System

Abstract

To provide a system for making a personalized health proposal on the basis of a user's health information.SOLUTION: A system includes a reception section, a recording section, a recommendation section, and a proposal section. The reception section inputs a user's health information. The recording section records information inputted by the reception section. The recommendation section recommends prevention on the basis of a user's age. The proposal section makes a personalized proposal to a user on the basis of information recorded in the recording section.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies do not adequately provide personalized health suggestions based on a user's health information, and there is room for improvement.

[0005] The system according to the embodiment aims to provide personalized health suggestions based on the user's health information. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, a recording unit, a recommending unit, and a suggesting unit. The receiving unit inputs health information of a user. The recording unit records the information input by the receiving unit. The recommending unit recommends prevention based on the age of the user. The suggesting unit makes personalized suggestions to the user based on the information stored in the recording unit. Effect of the Invention

[0007] The system according to the embodiment can provide personalized health suggestions based on the user's health information. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

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

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

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

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

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

[0028] (Example 1) A health support system according to an embodiment of the present invention is a system that supports the mental and physical health of a user. This system starts health advice and recording from the day of birth based on Ericson's developmental stages. Specifically, the system includes a reception unit that inputs the user's health information, a recording unit that records the input information, a recommendation unit that recommends prevention according to the user's developmental stage, and a suggestion unit that makes personalized suggestions based on the recorded information. For example, information such as the user's weight, height, dietary content, and amount of exercise is input, and this information is input by the user himself or her or his guardian. Next, the input information is recorded in the recording unit and stored for a long period of time. The recommendation unit suggests appropriate preventive measures based on the user's age and developmental stage, for example, recommending a vaccination schedule for infancy and recommending regular health checkups for school-age children. In addition, there is a suggestion unit that makes personalized suggestions based on the information stored in the recording unit, which analyzes the user's health information and suggests optimal health habits and lifestyle habits for each individual user. For example, if a specific nutrient is lacking, a meal containing that nutrient is suggested. This allows the user to receive support for maintaining health throughout his or her life. This allows the health support system to efficiently input, record, recommend, and suggest the user's health information.

[0029] The health support system according to the embodiment includes a reception unit, a recording unit, a recommendation unit, and a suggestion unit. The reception unit inputs the user's health information. The user's health information includes, for example, weight, blood pressure, dietary content, and amount of exercise, but is not limited to such examples. The reception unit, for example, receives the health information input by the user in a digital format. The reception unit can also receive the user's health information in various formats such as voice input and image input. For example, the reception unit enables the user to input the health information by voice using a smartphone. The reception unit can also analyze an image of a meal taken by the user and automatically input the meal content. The recording unit records the information input by the reception unit. The recording unit, for example, stores the user's health information in cloud storage and makes it accessible for a long period of time. The recording unit also encrypts and stores the user's health information to protect privacy. For example, the recording unit regularly backs up the user's health information to prevent data loss. The recommendation unit recommends prevention based on the user's age. The recommendation unit, for example, suggests a vaccination schedule according to the user's age. The recommendation unit can also recommend regular health checkups based on the user's age. For example, the recommendation unit notifies the user of necessary vaccinations and health checkups when the user reaches a certain age. The suggestion unit makes personalized suggestions based on the information stored in the recording unit. The suggestion unit, for example, analyzes the user's health information and suggests optimal health habits and lifestyle habits for each individual user. For example, the suggestion unit analyzes the user's dietary content, and if a specific nutrient is lacking, suggests a diet containing that nutrient. The suggestion unit can also analyze the user's amount of exercise and suggest an appropriate exercise plan. As a result, the health support system according to the embodiment can efficiently input, record, recommend, and suggest the user's health information.

[0030] The reception unit inputs the health information of the user. The health information of the user includes, for example, weight, blood pressure, dietary content, and amount of exercise, but is not limited to such examples. The reception unit, for example, receives the health information input by the user in a digital format. Specifically, the user can input health information through a dedicated application or web interface using a device such as a smartphone, tablet, or personal computer. This allows the user to easily record health information on a daily basis. The reception unit can also receive the health information of the user in various formats such as voice input and image input. For example, in the case of voice input, the user can use the microphone of the smartphone to input weight, blood pressure, dietary content, and the like by voice. Using voice recognition technology, the input voice can be converted into text data and imported into the system. Furthermore, in the case of image input, the user can use the camera of the smartphone to take an image of the meal and upload it to the system. Using image analysis technology, the meal content, calories, and nutrients can be automatically analyzed and registered in the database. This allows the user to input detailed health information without any effort. By providing these various input methods, the reception unit can improve the convenience of the user and increase the accuracy and comprehensiveness of the health information.

[0031] The recording unit records the information input by the reception unit. For example, the recording unit stores the user's health information in cloud storage and makes it accessible for a long period of time. By using cloud storage, the user can consistently access the health information even from different devices. In addition, the recording unit encrypts and stores the user's health information to protect privacy. By using encryption technology, the risk of unauthorized access by third parties and data leakage can be reduced. For example, the recording unit periodically backs up the user's health information to prevent data loss. The backups are distributed and stored on multiple servers in different geographical locations, so that data can be restored even in the event of a disaster or system failure. Furthermore, the recording unit can organize the user's health information in chronological order and compare past data with current data. This allows the user to visually check changes in his or her health condition and take necessary measures. The recording unit can efficiently and safely manage the user's health information and provide reliable data for a long period of time.

[0032] The recommendation unit recommends prevention based on the user's age. For example, the recommendation unit proposes a vaccination schedule according to the user's age. Specifically, the recommendation unit can calculate the timing of necessary vaccinations based on the user's age, sex, and past vaccination history, and notify the user. The recommendation unit can also recommend regular health checkups based on the user's age. For example, when a certain age is reached, the recommendation unit proposes the type and frequency of necessary health checkups and notifies the user. This allows the user to check his or her health condition regularly and discover abnormalities early. Furthermore, the recommendation unit can also propose preventive measures according to the user's lifestyle and health condition. For example, the recommendation unit proposes an appropriate exercise plan for a user who is not getting enough exercise, and proposes a balanced meal menu for a user who has an unbalanced diet. This allows the recommendation unit to support the user in maintaining and improving their health, and to raise awareness of the importance of preventive medicine.

[0033] The suggestion unit makes personalized suggestions based on the information stored in the recording unit. For example, the suggestion unit analyzes the health information of the user and suggests optimal health habits and lifestyle habits for each individual user. Specifically, the suggestion unit analyzes the dietary content of the user, and if a specific nutrient is lacking, suggests a meal containing that nutrient. For example, for a user who is lacking in vitamin D, foods and supplements containing a lot of vitamin D can be suggested. The suggestion unit can also analyze the amount of exercise of the user and suggest an appropriate exercise plan. For example, for a user who is lacking in exercise, a specific exercise menu such as walking, jogging, and strength training can be suggested, and the frequency and intensity of exercise can be adjusted. Furthermore, the suggestion unit can analyze the stress level and sleep pattern of the user and suggest relaxation methods and measures to improve sleep. For example, for a user with high stress, relaxation methods such as meditation, deep breathing, and yoga are suggested, and for a user who is lacking in sleep, improvement of the sleep environment and review of sleep habits are suggested. As a result, the suggestion unit can comprehensively analyze the health condition of the user and make specific and practical suggestions according to individual needs. As a result, the health support system according to the embodiment can efficiently input, record, recommend, and suggest the health information of the user.

[0034] The health support system includes a management unit that manages vaccination and examination schedules, and a recommendation unit that recommends prevention based on information managed by the management unit. The management unit manages vaccination and examination schedules. For example, the management unit registers the user's vaccination and examination schedules in a digital calendar and notifies the user at an appropriate time. The management unit can also record the user's vaccination and examination history and automatically calculate the next schedule. For example, the management unit suggests the next vaccination required based on the history of vaccinations the user has received in the past. The recommendation unit recommends prevention based on information managed by the management unit. The recommendation unit suggests appropriate vaccinations and examinations based on the user's age and health condition, for example. For example, the recommendation unit notifies the user of necessary vaccinations and health checkups when the user reaches a certain age. As a result, the management unit appropriately manages vaccination and examination schedules, and the recommendation unit recommends prevention based on the information, thereby making the user's health management more efficient. A part or all of the above-mentioned processing in the management unit may be performed using AI, for example, or may be performed without using AI. For example, the management department can input a user's vaccination or testing schedule into the AI ​​model to automatically calculate the next schedule.

[0035] The health support system includes a providing unit that provides an advertisement to a user based on a proposal by the suggestion unit. The providing unit provides an advertisement to a user based on a proposal by the suggestion unit. The providing unit displays, for example, advertisements related to the user's health information and lifestyle habits. For example, the providing unit displays advertisements for health foods containing nutrients that the user is lacking. The providing unit can also display advertisements for fitness programs based on the user's amount of exercise. For example, when the user is not getting enough exercise, the providing unit displays advertisements for appropriate fitness programs. As a result, by providing advertisements based on the proposal by the suggestion unit, it is possible to display advertisements that are highly relevant to the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's health information into an AI model and automatically select the optimal advertisement.

