system

Abstract

The system according to this embodiment aims to analyze the user's health data in real time and provide personalized meal suggestions. [Solution] The system according to the embodiment comprises a selection unit, an analysis unit, a response unit, and a suggestion unit. The selection unit allows the user to select a generated AI service. The analysis unit analyzes health data based on the service selected by the selection unit. The response unit responds to changes in physical condition in real time based on the data analyzed by the analysis unit. The suggestion unit makes individual meal suggestions based on the information obtained by the response unit.

Description

Technical Field

[0006] ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the real-time analysis of the user's health data and the provision of individual dietary suggestions have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze the user's health data in real time and provide individual dietary suggestions.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a selection unit, an analysis unit, a response unit, and a suggestion unit. The selection unit allows the user to select a generated AI service. The analysis unit analyzes health data based on the service selected by the selection unit. The response unit responds to changes in physical condition in real time based on the data analyzed by the analysis unit. The suggestion unit makes individual meal suggestions based on the information obtained by the response unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the user's health data in real time and provide personalized meal suggestions. [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 manages 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 health management system according to an embodiment of the present invention is a system that utilizes generative AI to personalize user health management. This health management system allows users to select generative AI services individually or in combination. Next, based on the selected services, the generative AI analyzes the user's health data and trends. Furthermore, the generative AI responds to changes in the user's physical condition in real time and provides appropriate advice. The generative AI also provides personalized meal suggestions. This mechanism allows users to receive optimal health management, making it extremely useful for health-conscious consumers and those seeking to improve their health. For example, health data analysis analyzes the user's past health data to predict future health risks. In real-time response, if the user experiences a change in their physical condition, the generative AI analyzes that information and provides appropriate advice. Personalized meal suggestions consider the user's nutritional status and dietary preferences to propose an optimal meal plan. Thus, a personalized healthcare plan utilizing generative AI makes user health management more personalized and is extremely useful for health-conscious consumers and those seeking to improve their health. As a result, the health management system can personalize user health management and provide appropriate advice and meal suggestions in real time.

[0029] The health management system according to this embodiment comprises a selection unit, an analysis unit, a response unit, and a suggestion unit. The selection unit allows the user to select a generated AI service. For example, the selection unit allows the user to select from multiple generated AI services related to health management. The selection unit can also suggest the optimal service based on the user's preferences and past selection history. The analysis unit analyzes health data based on the service selected by the selection unit. For example, the analysis unit collects and analyzes health data such as the user's heart rate, blood pressure, and body temperature. The analysis unit can use AI to detect patterns in health data and detect abnormalities. The response unit responds to changes in physical condition in real time based on the data analyzed by the analysis unit. For example, if the user feels a change in physical condition, the response unit analyzes that information and provides appropriate advice. The response unit can use AI to quickly respond to changes in the user's physical condition. The suggestion unit makes individual meal suggestions based on the information obtained by the response unit. For example, the suggestion unit considers the user's nutritional status and food preferences and proposes the optimal meal plan. The suggestion unit can use AI to make optimal meal suggestions to the user. As a result, the health management system according to this embodiment can personalize the user's health management and provide appropriate advice and dietary suggestions in real time.

[0030] The selection section allows the user to choose a generative AI service. For example, the selection section allows the user to choose from multiple generative AI services related to health management. Specifically, the selection section displays a list of available generative AI services to the user through the user interface. This list includes the characteristics and functions offered for each service, as well as past user reviews, allowing the user to select the most suitable service based on this information. Furthermore, the selection section also has a function to analyze the user's past selection history and usage to suggest the service best suited to the user's preferences and needs. For example, it will prioritize displaying a service to a user who has frequently used a particular generative AI service in the past. The selection section can also recommend specific generative AI services based on the user's health status and goals. For example, if a user is aiming to lose weight, it will suggest a generative AI service specializing in diet management. In this way, the selection section supports users in quickly and easily selecting the optimal generative AI service, improving the efficiency of health management.

[0031] The Analysis Department analyzes health data based on the services selected by the Selection Department. Specifically, the Analysis Department collects and analyzes health data such as the user's heart rate, blood pressure, and body temperature. This data is collected in real time through wearable devices worn by the user or smartphone apps. The Analysis Department uses AI to analyze this data and detect patterns in health data. For example, it can analyze patterns of heart rate fluctuations and blood pressure increases and decreases to detect abnormalities. Furthermore, by comparing current data with past data, the Analysis Department grasps long-term health trends and monitors changes in the user's health status. The AI ​​uses machine learning algorithms to identify abnormal patterns and risk factors from the user's health data. For example, if the heart rate remains higher than normal, it can identify causes such as stress or lack of exercise and issue a warning to the user. This allows the Analysis Department to understand the user's health status in detail and respond quickly if an abnormality occurs.

[0032] The response unit responds to changes in the user's physical condition in real time based on data analyzed by the analysis unit. Specifically, when the response unit senses a change in the user's physical condition, it analyzes the information and provides appropriate advice. For example, if the user feels fatigued, the response unit collects this information and compares it with past data to identify the cause. The AI ​​analyzes the user's health data to determine whether the fatigue is caused by lack of sleep or nutritional deficiencies. Furthermore, the response unit provides specific advice to the user. For example, if lack of sleep is the cause, it suggests going to bed earlier or finding ways to relax. If nutritional deficiencies are the cause, it recommends consuming foods containing specific nutrients. By responding quickly to changes in the user's physical condition, the response unit helps prevent a deterioration of their health and supports them in living a comfortable daily life. In addition, the response unit can collect user feedback and continuously improve the accuracy and effectiveness of its advice. This allows the response unit to support the user's health management in real time and provide quick and appropriate responses.

[0033] The suggestion department provides personalized meal recommendations based on information obtained by the response department. Specifically, the suggestion department considers the user's nutritional status and dietary preferences to propose an optimal meal plan. For example, if a user is deficient in a particular nutrient, it will suggest foods rich in that nutrient. The AI ​​analyzes the user's health data and dietary history to generate a meal plan that optimizes nutritional balance. Furthermore, the suggestion department considers the user's dietary preferences and allergy information to provide individually customized meal recommendations. For example, if a user is vegetarian, it will propose a meal plan that does not include meat. Also, if a user is allergic to a particular food, it will suggest a menu that avoids that food. The suggestion department provides users with specific recipes and ingredient lists to support meal preparation. In addition, the suggestion department monitors the user's dietary history and evaluates the effectiveness of the meal plan. For example, it analyzes health data after the user has followed the suggested meal plan to check for improvements in nutritional balance and changes in physical condition. This allows the suggestion department to provide users with optimal meal recommendations and support their health management.

