides the created meal plan to the patient. For example, the meal plan can be provided via email or app notification. The delivery unit can also provide it in paper form. For example, the meal plan can be printed and mailed to the patient.Some or all of the processing described above in the serving section may be performed using AI or not. This allows the dietary guidance system according to the embodiment to support the improvement of the patient's health condition and maximize medical benefits.

[0063] The data collection unit collects data on the patient's medical condition, sugar and salt intake, and energy expenditure. Specifically, it collects detailed data on the patient's daily diet and exercise levels. For example, patients can record their sugar and salt intake by entering their meal contents into a dedicated app. This app has functions to scan food barcodes and automatically analyze nutrients from photos of meals. The data collection unit can also monitor energy expenditure using devices such as smartwatches and fitness trackers. These devices measure the patient's heart rate, steps, exercise level, and sleep patterns to calculate energy expenditure. Furthermore, the data collection unit can acquire biometric data such as blood glucose levels and blood pressure from medical devices. This allows the data collection unit to comprehensively understand the patient's health status and collect data in real time. The collected data is stored on a cloud server and made accessible to the analysis and creation units. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0064] The analysis department analyzes data collected by the data collection department in real time. Specifically, it analyzes data in real time based on previous patient cases and the latest medical research. For example, a generative AI refers to past patient data and compares it with current patient data for analysis. The generative AI can learn from vast amounts of past patient data and extract patterns and trends. This allows it to identify abnormal values ​​and high-risk patterns by comparing them with current patient data. The analysis department can also analyze data based on the latest medical research. For example, it refers to the latest research papers and medical guidelines to evaluate patient data and propose optimal treatment and dietary plans. The generative AI uses natural language processing technology to automatically analyze the latest research papers and medical guidelines and extract relevant information. This allows the analysis department to analyze collected data quickly and accurately and understand patients' health status in real time. Furthermore, the analysis department can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, it can predict fluctuations in risk for specific patients based on historical data and formulate future countermeasures. This allows the analysis department to not only understand the situation in real time but also to handle long-term risk management, improving the reliability and safety of the entire system.

[0065] The creation unit creates personalized meal plans based on the analysis results obtained by the analysis unit. Specifically, it creates meal plans that are optimal for the patient's health condition. For example, it can suggest a meal plan that reduces sugar intake for diabetic patients and a meal plan that reduces salt intake for hypertensive patients. The generation AI calculates the optimal nutritional balance based on the patient's individual health data and proposes specific meal menus. For example, the generation AI can suggest ingredient selections and cooking methods considering the patient's preferences and allergy information. The creation unit can also adjust meal plans considering interactions between food and medication. For example, for patients taking a specific medication, it can suggest meals that are rich in specific nutrients to maximize the effect of that medication. The generation AI can refer to a drug database and evaluate drug-food interactions. This allows the creation unit to provide meal plans that are optimal for the patient's health condition and maximize treatment effectiveness. Furthermore, the creation unit can adjust meal plans considering seasonal and regional characteristics. For example, it can propose menus that utilize seasonal ingredients or plans that incorporate traditional local dishes. This allows the creation unit to provide attractive and easy-to-follow meal plans for patients and support the improvement of their health condition.

[0066] The service provider provides the meal plans created by the development team. Specifically, they provide the created meal plans to patients. For example, meal plans can be provided via email or app notifications. Patients can check their meal plans anytime, anywhere using their smartphones or tablets. The service provider can also provide meal plans in paper form. For example, they can print out the meal plans and mail them to patients. This allows them to serve patients who do not use digital devices. Furthermore, the service provider can provide additional information and resources to support the implementation of the meal plans. For example, they can provide detailed explanations of recipes and cooking methods, shopping lists for ingredients, and advice on meal timing and quantity. The service provider can also collect feedback from patients and use it to improve meal plans. For example, by having patients input the results and their impressions of implementing the meal plan into the app, the service provider can collect this information and incorporate it into creating the next plan. This allows the service provider to continuously provide patients with optimal meal plans and support the improvement of their health. Furthermore, the service provider can collaborate with patients' families and medical staff to support the implementation of meal plans. For example, by sharing the contents of the meal plan with family members and requesting their cooperation, they can help patients implement the plan more effectively. This allows the service provider to comprehensively support the improvement of patients' health conditions and maximize medical benefits.