[0036] The reception unit can analyze the user's past health information input history and select an appropriate input method. The reception unit, for example, preferentially suggests an input method (such as voice or text) that the user has used frequently in the past. For example, if the user has used voice input a lot in the past, the reception unit preferentially suggests voice input. The reception unit can also analyze the pattern of health information previously input by the user and suggest the optimal input method. For example, if the user has input data during a specific time period, the reception unit suggests the optimal input method for that time period. In this way, the optimal input method can be provided to the user by analyzing the past input history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history into an AI model and automatically select the optimal input method.

[0037] The reception unit can filter the health information based on the user's current living situation and field of interest when inputting the health information. For example, if the user is currently on a diet, the reception unit prioritizes input of health information related to the diet. For example, the reception unit recognizes that the user is on a diet and prioritizes input of information related to the diet and the amount of exercise. In addition, if the user is interested in a specific disease, the reception unit can also prompt the user to input health information related to the disease. For example, if the user is interested in diabetes, the reception unit prioritizes input of information related to blood glucose levels and diet. In addition, if the user is interested in exercise, the reception unit can also prioritize input of health information related to exercise. For example, the reception unit recognizes that the user is interested in exercise and prioritizes input of information related to the amount of exercise and the type of exercise. This allows more relevant information to be input by filtering information based on the user's living situation and field of interest. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data related to the user's living situation and field of interest into an AI model and automatically select the optimal input method.

[0038] When inputting health information, the reception unit can preferentially input information that is highly relevant to the user in consideration of the geographical location information of the user. For example, when the user lives in a specific area, the reception unit prioritizes input of health information related to the area. For example, the reception unit recognizes that the user lives in a specific area and prompts the user to input health information related to the area. In addition, when the user is traveling, the reception unit can also prompt the user to input health information related to the travel destination. For example, the reception unit recognizes that the user is traveling and prompts the user to input health information related to the travel destination. In addition, when the user lives in a specific climate condition, the reception unit can also prioritize input of health information related to the climate. For example, when the user lives in a cold area, the reception unit prompts the user to input health information related to the cold area. In this way, by considering the geographical location information, information that is highly relevant to the user can be preferentially input. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information into an AI model and automatically select an optimal input method.

[0039] The reception unit can analyze the social media activity of the user and input related information when inputting health information. The reception unit, for example, prompts the user to input related information based on health information shared by the user on social media. For example, the reception unit prompts the user to input diet information based on the meal content shared by the user on social media. The reception unit can also prioritize the input of related information based on health-related accounts followed by the user on social media. For example, the reception unit prompts the user to input exercise information based on fitness accounts followed by the user. The reception unit can also prompt the user to input related information based on health-related groups in which the user participates on social media. For example, the reception unit prompts the user to input diet information based on diet groups in which the user participates. This allows the user to input information related to the user by analyzing social media activity. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into an AI model and automatically select an optimal input method.

[0040] The recording unit can select the optimal recording method by referring to the user's past health information at the time of recording. For example, the recording unit preferentially suggests a recording method (such as voice or text) that the user has used frequently in the past. For example, if the user has used voice input a lot in the past, the recording unit preferentially suggests voice input. The recording unit can also analyze the pattern of health information that the user has recorded in the past and suggest the optimal recording method. For example, if the user has recorded during a specific time period, the recording unit suggests the optimal recording method for that time period. In this way, by referring to the past health information, the optimal recording method can be provided to the user. A part or all of the above-mentioned processing in the recording unit may be performed, for example, using AI, or may be performed without using AI. For example, the recording unit can input the user's past health information into an AI model and automatically select the optimal recording method.

[0041] The recording unit can customize the recording means based on the user's current living situation at the time of recording. For example, if the user is currently on a diet, the recording unit provides a recording method related to the diet. For example, the recording unit recognizes that the user is on a diet and provides a recording method related to the meal contents and the amount of exercise. In addition, if the user is interested in a specific disease, the recording unit can also provide a recording method related to the disease. For example, if the user is interested in diabetes, the recording unit provides a recording method related to blood sugar levels and meal contents. In addition, if the user is interested in exercise, the recording unit can also provide a recording method related to exercise. For example, the recording unit recognizes that the user is interested in exercise and provides a recording method related to the amount of exercise and the type of exercise. This allows information to be recorded in a more appropriate manner by customizing the recording means based on the user's living situation. Part or all of the above-mentioned processing in the recording unit may be performed, for example, using AI, or may be performed without using AI. For example, the recording unit can input data related to the user's living situation into an AI model and automatically select the optimal recording method.

[0042] The recording unit can select the optimal recording method in consideration of the geographical location information of the user at the time of recording. For example, if the user lives in a specific area, the recording unit provides a recording method related to the area. For example, the recording unit recognizes that the user lives in a specific area and provides a recording method related to the area. In addition, if the user is traveling, the recording unit can also provide a recording method related to the travel destination. For example, the recording unit recognizes that the user is traveling and provides a recording method related to the travel destination. In addition, if the user lives in a specific climate condition, the recording unit can also provide a recording method related to the climate. For example, if the user lives in a cold area, the recording unit provides a recording method related to the cold area. In this way, by considering the geographical location information, the optimal recording method can be provided to the user. Part or all of the above-mentioned processing in the recording unit may be performed, for example, using AI, or may be performed without using AI. For example, the recording unit can input the geographical location information of the user into an AI model and automatically select the optimal recording method.

[0043] The recording unit can analyze the user's social media activity at the time of recording and suggest a means of recording. The recording unit provides a related recording method based on, for example, health information shared by the user on social media. For example, the recording unit provides a method for recording diet information based on the meal content shared by the user on social media. The recording unit can also provide a related recording method based on health-related accounts followed by the user on social media. For example, the recording unit provides a method for recording exercise information based on fitness accounts followed by the user. The recording unit can also provide a related recording method based on health-related groups in which the user participates on social media. For example, the recording unit provides a method for recording diet information based on diet groups in which the user participates. This allows the user to be provided with an optimal recording means by analyzing social media activities. Part or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the user's social media activity data into an AI model and automatically select an optimal recording method.

[0044] The recommendation unit can select the optimal preventive measures by analyzing the user's past health information at the time of recommendation. The recommendation unit recommends the next necessary vaccination based on, for example, the history of vaccinations the user has received in the past. For example, the recommendation unit suggests the next vaccination based on the history of influenza vaccinations the user has received in the past. The recommendation unit can also analyze the user's past health check results and recommend necessary preventive measures. For example, the recommendation unit suggests preventive measures for a specific disease based on the user's past health check results. The recommendation unit can also recommend preventive measures for a specific disease from the user's past health information. For example, if the user has been diagnosed with a high risk of diabetes in the past, the recommendation unit suggests preventive measures for diabetes. This makes it possible to provide the user with optimal preventive measures by analyzing past health information. Part or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the user's past health information into an AI model and automatically select the optimal preventive measures.

[0045] The recommendation unit can customize preventive measures based on the user's current living situation at the time of recommendation. For example, if the user is currently on a diet, the recommendation unit recommends preventive measures related to dieting. For example, the recommendation unit recognizes that the user is on a diet and suggests preventive measures related to diet content and exercise amount. In addition, the recommendation unit can also recommend preventive measures related to a particular disease if the user is interested in the disease. For example, the recommendation unit suggests preventive measures related to blood sugar level management and diet content if the user is interested in diabetes. In addition, the recommendation unit can also recommend preventive measures related to exercise if the user is interested in exercise. For example, the recommendation unit recognizes that the user is interested in exercise and suggests preventive measures related to the amount of exercise and the type of exercise. In this way, more appropriate preventive measures can be provided by customizing preventive measures based on the user's living situation. Part or all of the above-mentioned processing in the recommendation unit may be performed, for example, using AI, or may be performed without using AI. For example, the recommendation unit can input data related to the user's living situation into an AI model and automatically select the optimal preventive measures.

[0046] The recommendation unit can select the optimal preventive measures by considering the geographical location information of the user at the time of recommendation. For example, if the user lives in a specific area, the recommendation unit recommends preventive measures related to the area. For example, the recommendation unit recognizes that the user lives in a specific area and proposes preventive measures related to the area. In addition, the recommendation unit can also recommend preventive measures related to the travel destination when the user is traveling. For example, the recommendation unit recognizes that the user is traveling and proposes preventive measures related to the travel destination. In addition, the recommendation unit can also recommend preventive measures related to the climate when the user lives in a specific climate condition. For example, the recommendation unit proposes preventive measures related to the cold climate when the user lives in a cold climate. In this way, by considering the geographical location information, the optimal preventive measures can be provided to the user. Part or all of the above-mentioned processing in the recommendation unit may be performed, for example, using AI, or may be performed without using AI. For example, the recommendation unit can input the geographical location information of the user into an AI model and automatically select the optimal preventive measures.