[0034] The analysis unit includes a prediction unit that analyzes users' past health data and current trends to predict future health risks. For example, the analysis unit collects users' past health data and analyzes it using AI. Considering current health trends, the analysis unit can predict future health risks. For instance, it can predict risks such as heart disease and diabetes. This allows the system to predict users' future health risks and support preventative health management.

[0035] The support unit includes an advice unit that analyzes information when a user experiences a change in their physical condition and provides appropriate advice. For example, if a user experiences a change in physical condition such as fatigue, headache, or fever, the support unit collects this information and analyzes it using AI. Based on the analysis results, the support unit can provide appropriate advice to the user. For example, the support unit may advise the user to rest. It can also advise the user to visit a medical institution. This allows for a quick response to changes in the user's physical condition and the provision of appropriate advice.

[0036] The suggestion unit includes a meal suggestion unit that proposes an optimal meal plan considering the user's nutritional status and dietary preferences. For example, the suggestion unit collects nutritional information such as the user's vitamin intake and calorie intake, and analyzes it using AI. The suggestion unit can also propose an optimal meal plan considering the user's dietary preferences, such as favorite and disliked foods. For instance, the suggestion unit can propose a balanced meal plan. Furthermore, the suggestion unit can propose a meal plan tailored to the user's preferences. This allows the system to provide users with the most suitable meal plan and support their health management.

[0037] The selection function analyzes the user's past selection history and suggests the optimal service selection. For example, the selection function prioritizes displaying services that the user has frequently selected in the past. Based on the user's past selection history, the selection function can predict and suggest services that the user will use during a specific time period. The selection function can also analyze the user's past selection history and suggest relevant new services. In this way, it can suggest the optimal service based on the user's past selection history.

[0038] The selection unit automatically selects the most suitable service based on the user's current health status and lifestyle. For example, it can automatically select the most suitable health management service based on the user's current health data. The selection unit can also analyze the user's lifestyle data and suggest appropriate services. Furthermore, the selection unit can automatically select customized services based on the user's health status and lifestyle. This allows the system to automatically select the most suitable service based on the user's health status and lifestyle.

[0039] The selection section prioritizes displaying region-specific health services, taking into account the user's geographical location. For example, the selection section displays region-specific health services based on the user's current location. Based on the user's geographical location, the selection section can suggest nearby health facilities and services. The selection section can also display local health events and programs, taking the user's location into consideration. This allows for the provision of region-specific health services based on the user's geographical location.

[0040] The selection unit analyzes the user's social media activity and suggests relevant health services. For example, the selection unit can analyze the user's social media posts and suggest health services based on their interests. The selection unit can also suggest health services used by the user's followers and friends. The selection unit can also suggest health services based on trends derived from the user's social media activity. This allows the system to provide relevant health services based on the user's social media activity.

[0041] The analytics department improves the accuracy of health data analysis by comparing users' past health data with current trends. For example, the analytics department compares and analyzes a user's current health status based on their past health data. The analytics department can analyze a user's health data while considering current health trends. The analytics department can also predict future health risks by comparing past health data with current trends. This allows for improved analytical accuracy by comparing past health data with current trends.

[0042] The analysis department considers the user's lifestyle and environmental factors when analyzing health data. For example, the analysis department analyzes health data based on the user's lifestyle data. The analysis department can analyze health data while considering the user's environmental factors (climate, region, etc.). The analysis department can also comprehensively analyze the user's lifestyle and environmental factors to evaluate their health status. This allows for the analysis of health data while considering the user's lifestyle and environmental factors.

[0043] The analysis department analyzes region-specific health risks by considering the user's geographical location when analyzing health data. For example, the analysis department analyzes region-specific health risks based on the user's current location. The analysis department can analyze regional health trends based on the user's geographical location. The analysis department can also evaluate regional health risks by considering the user's location. This allows for the analysis of region-specific health risks based on the user's geographical location.

[0044] The analytics department analyzes relevant health information by referencing users' social media activity when analyzing health data. For example, the analytics department analyzes users' social media posts and extracts information related to their health status. The analytics department can also analyze relevant data by referring to the health information of users' followers and friends. The analytics department can also analyze health trends from users' social media activity. This allows the department to provide relevant health information based on users' social media activity.

[0045] The response unit, when responding to changes in the user's physical condition, refers to the user's past physical condition change data to provide the optimal response. For example, the response unit proposes the optimal response method based on the user's past physical condition change data. The response unit can analyze the user's past physical condition change patterns and take appropriate action. The response unit can also refer to the user's past physical condition change data to propose preventative measures. In this way, it can provide the optimal response based on the user's past physical condition change data.

[0046] The response unit takes into account the user's lifestyle and environmental factors when responding to changes in their physical condition. For example, the response unit proposes appropriate response methods based on the user's lifestyle data. The response unit can also take into account the user's environmental factors (climate, region, etc.) when responding. The response unit can also comprehensively analyze the user's lifestyle and environmental factors and propose the optimal response method. This allows the system to provide the best possible response based on the user's lifestyle and environmental factors.

[0047] The response unit, when responding to changes in the user's physical condition, selects a region-specific response method by considering the user's geographical location. For example, the response unit proposes a region-specific response method based on the user's current location. Based on the user's geographical location, the response unit can suggest nearby medical facilities and services. The response unit can also propose a response method that utilizes local health resources, taking the user's location into consideration. In this way, it can provide region-specific response methods based on the user's geographical location.

[0048] The support unit provides relevant advice by referencing the user's social media activity when responding to changes in their health. For example, the support unit analyzes the user's social media posts and provides advice related to changes in their health. The support unit can also provide appropriate advice by referring to the health information of the user's followers and friends. The support unit can also provide trend-based advice based on the user's social media activity. This allows the support unit to provide relevant advice based on the user's social media activity.

[0049] The recommendation department adjusts the level of detail in meal suggestions, taking into account the user's nutritional status and dietary preferences. For example, the recommendation department can provide balanced meal suggestions based on the user's nutritional status. The recommendation department can also provide meal suggestions tailored to the user's preferences, taking into account their dietary needs. The recommendation department can also comprehensively analyze the user's nutritional status and dietary preferences to provide optimal meal suggestions. This allows the department to provide the most suitable meal suggestions based on the user's nutritional status and dietary preferences.

[0050] The recommendation unit makes optimal suggestions by referring to the user's past eating history when suggesting meals. For example, the recommendation unit makes optimal meal suggestions based on the user's past eating history. The recommendation unit can analyze the user's past eating patterns and make appropriate meal suggestions. The recommendation unit can also make balanced meal suggestions by referring to the user's past eating history. In this way, it can provide optimal meal suggestions based on the user's past eating history.