[0067] The data collection unit can collect detailed data on the patient's daily diet and exercise levels. For example, the unit can record sugar and salt intake by having the patient input their meal details into an app. The data collection unit can also monitor energy expenditure using devices such as smartwatches. For example, a smartwatch can measure the patient's heart rate and exercise level and calculate energy expenditure. The data collection unit can also provide a dedicated application to collect detailed data on the patient's daily diet and exercise levels. For example, the application can automatically record and collect data on the patient's diet and exercise levels. This allows for more accurate analysis by collecting detailed data on the patient's daily diet and exercise levels. Detailed data may include, but is not limited to, the type, quantity, and timing of meals, and the type, duration, and intensity of exercise. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the patient's entered data on diet and exercise levels into a generating AI, which can then collect and organize the data.

[0068] The analysis unit can analyze data in real time based on previous patient cases and the latest medical research. For example, the analysis unit can refer to previous patient cases and compare them with current patient data for analysis. For example, the generating AI can refer to past patient data and compare it with current patient data for analysis. The analysis unit can also analyze data based on the latest medical research. For example, it can refer to the latest research papers and medical guidelines for analysis. Furthermore, in order to analyze data in real time, the analysis unit can start analysis immediately as soon as data is collected. For example, it can analyze data sent from the collection unit in real time and output the analysis results immediately. This allows for more accurate analysis results by analyzing data in real time based on previous patient cases and the latest medical research. Previous patient cases include, but are not limited to, past medical records and treatment results. The latest medical research includes, but are not limited to, the latest papers and research data. Some or all of the above processing in the analysis unit is performed using the generating AI. For example, the analysis unit can input past patient data and the latest medical research into the generating AI, which can then analyze the data.

[0069] The creation unit can create a meal plan that is optimal for the patient's health condition. For example, it can propose a meal plan that limits sugar intake for diabetic patients and a meal plan that limits salt intake for hypertensive patients. The creation unit can also create meal plans that take into account the patient's preferences and allergy information. For example, if a patient is allergic to a particular food, it will create a meal plan that does not include that food. Furthermore, the creation unit can create meal plans that take into account the patient's nutritional balance. For example, to optimize the patient's nutritional balance, it will propose a meal plan that is rich in a particular nutrient. In this way, by creating a meal plan that is optimal for the patient's health condition, it is possible to support the improvement of the patient's health condition. An optimal meal plan includes, but is not limited to, nutritional balance, calorie restriction, and patient preferences. Some or all of the above processing in the creation unit is performed using a generation AI. For example, the creation unit inputs the patient's health condition, preferences, and allergy information into the generation AI, which can then create an optimal meal plan.

[0070] The planning unit can adjust meal plans considering interactions between food and medication. For example, for a patient taking a specific medication, the planning unit can suggest a diet rich in specific nutrients to maximize the effect of that medication. For example, for a patient taking antibiotics, it can suggest a diet rich in probiotics. The planning unit can also adjust meal plans to avoid foods that affect the absorption of medication. For example, for a patient taking a specific medication, it can suggest avoiding grapefruit. Furthermore, the planning unit can adjust meal plans to avoid foods that reduce the effect of medication. For example, for a patient taking anticoagulants, it can suggest avoiding foods rich in vitamin K. By adjusting meal plans to consider interactions between food and medication, the effect of medication can be maximized, and the medical benefits can be maximized. Interactions between food and medication include, but are not limited to, foods that affect the absorption of medication or foods that reduce the effect of medication. Some or all of the above processing in the planning unit is performed using a generating AI. For example, the planning unit inputs information about the medications the patient is taking into the generating AI, and the generating AI can adjust the meal plan.