[0047] At the time of making a proposal, the suggestion unit can select an optimal proposal by analyzing the health information of the user. For example, based on the health information of the user, when a specific nutrient is lacking, the suggestion unit proposes a meal containing the nutrient. For example, the suggestion unit analyzes the dietary content of the user, and when a specific nutrient is lacking, proposes a meal containing the nutrient. In addition, the suggestion unit can also propose an appropriate exercise plan when the user's amount of exercise is insufficient. For example, the suggestion unit analyzes the amount of exercise of the user and proposes an appropriate exercise plan. In addition, the suggestion unit can also propose a necessary health habit based on the user's health checkup result. For example, the suggestion unit analyzes the user's health checkup result and proposes a necessary health habit. In this way, the optimal proposal can be provided to the user by analyzing the health information. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's health information into an AI model and automatically select an optimal proposal.

[0048] The suggestion unit can customize the content of the suggestion based on the user's current living situation at the time of suggestion. For example, if the user is currently on a diet, the suggestion unit makes a suggestion related to diet. For example, the suggestion unit recognizes that the user is on a diet and makes a suggestion regarding the meal content and the amount of exercise. In addition, if the user is interested in a specific disease, the suggestion unit can also make a suggestion related to the disease. For example, if the user is interested in diabetes, the suggestion unit makes a suggestion regarding blood sugar level management and meal content. In addition, if the user is interested in exercise, the suggestion unit can also make a suggestion related to exercise. For example, the suggestion unit recognizes that the user is interested in exercise and makes a suggestion regarding the amount of exercise and the type of exercise. As a result, by customizing the content of the suggestion based on the living situation, more appropriate suggestions can be made. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input data regarding the user's living situation into an AI model and automatically select the optimal suggestion.

[0049] The suggestion unit can select the optimal suggestion by considering the geographical location information of the user at the time of suggestion. For example, if the user lives in a specific area, the suggestion unit makes a suggestion related to the area. For example, the suggestion unit recognizes that the user lives in a specific area and makes a suggestion related to the area. In addition, the suggestion unit can also make a suggestion related to the travel destination when the user is traveling. For example, the suggestion unit recognizes that the user is traveling and makes a suggestion related to the travel destination. In addition, the suggestion unit can also make a suggestion related to the climate when the user lives in a specific climate condition. For example, the suggestion unit makes a suggestion related to the cold climate when the user lives in a cold climate. In this way, the optimal suggestion can be provided to the user by considering the geographical location information. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the geographical location information of the user into an AI model and automatically select the optimal suggestion.

[0050] At the time of making a proposal, the suggestion unit can adjust the contents of the proposal by analyzing the social media activity of the user. The suggestion unit makes a relevant proposal based on, for example, health information shared by the user on social media. For example, the suggestion unit makes a meal-related proposal based on the meal contents shared by the user on social media. The suggestion unit can also make a relevant proposal based on health-related accounts followed by the user on social media. For example, the suggestion unit makes an exercise-related proposal based on fitness accounts followed by the user. The suggestion unit can also make a relevant proposal based on health-related groups in which the user participates on social media. For example, the suggestion unit makes a diet-related proposal based on diet groups in which the user participates. This allows the user to be provided with optimal proposals by analyzing social media activities. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's social media activity data into an AI model and automatically select optimal proposals.

[0051] During management, the management unit can select the optimal management method by referring to the user's past schedule history. For example, the management unit preferentially proposes a schedule management method that the user has frequently used in the past. For example, the management unit proposes the optimal management method based on the schedule management method that the user has used in the past. The management unit can also analyze the user's past schedule history and propose the optimal management method. For example, if the user has set a schedule for a specific time period, the management unit proposes the optimal management method for that time period. In this way, the optimal management method can be provided to the user by referring to the past schedule history. A part or all of the above-mentioned processing in the management unit may be performed, for example, using AI, or may be performed without using AI. For example, the management unit can input the user's past schedule history into an AI model and automatically select the optimal management method.

[0052] The management unit can select the optimal management method by considering the geographical location information of the user at the time of management. For example, when the user lives in a specific area, the management unit provides a schedule management method related to the area. For example, the management unit recognizes that the user lives in a specific area and provides a schedule management method related to the area. In addition, when the user is traveling, the management unit can also provide a schedule management method related to the travel destination. For example, the management unit recognizes that the user is traveling and provides a schedule management method related to the travel destination. In addition, when the user lives in a specific climate condition, the management unit can also provide a schedule management method related to the climate. For example, when the user lives in a cold area, the management unit provides a schedule management method related to the cold area. In this way, by considering the geographical location information, the optimal management method can be provided to the user. Part or all of the above-mentioned processing in the management unit may be performed, for example, using AI, or may be performed without using AI. For example, the management unit can input the geographical location information of the user into an AI model and automatically select the optimal management method.

[0053] The providing unit can select an optimal advertisement by referring to the user's past advertisement viewing history when displaying an advertisement. The providing unit, for example, displays a related advertisement based on the history of advertisements clicked by the user in the past. For example, the providing unit displays an advertisement for a related health food based on an advertisement for a health food clicked by the user in the past. The providing unit can also analyze the user's past advertisement viewing history and select an optimal advertisement. For example, the providing unit displays an advertisement for a related fitness program based on an advertisement for a fitness program viewed by the user in the past. The providing unit can also display an optimal advertisement for a specific time period based on the user's past advertisement viewing history. For example, the providing unit displays an optimal advertisement for a specific time period based on the history of advertisements viewed by the user in that time period. In this way, the optimal advertisement can be provided to the user by referring to the past advertisement viewing history. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past advertisement viewing history into an AI model and automatically select an optimal advertisement.

[0054] The providing unit can select an optimal advertisement in consideration of the geographical location information of the user when displaying the advertisement. For example, when the user lives in a specific area, the providing unit displays an advertisement related to the area. For example, the providing unit recognizes that the user lives in a specific area and displays an advertisement related to the area. In addition, when the user is traveling, the providing unit can also display an advertisement related to the travel destination. For example, the providing unit recognizes that the user is traveling and displays an advertisement related to the travel destination. In addition, when the user lives in a specific climate condition, the providing unit can also display an advertisement related to the climate. For example, when the user lives in a cold area, the providing unit displays an advertisement related to the cold area. In this way, the optimal advertisement can be provided to the user by considering the geographical location information. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the geographical location information of the user into an AI model and automatically select the optimal advertisement.

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

[0056] The health support system may further include a sleep monitoring unit that monitors the user's sleep pattern. The sleep monitoring unit records the user's sleep time, sleep quality, wake-up time, etc., and analyzes these data. For example, if the user is not getting enough sleep, the recommendation unit may provide advice to improve the quality of sleep. The sleep monitoring unit may also suggest optimal wake-up and bedtime times based on the user's sleep pattern. Furthermore, the sleep monitoring unit may integrate the user's sleep data with other health information to evaluate the overall health condition. This allows the user to develop better sleep habits and improve overall health.

[0057] The health support system may further include a stress monitoring unit that monitors the user's stress level. The stress monitoring unit collects the user's heart rate, electrodermal activity, breathing pattern, and the like using a sensor to evaluate the stress level. For example, if the user's stress level is high, the recommendation unit may provide relaxation methods and stress management advice. The stress monitoring unit may also suggest appropriate exercise and rest timing based on the user's stress level. Furthermore, the stress monitoring unit may integrate the user's stress data with other health information to evaluate the overall health condition. This allows the user to effectively manage stress and improve overall health.

[0058] The health support system may further include a diet analysis unit that analyzes the user's dietary content in detail. The diet analysis unit analyzes images of meals taken by the user and automatically calculates the intake of nutrients. For example, if the user is consuming an excessive amount of a particular nutrient, the suggestion unit may provide a meal plan to reduce the nutrient. The diet analysis unit may also suggest a balanced meal based on the user's eating patterns. Furthermore, the diet analysis unit may integrate the user's dietary data with other health information to evaluate the overall health condition. This allows the user to live a healthier dietary life.

[0059] The health support system may further include an exercise monitoring unit that monitors the user's exercise pattern. The exercise monitoring unit records the user's exercise amount, type of exercise, exercise time, etc., and analyzes these data. For example, if the user is not getting enough exercise, the recommendation unit may provide an appropriate exercise plan. The exercise monitoring unit may also suggest optimal exercise time and type of exercise based on the user's exercise pattern. Furthermore, the exercise monitoring unit may integrate the user's exercise data with other health information to evaluate the overall health condition. This allows the user to develop more effective exercise habits and improve overall health.