[0051] The suggestion department, when suggesting meals, takes into account the user's geographical location and makes suggestions using ingredients specific to the region. For example, the suggestion department can suggest meals using ingredients specific to the region based on the user's current location. The suggestion department can also suggest meals using nearby ingredients based on the user's geographical location. The suggestion department can also suggest meals utilizing local ingredients, taking into account the user's location. This allows the system to provide meal suggestions using ingredients specific to the region based on the user's geographical location.

[0052] The suggestion department, when suggesting meals, refers to the user's social media activity to propose relevant meal plans. For example, the suggestion department can analyze the user's social media posts and propose meal plans based on their interests. The suggestion department can also suggest meal plans used by the user's followers and friends. The suggestion department can also propose meal plans based on trends derived from the user's social media activity. In this way, it can provide relevant meal plans based on the user's social media activity.

[0053] The prediction unit improves prediction accuracy by comparing the user's past health data with current trends when predicting health risks. For example, the prediction unit compares and analyzes the user's current health status based on their past health data. The prediction unit can predict the user's health risks by considering current health trends. The prediction unit can also predict future health risks by comparing past health data with current trends. This allows for improved prediction accuracy by comparing past health data with current trends.

[0054] The prediction unit predicts region-specific risks by considering the user's geographical location when predicting health risks. For example, the prediction unit predicts region-specific health risks based on the user's current location. The prediction unit can predict regional health trends based on the user's geographical location. The prediction unit can also evaluate regional health risks by considering the user's location. This allows for the prediction of region-specific risks based on the user's geographical location.

[0055] The advice unit provides optimal advice by referring to the user's past health change data. For example, the advice unit provides optimal advice based on the user's past health change data. The advice unit can analyze the user's past health change patterns and provide appropriate advice. The advice unit can also provide preventative advice by referring to the user's past health change data. This allows the system to provide optimal advice based on the user's past health change data.

[0056] The advice unit provides region-specific advice, taking into account the user's geographical location. For example, it provides region-specific advice based on the user's current location. Based on the user's geographical location, the advice unit can suggest nearby medical facilities and services. The advice unit can also provide advice that utilizes local health resources, taking into account the user's location. This allows the system to provide region-specific advice based on the user's geographical location.

[0057] The meal suggestion department adjusts the level of detail in its suggestions, taking into account the user's nutritional status and dietary preferences. For example, it can suggest balanced meals based on the user's nutritional status. It can also suggest meals tailored to the user's preferences, taking their dietary needs into consideration. Furthermore, it can comprehensively analyze the user's nutritional status and dietary preferences to provide optimal meal suggestions. This allows the department to provide the most suitable meal suggestions based on the user's nutritional status and dietary preferences.

[0058] The meal suggestion department, when suggesting meals, takes into account the user's geographical location and makes suggestions using ingredients specific to the region. For example, the meal suggestion department can suggest meals using ingredients specific to the region based on the user's current location. The meal suggestion department can also suggest meals using nearby ingredients based on the user's geographical location. The meal suggestion department can also suggest meals that utilize local ingredients, taking into account the user's location. This allows the department to provide meal suggestions using ingredients specific to the region based on the user's geographical location.

[0059] The meal suggestion department, when suggesting meals, refers to the user's social media activity to propose relevant meal plans. For example, the meal suggestion department can analyze the user's social media posts and propose meal plans based on their interests. The meal suggestion department can also suggest meal plans used by the user's followers and friends. The meal suggestion department can also propose meal plans based on trends derived from the user's social media activity. In this way, it can provide relevant meal plans based on the user's social media activity.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The analytics department can improve the accuracy of its analysis by comparing users' past health data with current trends. For example, it can compare and analyze a user's current health status based on their past health data. It can also analyze a user's health data while considering current health trends. Furthermore, it can predict future health risks by comparing past health data with current trends. In this way, the accuracy of the analysis can be improved by comparing past health data with current trends.

[0062] The proposal department can suggest the optimal meal plan by considering the user's nutritional status and dietary preferences. For example, it can collect and analyze the user's nutritional status, such as vitamin intake and calorie intake, using AI. It can also suggest the optimal meal plan by considering the user's dietary preferences, such as favorite and disliked foods. Furthermore, it can suggest a balanced meal plan to the user. In this way, it can provide the user with the optimal meal plan and support their health management.

[0063] The selection function can analyze the user's past selection history and suggest the most suitable service. For example, it can prioritize displaying services that the user has frequently selected in the past. It can also predict and suggest services that the user will use during specific time periods based on their past selection history. Furthermore, it can analyze the user's past selection history and suggest related new services. In this way, it can suggest the most suitable service based on the user's past selection history.

[0064] The analysis department can perform health data analysis while considering the user's lifestyle and environmental factors. For example, it can analyze health data based on the user's lifestyle data. It can also analyze health data while considering the user's environmental factors (climate, region, etc.). Furthermore, it can comprehensively analyze the user's lifestyle and environmental factors to evaluate their health status. This allows for health data analysis that takes into account the user's lifestyle and environmental factors.

[0065] The response unit can refer to the user's past health change data to provide the optimal response when responding to changes in the user's health. For example, it can suggest the most appropriate response method based on the user's past health change data. It can also analyze the user's past health change patterns and provide appropriate responses. Furthermore, it can suggest preventative measures by referring to the user's past health change data. In this way, it can provide the optimal response based on the user's past health change data.

[0066] The suggestion function can provide optimal meal recommendations by referencing the user's past eating history. For example, it can provide optimal meal recommendations based on the user's past eating history. It can also analyze the user's past eating patterns and provide appropriate meal recommendations. Furthermore, it can provide balanced meal recommendations by referring to the user's past eating history. In this way, it can provide optimal meal recommendations based on the user's past eating history.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The selection section allows the user to choose a generated AI service. For example, the selection section allows the user to choose from multiple generated AI services related to health management. The selection section can also suggest the most suitable service based on the user's preferences and past selection history. Step 2: The analysis unit analyzes health data based on the services selected by the selection unit. The analysis unit collects and analyzes health data such as the user's heart rate, blood pressure, and body temperature. The analysis unit can use AI to detect patterns in the health data and identify anomalies. Step 3: The response unit responds to changes in physical condition in real time based on the data analyzed by the analysis unit. For example, if the user feels a change in their physical condition, the response unit analyzes that information and provides appropriate advice. The response unit can use AI to respond quickly to changes in the user's physical condition. Step 4: The suggestion unit provides personalized meal suggestions based on the information obtained by the response unit. For example, the suggestion unit considers the user's nutritional status and dietary preferences to propose the optimal meal plan. The suggestion unit can use AI to provide the most suitable meal suggestions to the user.