[0071] The service provider can provide the created meal plan to the patient. For example, the service provider can provide the created meal plan to the patient via email or app notification. The service provider can also provide it in paper form. For example, the meal plan can be printed and mailed to the patient. Furthermore, the service provider can collect patient feedback when providing the meal plan. For example, the patient can provide feedback on the meal plan through an app. This ensures that the patient receives appropriate dietary guidance by providing them with the created meal plan. Delivery includes, but is not limited to, email, app notification, and paper form. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input the created meal plan into a generating AI, which can then determine how to provide it to the patient.

[0072] The data collection unit can estimate the patient's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the patient is stressed, the data collection unit will collect data during relaxed periods. For example, if the patient is relaxed, the data collection unit can collect detailed data. The data collection unit can also perform simplified data collection if the patient is in a hurry. For example, if the patient is in a hurry, the data collection unit can collect only the minimum necessary data. By adjusting the timing of data collection based on the patient's emotions, data can be collected at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input patient emotion data into a generative AI, which can then adjust the timing of data collection.

[0073] The data collection unit can analyze a patient's past health data and select the optimal data collection method. For example, the data collection unit can collect detailed data on the patient's diet based on their past eating history. For example, the data collection unit can collect data on energy expenditure based on the patient's exercise history. The data collection unit can also collect data on specific health indicators based on the patient's medical history. For example, the data collection unit can refer to the patient's past medical records to collect data on specific health indicators. By analyzing the patient's past health data, the optimal data collection method can be selected. The optimal data collection method includes, but is not limited to, data accuracy, ease of collection, and patient burden. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the patient's past health data into a generating AI, which can then select the optimal data collection method.

[0074] The data collection unit can filter data while considering the patient's lifestyle and environmental factors. For example, the data collection unit can collect dietary data based on the patient's lifestyle. For example, the data collection unit can collect energy consumption data based on the patient's living environment. The data collection unit can also collect stress level data based on the patient's occupation. For example, the data collection unit can collect stress level data based on the patient's occupation. By filtering the data while considering the patient's lifestyle and environmental factors, more relevant data can be collected. Lifestyle and environmental factors include, but are not limited to, the timing of meals, the frequency of exercise, and the living environment. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on the patient's lifestyle and environmental factors into a generating AI, which can then filter the data.

[0075] The data collection unit can estimate the patient's emotions and prioritize the data to collect based on the estimated emotions. For example, if the patient is stressed, the data collection unit will prioritize collecting stress-related data. For example, if the patient is relaxed, the data collection unit can prioritize collecting detailed dietary data. Also, if the patient is in a hurry, the data collection unit can prioritize collecting energy consumption data. For example, if the patient is in a hurry, the data collection unit can collect only the minimum necessary data. This allows for the priority collection of more important data by prioritizing the data to be collected based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the patient's emotion data into a generative AI and determine the priority of the data to be collected by the generative AI.

[0076] The data collection unit can prioritize the collection of highly relevant data by considering the patient's geographical location information during data collection. For example, if the patient lives in an urban area, the data collection unit can prioritize the collection of restaurant meal data. For example, if the patient lives in a rural area, the data collection unit can prioritize the collection of home-cooked meal data. Furthermore, if the patient is traveling, the data collection unit can prioritize the collection of meal data at their travel destination. For example, if the patient is traveling, the data collection unit can prioritize the collection of meal data at their travel destination. This allows for the collection of more appropriate data by prioritizing the collection of highly relevant data by considering the patient's geographical location information. Geographical location information includes, but is not limited to, GPS data and address information. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit can input the patient's geographical location information into a generating AI, which can then prioritize the collection of highly relevant data.

[0077] The data collection unit can analyze the patient's social media activity and collect relevant data during data collection. For example, the data collection unit can collect details of meals shared by the patient on social media. For example, the data collection unit can collect details of exercise mentioned by the patient on social media. The data collection unit can also collect details of health conditions mentioned by the patient on social media. For example, the data collection unit can collect details of health conditions mentioned by the patient on social media. This allows for the collection of relevant data by analyzing the patient's social media activity. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on the patient's social media activity into a generating AI, which can then collect relevant data.

