System

Abstract

To provide a system that determines an onset risk of disease based on user's genetic information, health condition, and lifestyle habits, and notifies the user of appropriate countermeasures at the appropriate time.SOLUTION: A system according to the embodiment comprises a reception unit, a collection unit, a determination unit, and a notification unit. The reception unit receives genetic information of a user. The collection unit collects questionnaire information on the user's health condition and lifestyle habits. The determination unit determines an onset risk of disease at the user's life stage based on the genetic information received by the reception unit and the questionnaire information collected by the collection unit. The notification unit notifies the user of the onset risk of the disease and measures to combat the disease determined by the determination unit at a timing depending on the user's life stage.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 technology has the problem that it is not sufficient to properly assess a user's risk of developing a disease based on their genetic information, health condition, and lifestyle habits, and to notify them of countermeasures in a timely manner.

[0005] The system according to the embodiment aims to determine the risk of developing a disease based on the user's genetic information, health condition, and lifestyle habits, and to notify the user of measures to take at an appropriate time. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a collection unit, a determination unit, and a notification unit. The reception unit receives genetic information of a user. The collection unit collects questionnaire information on the user's health condition and lifestyle habits. The determination unit determines a risk of developing a disease at the user's life stage based on the genetic information received by the reception unit and the questionnaire information collected by the collection unit. The notification unit notifies the user of the risk of developing the disease and measures against the disease determined by the determination unit at a timing according to the user's life stage. Effect of the Invention

[0007] The system according to the embodiment can determine the risk of developing a disease based on the user's genetic information, health condition, and lifestyle habits, and notify the user of measures at an appropriate time. [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 management system according to an embodiment of the present invention is a system in which a user undergoes a human genetic test and uploads the results to a generating AI. This system allows the user to grasp the situation regarding at least a portion of the approximately 21,000 gene sets, and further inputs a simple questionnaire regarding the user's current health condition and lifestyle habits, thereby determining the risk of developing a disease according to the user's life stage, and delivering countermeasures at an appropriate time. For example, a user undergoes a genetic test and uploads the results to the system. Next, questionnaire information regarding the user's health condition and lifestyle habits is collected. Based on this information, the system uses a generating AI to determine the risk of developing a disease in the user's life stage. For example, if it is determined that the user is at high risk of a specific disease based on the genetic information, the system notifies the user of countermeasures for that disease. The notification is performed at a timing according to the user's life stage. For example, if the user is at high risk of lifestyle-related diseases in their youth, the system notifies the user of countermeasures for that disease, and if the user is at high risk of cancer in their middle age, the system notifies the user of countermeasures for that disease. This allows the user to take appropriate countermeasures according to their health condition. This system enables the user to manage their health individually based on their genetic information and health condition, which is expected to prevent and detect diseases early. This allows the health management system to provide individualized health management based on the user's genetic information and health condition.

[0029] The health management system according to the embodiment includes a reception unit, a collection unit, a determination unit, and a notification unit. The reception unit receives genetic information indicating a human genetic test of a user. The genetic information of the user includes, for example, a specific gene mutation and a gene expression level, but is not limited to such examples. The reception unit provides, for example, an interface for the user to upload the genetic information. The reception unit also safely stores the genetic information and makes it accessible as needed. The collection unit collects questionnaire information on the user's health condition and lifestyle habits. The health condition includes, for example, blood pressure, blood sugar level, weight, etc., but is not limited to such examples. The lifestyle habits include, for example, diet, exercise, sleep, etc., but is not limited to such examples. The collection unit collects information, for example, through an online questionnaire or a telephone interview. The determination unit uses a generation AI to determine the risk of developing a disease in the user's life stage based on the genetic information received by the reception unit and the questionnaire information collected by the collection unit. The life stages include, for example, childhood, adolescence, middle age, old age, etc., but are not limited to such examples. The determination unit, for example, uses a generation AI to analyze genetic information and questionnaire information to evaluate the risk of a specific disease. The notification unit notifies the user of the disease onset risk and disease countermeasures determined by the determination unit at a timing according to the user's life stage. Notification timing includes, for example, periodic notification and notification at the time of a specific event, but is not limited to such examples. The notification unit provides information to the user, for example, through email or app notification. As a result, the health management system according to the embodiment can provide individual health management based on the user's genetic information and health condition.

[0030] The reception unit receives the genetic information of the user. The genetic information of the user includes, for example, specific gene mutations and gene expression levels, but is not limited to such examples. The reception unit provides, for example, an interface for the user to upload the genetic information. Specifically, the user can upload a file of genetic information through a dedicated web portal or mobile app. This interface is designed to be easy for the user to operate, and has functions such as automatic recognition of file formats and display of upload progress. In addition, the reception unit securely stores the genetic information and makes it accessible as needed. This includes data encryption and access control, and strict security measures are taken to protect the privacy of the user. In addition, the reception unit also has a function of automatically classifying the genetic information uploaded by the user and extracting information required for analysis. For example, data related to specific gene mutations and expression levels can be extracted and used for a subsequent analysis process. In this way, the reception unit can efficiently and safely manage the genetic information of the user, and improve the reliability and convenience of the entire system.

[0031] The collection unit collects questionnaire information on the user's health condition and lifestyle habits. Examples of health conditions include, but are not limited to, blood pressure, blood sugar level, and weight. Specifically, the user periodically answers online questionnaires to provide data on his or her health condition. The questionnaires are designed to be easily answered by the user, and include questions in various formats, such as multiple choice format and numerical input format. Examples of lifestyle habits include, but are not limited to, diet, exercise, and sleep. The collection unit collects information, for example, through online questionnaires and telephone interviews. Online questionnaires are highly convenient because the user can answer at his or her own pace. In addition, telephone interviews are effective for obtaining detailed answers to specific questions, and deeper information can be collected by the person in charge of the collection unit directly interacting with the user. Furthermore, the collection unit has a function of automatically organizing data provided by the user and converting it into a format required for analysis. For example, the collected data is converted into a unified format and stored in a database to make the subsequent analysis process more efficient. This allows the collection unit to efficiently collect a wide range of data on the user's health condition and lifestyle habits, and improve the data quality of the entire system.