[0060] The process flow of the first embodiment will be briefly described below.

[0061] Step 1: The reception unit inputs the user's health information. The user's health information includes weight, blood pressure, dietary details, and amount of exercise. The reception unit accepts the health information in various formats, such as digital format, voice input, and image input. For example, the user can input the health information by voice using a smartphone, or the dietary details can be automatically input by analyzing images of the meal. Step 2: The recording unit records the information input by the reception unit. The recording unit stores the user's health information in cloud storage to make it accessible for a long period of time. The recording unit also encrypts and stores the user's health information to protect privacy. Furthermore, the recording unit regularly backs up the user's health information to prevent data loss. Step 3: The recommendation module recommends preventive measures based on the user's age. For example, it can suggest a vaccination schedule based on the user's age, or recommend regular health checkups. It can also notify the user of required vaccinations or health checkups when they reach a certain age. Step 4: The suggestion unit makes personalized suggestions based on the information stored in the recording unit. For example, the suggestion unit analyzes the user's health information and suggests optimal health and lifestyle habits for each individual user. The suggestion unit analyzes the user's dietary content, and if a specific nutrient is lacking, suggests a meal that contains that nutrient. The suggestion unit can also analyze the user's amount of exercise and suggest an appropriate exercise plan.

[0062] (Example 2) A health support system according to an embodiment of the present invention is a system that supports the mental and physical health of a user. This system starts health advice and recording from the day of birth based on Ericson's developmental stages. Specifically, the system includes a reception unit that inputs the user's health information, a recording unit that records the input information, a recommendation unit that recommends prevention according to the user's developmental stage, and a suggestion unit that makes personalized suggestions based on the recorded information. For example, information such as the user's weight, height, dietary content, and amount of exercise is input, and this information is input by the user himself or her or his guardian. Next, the input information is recorded in the recording unit and stored for a long period of time. The recommendation unit suggests appropriate preventive measures based on the user's age and developmental stage, for example, recommending a vaccination schedule for infancy and recommending regular health checkups for school-age children. In addition, there is a suggestion unit that makes personalized suggestions based on the information stored in the recording unit, which analyzes the user's health information and suggests optimal health habits and lifestyle habits for each individual user. For example, if a specific nutrient is lacking, a meal containing that nutrient is suggested. This allows the user to receive support for maintaining health throughout his or her life. This allows the health support system to efficiently input, record, recommend, and suggest the user's health information.

[0063] The health support system according to the embodiment includes a reception unit, a recording unit, a recommendation unit, and a suggestion unit. The reception unit inputs the user's health information. The user's health information includes, for example, weight, blood pressure, dietary content, and amount of exercise, but is not limited to such examples. The reception unit, for example, receives the health information input by the user in a digital format. The reception unit can also receive the user's health information in various formats such as voice input and image input. For example, the reception unit enables the user to input the health information by voice using a smartphone. The reception unit can also analyze an image of a meal taken by the user and automatically input the meal content. The recording unit records the information input by the reception unit. The recording unit, for example, stores the user's health information in cloud storage and makes it accessible for a long period of time. The recording unit also encrypts and stores the user's health information to protect privacy. For example, the recording unit regularly backs up the user's health information to prevent data loss. The recommendation unit recommends prevention based on the user's age. The recommendation unit, for example, suggests a vaccination schedule according to the user's age. The recommendation unit can also recommend regular health checkups based on the user's age. For example, the recommendation unit notifies the user of necessary vaccinations and health checkups when the user reaches a certain age. The suggestion unit makes personalized suggestions based on the information stored in the recording unit. The suggestion unit, for example, analyzes the user's health information and suggests optimal health habits and lifestyle habits for each individual user. For example, the suggestion unit analyzes the user's dietary content, and if a specific nutrient is lacking, suggests a diet containing that nutrient. The suggestion unit can also analyze the user's amount of exercise and suggest an appropriate exercise plan. As a result, the health support system according to the embodiment can efficiently input, record, recommend, and suggest the user's health information.

[0064] The reception unit inputs the health information of the user. The health information of the user includes, for example, weight, blood pressure, dietary content, and amount of exercise, but is not limited to such examples. The reception unit, for example, receives the health information input by the user in a digital format. Specifically, the user can input health information through a dedicated application or web interface using a device such as a smartphone, tablet, or personal computer. This allows the user to easily record health information on a daily basis. The reception unit can also receive the health information of the user in various formats such as voice input and image input. For example, in the case of voice input, the user can use the microphone of the smartphone to input weight, blood pressure, dietary content, and the like by voice. Using voice recognition technology, the input voice can be converted into text data and imported into the system. Furthermore, in the case of image input, the user can use the camera of the smartphone to take an image of the meal and upload it to the system. Using image analysis technology, the meal content, calories, and nutrients can be automatically analyzed and registered in the database. This allows the user to input detailed health information without any effort. By providing these various input methods, the reception unit can improve the convenience of the user and increase the accuracy and comprehensiveness of the health information.

[0065] The recording unit records the information input by the reception unit. For example, the recording unit stores the user's health information in cloud storage and makes it accessible for a long period of time. By using cloud storage, the user can consistently access the health information even from different devices. In addition, the recording unit encrypts and stores the user's health information to protect privacy. By using encryption technology, the risk of unauthorized access by third parties and data leakage can be reduced. For example, the recording unit periodically backs up the user's health information to prevent data loss. The backups are distributed and stored on multiple servers in different geographical locations, so that data can be restored even in the event of a disaster or system failure. Furthermore, the recording unit can organize the user's health information in chronological order and compare past data with current data. This allows the user to visually check changes in his or her health condition and take necessary measures. The recording unit can efficiently and safely manage the user's health information and provide reliable data for a long period of time.

[0066] The recommendation unit recommends prevention based on the user's age. For example, the recommendation unit proposes a vaccination schedule according to the user's age. Specifically, the recommendation unit can calculate the timing of necessary vaccinations based on the user's age, sex, and past vaccination history, and notify the user. The recommendation unit can also recommend regular health checkups based on the user's age. For example, when a certain age is reached, the recommendation unit proposes the type and frequency of necessary health checkups and notifies the user. This allows the user to check his or her health condition regularly and discover abnormalities early. Furthermore, the recommendation unit can also propose preventive measures according to the user's lifestyle and health condition. For example, the recommendation unit proposes an appropriate exercise plan for a user who is not getting enough exercise, and proposes a balanced meal menu for a user who has an unbalanced diet. This allows the recommendation unit to support the user in maintaining and improving their health, and to raise awareness of the importance of preventive medicine.

[0067] The suggestion unit makes personalized suggestions based on the information stored in the recording unit. For example, the suggestion unit analyzes the health information of the user and suggests optimal health habits and lifestyle habits for each individual user. Specifically, the suggestion unit analyzes the dietary content of the user, and if a specific nutrient is lacking, suggests a meal containing that nutrient. For example, for a user who is lacking in vitamin D, foods and supplements containing a lot of vitamin D can be suggested. The suggestion unit can also analyze the amount of exercise of the user and suggest an appropriate exercise plan. For example, for a user who is lacking in exercise, a specific exercise menu such as walking, jogging, and strength training can be suggested, and the frequency and intensity of exercise can be adjusted. Furthermore, the suggestion unit can analyze the stress level and sleep pattern of the user and suggest relaxation methods and measures to improve sleep. For example, for a user with high stress, relaxation methods such as meditation, deep breathing, and yoga are suggested, and for a user who is lacking in sleep, improvement of the sleep environment and review of sleep habits are suggested. As a result, the suggestion unit can comprehensively analyze the health condition of the user and make specific and practical suggestions according to individual needs. As a result, the health support system according to the embodiment can efficiently input, record, recommend, and suggest the health information of the user.

[0068] The health support system includes a management unit that manages vaccination and examination schedules, and a recommendation unit that recommends prevention based on information managed by the management unit. The management unit manages vaccination and examination schedules. For example, the management unit registers the user's vaccination and examination schedules in a digital calendar and notifies the user at an appropriate time. The management unit can also record the user's vaccination and examination history and automatically calculate the next schedule. For example, the management unit suggests the next vaccination required based on the history of vaccinations the user has received in the past. The recommendation unit recommends prevention based on information managed by the management unit. The recommendation unit suggests appropriate vaccinations and examinations based on the user's age and health condition, for example. For example, the recommendation unit notifies the user of necessary vaccinations and health checkups when the user reaches a certain age. As a result, the management unit appropriately manages vaccination and examination schedules, and the recommendation unit recommends prevention based on the information, thereby making the user's health management more efficient. A part or all of the above-mentioned processing in the management unit may be performed using AI, for example, or may be performed without using AI. For example, the management department can input a user's vaccination or testing schedule into the AI ​​model to automatically calculate the next schedule.