[0078] The analysis unit can estimate the patient's emotions and adjust the analysis criteria based on the estimated emotions. For example, if the patient is stressed, the analysis unit will prioritize stress-related data in its analysis. For example, if the patient is relaxed, the analysis unit can prioritize detailed dietary data in its analysis. Furthermore, if the patient is in a hurry, the analysis unit can prioritize energy expenditure data in its analysis. For example, if the patient is in a hurry, the analysis unit will prioritize the minimum necessary data in its analysis. This allows for more appropriate analysis by adjusting the analysis criteria based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 in the analysis unit are performed using generative AI. For example, the analysis unit can input patient emotion data into the generative AI, which can then adjust the analysis criteria.

[0079] The analysis unit can improve the accuracy of its analysis by considering the interrelationships between data. For example, the analysis unit can analyze by considering the interrelationships between dietary data and energy expenditure data. For example, the analysis unit can analyze by considering the interrelationships between sugar intake and blood glucose levels. The analysis unit can also analyze by considering the interrelationships between salt intake and blood pressure. For example, the analysis unit can analyze by considering the interrelationships between salt intake and blood pressure. This improves the accuracy of the analysis by considering the interrelationships between data. Interrelationships between data include, but are not limited to, correlation analysis and identification of causal relationships. Some or all of the above processing in the analysis unit is performed using generative AI. For example, the analysis unit can input the interrelationships between dietary data and energy expenditure data into the generative AI, which can then improve the accuracy of the analysis.

[0080] The analysis unit can perform analysis while considering patient attribute information. For example, the analysis unit can perform analysis while considering the patient's age. For example, the analysis unit can perform analysis while considering the patient's gender. Furthermore, the analysis unit can also perform analysis while considering the patient's medical history. For example, the analysis unit can perform analysis while considering the patient's medical history. This allows for more individualized analysis by considering patient attribute information. Patient attribute information includes, but is not limited to, age, gender, and medical history. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input patient attribute information into the generation AI, and the generation AI can perform the analysis.

[0081] The analysis unit can estimate the patient's emotions and adjust the display order of the analysis results based on the estimated emotions. For example, if the patient is stressed, the analysis unit can prioritize displaying stress-related analysis results. For example, if the patient is relaxed, the analysis unit can prioritize displaying detailed dietary analysis results. The analysis unit can also prioritize displaying energy consumption analysis results if the patient is in a hurry. For example, if the patient is in a hurry, the analysis unit can prioritize displaying only the essential analysis results. This allows for the prioritization of more relevant information by adjusting the display order of analysis results based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit is performed using generative AI. For example, the analysis unit inputs patient emotion data into the generative AI, which can then adjust the display order of the analysis results.

[0082] The analysis unit can perform analyses while considering the geographical distribution of the data. For example, the analysis unit can perform analyses while considering the dietary habits of the area where the patient lives. For example, the analysis unit can perform analyses while considering the exercise habits of the area where the patient lives. Furthermore, the analysis unit can also perform analyses while considering the medical resources of the area where the patient lives. For example, the analysis unit can perform analyses while considering the medical resources of the area where the patient lives. This allows for more region-appropriate analysis by considering the geographical distribution of the data. Geographical distribution includes, but is not limited to, differences in data from different regions and region-specific factors. Some or all of the above processing in the analysis unit is performed using generative AI. For example, the analysis unit can input data from the area where the patient lives into the generative AI, and the generative AI can perform the analysis.

[0083] The analysis department can improve the accuracy of its analysis by referring to relevant literature during the analysis process. For example, the analysis department can perform analysis by referring to the latest medical research. For example, the analysis department can perform analysis by referring to previous patient cases. The analysis department can also perform analysis by referring to medical guidelines. For example, the analysis department can perform analysis by referring to medical guidelines. This improves the accuracy of the analysis by referring to relevant literature. Relevant literature includes, but is not limited to, specific journals and research papers. Some or all of the above processing in the analysis department is performed using generative AI. For example, the analysis department can input relevant literature into the generative AI, which can then improve the accuracy of the analysis.