[0069] (Example of form 2) The health management system according to an embodiment of the present invention is a system that utilizes generative AI to personalize user health management. This health management system allows users to select generative AI services individually or in combination. Next, based on the selected services, the generative AI analyzes the user's health data and trends. Furthermore, the generative AI responds to changes in the user's physical condition in real time and provides appropriate advice. The generative AI also provides personalized meal suggestions. This mechanism allows users to receive optimal health management, making it extremely useful for health-conscious consumers and those seeking to improve their health. For example, health data analysis analyzes the user's past health data to predict future health risks. In real-time response, if the user experiences a change in their physical condition, the generative AI analyzes that information and provides appropriate advice. Personalized meal suggestions consider the user's nutritional status and dietary preferences to propose an optimal meal plan. Thus, a personalized healthcare plan utilizing generative AI makes user health management more personalized and is extremely useful for health-conscious consumers and those seeking to improve their health. As a result, the health management system can personalize user health management and provide appropriate advice and meal suggestions in real time.

[0070] The health management system according to this embodiment comprises a selection unit, an analysis unit, a response unit, and a suggestion unit. The selection unit allows the user to select a generated AI service. For example, the selection unit allows the user to select from multiple generated AI services related to health management. The selection unit can also suggest the optimal service based on the user's preferences and past selection history. The analysis unit analyzes health data based on the service selected by the selection unit. For example, the analysis unit collects and analyzes health data such as the user's heart rate, blood pressure, and body temperature. The analysis unit can use AI to detect patterns in health data and detect abnormalities. The response unit responds to changes in physical condition in real time based on the data analyzed by the analysis unit. For example, if the user feels a change in physical condition, the response unit analyzes that information and provides appropriate advice. The response unit can use AI to quickly respond to changes in the user's physical condition. The suggestion unit makes individual meal suggestions based on the information obtained by the response unit. For example, the suggestion unit considers the user's nutritional status and food preferences and proposes the optimal meal plan. The suggestion unit can use AI to make optimal meal suggestions to the user. As a result, the health management system according to this embodiment can personalize the user's health management and provide appropriate advice and dietary suggestions in real time.

[0071] The selection section allows the user to choose a generative AI service. For example, the selection section allows the user to choose from multiple generative AI services related to health management. Specifically, the selection section displays a list of available generative AI services to the user through the user interface. This list includes the characteristics and functions offered for each service, as well as past user reviews, allowing the user to select the most suitable service based on this information. Furthermore, the selection section also has a function to analyze the user's past selection history and usage to suggest the service best suited to the user's preferences and needs. For example, it will prioritize displaying a service to a user who has frequently used a particular generative AI service in the past. The selection section can also recommend specific generative AI services based on the user's health status and goals. For example, if a user is aiming to lose weight, it will suggest a generative AI service specializing in diet management. In this way, the selection section supports users in quickly and easily selecting the optimal generative AI service, improving the efficiency of health management.

[0072] The Analysis Department analyzes health data based on the services selected by the Selection Department. Specifically, the Analysis Department collects and analyzes health data such as the user's heart rate, blood pressure, and body temperature. This data is collected in real time through wearable devices worn by the user or smartphone apps. The Analysis Department uses AI to analyze this data and detect patterns in health data. For example, it can analyze patterns of heart rate fluctuations and blood pressure increases and decreases to detect abnormalities. Furthermore, by comparing current data with past data, the Analysis Department grasps long-term health trends and monitors changes in the user's health status. The AI ​​uses machine learning algorithms to identify abnormal patterns and risk factors from the user's health data. For example, if the heart rate remains higher than normal, it can identify causes such as stress or lack of exercise and issue a warning to the user. This allows the Analysis Department to understand the user's health status in detail and respond quickly if an abnormality occurs.

[0073] The response unit responds to changes in the user's physical condition in real time based on data analyzed by the analysis unit. Specifically, when the response unit senses a change in the user's physical condition, it analyzes the information and provides appropriate advice. For example, if the user feels fatigued, the response unit collects this information and compares it with past data to identify the cause. The AI ​​analyzes the user's health data to determine whether the fatigue is caused by lack of sleep or nutritional deficiencies. Furthermore, the response unit provides specific advice to the user. For example, if lack of sleep is the cause, it suggests going to bed earlier or finding ways to relax. If nutritional deficiencies are the cause, it recommends consuming foods containing specific nutrients. By responding quickly to changes in the user's physical condition, the response unit helps prevent a deterioration of their health and supports them in living a comfortable daily life. In addition, the response unit can collect user feedback and continuously improve the accuracy and effectiveness of its advice. This allows the response unit to support the user's health management in real time and provide quick and appropriate responses.

[0074] The suggestion department provides personalized meal recommendations based on information obtained by the response department. Specifically, the suggestion department considers the user's nutritional status and dietary preferences to propose an optimal meal plan. For example, if a user is deficient in a particular nutrient, it will suggest foods rich in that nutrient. The AI ​​analyzes the user's health data and dietary history to generate a meal plan that optimizes nutritional balance. Furthermore, the suggestion department considers the user's dietary preferences and allergy information to provide individually customized meal recommendations. For example, if a user is vegetarian, it will propose a meal plan that does not include meat. Also, if a user is allergic to a particular food, it will suggest a menu that avoids that food. The suggestion department provides users with specific recipes and ingredient lists to support meal preparation. In addition, the suggestion department monitors the user's dietary history and evaluates the effectiveness of the meal plan. For example, it analyzes health data after the user has followed the suggested meal plan to check for improvements in nutritional balance and changes in physical condition. This allows the suggestion department to provide users with optimal meal recommendations and support their health management.

[0075] The analysis unit includes a prediction unit that analyzes users' past health data and current trends to predict future health risks. For example, the analysis unit collects users' past health data and analyzes it using AI. Considering current health trends, the analysis unit can predict future health risks. For instance, it can predict risks such as heart disease and diabetes. This allows the system to predict users' future health risks and support preventative health management.

[0076] The support unit includes an advice unit that analyzes information when a user experiences a change in their physical condition and provides appropriate advice. For example, if a user experiences a change in physical condition such as fatigue, headache, or fever, the support unit collects this information and analyzes it using AI. Based on the analysis results, the support unit can provide appropriate advice to the user. For example, the support unit may advise the user to rest. It can also advise the user to visit a medical institution. This allows for a quick response to changes in the user's physical condition and the provision of appropriate advice.