[0032] The determination unit uses the generation AI to determine the risk of developing a disease in the user's life stage based on the genetic information accepted by the acceptance unit and the questionnaire information collected by the collection unit. The life stages include, but are not limited to, childhood, adolescence, middle age, and old age. In the determination unit, for example, the generation AI analyzes the genetic information and the questionnaire information to evaluate the risk of a specific disease. Specifically, the generation AI extracts specific gene mutations and expression levels from the genetic information and combines them with the questionnaire information for analysis. The generation AI predicts the risk of developing a disease in each life stage using a model based on past data and medical knowledge. For example, if a user with a specific genetic mutation is at risk of high blood pressure, the generation AI evaluates the risk and provides the user with appropriate advice. In addition, the generation AI analyzes the user's lifestyle data and identifies factors that affect the health condition. For example, if lack of exercise or an unhealthy diet increases the risk of a specific disease, the generation AI evaluates the risk and provides the user with specific advice for improvement. As a result, the determination unit can perform an individual risk assessment based on the user's genetic information and health condition, and provide the user with useful information for managing his or her own health.

[0033] The notification unit notifies the user of the disease onset risk and disease countermeasures determined by the determination unit at a timing according to the user's life stage. The timing of the notification includes, for example, periodic notification and notification at the time of a specific event, but is not limited to such examples. Specifically, the notification unit provides notification at an optimal timing according to the user's life stage and health condition. For example, the notification can be made in accordance with the results of a periodic health check or a specific event (for example, before or after a birthday or a health check). The notification unit provides information to the user through, for example, email or app notification. Email notification is sent to an email address that the user uses on a daily basis, so that the information is surely delivered. In addition, app notification can provide information to the user in real time using the push notification function of the smartphone. Furthermore, the notification unit can use visual graphs and diagrams to provide the notification content in a format that is easy for the user to understand. For example, the disease risk assessment result is displayed in a graph so that the user can intuitively understand. In addition, specific countermeasures and advice are shown in bullet points, and are devised to be easy for the user to put into practice. In this way, the notification unit can provide useful information to the user at an appropriate timing and support the user to effectively manage his or her health.

[0034] The reception unit can analyze the user's past genetic testing history and select an appropriate reception method. The reception unit can, for example, suggest an optimal reception method based on the reception method used by the user in the past. The reception unit can also select the most efficient reception method from the user's past genetic testing history. Furthermore, the reception unit can analyze the user's past genetic testing history and suggest a reception method that suits the user. This makes it possible to provide an optimal reception method based on the user's past history. A 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 past genetic testing history data to the generation AI and cause the generation AI to select the optimal reception method.

[0035] The reception unit can perform filtering based on the user's current health condition and lifestyle habits when receiving the genetic information. The reception unit preferentially receives important genetic information based on the user's current health condition, for example. The reception unit can also filter highly relevant genetic information based on the user's lifestyle habits. Furthermore, the reception unit can also exclude unnecessary genetic information based on the user's health condition and lifestyle habits. This makes it possible to preferentially receive important genetic information based on the user's health condition and lifestyle habits. 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 health condition and lifestyle habit data to the generation AI and cause the generation AI to perform filtering.

[0036] The reception unit can preferentially receive highly relevant results in consideration of the geographical location information of the user when receiving the genetic information. The reception unit preferentially receives region-specific genetic information based on the geographical location information of the user, for example. The reception unit can also filter highly relevant genetic information in consideration of the geographical location information of the user. Furthermore, the reception unit can preferentially receive important genetic information based on the geographical location information of the user. This makes it possible to preferentially receive highly relevant genetic information based on the geographical location information of the user. A 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 geographical location information data to the generation AI and cause the generation AI to select highly relevant results.

[0037] The reception unit can analyze the social media activity of the user and receive related results when receiving the genetic information. The reception unit, for example, analyzes the social media activity of the user and preferentially receives related genetic information. The reception unit can also filter important genetic information based on the social media activity of the user. Furthermore, the reception unit can also receive highly relevant genetic information in consideration of the social media activity of the user. This makes it possible to preferentially receive related genetic information based on the social media activity of the user. A 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 social media activity data to the generation AI and cause the generation AI to select related results.

[0038] The collection unit can customize the question content based on the user's past health condition and lifestyle habits when collecting the questionnaire. The collection unit can add related questions based on the user's past health condition, for example. The collection unit can also provide customized questions based on the user's lifestyle habits. Furthermore, the collection unit can adjust the question content in consideration of the user's past health condition and lifestyle habits. This makes it possible to provide customized questions based on the user's past health condition and lifestyle habits. A part or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or can be performed without using AI. For example, the collection unit can input past health condition and lifestyle habit data to the generation AI and cause the generation AI to customize the question content.

[0039] The collection unit can adjust the timing of collection based on the user's current living situation when collecting the questionnaire. The collection unit collects the questionnaire at an optimal timing based on the user's current living situation, for example. The collection unit can also adjust the timing of collection of the questionnaire in accordance with the user's life rhythm. Furthermore, the collection unit can also optimize the timing of collection of the questionnaire in consideration of the user's current living situation. This makes it possible to collect the questionnaire at an optimal timing according to the user's living situation. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the living situation data to the generation AI and cause the generation AI to adjust the collection timing.

[0040] The collection unit can preferentially collect highly relevant information in consideration of the geographical location information of the user when collecting the questionnaire. The collection unit, for example, preferentially collects area-specific information based on the geographical location information of the user. The collection unit can also filter highly relevant information in consideration of the geographical location information of the user. Furthermore, the collection unit can preferentially collect important information based on the geographical location information of the user. This makes it possible to preferentially collect highly relevant information based on the geographical location information of the user. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input geographical location information data to the generation AI and cause the generation AI to select highly relevant information.