[0084] The creation unit can estimate the patient's emotions and adjust the method of creating the meal plan based on the estimated emotions. For example, if the patient is stressed, the creation unit can create a meal plan that is effective in reducing stress. For example, if the patient is relaxed, the creation unit can create a detailed meal plan. Also, if the patient is in a hurry, the creation unit can create a simplified meal plan. For example, if the patient is in a hurry, the creation unit can create a minimum-necessary meal plan. In this way, by adjusting the method of creating the meal plan based on the patient's emotions, a more appropriate meal plan can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the creation unit is performed using generative AI. For example, the creation unit can input patient emotion data into the generative AI, and the generative AI can adjust the method of creating the meal plan.

[0085] The creation unit can analyze a patient's past eating history to create an optimal meal plan. For example, the creation unit can create a plan that limits sugar intake based on the patient's past eating history. For example, the creation unit can create a plan that limits salt intake based on the patient's past eating history. The creation unit can also create a balanced plan based on the patient's past eating history. For example, the creation unit can create a balanced plan based on the patient's past eating history. This allows for the creation of a more appropriate meal plan by analyzing the patient's past eating history. Past eating history includes, but is not limited to, the type, quantity, and timing of meals. Some or all of the above processing in the creation unit is performed using a generation AI. For example, the creation unit can input the patient's past eating history into the generation AI, which can then create an optimal plan.

[0086] The creation unit can customize meal plans based on the patient's current health condition. For example, if the patient has diabetes, the creation unit can create a plan that limits sugar intake. For example, if the patient has high blood pressure, the creation unit can create a plan that limits salt intake. Furthermore, if the patient is obese, the creation unit can create a plan that includes calorie restriction. For example, if the patient is obese, the creation unit can create a plan that includes calorie restriction. This allows for the provision of more appropriate meal plans by customizing them based on the patient's current health condition. Current health conditions include, but are not limited to, blood glucose levels, blood pressure, and weight. Some or all of the above processing in the creation unit is performed using a generating AI. For example, the creation unit inputs the patient's current health condition into the generating AI, which can then customize the plan.

[0087] The creation unit can estimate the patient's emotions and prioritize meal plans based on those emotions. For example, if the patient is stressed, the creation unit will prioritize meal plans that are effective in reducing stress. For example, if the patient is relaxed, the creation unit may prioritize detailed meal plans. The creation unit may also prioritize simplified meal plans if the patient is in a hurry. For example, if the patient is in a hurry, the creation unit may prioritize a minimum-necessary meal plan. This allows for the prioritization of more important plans by determining meal plan priorities based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the creation unit is performed using generative AI. For example, the creation unit can input patient emotion data into the generative AI, which can then determine the priority of meal plans.

[0088] The creation unit can create an optimal meal plan by considering the patient's geographical location information. For example, if the patient lives in an urban area, the creation unit can create a plan that takes into account eating out. For example, if the patient lives in a rural area, the creation unit can create a plan that takes into account home-cooked meals. Furthermore, if the patient is traveling, the creation unit can create a plan that takes into account meals at the travel destination. For example, if the patient is traveling, the creation unit can create a plan that takes into account meals at the travel destination. This allows for the creation of a more appropriate meal plan by considering the patient's geographical location information. Geographical location information includes, but is not limited to, GPS data and address information. Some or all of the above processing in the creation unit is performed using a generating AI. For example, the creation unit can input the patient's geographical location information into the generating AI, which can then create an optimal plan.

[0089] The creation unit can analyze a patient's social media activity when creating a meal plan and propose a plan based on that. For example, the creation unit can create a plan based on the meal content the patient has shared on social media. For example, the creation unit can create a plan based on health goals the patient has mentioned on social media. The creation unit can also create a plan based on the food preferences the patient has mentioned on social media. For example, the creation unit can create a plan based on food preferences the patient has mentioned on social media. This allows the creation unit to propose a more appropriate meal plan by analyzing the patient's social media activity. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. Some or all of the above processing in the creation unit is performed using a generative AI. For example, the creation unit can input data on the patient's social media activity into the generative AI, which can then propose a plan.