[0077] The suggestion unit includes a meal suggestion unit that proposes an optimal meal plan considering the user's nutritional status and dietary preferences. For example, the suggestion unit collects nutritional information such as the user's vitamin intake and calorie intake, and analyzes it using AI. The suggestion unit can also propose an optimal meal plan considering the user's dietary preferences, such as favorite and disliked foods. For instance, the suggestion unit can propose a balanced meal plan. Furthermore, the suggestion unit can propose a meal plan tailored to the user's preferences. This allows the system to provide users with the most suitable meal plan and support their health management.

[0078] The selection unit estimates the user's emotions and presents a selection of generative AI services based on the estimated emotions. For example, if the user is stressed, the selection unit may prioritize presenting health services with a relaxing effect. If the user is agitated, the selection unit may suggest active health management services. If the user is tired, the selection unit may also prioritize displaying services related to relaxation and rest. This allows for the provision of appropriate service options according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The selection function analyzes the user's past selection history and suggests the optimal service selection. For example, the selection function prioritizes displaying services that the user has frequently selected in the past. Based on the user's past selection history, the selection function can predict and suggest services that the user will use during a specific time period. The selection function can also analyze the user's past selection history and suggest relevant new services. In this way, it can suggest the optimal service based on the user's past selection history.

[0080] The selection unit automatically selects the most suitable service based on the user's current health status and lifestyle. For example, it can automatically select the most suitable health management service based on the user's current health data. The selection unit can also analyze the user's lifestyle data and suggest appropriate services. Furthermore, the selection unit can automatically select customized services based on the user's health status and lifestyle. This allows the system to automatically select the most suitable service based on the user's health status and lifestyle.

[0081] The selection unit estimates the user's emotions and adjusts the display order of the options based on the estimated emotions. For example, if the user is relaxed, the selection unit may display services with a relaxing effect at the top. If the user is stressed, the selection unit may display services that help relieve stress at the top. If the user is excited, the selection unit may also display active services at the top. This allows for a display order that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The selection section prioritizes displaying region-specific health services, taking into account the user's geographical location. For example, the selection section displays region-specific health services based on the user's current location. Based on the user's geographical location, the selection section can suggest nearby health facilities and services. The selection section can also display local health events and programs, taking the user's location into consideration. This allows for the provision of region-specific health services based on the user's geographical location.

[0083] The selection unit analyzes the user's social media activity and suggests relevant health services. For example, the selection unit can analyze the user's social media posts and suggest health services based on their interests. The selection unit can also suggest health services used by the user's followers and friends. The selection unit can also suggest health services based on trends derived from the user's social media activity. This allows the system to provide relevant health services based on the user's social media activity.

[0084] The analytics unit estimates the user's emotions and adjusts the analysis method of health data based on the estimated user emotions. For example, if the user is stressed, the analytics unit prioritizes analyzing stress-related health data. If the user is relaxed, the analytics unit can comprehensively analyze the overall health status. If the user is excited, the analytics unit can also analyze health data related to active activities. This provides a method of analyzing health data that is tailored to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The analytics department improves the accuracy of health data analysis by comparing users' past health data with current trends. For example, the analytics department compares and analyzes a user's current health status based on their past health data. The analytics department can analyze a user's health data while considering current health trends. The analytics department can also predict future health risks by comparing past health data with current trends. This allows for improved analytical accuracy by comparing past health data with current trends.

[0086] The analysis department considers the user's lifestyle and environmental factors when analyzing health data. For example, the analysis department analyzes health data based on the user's lifestyle data. The analysis department can analyze health data while considering the user's environmental factors (climate, region, etc.). The analysis department can also comprehensively analyze the user's lifestyle and environmental factors to evaluate their health status. This allows for the analysis of health data while considering the user's lifestyle and environmental factors.

[0087] The analysis unit estimates the user's emotions and adjusts the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. This allows for a display method that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The analysis department analyzes region-specific health risks by considering the user's geographical location when analyzing health data. For example, the analysis department analyzes region-specific health risks based on the user's current location. The analysis department can analyze regional health trends based on the user's geographical location. The analysis department can also evaluate regional health risks by considering the user's location. This allows for the analysis of region-specific health risks based on the user's geographical location.

[0089] The analytics department analyzes relevant health information by referencing users' social media activity when analyzing health data. For example, the analytics department analyzes users' social media posts and extracts information related to their health status. The analytics department can also analyze relevant data by referring to the health information of users' followers and friends. The analytics department can also analyze health trends from users' social media activity. This allows the department to provide relevant health information based on users' social media activity.

[0090] The response unit estimates the user's emotions and adjusts its response to changes in physical condition based on the estimated emotions. For example, if the user is stressed, the response unit may suggest a relaxing response. If the user is excited, the response unit may suggest an active response. If the user is tired, the response unit may also suggest a response related to rest or relaxation. This allows the system to provide a response that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The response unit, when responding to changes in the user's physical condition, refers to the user's past physical condition change data to provide the optimal response. For example, the response unit proposes the optimal response method based on the user's past physical condition change data. The response unit can analyze the user's past physical condition change patterns and take appropriate action. The response unit can also refer to the user's past physical condition change data to propose preventative measures. In this way, it can provide the optimal response based on the user's past physical condition change data.

[0092] The response unit takes into account the user's lifestyle and environmental factors when responding to changes in their physical condition. For example, the response unit proposes appropriate response methods based on the user's lifestyle data. The response unit can also take into account the user's environmental factors (climate, region, etc.) when responding. The response unit can also comprehensively analyze the user's lifestyle and environmental factors and propose the optimal response method. This allows the system to provide the best possible response based on the user's lifestyle and environmental factors.

[0093] The response unit estimates the user's emotions and determines the priority of responses based on the estimated emotions. For example, if the user is tense, the response unit will prioritize responses that have a relaxing effect. If the user is relaxed, the response unit may prioritize overall well-being. If the user is in a hurry, the response unit may also prioritize a quick response. This allows for the provision of response priorities that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The response unit, when responding to changes in the user's physical condition, selects a region-specific response method by considering the user's geographical location. For example, the response unit proposes a region-specific response method based on the user's current location. Based on the user's geographical location, the response unit can suggest nearby medical facilities and services. The response unit can also propose a response method that utilizes local health resources, taking the user's location into consideration. In this way, it can provide region-specific response methods based on the user's geographical location.