[0041] The collection unit can analyze the user's social media activity and collect related information when collecting the questionnaire. For example, the collection unit analyzes the user's social media activity and preferentially collects related information. The collection unit can also filter important information based on the user's social media activity. Furthermore, the collection unit can also collect highly relevant information taking into account the user's social media activity. This makes it possible to preferentially collect related information based on the user's social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input social media activity data to the generation AI and cause the generation AI to select related information.

[0042] The determination unit can improve the accuracy of the determination when determining the risk of onset by considering the interrelationship between the genetic information and the questionnaire information. The determination unit, for example, analyzes the interrelationship between the genetic information and the questionnaire information to perform a highly accurate onset risk determination. The determination unit can also optimize the risk determination algorithm by considering the interrelationship between the genetic information and the questionnaire information. Furthermore, the determination unit can improve the accuracy of the onset risk determination based on the interrelationship between the genetic information and the questionnaire information. As a result, by considering the interrelationship between the genetic information and the questionnaire information, a highly accurate onset risk determination is possible. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input the genetic information and the questionnaire information data to the generation AI and cause the generation AI to execute the analysis of the interrelationship.

[0043] The determination unit can apply different determination algorithms for each life stage of the user when determining the risk of onset. For example, the determination unit applies a determination algorithm that emphasizes the risk of lifestyle-related diseases to the young age. The determination unit can also apply a determination algorithm that emphasizes the risk of cancer to the middle age. Furthermore, the determination unit can apply a determination algorithm that emphasizes the risk of dementia to the elderly age. This enables appropriate risk determination according to the life stage of the user. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input life stage data to the generation AI and cause the generation AI to apply the determination algorithm.

[0044] The determination unit can make a determination taking into consideration the geographical distribution of the user when determining the risk of onset. The determination unit makes a determination taking into consideration a region-specific risk based on the geographical distribution of the user, for example. The determination unit can also optimize the risk determination algorithm taking into consideration the geographical distribution of the user. Furthermore, the determination unit can improve the accuracy of the determination of the risk of onset based on the geographical distribution of the user. This makes it possible to make a determination taking into consideration a region-specific risk based on the geographical distribution of the user. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input geographical distribution data to the generation AI and cause the generation AI to execute the risk determination.

[0045] The determination unit can improve the accuracy of the determination when determining the risk of onset by referring to related literature. The determination unit, for example, refers to related literature and performs risk determination based on the latest research results. The determination unit can also optimize the risk determination algorithm based on the related literature. Furthermore, the determination unit can improve the accuracy of the determination of the risk of onset by referring to related literature. This makes it possible to perform a highly accurate risk determination based on the latest research results by referring to related literature. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input related literature data to the generation AI and cause the generation AI to execute the improvement of the accuracy of the risk determination.

[0046] The notification unit can customize the notification content based on the user's past health condition and lifestyle habits at the time of notification. The notification unit notifies the user of related information based on the user's past health condition, for example. The notification unit can also perform customized notification based on the user's lifestyle habits. Furthermore, the notification unit can adjust the notification content in consideration of the user's past health condition and lifestyle habits. This makes it possible to perform customized notification based on the user's past health condition and lifestyle habits. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input past health condition and lifestyle habit data to the generation AI and cause the generation AI to customize the notification content.

[0047] The notification unit can adjust the timing of notification according to the life stage of the user at the time of notification. For example, the notification unit notifies the user about the risk of lifestyle-related diseases in the younger age. The notification unit can also notify the user about the risk of cancer in the middle age. Furthermore, the notification unit can also notify the user about the risk of dementia in the elderly age. This makes it possible to notify at an appropriate timing according to the user's life stage. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input life stage data to the generation AI and cause the generation AI to adjust the notification timing.

[0048] The notification unit can give priority to notifying highly relevant information in consideration of the geographical location information of the user at the time of notification. The notification unit gives priority to notifying information specific to a region based on the geographical location information of the user, for example. The notification unit can also filter highly relevant information in consideration of the geographical location information of the user. Furthermore, the notification unit can also give priority to notifying important information based on the geographical location information of the user. This makes it possible to give priority to notifying information that is highly relevant based on the geographical location information of the user. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input geographical location information data to the generation AI and cause the generation AI to select highly relevant information.

[0049] The notification unit can analyze the social media activity of the user at the time of notification and notify the user of related information. For example, the notification unit analyzes the social media activity of the user and notifies the user of related information preferentially. The notification unit can also filter important information based on the user's social media activity. Furthermore, the notification unit can also notify highly relevant information taking into account the user's social media activity. This makes it possible to preferentially notify related information based on the user's social media activity. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input social media activity data to the generation AI and cause the generation AI to select related information.

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

[0051] The health management system may further include a meal record acquisition unit that automatically acquires the user's meal record. The meal record acquisition unit allows the user to record the contents of daily meals as photos, and analyzes the photos to automatically calculate nutrients and calories. For example, when the user takes a photo of a meal and uploads it to the system, the meal record acquisition unit analyzes the photo and identifies the type and amount of ingredients. The meal record acquisition unit may also analyze the user's eating pattern and suggest improvements to the diet according to the health condition. Furthermore, the meal record acquisition unit may accumulate the user's meal history and grasp the long-term eating tendency, thereby realizing more accurate health management. This allows the user to easily record the contents of their meals and use them for health management.

[0052] The health management system may further include an exercise data acquisition unit that automatically acquires the user's exercise data. The exercise data acquisition unit records the user's daily exercise content, analyzes the data, and automatically calculates the amount of exercise and calories burned. For example, the user records exercise data using a smart watch or fitness tracker and synchronizes it with the system, and the exercise data acquisition unit analyzes the data and identifies the type and intensity of exercise. The exercise data acquisition unit can also analyze the user's exercise pattern and make suggestions for improving exercise according to the user's health condition. Furthermore, the exercise data acquisition unit can accumulate the user's exercise history and grasp the long-term exercise tendency, thereby achieving more accurate health management. This allows the user to easily record their exercise content and use it for health management.