[0090] The service provider can estimate the patient's emotions and adjust the delivery method of the meal plan based on the estimated emotions. For example, if the patient is stressed, the service provider can provide the meal plan with a simple interface. For example, if the patient is relaxed, the service provider can provide the meal plan with an interface containing detailed information. The service provider can also provide the meal plan with voice guidance if the patient is in a hurry. For example, if the service provider is in a hurry, the service provider can provide the meal plan with voice guidance. This allows the service provider to deliver the plan in a more appropriate way by adjusting the delivery method based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input patient emotion data into a generative AI, which can then adjust the delivery method.

[0091] The service provider can select the optimal service method when providing a meal plan by referring to the patient's past feedback. For example, the service provider can select the optimal service method based on the service method the patient has preferred in the past. For example, the service provider can select a service method that reflects improvements based on the patient's past feedback. The service provider can also select a customized service method based on the patient's past feedback. For example, the service provider can select a customized service method based on the patient's past feedback. This allows the plan to be provided in a more appropriate way by referring to the patient's past feedback. Past feedback includes, but is not limited to, patient evaluations and areas for improvement. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the patient's past feedback into a generating AI, which can then select the optimal service method.

[0092] The service provider can estimate the patient's emotions and adjust the order in which meal plans are provided based on the estimated emotions. For example, if the patient is stressed, the service provider can prioritize providing meal plans that are effective in reducing stress. For example, if the patient is relaxed, the service provider can prioritize providing detailed meal plans. The service provider can also prioritize providing simplified meal plans if the patient is in a hurry. For example, if the service provider is in a hurry, the service provider can prioritize providing the minimum necessary meal plans. This allows for the prioritization of more important plans by adjusting the order in which meal plans are provided based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input patient emotion data into a generative AI, which can then adjust the order in which meals are provided.

[0093] The service provider can select the optimal service method when providing a meal plan, taking into account the patient's device information. For example, if the patient is using a smartphone, the service provider can select a service method that matches the screen size. For example, if the patient is using a tablet, the service provider can select a service method optimized for a larger screen. Furthermore, if the patient is using a smartwatch, the service provider can select a concise and highly visible service method. For example, if the patient is using a smartwatch, the service provider can select a concise and highly visible service method. This allows the service provider to deliver the plan in a more appropriate way by considering the patient's device information. Device information includes, but is not limited to, smartphones, tablets, and personal computers. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the patient's device information into a generating AI, which can then select the optimal service method.

[0094] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0095] The data collection unit can estimate the patient's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the patient is stressed, data collection can be performed during a relaxed time. If the patient is in a hurry, simplified data collection can be performed. By adjusting the timing of data collection based on the patient's emotions, data can be collected at a more appropriate time. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0096] The data collection unit can analyze the patient's past health data and select the optimal data collection method. For example, it can collect detailed data on the patient's diet based on their past eating history. It can also collect data on energy expenditure based on the patient's exercise history. By analyzing the patient's past health data, the optimal data collection method can be selected. Some or all of the above processing in the data collection unit may be performed using AI, or it may be performed without using AI.

[0097] The analysis unit can estimate the patient's emotions and adjust the analysis criteria based on those estimated emotions. For example, if the patient is stressed, stress-related data can be given more weight in the analysis. Conversely, if the patient is relaxed, detailed dietary data can be given more weight in the analysis. By adjusting the analysis criteria based on the patient's emotions, a more appropriate analysis becomes possible. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the processing described above in the analysis unit is performed using generative AI.

[0098] The analysis unit can improve the accuracy of its analysis by considering the interrelationships between data. For example, it can analyze the relationship between dietary data and energy expenditure data. It can also analyze the relationship between sugar intake and blood glucose levels. By considering the interrelationships between data, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit is performed using generative AI.

[0099] The creation unit can estimate the patient's emotions and adjust the method of creating the meal plan based on the estimated emotions. For example, if the patient is stressed, it can create a meal plan that is effective in reducing stress. If the patient is relaxed, it can also create a more detailed meal plan. In this way, by adjusting the method of creating the meal plan based on the patient's emotions, a more appropriate meal plan can be provided. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the above-mentioned processes in the creation unit are performed using generative AI.