[0095] The support unit provides relevant advice by referencing the user's social media activity when responding to changes in their health. For example, the support unit analyzes the user's social media posts and provides advice related to changes in their health. The support unit can also provide appropriate advice by referring to the health information of the user's followers and friends. The support unit can also provide trend-based advice based on the user's social media activity. This allows the support unit to provide relevant advice based on the user's social media activity.

[0096] The suggestion unit estimates the user's emotions and adjusts the presentation of meal suggestions based on the estimated emotions. For example, if the user is relaxed, the suggestion unit can provide detailed meal suggestions. If the user is stressed, the suggestion unit can provide simple and easy-to-understand meal suggestions. If the user is in a hurry, the suggestion unit can also provide meal suggestions that can be quickly understood. This allows for the presentation of meal suggestions to be tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The recommendation department adjusts the level of detail in meal suggestions, taking into account the user's nutritional status and dietary preferences. For example, the recommendation department can provide balanced meal suggestions based on the user's nutritional status. The recommendation department can also provide meal suggestions tailored to the user's preferences, taking into account their dietary needs. The recommendation department can also comprehensively analyze the user's nutritional status and dietary preferences to provide optimal meal suggestions. This allows the department to provide the most suitable meal suggestions based on the user's nutritional status and dietary preferences.

[0098] The recommendation unit makes optimal suggestions by referring to the user's past eating history when suggesting meals. For example, the recommendation unit makes optimal meal suggestions based on the user's past eating history. The recommendation unit can analyze the user's past eating patterns and make appropriate meal suggestions. The recommendation unit can also make balanced meal suggestions by referring to the user's past eating history. In this way, it can provide optimal meal suggestions based on the user's past eating history.

[0099] The suggestion function estimates the user's emotions and prioritizes suggestions based on those emotions. For example, if the user is relaxed, the suggestion function may prioritize detailed meal suggestions. If the user is stressed, the suggestion function may prioritize simple and easy-to-understand meal suggestions. If the user is in a hurry, the suggestion function may also prioritize meal suggestions that can be quickly understood. This allows for the provision of suggestion prioritization according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The suggestion department, when suggesting meals, takes into account the user's geographical location and makes suggestions using ingredients specific to the region. For example, the suggestion department can suggest meals using ingredients specific to the region based on the user's current location. The suggestion department can also suggest meals using nearby ingredients based on the user's geographical location. The suggestion department can also suggest meals utilizing local ingredients, taking into account the user's location. This allows the system to provide meal suggestions using ingredients specific to the region based on the user's geographical location.

[0101] The suggestion department, when suggesting meals, refers to the user's social media activity to propose relevant meal plans. For example, the suggestion department can analyze the user's social media posts and propose meal plans based on their interests. The suggestion department can also suggest meal plans used by the user's followers and friends. The suggestion department can also propose meal plans based on trends derived from the user's social media activity. In this way, it can provide relevant meal plans based on the user's social media activity.

[0102] The prediction unit estimates the user's emotions and adjusts the health risk prediction method based on the estimated user emotions. For example, if the user is stressed, the prediction unit prioritizes predicting stress-related health risks. If the user is relaxed, the prediction unit can comprehensively predict overall health risks. If the user is excited, the prediction unit can also predict health risks related to active activities. This provides a health risk prediction method that is tailored to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The prediction unit improves prediction accuracy by comparing the user's past health data with current trends when predicting health risks. For example, the prediction unit compares and analyzes the user's current health status based on their past health data. The prediction unit can predict the user's health risks by considering current health trends. The prediction unit can also predict future health risks by comparing past health data with current trends. This allows for improved prediction accuracy by comparing past health data with current trends.

[0104] The prediction unit estimates the user's emotions and adjusts the display method of the prediction results based on the estimated emotions. For example, if the user is nervous, the prediction unit can provide a simple and highly visible display method. If the user is relaxed, the prediction unit can provide a display method that includes detailed information. If the user is in a hurry, the prediction unit can also provide a display method that gets straight to the point. This allows for the display of prediction results to be tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The prediction unit predicts region-specific risks by considering the user's geographical location when predicting health risks. For example, the prediction unit predicts region-specific health risks based on the user's current location. The prediction unit can predict regional health trends based on the user's geographical location. The prediction unit can also evaluate regional health risks by considering the user's location. This allows for the prediction of region-specific risks based on the user's geographical location.

[0106] The advice unit estimates the user's emotions and adjusts the way it presents advice based on those emotions. For example, if the user is relaxed, the advice unit can provide detailed advice. If the user is stressed, the advice unit can provide simple and easy-to-understand advice. If the user is in a hurry, the advice unit can also provide advice that can be quickly understood. This allows for the presentation of advice tailored to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0107] The advice unit provides optimal advice by referring to the user's past health change data. For example, the advice unit provides optimal advice based on the user's past health change data. The advice unit can analyze the user's past health change patterns and provide appropriate advice. The advice unit can also provide preventative advice by referring to the user's past health change data. This allows the system to provide optimal advice based on the user's past health change data.

[0108] The advice unit estimates the user's emotions and prioritizes advice based on those emotions. For example, if the user is stressed, the advice unit will prioritize advice that promotes relaxation. If the user is relaxed, the advice unit may prioritize advice focused on overall well-being. If the user is in a hurry, the advice unit may also prioritize quick advice. This allows for prioritizing advice according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0109] The advice unit provides region-specific advice, taking into account the user's geographical location. For example, it provides region-specific advice based on the user's current location. Based on the user's geographical location, the advice unit can suggest nearby medical facilities and services. The advice unit can also provide advice that utilizes local health resources, taking into account the user's location. This allows the system to provide region-specific advice based on the user's geographical location.

[0110] The meal suggestion function estimates the user's emotions and adjusts the presentation of meal suggestions based on the estimated emotions. For example, if the user is relaxed, the meal suggestion function can provide detailed meal suggestions. If the user is stressed, the meal suggestion function can provide simple and easy-to-understand meal suggestions. If the user is in a hurry, the meal suggestion function can also provide meal suggestions that can be quickly understood. This allows for the presentation of meal suggestions to be tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0111] The meal suggestion department adjusts the level of detail in its suggestions, taking into account the user's nutritional status and dietary preferences. For example, it can suggest balanced meals based on the user's nutritional status. It can also suggest meals tailored to the user's preferences, taking their dietary needs into consideration. Furthermore, it can comprehensively analyze the user's nutritional status and dietary preferences to provide optimal meal suggestions. This allows the department to provide the most suitable meal suggestions based on the user's nutritional status and dietary preferences.