[0053] The health management system may further include a sleep data acquisition unit that automatically acquires the user's sleep data. The sleep data acquisition unit allows the user to record daily sleep details, analyzes the data, and automatically calculates the quality and duration of sleep. For example, the user may record sleep data using a smart watch or sleep tracker and synchronize the data with the system, whereby the sleep data acquisition unit analyzes the data and identifies the depth of sleep and the number of interruptions. The sleep data acquisition unit may also analyze the user's sleep patterns and provide suggestions for improving sleep according to the user's health condition. Furthermore, the sleep data acquisition unit may accumulate the user's sleep history and grasp the long-term sleep tendency, thereby realizing more accurate health management. This allows the user to easily record their sleep details and use them for health management.

[0054] The health management system may further include a stress level acquisition unit that automatically acquires the user's stress level. The stress level acquisition unit allows the user to record his or her daily stress level, analyzes the data, and automatically identifies the cause and effect of stress. For example, the user records his or her stress level using a stress check app and synchronizes the data with the system, whereby the stress level acquisition unit analyzes the data and identifies the cause and effect of stress. The stress level acquisition unit may also analyze the user's stress pattern and provide suggestions for improving stress according to the user's health condition. Furthermore, the stress level acquisition unit may accumulate the user's stress history and grasp the long-term stress trend, thereby achieving more accurate health management. This allows the user to easily record his or her own stress level and use it for health management.

[0055] The health management system may further include a water intake acquisition unit that automatically acquires the water intake of the user. The water intake acquisition unit allows the user to record the water intake amount on a daily basis, analyzes the data, and automatically calculates an appropriate water intake amount. For example, the user records the water intake amount using a water intake app and synchronizes with the system, whereby the water intake acquisition unit analyzes the data and identifies an appropriate water intake amount. The water intake acquisition unit may also analyze the water intake pattern of the user and provide suggestions for improving water intake according to the health condition. Furthermore, the water intake acquisition unit may accumulate the water intake history of the user and grasp the long-term water intake tendency, thereby realizing more accurate health management. This allows the user to easily record his or her water intake amount and use it for health management.

[0056] The process flow of the first embodiment will be briefly described below.

[0057] Step 1: The reception unit receives the user's genetic information. The user's genetic information includes specific gene mutations and gene expression levels. The reception unit provides an interface for the user to upload the genetic information, safely stores the genetic information, and enables it to be accessed as needed. Step 2: The collection unit collects questionnaire information on the user's health condition and lifestyle habits. The health condition includes blood pressure, blood sugar level, weight, etc., and the lifestyle habits include diet, exercise, sleep, etc. The collection unit collects information through online questionnaires and telephone interviews. Step 3: The determination unit uses the generation AI to determine the risk of developing a disease at the user's life stage based on the genetic information accepted by the acceptance unit and the questionnaire information collected by the collection unit. Life stages include childhood, adolescence, middle age, old age, etc. In the determination unit, the generation AI analyzes the genetic information and the questionnaire information to evaluate the risk of a specific disease. Step 4: The notification unit notifies the user of the disease onset risk and disease countermeasures determined by the determination unit at a timing appropriate to the user's life stage. Notification timing includes periodic notifications and notifications at the time of specific events. The notification unit provides information to the user through emails and app notifications.

[0058] (Example 2) A health management system according to an embodiment of the present invention is a system in which a user undergoes a human genetic test and uploads the results to a generating AI. This system allows the user to grasp the situation regarding at least a portion of the approximately 21,000 gene sets, and further inputs a simple questionnaire regarding the user's current health condition and lifestyle habits, thereby determining the risk of developing a disease according to the user's life stage, and delivering countermeasures at an appropriate time. For example, a user undergoes a genetic test and uploads the results to the system. Next, questionnaire information regarding the user's health condition and lifestyle habits is collected. Based on this information, the system uses a generating AI to determine the risk of developing a disease in the user's life stage. For example, if it is determined that the user is at high risk of a specific disease based on the genetic information, the system notifies the user of countermeasures for that disease. The notification is performed at a timing according to the user's life stage. For example, if the user is at high risk of lifestyle-related diseases in their youth, the system notifies the user of countermeasures for that disease, and if the user is at high risk of cancer in their middle age, the system notifies the user of countermeasures for that disease. This allows the user to take appropriate countermeasures according to their health condition. This system enables the user to manage their health individually based on their genetic information and health condition, which is expected to prevent and detect diseases early. This allows the health management system to provide individualized health management based on the user's genetic information and health condition.

[0059] The health management system according to the embodiment includes a reception unit, a collection unit, a determination unit, and a notification unit. The reception unit receives genetic information indicating a human genetic test of a user. The genetic information of the user includes, for example, a specific gene mutation and a gene expression level, but is not limited to such examples. The reception unit provides, for example, an interface for the user to upload the genetic information. The reception unit also safely stores the genetic information and makes it accessible as needed. The collection unit collects questionnaire information on the user's health condition and lifestyle habits. The health condition includes, for example, blood pressure, blood sugar level, weight, etc., but is not limited to such examples. The lifestyle habits include, for example, diet, exercise, sleep, etc., but is not limited to such examples. The collection unit collects information, for example, through an online questionnaire or a telephone interview. The determination unit uses a generation AI to determine the risk of developing a disease in the user's life stage based on the genetic information received by the reception unit and the questionnaire information collected by the collection unit. The life stages include, for example, childhood, adolescence, middle age, old age, etc., but are not limited to such examples. The determination unit, for example, uses a generation AI to analyze genetic information and questionnaire information to evaluate the risk of a specific disease. The notification unit notifies the user of the disease onset risk and disease countermeasures determined by the determination unit at a timing according to the user's life stage. Notification timing includes, for example, periodic notification and notification at the time of a specific event, but is not limited to such examples. The notification unit provides information to the user, for example, through email or app notification. As a result, the health management system according to the embodiment can provide individual health management based on the user's genetic information and health condition.