[0069] The health support system includes a providing unit that provides an advertisement to a user based on a proposal by the suggestion unit. The providing unit provides an advertisement to a user based on a proposal by the suggestion unit. The providing unit displays, for example, advertisements related to the user's health information and lifestyle habits. For example, the providing unit displays advertisements for health foods containing nutrients that the user is lacking. The providing unit can also display advertisements for fitness programs based on the user's amount of exercise. For example, when the user is not getting enough exercise, the providing unit displays advertisements for appropriate fitness programs. As a result, by providing advertisements based on the proposal by the suggestion unit, it is possible to display advertisements that are highly relevant to the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's health information into an AI model and automatically select the optimal advertisement.

[0070] The reception unit can estimate the user's emotion and adjust the input timing of the health information based on the emotion. The reception unit, for example, captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on the change in facial expression. The reception unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the reception unit analyzes the tone and speed of the voice and calculates the emotion score. The reception unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit calculates the emotion score based on the fluctuation of the heart rate. This allows the information to be input at a more appropriate timing by adjusting the input timing of the health information according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input image data of a user captured by a camera to the generation AI and have the generation AI estimate the user's emotions.

[0071] The reception unit can analyze the user's past health information input history and select an appropriate input method. The reception unit, for example, preferentially suggests an input method (such as voice or text) that the user has used frequently in the past. For example, if the user has used voice input a lot in the past, the reception unit preferentially suggests voice input. The reception unit can also analyze the pattern of health information previously input by the user and suggest the optimal input method. For example, if the user has input data during a specific time period, the reception unit suggests the optimal input method for that time period. In this way, the optimal input method can be provided to the user by analyzing the past input history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history into an AI model and automatically select the optimal input method.

[0072] The reception unit can filter the health information based on the user's current living situation and field of interest when inputting the health information. For example, if the user is currently on a diet, the reception unit prioritizes input of health information related to the diet. For example, the reception unit recognizes that the user is on a diet and prioritizes input of information related to the diet and the amount of exercise. In addition, if the user is interested in a specific disease, the reception unit can also prompt the user to input health information related to the disease. For example, if the user is interested in diabetes, the reception unit prioritizes input of information related to blood glucose levels and diet. In addition, if the user is interested in exercise, the reception unit can also prioritize input of health information related to exercise. For example, the reception unit recognizes that the user is interested in exercise and prioritizes input of information related to the amount of exercise and the type of exercise. This allows more relevant information to be input by filtering information based on the user's living situation and field of interest. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data related to the user's living situation and field of interest into an AI model and automatically select the optimal input method.

[0073] The reception unit can estimate the user's emotion and determine the priority of the health information to be input based on the emotion. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on the change in facial expression. The reception unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the reception unit analyzes the tone and speed of the voice and calculates the emotion score. The reception unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit calculates the emotion score based on the fluctuation of the heart rate. As a result, important information can be preferentially input by determining the priority of the health information according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input image data of a user captured by a camera to the generation AI and have the generation AI estimate the user's emotions.

[0074] When inputting health information, the reception unit can preferentially input information that is highly relevant to the user in consideration of the geographical location information of the user. For example, when the user lives in a specific area, the reception unit prioritizes input of health information related to the area. For example, the reception unit recognizes that the user lives in a specific area and prompts the user to input health information related to the area. In addition, when the user is traveling, the reception unit can also prompt the user to input health information related to the travel destination. For example, the reception unit recognizes that the user is traveling and prompts the user to input health information related to the travel destination. In addition, when the user lives in a specific climate condition, the reception unit can also prioritize input of health information related to the climate. For example, when the user lives in a cold area, the reception unit prompts the user to input health information related to the cold area. In this way, by considering the geographical location information, information that is highly relevant to the user can be preferentially input. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information into an AI model and automatically select an optimal input method.

[0075] The reception unit can analyze the social media activity of the user and input related information when inputting health information. The reception unit, for example, prompts the user to input related information based on health information shared by the user on social media. For example, the reception unit prompts the user to input diet information based on the meal content shared by the user on social media. The reception unit can also prioritize the input of related information based on health-related accounts followed by the user on social media. For example, the reception unit prompts the user to input exercise information based on fitness accounts followed by the user. The reception unit can also prompt the user to input related information based on health-related groups in which the user participates on social media. For example, the reception unit prompts the user to input diet information based on diet groups in which the user participates. This allows the user to input information related to the user by analyzing social media activity. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into an AI model and automatically select an optimal input method.

[0076] The recording unit can estimate the user's emotion and adjust the recording method based on the emotion. For example, the recording unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the recording unit calculates an emotion score based on the change in facial expression. The recording unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the recording unit analyzes the tone and speed of the voice and calculates the emotion score. The recording unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the recording unit calculates the emotion score based on the fluctuation of the heart rate. This allows the information to be recorded in a more appropriate manner by adjusting the recording method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the recording unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recording unit can input image data of a user captured by a camera into the generation AI and have the generation AI estimate the user's emotions.

[0077] The recording unit can select the optimal recording method by referring to the user's past health information at the time of recording. For example, the recording unit preferentially suggests a recording method (such as voice or text) that the user has used frequently in the past. For example, if the user has used voice input a lot in the past, the recording unit preferentially suggests voice input. The recording unit can also analyze the pattern of health information that the user has recorded in the past and suggest the optimal recording method. For example, if the user has recorded during a specific time period, the recording unit suggests the optimal recording method for that time period. In this way, by referring to the past health information, the optimal recording method can be provided to the user. A part or all of the above-mentioned processing in the recording unit may be performed, for example, using AI, or may be performed without using AI. For example, the recording unit can input the user's past health information into an AI model and automatically select the optimal recording method.

[0078] The recording unit can customize the recording means based on the user's current living situation at the time of recording. For example, if the user is currently on a diet, the recording unit provides a recording method related to the diet. For example, the recording unit recognizes that the user is on a diet and provides a recording method related to the meal contents and the amount of exercise. In addition, if the user is interested in a specific disease, the recording unit can also provide a recording method related to the disease. For example, if the user is interested in diabetes, the recording unit provides a recording method related to blood sugar levels and meal contents. In addition, if the user is interested in exercise, the recording unit can also provide a recording method related to exercise. For example, the recording unit recognizes that the user is interested in exercise and provides a recording method related to the amount of exercise and the type of exercise. This allows information to be recorded in a more appropriate manner by customizing the recording means based on the user's living situation. Part or all of the above-mentioned processing in the recording unit may be performed, for example, using AI, or may be performed without using AI. For example, the recording unit can input data related to the user's living situation into an AI model and automatically select the optimal recording method.

[0079] The recording unit can estimate the user's emotion and determine the priority of information to be recorded based on the emotion. For example, the recording unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the recording unit calculates an emotion score based on the change in facial expression. The recording unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the recording unit analyzes the tone and speed of the voice and calculates the emotion score. The recording unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the recording unit calculates the emotion score based on the fluctuation of the heart rate. As a result, the priority of information to be recorded according to the user's emotion can be determined, and important information can be preferentially recorded. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the recording unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recording unit can input image data of a user captured by a camera into the generation AI and have the generation AI estimate the user's emotions.

[0080] The recording unit can select the optimal recording method in consideration of the geographical location information of the user at the time of recording. For example, if the user lives in a specific area, the recording unit provides a recording method related to the area. For example, the recording unit recognizes that the user lives in a specific area and provides a recording method related to the area. In addition, if the user is traveling, the recording unit can also provide a recording method related to the travel destination. For example, the recording unit recognizes that the user is traveling and provides a recording method related to the travel destination. In addition, if the user lives in a specific climate condition, the recording unit can also provide a recording method related to the climate. For example, if the user lives in a cold area, the recording unit provides a recording method related to the cold area. In this way, by considering the geographical location information, the optimal recording method can be provided to the user. Part or all of the above-mentioned processing in the recording unit may be performed, for example, using AI, or may be performed without using AI. For example, the recording unit can input the geographical location information of the user into an AI model and automatically select the optimal recording method.

[0081] The recording unit can analyze the user's social media activity at the time of recording and suggest a means of recording. The recording unit provides a related recording method based on, for example, health information shared by the user on social media. For example, the recording unit provides a method for recording diet information based on the meal content shared by the user on social media. The recording unit can also provide a related recording method based on health-related accounts followed by the user on social media. For example, the recording unit provides a method for recording exercise information based on fitness accounts followed by the user. The recording unit can also provide a related recording method based on health-related groups in which the user participates on social media. For example, the recording unit provides a method for recording diet information based on diet groups in which the user participates. This allows the user to be provided with an optimal recording means by analyzing social media activities. Part or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the user's social media activity data into an AI model and automatically select an optimal recording method.