[0100] The creation unit can analyze a patient's past eating history to create an optimal meal plan. For example, it can create a plan that limits sugar intake based on the patient's past eating history. It can also create a plan that limits salt intake based on the patient's past eating history. In this way, a more appropriate meal plan can be created by analyzing the patient's past eating history. Some or all of the above processing in the creation unit is performed using generation AI.

[0101] The service provider can estimate the patient's emotions and adjust the way the meal plan is delivered based on those emotions. For example, if the patient is stressed, the meal plan can be delivered with a simple interface. Conversely, if the patient is relaxed, the meal plan can be delivered with an interface that includes detailed information. By adjusting the delivery method of the meal plan based on the patient's emotions, the plan can be delivered in a more appropriate way. Emotion estimation is achieved using an emotion engine or generative AI, etc. Some or all of the processing described above in the service provider may be performed using AI or not.

[0102] The service provider can select the optimal service method when providing a meal plan by referring to the patient's past feedback. For example, it can select the optimal service method based on the service method the patient has preferred in the past. It can also select a service method that reflects improvements based on the patient's past feedback. This allows the plan to be provided in a more appropriate way by referring to the patient's past feedback. Some or all of the above processing in the service provider may be performed using AI or not.

[0103] The service provider can estimate the patient's emotions and adjust the order in which meal plans are provided based on the estimated emotions. For example, if the patient is stressed, meal plans effective in reducing stress can be prioritized. Conversely, if the patient is relaxed, more detailed meal plans can be prioritized. By adjusting the order in which meal plans are provided based on the patient's emotions, more important plans can be prioritized. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the processing described above in the service provider may be performed using AI or not.

[0104] The service delivery unit can select the optimal delivery method when providing a meal plan, taking into account the patient's device information. For example, if the patient is using a smartphone, the service delivery unit can select a delivery method that matches the screen size. Similarly, if the patient is using a tablet, the service delivery unit can select a delivery method optimized for a larger screen. This allows for the delivery of the plan in a more appropriate manner by considering the patient's device information. Some or all of the above processing in the service delivery unit may be performed using AI, or it may be performed without AI.

[0105] The following briefly describes the processing flow for example form 2.

[0106] Step 1: The data collection unit collects data such as the patient's medical condition, sugar and salt intake, and energy expenditure. For example, it collects detailed data such as the patient's daily diet and exercise levels. By entering the contents of their meals into the app, patients can record their sugar and salt intake. Energy expenditure can also be monitored using devices such as smartwatches. For example, a smartwatch can measure the patient's heart rate and exercise level and calculate energy expenditure. Step 2: The analysis unit analyzes the data collected by the collection unit in real time. For example, it analyzes the data in real time based on previous patient cases and the latest medical research. The generating AI refers to past patient data and compares it with current patient data for analysis. It also performs analysis by referring to the latest research papers and medical guidelines. Step 3: The creation department creates a personalized meal plan based on the analysis results obtained by the analysis department. For example, it can create a meal plan that is optimal for the patient's health condition. For example, it can suggest a meal plan that reduces sugar intake for diabetic patients and a meal plan that reduces salt intake for hypertensive patients. It can also adjust the meal plan to take into account interactions between food and medication. For patients taking specific medications, it can suggest a diet that is rich in specific nutrients to maximize the effects of those medications. Step 4: The delivery department provides the meal plan created by the creation department. For example, the meal plan can be provided via email or app notification. It can also be provided in paper form, and the meal plan can be printed and mailed to the patient.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] Each of the multiple elements described above, including the collection unit, analysis unit, creation unit, and provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects data such as the patient's medical condition, sugar and salt intake, and energy consumption using an app on the smart device 14 or a smartwatch. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data in real time. The creation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and creates a personalized meal plan based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the smart device 14, and provides the created meal plan to the patient. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0111] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the data collection unit, analysis unit, creation unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects data such as the patient's medical condition, sugar and salt intake, and energy expenditure using an app on the smart glasses 214 or a smartwatch. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data in real time. The creation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and creates a personalized meal plan based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214, and provides the created meal plan to the patient. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0127] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the collection unit, analysis unit, creation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects data such as the patient's medical condition, sugar and salt intake, and energy consumption using an app on the headset terminal 314 or a smartwatch. The analysis unit is implemented in real time by the specific processing unit 290 of the data processing unit 12. The creation unit is implemented in real time by the specific processing unit 290 of the data processing unit 12. Based on the analysis results, a personalized meal plan is created. The provision unit is implemented in real time by the control unit 46A of the headset terminal 314. 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.