[0060] The reception unit receives the genetic information of the user. The genetic information of the user includes, for example, specific gene mutations and gene expression levels, but is not limited to such examples. The reception unit provides, for example, an interface for the user to upload the genetic information. Specifically, the user can upload a file of genetic information through a dedicated web portal or mobile app. This interface is designed to be easy for the user to operate, and has functions such as automatic recognition of file formats and display of upload progress. In addition, the reception unit securely stores the genetic information and makes it accessible as needed. This includes data encryption and access control, and strict security measures are taken to protect the privacy of the user. In addition, the reception unit also has a function of automatically classifying the genetic information uploaded by the user and extracting information required for analysis. For example, data related to specific gene mutations and expression levels can be extracted and used for a subsequent analysis process. In this way, the reception unit can efficiently and safely manage the genetic information of the user, and improve the reliability and convenience of the entire system.

[0061] The collection unit collects questionnaire information on the user's health condition and lifestyle habits. Examples of health conditions include, but are not limited to, blood pressure, blood sugar level, and weight. Specifically, the user periodically answers online questionnaires to provide data on his or her health condition. The questionnaires are designed to be easily answered by the user, and include questions in various formats, such as multiple choice format and numerical input format. Examples of lifestyle habits include, but are not limited to, diet, exercise, and sleep. The collection unit collects information, for example, through online questionnaires and telephone interviews. Online questionnaires are highly convenient because the user can answer at his or her own pace. In addition, telephone interviews are effective for obtaining detailed answers to specific questions, and deeper information can be collected by the person in charge of the collection unit directly interacting with the user. Furthermore, the collection unit has a function of automatically organizing data provided by the user and converting it into a format required for analysis. For example, the collected data is converted into a unified format and stored in a database to make the subsequent analysis process more efficient. This allows the collection unit to efficiently collect a wide range of data on the user's health condition and lifestyle habits, and improve the data quality of the entire system.

[0062] The determination unit uses the generation AI to determine the risk of developing a disease in the user's life stage based on the genetic information accepted by the acceptance unit and the questionnaire information collected by the collection unit. The life stages include, but are not limited to, childhood, adolescence, middle age, and old age. In the determination unit, for example, the generation AI analyzes the genetic information and the questionnaire information to evaluate the risk of a specific disease. Specifically, the generation AI extracts specific gene mutations and expression levels from the genetic information and combines them with the questionnaire information for analysis. The generation AI predicts the risk of developing a disease in each life stage using a model based on past data and medical knowledge. For example, if a user with a specific genetic mutation is at risk of high blood pressure, the generation AI evaluates the risk and provides the user with appropriate advice. In addition, the generation AI analyzes the user's lifestyle data and identifies factors that affect the health condition. For example, if lack of exercise or an unhealthy diet increases the risk of a specific disease, the generation AI evaluates the risk and provides the user with specific advice for improvement. As a result, the determination unit can perform an individual risk assessment based on the user's genetic information and health condition, and provide the user with useful information for managing his or her own health.

[0063] The notification unit notifies the user of the disease onset risk and disease countermeasures determined by the determination unit at a timing according to the user's life stage. The timing of the notification includes, for example, periodic notification and notification at the time of a specific event, but is not limited to such examples. Specifically, the notification unit provides notification at an optimal timing according to the user's life stage and health condition. For example, the notification can be made in accordance with the results of a periodic health check or a specific event (for example, before or after a birthday or a health check). The notification unit provides information to the user through, for example, email or app notification. Email notification is sent to an email address that the user uses on a daily basis, so that the information is surely delivered. In addition, app notification can provide information to the user in real time using the push notification function of the smartphone. Furthermore, the notification unit can use visual graphs and diagrams to provide the notification content in a format that is easy for the user to understand. For example, the disease risk assessment result is displayed in a graph so that the user can intuitively understand. In addition, specific countermeasures and advice are shown in bullet points, and are devised to be easy for the user to put into practice. In this way, the notification unit can provide useful information to the user at an appropriate timing and support the user to effectively manage his or her health.

[0064] The reception unit can estimate the user's emotion and adjust the timing of receiving the genetic information based on the estimated emotion of the user. For example, when the user is stressed, the reception unit receives the genetic information during a time period when the user can relax. Also, when the user is relaxed, the reception unit can immediately receive the genetic information. Furthermore, when the user is busy, the reception unit can receive the genetic information according to the schedule. This makes it possible to receive the genetic information at an appropriate timing according to the emotion of the user. The estimation of the emotion 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 the user's facial expression data to the generation AI and cause the generation AI to execute the emotion estimation.

[0065] The reception unit can analyze the user's past genetic testing history and select an appropriate reception method. The reception unit can, for example, suggest an optimal reception method based on the reception method used by the user in the past. The reception unit can also select the most efficient reception method from the user's past genetic testing history. Furthermore, the reception unit can analyze the user's past genetic testing history and suggest a reception method that suits the user. This makes it possible to provide an optimal reception method based on the user's past history. A 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 past genetic testing history data to the generation AI and cause the generation AI to select the optimal reception method.

[0066] The reception unit can perform filtering based on the user's current health condition and lifestyle habits when receiving the genetic information. The reception unit preferentially receives important genetic information based on the user's current health condition, for example. The reception unit can also filter highly relevant genetic information based on the user's lifestyle habits. Furthermore, the reception unit can also exclude unnecessary genetic information based on the user's health condition and lifestyle habits. This makes it possible to preferentially receive important genetic information based on the user's health condition and lifestyle habits. 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 health condition and lifestyle habit data to the generation AI and cause the generation AI to perform filtering.