[0082] The recommendation unit can estimate the user's emotion and adjust the recommended prevention method based on the emotion. For example, the recommendation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the recommendation unit calculates an emotion score based on the change in facial expression. The recommendation unit can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the recommendation unit analyzes the tone and speed of the voice and calculates the emotion score. The recommendation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the recommendation unit calculates the emotion score based on the fluctuation of the heart rate. This makes it possible to provide a more appropriate preventive measure by adjusting the recommended prevention method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the recommendation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recommendation unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0083] The recommendation unit can select the optimal preventive measures by analyzing the user's past health information at the time of recommendation. The recommendation unit recommends the next necessary vaccination based on, for example, the history of vaccinations the user has received in the past. For example, the recommendation unit suggests the next vaccination based on the history of influenza vaccinations the user has received in the past. The recommendation unit can also analyze the user's past health check results and recommend necessary preventive measures. For example, the recommendation unit suggests preventive measures for a specific disease based on the user's past health check results. The recommendation unit can also recommend preventive measures for a specific disease from the user's past health information. For example, if the user has been diagnosed with a high risk of diabetes in the past, the recommendation unit suggests preventive measures for diabetes. This makes it possible to provide the user with optimal preventive measures by analyzing past health information. Part or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the user's past health information into an AI model and automatically select the optimal preventive measures.

[0084] The recommendation unit can customize preventive measures based on the user's current living situation at the time of recommendation. For example, if the user is currently on a diet, the recommendation unit recommends preventive measures related to dieting. For example, the recommendation unit recognizes that the user is on a diet and suggests preventive measures related to diet content and exercise amount. In addition, the recommendation unit can also recommend preventive measures related to a particular disease if the user is interested in the disease. For example, the recommendation unit suggests preventive measures related to blood sugar level management and diet content if the user is interested in diabetes. In addition, the recommendation unit can also recommend preventive measures related to exercise if the user is interested in exercise. For example, the recommendation unit recognizes that the user is interested in exercise and suggests preventive measures related to the amount of exercise and the type of exercise. In this way, more appropriate preventive measures can be provided by customizing preventive measures based on the user's living situation. Part or all of the above-mentioned processing in the recommendation unit may be performed, for example, using AI, or may be performed without using AI. For example, the recommendation unit can input data related to the user's living situation into an AI model and automatically select the optimal preventive measures.

[0085] The recommendation unit can estimate the user's emotions and determine the priority of prevention based on the emotions. For example, the recommendation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the recommendation unit calculates an emotion score based on the change in facial expression. The recommendation unit can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the recommendation unit analyzes the tone and speed of the voice and calculates the emotion score. The recommendation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the recommendation unit calculates the emotion score based on the fluctuation of the heart rate. As a result, important preventive measures can be provided preferentially by determining the priority of prevention according to the user's emotions. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the recommendation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recommendation unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0086] The recommendation unit can select the optimal preventive measures by considering the geographical location information of the user at the time of recommendation. For example, if the user lives in a specific area, the recommendation unit recommends preventive measures related to the area. For example, the recommendation unit recognizes that the user lives in a specific area and proposes preventive measures related to the area. In addition, the recommendation unit can also recommend preventive measures related to the travel destination when the user is traveling. For example, the recommendation unit recognizes that the user is traveling and proposes preventive measures related to the travel destination. In addition, the recommendation unit can also recommend preventive measures related to the climate when the user lives in a specific climate condition. For example, the recommendation unit proposes preventive measures related to the cold climate when the user lives in a cold climate. In this way, by considering the geographical location information, the optimal preventive measures can be provided to the user. Part or all of the above-mentioned processing in the recommendation unit may be performed, for example, using AI, or may be performed without using AI. For example, the recommendation unit can input the geographical location information of the user into an AI model and automatically select the optimal preventive measures.

[0087] The suggestion unit can estimate the user's emotion and adjust the expression method of the proposal based on the emotion. For example, the suggestion unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the suggestion unit calculates an emotion score based on the change in the facial expression. The suggestion unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the suggestion unit analyzes the tone and speed of the voice and calculates the emotion score. The suggestion unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the suggestion unit calculates the emotion score based on the fluctuation of the heart rate. As a result, a more appropriate suggestion can be made by adjusting the expression method of the proposal according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input image data of a user captured by a camera into the generation AI and have the generation AI estimate the user's emotions.

[0088] At the time of making a proposal, the suggestion unit can select an optimal proposal by analyzing the health information of the user. For example, based on the health information of the user, when a specific nutrient is lacking, the suggestion unit proposes a meal containing the nutrient. For example, the suggestion unit analyzes the dietary content of the user, and when a specific nutrient is lacking, proposes a meal containing the nutrient. In addition, the suggestion unit can also propose an appropriate exercise plan when the user's amount of exercise is insufficient. For example, the suggestion unit analyzes the amount of exercise of the user and proposes an appropriate exercise plan. In addition, the suggestion unit can also propose a necessary health habit based on the user's health checkup result. For example, the suggestion unit analyzes the user's health checkup result and proposes a necessary health habit. In this way, the optimal proposal can be provided to the user by analyzing the health information. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's health information into an AI model and automatically select an optimal proposal.

[0089] The suggestion unit can customize the content of the suggestion based on the user's current living situation at the time of suggestion. For example, if the user is currently on a diet, the suggestion unit makes a suggestion related to diet. For example, the suggestion unit recognizes that the user is on a diet and makes a suggestion regarding the meal content and the amount of exercise. In addition, if the user is interested in a specific disease, the suggestion unit can also make a suggestion related to the disease. For example, if the user is interested in diabetes, the suggestion unit makes a suggestion regarding blood sugar level management and meal content. In addition, if the user is interested in exercise, the suggestion unit can also make a suggestion related to exercise. For example, the suggestion unit recognizes that the user is interested in exercise and makes a suggestion regarding the amount of exercise and the type of exercise. As a result, by customizing the content of the suggestion based on the living situation, more appropriate suggestions can be made. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input data regarding the user's living situation into an AI model and automatically select the optimal suggestion.

[0090] The suggestion unit can estimate the user's emotion and determine the priority of suggestions based on the emotion. For example, the suggestion unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the suggestion unit calculates an emotion score based on the change in facial expression. The suggestion unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the suggestion unit analyzes the tone and speed of the voice and calculates the emotion score. The suggestion unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the suggestion unit calculates the emotion score based on the fluctuation of the heart rate. As a result, the priority of suggestions can be determined according to the user's emotion, and important suggestions can be provided preferentially. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input image data of a user captured by a camera into the generation AI and have the generation AI estimate the user's emotions.

[0091] The suggestion unit can select the optimal suggestion by considering the geographical location information of the user at the time of suggestion. For example, if the user lives in a specific area, the suggestion unit makes a suggestion related to the area. For example, the suggestion unit recognizes that the user lives in a specific area and makes a suggestion related to the area. In addition, the suggestion unit can also make a suggestion related to the travel destination when the user is traveling. For example, the suggestion unit recognizes that the user is traveling and makes a suggestion related to the travel destination. In addition, the suggestion unit can also make a suggestion related to the climate when the user lives in a specific climate condition. For example, the suggestion unit makes a suggestion related to the cold climate when the user lives in a cold climate. In this way, the optimal suggestion can be provided to the user by considering the geographical location information. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the geographical location information of the user into an AI model and automatically select the optimal suggestion.

[0092] At the time of making a proposal, the suggestion unit can adjust the contents of the proposal by analyzing the social media activity of the user. The suggestion unit makes a relevant proposal based on, for example, health information shared by the user on social media. For example, the suggestion unit makes a meal-related proposal based on the meal contents shared by the user on social media. The suggestion unit can also make a relevant proposal based on health-related accounts followed by the user on social media. For example, the suggestion unit makes an exercise-related proposal based on fitness accounts followed by the user. The suggestion unit can also make a relevant proposal based on health-related groups in which the user participates on social media. For example, the suggestion unit makes a diet-related proposal based on diet groups in which the user participates. This allows the user to be provided with optimal proposals by analyzing social media activities. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's social media activity data into an AI model and automatically select optimal proposals.

[0093] The management unit can estimate the user's emotion and adjust the schedule management method based on the emotion. For example, the management unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the management unit calculates an emotion score based on the change in facial expression. The management unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the management unit analyzes the tone and speed of the voice and calculates the emotion score. The management unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the management unit calculates the emotion score based on the fluctuation of the heart rate. This enables more appropriate schedule management by adjusting the schedule management method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the management unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the management unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0094] During management, the management unit can select the optimal management method by referring to the user's past schedule history. For example, the management unit preferentially proposes a schedule management method that the user has frequently used in the past. For example, the management unit proposes the optimal management method based on the schedule management method that the user has used in the past. The management unit can also analyze the user's past schedule history and propose the optimal management method. For example, if the user has set a schedule for a specific time period, the management unit proposes the optimal management method for that time period. In this way, the optimal management method can be provided to the user by referring to the past schedule history. A part or all of the above-mentioned processing in the management unit may be performed, for example, using AI, or may be performed without using AI. For example, the management unit can input the user's past schedule history into an AI model and automatically select the optimal management method.