[0143] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements described above, including the collection unit, analysis unit, creation unit, and provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects data such as the patient's medical condition, sugar and salt intake, and energy consumption using an app or smartwatch on the robot 414. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data in real time. The creation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and creates a personalized meal plan based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the robot 414, and provides the created meal plan to the patient. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] (Note 1) A data collection unit that collects data such as the patient's medical condition, sugar and salt intake, and energy expenditure, An analysis unit analyzes the data collected by the aforementioned collection unit in real time, A creation unit that creates a personalized meal plan based on the analysis results obtained by the aforementioned analysis unit, The system includes a provisioning unit that provides the meal plan created by the creation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect detailed data on the patient's daily diet, exercise levels, and other relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is Data is analyzed in real time based on previous patient cases and the latest medical research. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned creation unit, We create a meal plan that is best suited to the patient's health condition. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned creation unit, Adjust your meal plan to take into account food-medication interactions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Provide the created meal plan to the patient. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the patient's emotions and adjust the timing of data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the patient's past health data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed considering the patient's lifestyle and environmental factors. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system estimates the patient's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, analyze patients' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is We estimate the patient's emotions and adjust the analysis criteria based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is When performing analysis, consider the interrelationships between data to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is When performing the analysis, consider the patient's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is The system estimates the patient's emotions and adjusts the display order of the analysis results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is When performing analysis, consider the geographical distribution of the data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, refer to relevant literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned creation unit, We estimate the patient's emotions and adjust the meal plan creation method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned creation unit, When creating a meal plan, we analyze the patient's past eating history to create the optimal plan. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned creation unit, When creating a meal plan, customize the plan based on the patient's current health condition. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned creation unit, The system estimates the patient's emotions and prioritizes meal plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned creation unit, When creating a meal plan, we take the patient's geographical location into consideration to create the optimal plan. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned creation unit, When creating a meal plan, we analyze the patient's social media activity and propose a plan based on that analysis. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, The system estimates the patient's emotions and adjusts the delivery method of the meal plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing meal plans, we select the optimal delivery method by referring to the patient's past feedback. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, The system estimates the patient's emotions and adjusts the order in which meals are served based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing meal plans, the optimal delivery method is selected considering the patient's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0179] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data such as the patient's medical condition, sugar and salt intake, and energy expenditure, An analysis unit analyzes the data collected by the aforementioned collection unit in real time, A creation unit that creates a personalized meal plan based on the analysis results obtained by the aforementioned analysis unit, The system includes a provisioning unit that provides the meal plan created by the creation unit. A system characterized by the following features.

2. The aforementioned collection unit is We collect detailed data on the patient's daily diet, exercise levels, and other relevant information. The system according to feature 1.

3. The aforementioned analysis unit is Data is analyzed in real time based on previous patient cases and the latest medical research. The system according to feature 1.

4. The aforementioned creation unit, We create a meal plan that is best suited to the patient's health condition. The system according to feature 1.

5. The aforementioned creation unit, Adjust your meal plan to take into account food-medication interactions. The system according to feature 1.

6. The aforementioned supply unit is, Provide the created meal plan to the patient. The system according to feature 1.

7. The aforementioned collection unit is We estimate the patient's emotions and adjust the timing of data collection based on the estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the patient's past health data and select the optimal data collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting data, filtering is performed considering the patient's lifestyle and environmental factors. The system according to feature 1.

10. The aforementioned collection unit is The system estimates the patient's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

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