[0067] The reception unit can estimate the user's emotion and determine the priority of the genetic information to be received based on the estimated user's emotion. For example, when the user feels anxious, the reception unit preferentially receives important genetic information. In addition, when the user is relaxed, the reception unit can also receive all genetic information equally. Furthermore, when the user is in a hurry, the reception unit can also preferentially receive genetic information that needs to be confirmed quickly. This makes it possible to preferentially receive important genetic 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 the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority order.

[0068] The reception unit can preferentially receive highly relevant results in consideration of the geographical location information of the user when receiving the genetic information. The reception unit preferentially receives region-specific genetic information based on the geographical location information of the user, for example. The reception unit can also filter highly relevant genetic information in consideration of the geographical location information of the user. Furthermore, the reception unit can preferentially receive important genetic information based on the geographical location information of the user. This makes it possible to preferentially receive highly relevant genetic information based on the geographical location information of the user. A 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 geographical location information data to the generation AI and cause the generation AI to select highly relevant results.

[0069] The reception unit can analyze the social media activity of the user and receive related results when receiving the genetic information. The reception unit, for example, analyzes the social media activity of the user and preferentially receives related genetic information. The reception unit can also filter important genetic information based on the social media activity of the user. Furthermore, the reception unit can also receive highly relevant genetic information in consideration of the social media activity of the user. This makes it possible to preferentially receive related genetic information based on the social media activity of the user. A 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 social media activity data to the generation AI and cause the generation AI to select related results.

[0070] The collection unit can estimate the user's emotions and adjust the survey collection method based on the estimated user's emotions. For example, when the user is stressed, the collection unit provides a simple question-type survey. Also, when the user is relaxed, the collection unit can provide a detailed question-type survey. Furthermore, when the user is in a hurry, the collection unit can provide a survey that can be answered in a short time. This makes it possible to provide an appropriate survey collection method according to the user's emotions. The estimation of emotions 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 collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the collection method.

[0071] The collection unit can customize the question content based on the user's past health condition and lifestyle habits when collecting the questionnaire. The collection unit can add related questions based on the user's past health condition, for example. The collection unit can also provide customized questions based on the user's lifestyle habits. Furthermore, the collection unit can adjust the question content in consideration of the user's past health condition and lifestyle habits. This makes it possible to provide customized questions based on the user's past health condition and lifestyle habits. A part or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or can be performed without using AI. For example, the collection unit can input past health condition and lifestyle habit data to the generation AI and cause the generation AI to customize the question content.

[0072] The collection unit can adjust the timing of collection based on the user's current living situation when collecting the questionnaire. The collection unit collects the questionnaire at an optimal timing based on the user's current living situation, for example. The collection unit can also adjust the timing of collection of the questionnaire in accordance with the user's life rhythm. Furthermore, the collection unit can also optimize the timing of collection of the questionnaire in consideration of the user's current living situation. This makes it possible to collect the questionnaire at an optimal timing according to the user's living situation. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the living situation data to the generation AI and cause the generation AI to adjust the collection timing.

[0073] The collection unit can estimate the user's emotion and determine the priority of the questionnaires to be collected based on the estimated user's emotion. For example, when the user feels anxious, the collection unit preferentially collects important questionnaires. In addition, when the user is relaxed, the collection unit can also collect all questionnaires evenly. Furthermore, when the user is in a hurry, the collection unit can also preferentially collect questionnaires that require a quick response. This makes it possible to preferentially collect important questionnaires 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 collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority order.

[0074] The collection unit can preferentially collect highly relevant information in consideration of the geographical location information of the user when collecting the questionnaire. The collection unit, for example, preferentially collects area-specific information based on the geographical location information of the user. The collection unit can also filter highly relevant information in consideration of the geographical location information of the user. Furthermore, the collection unit can preferentially collect important information based on the geographical location information of the user. This makes it possible to preferentially collect highly relevant information based on the geographical location information of the user. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input geographical location information data to the generation AI and cause the generation AI to select highly relevant information.

[0075] The collection unit can analyze the user's social media activity and collect related information when collecting the questionnaire. For example, the collection unit analyzes the user's social media activity and preferentially collects related information. The collection unit can also filter important information based on the user's social media activity. Furthermore, the collection unit can also collect highly relevant information taking into account the user's social media activity. This makes it possible to preferentially collect related information based on the user's social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input social media activity data to the generation AI and cause the generation AI to select related information.

[0076] The determination unit can estimate the user's emotion and adjust the judgment criteria for the risk of onset based on the estimated user's emotion. For example, when the user feels anxious, the determination unit applies strict judgment criteria. In addition, when the user is relaxed, the determination unit can also apply standard judgment criteria. Furthermore, when the user is in a hurry, the determination unit can apply criteria that allow for quick judgment. This makes it possible to apply appropriate judgment criteria according to the user's emotion. The estimation of emotion 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 determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the judgment criteria.

[0077] The determination unit can improve the accuracy of the determination when determining the risk of onset by considering the interrelationship between the genetic information and the questionnaire information. The determination unit, for example, analyzes the interrelationship between the genetic information and the questionnaire information to perform a highly accurate onset risk determination. The determination unit can also optimize the risk determination algorithm by considering the interrelationship between the genetic information and the questionnaire information. Furthermore, the determination unit can improve the accuracy of the onset risk determination based on the interrelationship between the genetic information and the questionnaire information. As a result, by considering the interrelationship between the genetic information and the questionnaire information, a highly accurate onset risk determination is possible. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input the genetic information and the questionnaire information data to the generation AI and cause the generation AI to execute the analysis of the interrelationship.

[0078] The determination unit can apply different determination algorithms for each life stage of the user when determining the risk of onset. For example, the determination unit applies a determination algorithm that emphasizes the risk of lifestyle-related diseases to the young age. The determination unit can also apply a determination algorithm that emphasizes the risk of cancer to the middle age. Furthermore, the determination unit can apply a determination algorithm that emphasizes the risk of dementia to the elderly age. This enables appropriate risk determination according to the life stage of the user. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input life stage data to the generation AI and cause the generation AI to apply the determination algorithm.