[0112] The meal suggestion system estimates the user's emotions and prioritizes suggestions based on those emotions. For example, if the user is relaxed, the system may prioritize detailed meal suggestions. If the user is stressed, the system may prioritize simple and easy-to-understand meal suggestions. If the user is in a hurry, the system may also prioritize meal suggestions that can be quickly understood. This allows the system to provide a priority of suggestions that aligns with the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0113] The meal suggestion department, when suggesting meals, takes into account the user's geographical location and makes suggestions using ingredients specific to the region. For example, the meal suggestion department can suggest meals using ingredients specific to the region based on the user's current location. The meal suggestion department can also suggest meals using nearby ingredients based on the user's geographical location. The meal suggestion department can also suggest meals that utilize local ingredients, taking into account the user's location. This allows the department to provide meal suggestions using ingredients specific to the region based on the user's geographical location.

[0114] The meal suggestion department, when suggesting meals, refers to the user's social media activity to propose relevant meal plans. For example, the meal suggestion department can analyze the user's social media posts and propose meal plans based on their interests. The meal suggestion department can also suggest meal plans used by the user's followers and friends. The meal suggestion department can also propose meal plans based on trends derived from the user's social media activity. In this way, it can provide relevant meal plans based on the user's social media activity.

[0115] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0116] The selection unit can estimate the user's emotions and present a selection of AI-generated services based on those emotions. For example, if the user is stressed, it can prioritize presenting health services with a relaxing effect. If the user is excited, it can suggest active health management services. Furthermore, if the user is tired, it can prioritize displaying services related to relaxation and rest. This allows for the provision of appropriate service options tailored to the user's emotions.

[0117] The analytics department can improve the accuracy of its analysis by comparing users' past health data with current trends. For example, it can compare and analyze a user's current health status based on their past health data. It can also analyze a user's health data while considering current health trends. Furthermore, it can predict future health risks by comparing past health data with current trends. In this way, the accuracy of the analysis can be improved by comparing past health data with current trends.

[0118] The response unit can estimate the user's emotions and adjust its response to changes in physical condition based on those estimated emotions. For example, if the user is stressed, it can suggest a response that promotes relaxation. If the user is excited, it can suggest an active response. Furthermore, if the user is tired, it can suggest a response related to rest and relaxation. This allows the system to provide responses that are tailored to the user's emotions.

[0119] The proposal department can suggest the optimal meal plan by considering the user's nutritional status and dietary preferences. For example, it can collect and analyze the user's nutritional status, such as vitamin intake and calorie intake, using AI. It can also suggest the optimal meal plan by considering the user's dietary preferences, such as favorite and disliked foods. Furthermore, it can suggest a balanced meal plan to the user. In this way, it can provide the user with the optimal meal plan and support their health management.

[0120] The selection function can analyze the user's past selection history and suggest the most suitable service. For example, it can prioritize displaying services that the user has frequently selected in the past. It can also predict and suggest services that the user will use during specific time periods based on their past selection history. Furthermore, it can analyze the user's past selection history and suggest related new services. In this way, it can suggest the most suitable service based on the user's past selection history.

[0121] The selection unit can estimate the user's emotions and adjust the display order of options based on those emotions. For example, if the user is relaxed, services with a relaxing effect can be displayed higher up. If the user is stressed, services that help relieve stress can be displayed higher up. Furthermore, if the user is excited, active services can be displayed higher up. This allows the system to provide a display order that is tailored to the user's emotions.

[0122] The analysis department can perform health data analysis while considering the user's lifestyle and environmental factors. For example, it can analyze health data based on the user's lifestyle data. It can also analyze health data while considering the user's environmental factors (climate, region, etc.). Furthermore, it can comprehensively analyze the user's lifestyle and environmental factors to evaluate their health status. This allows for health data analysis that takes into account the user's lifestyle and environmental factors.

[0123] The response unit can refer to the user's past health change data to provide the optimal response when responding to changes in the user's health. For example, it can suggest the most appropriate response method based on the user's past health change data. It can also analyze the user's past health change patterns and provide appropriate responses. Furthermore, it can suggest preventative measures by referring to the user's past health change data. In this way, it can provide the optimal response based on the user's past health change data.

[0124] The suggestion function can estimate the user's emotions and adjust the presentation of meal suggestions based on those emotions. For example, if the user is relaxed, it can provide detailed meal suggestions. If the user is stressed, it can provide simple and easy-to-understand meal suggestions. Furthermore, if the user is in a hurry, it can provide meal suggestions that can be quickly understood. This allows the system to provide meal suggestions in a way that is tailored to the user's emotions.

[0125] The suggestion function can provide optimal meal recommendations by referencing the user's past eating history. For example, it can provide optimal meal recommendations based on the user's past eating history. It can also analyze the user's past eating patterns and provide appropriate meal recommendations. Furthermore, it can provide balanced meal recommendations by referring to the user's past eating history. In this way, it can provide optimal meal recommendations based on the user's past eating history.

[0126] The following briefly describes the processing flow for example form 2.

[0127] Step 1: The selection section allows the user to choose a generated AI service. For example, the selection section allows the user to choose from multiple generated AI services related to health management. The selection section can also suggest the most suitable service based on the user's preferences and past selection history. Step 2: The analysis unit analyzes health data based on the services selected by the selection unit. The analysis unit collects and analyzes health data such as the user's heart rate, blood pressure, and body temperature. The analysis unit can use AI to detect patterns in the health data and identify anomalies. Step 3: The response unit responds to changes in physical condition in real time based on the data analyzed by the analysis unit. For example, if the user feels a change in their physical condition, the response unit analyzes that information and provides appropriate advice. The response unit can use AI to respond quickly to changes in the user's physical condition. Step 4: The suggestion unit provides personalized meal suggestions based on the information obtained by the response unit. For example, the suggestion unit considers the user's nutritional status and dietary preferences to propose the optimal meal plan. The suggestion unit can use AI to provide the most suitable meal suggestions to the user.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the selection unit, analysis unit, response unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the selection unit is implemented by the control unit 46A of the smart device 14, allowing the user to select a generated AI service. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, to analyze the user's health data. The response unit is implemented by the control unit 46A of the smart device 14, for example, to respond in real time to changes in the user's physical condition. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, to propose an optimal meal plan to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0132] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] Each of the multiple elements described above, including the selection unit, analysis unit, response unit, and proposal unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the selection unit is implemented by the control unit 46A of the smart glasses 214, allowing the user to select a generated AI service. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, to analyze the user's health data. The response unit is implemented by the control unit 46A of the smart glasses 214, for example, to respond in real time to changes in the user's physical condition. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, to propose an optimal meal plan to the user. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0148] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

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

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

[0158] In 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.