[0095] The management unit can estimate the user's emotions and determine the priority of the schedule based on the emotions. For example, the management unit captures the user's facial expression with a camera and estimates the emotions using an emotion estimation algorithm. For example, the management unit calculates an emotion score based on the change in facial expression. The management unit can also record the user's voice and estimate the emotions using a voice analysis technique. For example, the management unit analyzes the tone and speed of the voice and calculates the emotion score. The management unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the management unit calculates the emotion score based on the fluctuation of the heart rate. As a result, important schedules can be managed preferentially by determining the priority of the schedule according to the user's emotions. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the management unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the management unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0096] The management unit can select the optimal management method by considering the geographical location information of the user at the time of management. For example, when the user lives in a specific area, the management unit provides a schedule management method related to the area. For example, the management unit recognizes that the user lives in a specific area and provides a schedule management method related to the area. In addition, when the user is traveling, the management unit can also provide a schedule management method related to the travel destination. For example, the management unit recognizes that the user is traveling and provides a schedule management method related to the travel destination. In addition, when the user lives in a specific climate condition, the management unit can also provide a schedule management method related to the climate. For example, when the user lives in a cold area, the management unit provides a schedule management method related to the cold area. In this way, by considering the geographical location information, the optimal management method can be provided to the user. Part or all of the above-mentioned processing in the management unit may be performed, for example, using AI, or may be performed without using AI. For example, the management unit can input the geographical location information of the user into an AI model and automatically select the optimal management method.

[0097] The providing unit can estimate the user's emotion and adjust the advertisement display method based on the emotion. The providing unit, for example, captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on the change in facial expression. The providing unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the providing unit analyzes the tone and speed of the voice and calculates the emotion score. The providing unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the providing unit calculates the emotion score based on the fluctuation of the heart rate. As a result, the advertisement display method can be adjusted according to the user's emotion, thereby displaying a more appropriate advertisement. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input image data of a user captured by a camera into the generating AI and cause the generating AI to estimate the user's emotions.

[0098] The providing unit can select an optimal advertisement by referring to the user's past advertisement viewing history when displaying an advertisement. The providing unit, for example, displays a related advertisement based on the history of advertisements clicked by the user in the past. For example, the providing unit displays an advertisement for a related health food based on an advertisement for a health food clicked by the user in the past. The providing unit can also analyze the user's past advertisement viewing history and select an optimal advertisement. For example, the providing unit displays an advertisement for a related fitness program based on an advertisement for a fitness program viewed by the user in the past. The providing unit can also display an optimal advertisement for a specific time period based on the user's past advertisement viewing history. For example, the providing unit displays an optimal advertisement for a specific time period based on the history of advertisements viewed by the user in that time period. In this way, the optimal advertisement can be provided to the user by referring to the past advertisement viewing history. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past advertisement viewing history into an AI model and automatically select an optimal advertisement.

[0099] The providing unit can estimate the user's emotion and determine the priority of advertisements based on the emotion. The providing unit, for example, captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on the change in facial expression. The providing unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the providing unit analyzes the tone and speed of the voice and calculates the emotion score. The providing unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the providing unit calculates the emotion score based on the fluctuation of the heart rate. As a result, important advertisements can be preferentially displayed by determining the priority of advertisements according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input image data of a user captured by a camera into the generating AI and cause the generating AI to estimate the user's emotions.

[0100] The providing unit can select an optimal advertisement in consideration of the geographical location information of the user when displaying the advertisement. For example, when the user lives in a specific area, the providing unit displays an advertisement related to the area. For example, the providing unit recognizes that the user lives in a specific area and displays an advertisement related to the area. In addition, when the user is traveling, the providing unit can also display an advertisement related to the travel destination. For example, the providing unit recognizes that the user is traveling and displays an advertisement related to the travel destination. In addition, when the user lives in a specific climate condition, the providing unit can also display an advertisement related to the climate. For example, when the user lives in a cold area, the providing unit displays an advertisement related to the cold area. In this way, the optimal advertisement can be provided to the user by considering the geographical location information. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the geographical location information of the user into an AI model and automatically select the optimal advertisement.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] The health support system may further include a sleep monitoring unit that monitors the user's sleep pattern. The sleep monitoring unit records the user's sleep time, sleep quality, wake-up time, etc., and analyzes these data. For example, if the user is not getting enough sleep, the recommendation unit may provide advice to improve the quality of sleep. The sleep monitoring unit may also suggest optimal wake-up and bedtime times based on the user's sleep pattern. Furthermore, the sleep monitoring unit may integrate the user's sleep data with other health information to evaluate the overall health condition. This allows the user to develop better sleep habits and improve overall health.

[0103] The health support system may further include a stress monitoring unit that monitors the user's stress level. The stress monitoring unit collects the user's heart rate, electrodermal activity, breathing pattern, and the like using a sensor to evaluate the stress level. For example, if the user's stress level is high, the recommendation unit may provide relaxation methods and stress management advice. The stress monitoring unit may also suggest appropriate exercise and rest timing based on the user's stress level. Furthermore, the stress monitoring unit may integrate the user's stress data with other health information to evaluate the overall health condition. This allows the user to effectively manage stress and improve overall health.

[0104] The health support system may further include a diet analysis unit that analyzes the user's dietary content in detail. The diet analysis unit analyzes images of meals taken by the user and automatically calculates the intake of nutrients. For example, if the user is consuming an excessive amount of a particular nutrient, the suggestion unit may provide a meal plan to reduce the nutrient. The diet analysis unit may also suggest a balanced meal based on the user's eating patterns. Furthermore, the diet analysis unit may integrate the user's dietary data with other health information to evaluate the overall health condition. This allows the user to live a healthier dietary life.

[0105] The health support system may further include an exercise monitoring unit that monitors the user's exercise pattern. The exercise monitoring unit records the user's exercise amount, type of exercise, exercise time, etc., and analyzes these data. For example, if the user is not getting enough exercise, the recommendation unit may provide an appropriate exercise plan. The exercise monitoring unit may also suggest optimal exercise time and type of exercise based on the user's exercise pattern. Furthermore, the exercise monitoring unit may integrate the user's exercise data with other health information to evaluate the overall health condition. This allows the user to develop more effective exercise habits and improve overall health.

[0106] The health support system can further estimate the user's emotions and adjust the input method of health information based on the emotions. For example, if the user is feeling stressed, the reception unit can suggest a method of inputting health information in the form of a simple question. Also, if the user is relaxed, the reception unit can suggest a method of inputting detailed health information. Furthermore, the emotion estimation function can be used to automatically select an input method according to the user's emotions. This allows the user to efficiently input health information without feeling stressed.

[0107] The health support system can further estimate the user's emotions and adjust the method of recording health information based on the emotions. For example, if the user is tired, the recording unit can suggest a method of recording health information in a simple checklist format. Also, if the user is in good health, the recording unit can suggest a method of recording detailed health information. Furthermore, the emotion estimation function can be used to automatically select a recording method according to the user's emotions. This allows the user to record health information without strain.

[0108] The health support system can further estimate the user's emotions and adjust the method of suggesting preventive measures based on the emotions. For example, if the user feels anxious, the recommendation unit can suggest preventive measures in words that give a sense of security. Also, if the user feels positive, the recommendation unit can suggest preventive measures in positive words. Furthermore, the emotion estimation function can be used to automatically select the suggestion method according to the user's emotions. This allows the user to receive preventive measures in a more acceptable form.

[0109] The health support system can further estimate the user's emotions and adjust the display content of advertisements based on the emotions. For example, if the user is feeling stressed, the providing unit can display advertisements related to relaxation. Also, if the user is feeling energetic, advertisements related to fitness and activity can be displayed. Furthermore, the emotion estimation function can be used to automatically select advertisement content according to the user's emotions. This allows the user to receive more relevant advertisements.

[0110] The health support system can further estimate the user's emotions and determine the priority of health information based on the emotions. For example, if the user is feeling stressed, the recording unit can prioritize recording information related to stress management. Also, if the user is relaxed, detailed health information can be prioritized. Furthermore, the emotion estimation function can be used to automatically select the priority of information according to the user's emotions. This allows the user to record the most appropriate information according to their emotions at any given time.

[0111] The health support system can further estimate the user's emotions and adjust the timing of inputting health information based on the emotions. For example, if the user is busy, the reception unit can suggest a method that allows inputting information in a short time. Also, if the user is relaxed, the reception unit can suggest a method of inputting detailed information. Furthermore, the emotion estimation function can be used to automatically select the input timing according to the user's emotions. This allows the user to input health information without straining themselves.

[0112] The process flow of the second embodiment will be briefly described below.