[0079] The determination unit can estimate the user's emotion and adjust the order of displaying the assessment results of the risk of onset based on the estimated user's emotion. For example, when the user feels anxious, the determination unit displays important risks first. Also, when the user is relaxed, the determination unit can display all risks evenly. Furthermore, when the user is in a hurry, the determination unit can display risks that require quick confirmation first. This makes it possible to display important risks preferentially according to the user's emotion. The estimation of emotion 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 determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display order.

[0080] The determination unit can make a determination taking into consideration the geographical distribution of the user when determining the risk of onset. The determination unit makes a determination taking into consideration a region-specific risk based on the geographical distribution of the user, for example. The determination unit can also optimize the risk determination algorithm taking into consideration the geographical distribution of the user. Furthermore, the determination unit can improve the accuracy of the determination of the risk of onset based on the geographical distribution of the user. This makes it possible to make a determination taking into consideration a region-specific risk based on the geographical distribution of the user. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input geographical distribution data to the generation AI and cause the generation AI to execute the risk determination.

[0081] The determination unit can improve the accuracy of the determination when determining the risk of onset by referring to related literature. The determination unit, for example, refers to related literature and performs risk determination based on the latest research results. The determination unit can also optimize the risk determination algorithm based on the related literature. Furthermore, the determination unit can improve the accuracy of the determination of the risk of onset by referring to related literature. This makes it possible to perform a highly accurate risk determination based on the latest research results by referring to related literature. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input related literature data to the generation AI and cause the generation AI to execute the improvement of the accuracy of the risk determination.

[0082] The notification unit can estimate the user's emotion and adjust the expression method of the notification based on the estimated user's emotion. For example, when the user feels anxious, the notification unit can provide a notification using gentle language. Also, when the user is relaxed, the notification unit can provide a notification including detailed information. Furthermore, when the user is in a hurry, the notification unit can provide a concise and quick notification. This allows the notification to be provided in an appropriate expression 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 notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the expression method.

[0083] The notification unit can customize the notification content based on the user's past health condition and lifestyle habits at the time of notification. The notification unit notifies the user of related information based on the user's past health condition, for example. The notification unit can also perform customized notification based on the user's lifestyle habits. Furthermore, the notification unit can adjust the notification content in consideration of the user's past health condition and lifestyle habits. This makes it possible to perform customized notification based on the user's past health condition and lifestyle habits. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input past health condition and lifestyle habit data to the generation AI and cause the generation AI to customize the notification content.

[0084] The notification unit can adjust the timing of notification according to the life stage of the user at the time of notification. For example, the notification unit notifies the user about the risk of lifestyle-related diseases in the younger age. The notification unit can also notify the user about the risk of cancer in the middle age. Furthermore, the notification unit can also notify the user about the risk of dementia in the elderly age. This makes it possible to notify at an appropriate timing according to the user's life stage. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input life stage data to the generation AI and cause the generation AI to adjust the notification timing.

[0085] The notification unit can estimate the user's emotion and determine the priority of notifications based on the estimated user's emotion. For example, when the user feels anxious, the notification unit prioritizes important notifications. Also, when the user is relaxed, the notification unit can also perform all notifications equally. Furthermore, when the user is in a hurry, the notification unit can also prioritize notifications that require quick confirmation. This allows important notifications to be prioritized 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 notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority order.

[0086] The notification unit can give priority to notifying highly relevant information in consideration of the geographical location information of the user at the time of notification. The notification unit gives priority to notifying information specific to a region based on the geographical location information of the user, for example. The notification unit can also filter highly relevant information in consideration of the geographical location information of the user. Furthermore, the notification unit can also give priority to notifying important information based on the geographical location information of the user. This makes it possible to give priority to notifying information that is highly relevant based on the geographical location information of the user. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input geographical location information data to the generation AI and cause the generation AI to select highly relevant information.

[0087] The notification unit can analyze the social media activity of the user at the time of notification and notify the user of related information. For example, the notification unit analyzes the social media activity of the user and notifies the user of related information preferentially. The notification unit can also filter important information based on the user's social media activity. Furthermore, the notification unit can also notify highly relevant information taking into account the user's social media activity. This makes it possible to preferentially notify related information based on the user's social media activity. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input social media activity data to the generation AI and cause the generation AI to select related information.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The health management system may further include a meal record acquisition unit that automatically acquires the user's meal record. The meal record acquisition unit allows the user to record the contents of daily meals as photos, and analyzes the photos to automatically calculate nutrients and calories. For example, when the user takes a photo of a meal and uploads it to the system, the meal record acquisition unit analyzes the photo and identifies the type and amount of ingredients. The meal record acquisition unit may also analyze the user's eating pattern and suggest improvements to the diet according to the health condition. Furthermore, the meal record acquisition unit may accumulate the user's meal history and grasp the long-term eating tendency, thereby realizing more accurate health management. This allows the user to easily record the contents of their meals and use them for health management.

[0090] The health management system may further include an exercise data acquisition unit that automatically acquires the user's exercise data. The exercise data acquisition unit records the user's daily exercise content, analyzes the data, and automatically calculates the amount of exercise and calories burned. For example, the user records exercise data using a smart watch or fitness tracker and synchronizes it with the system, and the exercise data acquisition unit analyzes the data and identifies the type and intensity of exercise. The exercise data acquisition unit can also analyze the user's exercise pattern and make suggestions for improving exercise according to the user's health condition. Furthermore, the exercise data acquisition unit can accumulate the user's exercise history and grasp the long-term exercise tendency, thereby achieving more accurate health management. This allows the user to easily record their exercise content and use it for health management.