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

[0160] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0162] The data processing system 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.

[0163] Each of the multiple elements described above, including the selection unit, analysis unit, response unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the selection unit is implemented by the control unit 46A of the headset terminal 314, allowing the user to select a generated AI service. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, to analyze the user's health data. The response unit is implemented by the control unit 46A of the headset terminal 314, for example, to respond in real time to changes in the user's physical condition. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, to propose an optimal meal plan to the user. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0164] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] Each of the multiple elements described above, including the selection unit, analysis unit, response unit, and proposal unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the selection unit is implemented by the control unit 46A of the robot 414, allowing the user to select a generated AI service. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, to analyze the user's health data. The response unit is implemented by the control unit 46A of the robot 414, for example, to respond in real time to changes in the user's physical condition. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, to propose an optimal meal plan to the user. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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."

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] (Note 1) A selection section where the user selects the generated AI service, An analysis unit that analyzes health data based on the service selected by the selection unit, Based on the data analyzed by the aforementioned analysis unit, the response unit responds to changes in physical condition in real time, The system includes a proposal unit that makes individual meal suggestions based on the information obtained by the aforementioned correspondence unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit is It includes a predictive unit that analyzes the user's past health data and current trends to predict future health risks. The system described in Appendix 1, characterized by the features described herein. (Note 3) The corresponding part is, It includes an advice unit that analyzes information when a user experiences a change in their physical condition and provides appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, The company has a meal planning department that proposes the optimal meal plan, taking into account the user's nutritional status and dietary preferences. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned selection unit is It estimates the user's emotions and presents options for generative AI services based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned selection unit is We analyze the user's past selection history and suggest the optimal service selection. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned selection unit is The system automatically selects the most suitable service based on the user's current health status and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned selection unit is It estimates the user's emotions and adjusts the display order of options based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned selection unit is The system prioritizes displaying region-specific health services, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned selection unit is Analyze users' social media activity and suggest relevant health services. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is We estimate the user's emotions and adjust the analysis method of health data based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is When analyzing health data, we improve the accuracy of the analysis by comparing the user's past health data with current trends. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is When analyzing health data, the analysis should take into account the user's lifestyle and environmental factors. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is When analyzing health data, we consider the user's geographical location to analyze region-specific health risks. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is When analyzing health data, we refer to users' social media activity to analyze relevant health information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The corresponding part is, The system estimates the user's emotions and adjusts its response to changes in physical condition based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The corresponding part is, When responding to changes in the user's physical condition, the system refers to the user's past physical condition data to provide the most appropriate response. The system described in Appendix 1, characterized by the features described herein. (Note 19) The corresponding part is, When responding to changes in the user's physical condition, we take into consideration the user's lifestyle and environmental factors. The system described in Appendix 1, characterized by the features described herein. (Note 20) The corresponding part is, It estimates the user's emotions and determines the priority of responses based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The corresponding part is, When responding to changes in a user's physical condition, the system selects region-specific response methods by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 22) The corresponding part is, When responding to changes in the user's health, the system provides relevant advice by referencing the user's social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, The system estimates the user's emotions and adjusts the way meal suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When suggesting meals, the level of detail in the suggestions is adjusted to take into account the user's nutritional status and dietary preferences. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When suggesting meals, the system refers to the user's past meal history to provide the most suitable suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When suggesting meals, the system takes the user's geographical location into consideration and suggests dishes using ingredients unique to that region. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, When suggesting meals, the system refers to the user's social media activity to suggest relevant meal plans. The system described in Appendix 1, characterized by the features described herein. (Note 29) The prediction unit, We estimate the user's emotions and adjust the method of predicting health risks based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 30) The prediction unit, When predicting health risks, we improve prediction accuracy by comparing the user's past health data with current trends. The system described in Appendix 2, characterized by the features described herein. (Note 31) The prediction unit, It estimates the user's emotions and adjusts how the prediction results are displayed based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The prediction unit, When predicting health risks, the system takes into account the user's geographical location to predict region-specific risks. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned advice section, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned advice section, When providing advice, we refer to the user's past health data to provide the most appropriate advice. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned advice section, It estimates the user's emotions and prioritizes advice based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned advice section, When providing advice, we take the user's geographical location into consideration to provide region-specific advice. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned meal proposal department, The system estimates the user's emotions and adjusts the way meal suggestions are presented based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 38) The aforementioned meal proposal department, When suggesting meals, the level of detail in the suggestions is adjusted to take into account the user's nutritional status and dietary preferences. The system described in Appendix 4, characterized by the features described herein. (Note 39) The aforementioned meal proposal department, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 40) The aforementioned meal proposal department, When suggesting meals, the system takes the user's geographical location into consideration and suggests dishes using ingredients unique to that region. The system described in Appendix 4, characterized by the features described herein. (Note 41) The aforementioned meal proposal department, When suggesting meals, the system refers to the user's social media activity to suggest relevant meal plans. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0200] 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 selection section where the user selects the generated AI service, An analysis unit that analyzes health data based on the service selected by the selection unit, Based on the data analyzed by the aforementioned analysis unit, the response unit responds to changes in physical condition in real time, The system includes a proposal unit that makes individual meal suggestions based on the information obtained by the aforementioned correspondence unit. A system characterized by the following features.

2. The aforementioned analysis unit is It includes a predictive unit that analyzes the user's past health data and current trends to predict future health risks. The system according to feature 1.

3. The corresponding part is, It includes an advice unit that analyzes information when a user experiences a change in their physical condition and provides appropriate advice. The system according to feature 1.

4. The aforementioned proposal section is, The company has a meal planning department that proposes the optimal meal plan, taking into account the user's nutritional status and dietary preferences. The system according to feature 1.

5. The aforementioned selection unit is It estimates the user's emotions and presents options for generative AI services based on those estimated emotions. The system according to feature 1.

6. The aforementioned selection unit is We analyze the user's past selection history and suggest the optimal service selection. The system according to feature 1.

7. The aforementioned selection unit is The system automatically selects the most suitable service based on the user's current health status and lifestyle. The system according to feature 1.

8. The aforementioned selection unit is It estimates the user's emotions and adjusts the display order of options based on the estimated user emotions. The system according to feature 1.

9. The aforementioned selection unit is The system prioritizes displaying region-specific health services, taking into account the user's geographical location. The system according to feature 1.

10. The aforementioned selection unit is Analyze users' social media activity and suggest relevant health services. The system according to feature 1.

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