[0113] Step 1: The reception unit inputs the user's health information. The user's health information includes weight, blood pressure, dietary details, and amount of exercise. The reception unit accepts the health information in various formats, such as digital format, voice input, and image input. For example, the user can input the health information by voice using a smartphone, or the dietary details can be automatically input by analyzing images of the meal. Step 2: The recording unit records the information input by the reception unit. The recording unit stores the user's health information in cloud storage to make it accessible for a long period of time. The recording unit also encrypts and stores the user's health information to protect privacy. Furthermore, the recording unit regularly backs up the user's health information to prevent data loss. Step 3: The recommendation module recommends preventive measures based on the user's age. For example, it can suggest a vaccination schedule based on the user's age, or recommend regular health checkups. It can also notify the user of required vaccinations or health checkups when they reach a certain age. Step 4: The suggestion unit makes personalized suggestions based on the information stored in the recording unit. For example, the suggestion unit analyzes the user's health information and suggests optimal health and lifestyle habits for each individual user. The suggestion unit analyzes the user's dietary content, and if a specific nutrient is lacking, suggests a meal that contains that nutrient. The suggestion unit can also analyze the user's amount of exercise and suggest an appropriate exercise plan.

[0114] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0115] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0116] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0117] For example, the reception unit can input the user's health information using the reception device 38 of the smart device 14. The recording unit records the input information in the database 24 of the data processing device 12. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12, and suggests preventive measures based on the user's age and development stage. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and makes personalized suggestions based on the recorded information. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0120] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0125] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0128] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0130] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0132] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0133] For example, the reception unit can input the user's health information using the microphone 238 of the smart glasses 214. The recording unit records the input information in the database 24 of the data processing device 12. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12 and suggests preventive measures based on the user's age and development stage. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and makes personalized suggestions based on the recorded information. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0134] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0135] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0136] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0137] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0138] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0140] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0141] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0142] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0144] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0145] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0146] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0148] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0149] For example, the reception unit can input the user's health information using the microphone 238 of the headset type terminal 314. The recording unit records the input information in the database 24 of the data processing device 12. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12, and proposes preventive measures based on the user's age and development stage. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and makes personalized suggestions based on the recorded information. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0150] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0151] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0152] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0153] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0154] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0156] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0157] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0158] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0159] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0160] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0161] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0162] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0163] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0164] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0165] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0166] For example, the reception unit can input the user's health information using the microphone 238 of the robot 414. The recording unit records the input information in the database 24 of the data processing device 12. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12, and suggests preventive measures based on the user's age and development stage. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and makes personalized suggestions based on the recorded information. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0167] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0168] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0169] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0170] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0171] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0172] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0173] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0174] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0175] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0176] In addition, 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 upon request from the data processing device 12.

[0177] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0178] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0179] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0180] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0181] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0182] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0183] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0184] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0185] (Appendix 1) A reception unit for inputting health information of a user; a recording unit that records the information input by the receiving unit; A recommendation unit that recommends prevention based on the age of the user; A suggestion unit that provides personalized suggestions to a user based on the information stored in the recording unit. A system characterized by: (Appendix 2) It has an administration department that manages vaccination and testing schedules, The recommendation unit is The prevention recommendation is made based on the information managed by the management unit. 2. The system of claim 1, (Appendix 3) a provision unit that provides an advertisement to the user based on the proposal by the proposal unit; 2. The system of claim 1, (Appendix 4) The reception unit is Estimating user emotions and adjusting the timing of health information input based on those emotions 2. The system of claim 1, (Appendix 5) The reception unit is Analyze the user's past health information input history and select the appropriate input method 2. The system of claim 1, (Appendix 6) The reception unit is Filtering health information as it is entered based on the user's current life situation and areas of interest 2. The system of claim 1, (Appendix 7) The reception unit is Estimate the user's emotions and prioritize the health information to be entered based on those emotions 2. The system of claim 1, (Appendix 8) The reception unit is When entering health information, the system takes into account the user's geographic location to provide more relevant information 2. The system of claim 1, (Appendix 9) The reception unit is Analyze users' social media activity and enter health information when entering that information 2. The system of claim 1, (Appendix 10) The recording unit is Infer the user's emotions and adjust the recording method based on those emotions 2. The system of claim 1, (Appendix 11) The recording unit is When recording, the system selects the optimal recording method by referring to the user's past health information. 2. The system of claim 1, (Appendix 12) The recording unit is When logging, customize the logging experience based on the user's current life situation 2. The system of claim 1, (Appendix 13) The recording unit is To estimate a user's emotion and determine the priority of information to be recorded based on the estimated user's emotion. 2. The system of claim 1, (Appendix 14) The recording unit is When recording, the optimal recording method is selected based on the user's geographic location. 2. The system of claim 1, (Appendix 15) The recording unit is When recording, analyze users' social media activity and suggest recording methods 2. The system of claim 1, (Appendix 16) The recommendation unit is Inferring a user's emotions and adjusting a prevention recommendation method based on the inferred user's emotions 2. The system of claim 1, (Appendix 17) The recommendation unit is When making recommendations, the system analyzes the user's past health information to select the most appropriate preventive measures. 2. The system of claim 1, (Appendix 18) The recommendation unit is Customize preventative measures based on the user's current living situation when making recommendations 2. The system of claim 1, (Appendix 19) The recommendation unit is To estimate a user's emotion and determine a priority of prevention measures based on the estimated user's emotion. 2. The system of claim 1, (Appendix 20) The recommendation unit is When making recommendations, the system takes into account the user's geographic location to select the most appropriate preventative measures. 2. The system of claim 1, (Appendix 21) The suggestion unit, Infer user emotions and adjust the presentation of suggestions based on the inferred user emotions 2. The system of claim 1, (Appendix 22) The suggestion unit, When making a proposal, the system analyzes the user's health information to select the most appropriate proposal. 2. The system of claim 1, (Appendix 23) The suggestion unit, When making a suggestion, customize the suggestion content based on the user's current life situation 2. The system of claim 1, (Appendix 24) The suggestion unit, Estimate user sentiment and prioritize suggestions based on the estimated user sentiment 2. The system of claim 1, (Appendix 25) The suggestion unit, When making suggestions, the system considers the user's geographic location to select the most appropriate suggestions. 2. The system of claim 1, (Appendix 26) The suggestion unit, When making suggestions, the system analyzes the user's social media activity to tailor the suggestions 2. The system of claim 1, (Appendix 27) The management unit To estimate a user's emotion and adjust a schedule management method based on the estimated user's emotion. 3. The system of claim 2, (Appendix 28) The management unit When managing schedules, select the optimal management method by referring to the user's past schedule history. 3. The system of claim 2, (Appendix 29) The management unit Estimate user emotions and prioritize schedules based on the estimated user emotions 3. The system of claim 2, (Appendix 30) The management unit During management, the optimal management method is selected taking into account the user's geographic location information. 3. The system of claim 2, (Appendix 31) The providing unit is Inferring user emotions and adjusting how ads are displayed based on the inferred user emotions 4. The system of claim 3. (Appendix 32) The providing unit is When displaying an ad, the most suitable ad is selected by referring to the user's past ad viewing history. 4. The system of claim 3. (Appendix 33) The providing unit is Infer user sentiment and prioritize advertisements based on the inferred user sentiment 4. The system of claim 3. (Appendix 34) The providing unit is When displaying ads, the most appropriate ad is selected taking into account the user's geographic location information. 4. The system of claim 3. [Explanation of symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception unit for inputting health information of a user; a recording unit that records the information input by the receiving unit; A recommendation unit that recommends prevention based on the age of the user; A suggestion unit that provides personalized suggestions to the user based on the information stored in the recording unit. A system characterized by:

2. It has an administration department that manages vaccination and testing schedules, The recommendation unit is The prevention recommendation is made based on the information managed by the management unit.

2. The system of claim 1 .

3. a provision unit that provides an advertisement to the user based on the proposal by the proposal unit; 2. The system of claim 1 .

4. The reception unit is The feeling of the user is estimated, and the timing of inputting health information is adjusted based on the feeling.

2. The system of claim 1 .

5. The reception unit is Analyzing the user's past health information input history and selecting an appropriate input method 2. The system of claim 1 .

6. The reception unit is When entering health information, filtering is performed based on the user's current life situation and areas of interest.

2. The system of claim 1 .

7. The reception unit is The user's emotions are estimated, and a priority order of health information to be input is determined based on the emotions.

2. The system of claim 1 .

8. The reception unit is When inputting health information, highly relevant information is input in consideration of the geographical location information of the user.

2. The system of claim 1 .

9. The reception unit is Analyzing the user's social media activity and entering the health information when the information is entered 2. The system of claim 1 .

10. The recording unit is Inferring the user's emotion and adjusting the recording method based on the emotion.

2. The system of claim 1 .

Images