[0091] The health management system may further include a sleep data acquisition unit that automatically acquires the user's sleep data. The sleep data acquisition unit allows the user to record daily sleep details, analyzes the data, and automatically calculates the quality and duration of sleep. For example, the user may record sleep data using a smart watch or sleep tracker and synchronize the data with the system, whereby the sleep data acquisition unit analyzes the data and identifies the depth of sleep and the number of interruptions. The sleep data acquisition unit may also analyze the user's sleep patterns and provide suggestions for improving sleep according to the user's health condition. Furthermore, the sleep data acquisition unit may accumulate the user's sleep history and grasp the long-term sleep tendency, thereby realizing more accurate health management. This allows the user to easily record their sleep details and use them for health management.

[0092] The health management system may further include a stress level acquisition unit that automatically acquires the user's stress level. The stress level acquisition unit allows the user to record his or her daily stress level, analyzes the data, and automatically identifies the cause and effect of stress. For example, the user records his or her stress level using a stress check app and synchronizes the data with the system, whereby the stress level acquisition unit analyzes the data and identifies the cause and effect of stress. The stress level acquisition unit may also analyze the user's stress pattern and provide suggestions for improving stress according to the user's health condition. Furthermore, the stress level acquisition unit may accumulate the user's stress history and grasp the long-term stress trend, thereby achieving more accurate health management. This allows the user to easily record his or her own stress level and use it for health management.

[0093] The health management system may further include a water intake acquisition unit that automatically acquires the water intake of the user. The water intake acquisition unit allows the user to record the water intake amount on a daily basis, analyzes the data, and automatically calculates an appropriate water intake amount. For example, the user records the water intake amount using a water intake app and synchronizes with the system, whereby the water intake acquisition unit analyzes the data and identifies an appropriate water intake amount. The water intake acquisition unit may also analyze the water intake pattern of the user and provide suggestions for improving water intake according to the health condition. Furthermore, the water intake acquisition unit may accumulate the water intake history of the user and grasp the long-term water intake tendency, thereby realizing more accurate health management. This allows the user to easily record his or her water intake amount and use it for health management.

[0094] The health management system can further estimate the user's emotions and provide health advice based on the estimated emotions. For example, if the user feels stressed, advice to relax is provided. Also, if the user is relaxed, advice to maintain health can be provided. Furthermore, if the user feels anxious, advice to give a sense of security can be provided. This makes it possible to provide appropriate health advice according to the user's emotions. The estimation of emotions 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 (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the health management system may be performed, for example, using an AI, or may be performed without using an AI. For example, the health management system can input the user's emotion data into the generation AI and cause the generation AI to provide health advice.

[0095] The health management system can further estimate the user's emotions and adjust the exercise program based on the estimated emotions. For example, if the user is stressed, an exercise program with a relaxing effect is provided. Also, if the user is relaxed, an exercise program for improving physical strength can be provided. Furthermore, if the user is anxious, an exercise program for providing a sense of security can be provided. This makes it possible to provide an appropriate exercise program according to the user's emotions. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the health management system may be performed, for example, using an AI, or may be performed without using an AI. For example, the health management system can input the user's emotion data into the generation AI and have the generation AI execute the adjustment of the exercise program.

[0096] The health management system can further estimate the user's emotions and adjust the meal plan based on the estimated emotions. For example, if the user is stressed, a meal plan with a relaxing effect is provided. Also, if the user is relaxed, a meal plan with a balanced nutritional content can be provided. Furthermore, if the user is anxious, a meal plan to give a sense of security can be provided. This makes it possible to provide an appropriate meal plan according to the user's emotions. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the health management system may be performed, for example, using an AI, or may be performed without using an AI. For example, the health management system can input the user's emotion data into the generation AI and have the generation AI execute the adjustment of the meal plan.

[0097] The health management system can further estimate the user's emotions and provide sleep advice based on the estimated emotions. For example, if the user is stressed, sleep advice with a relaxing effect can be provided. Also, if the user is relaxed, advice for maintaining high quality sleep can be provided. Furthermore, if the user is anxious, sleep advice for providing a sense of security can be provided. This makes it possible to provide appropriate sleep advice according to the user's emotions. Estimation of emotions 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 health management system may be performed using, for example, an AI, or may be performed without using an AI. For example, the health management system can input the user's emotion data into the generation AI and cause the generation AI to provide sleep advice.

[0098] The health management system can further estimate the user's emotions and provide stress management advice based on the estimated emotions. For example, if the user feels stressed, advice to relax is provided. Also, if the user is relaxed, advice for stress prevention can be provided. Furthermore, if the user feels anxious, advice to give a sense of security can be provided. This makes it possible to provide appropriate stress management advice according to the user's emotions. The estimation of emotions 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 health management system may be performed, for example, using an AI, or may be performed without using an AI. For example, the health management system can input the user's emotion data into the generation AI and cause the generation AI to provide stress management advice.

[0099] The process flow of the second embodiment will be briefly described below.

[0100] Step 1: The reception unit receives the user's genetic information. The user's genetic information includes specific gene mutations and gene expression levels. The reception unit provides an interface for the user to upload the genetic information, safely stores the genetic information, and enables it to be accessed as needed. Step 2: The collection unit collects questionnaire information on the user's health condition and lifestyle habits. The health condition includes blood pressure, blood sugar level, weight, etc., and the lifestyle habits include diet, exercise, sleep, etc. The collection unit collects information through online questionnaires and telephone interviews. Step 3: The determination unit uses the generation AI to determine the risk of developing a disease at the user's life stage based on the genetic information accepted by the acceptance unit and the questionnaire information collected by the collection unit. Life stages include childhood, adolescence, middle age, old age, etc. In the determination unit, the generation AI analyzes the genetic information and the questionnaire information to evaluate the risk of a specific disease. Step 4: The notification unit notifies the user of the disease onset risk and disease countermeasures determined by the determination unit at a timing appropriate to the user's life stage. Notification timing includes periodic notifications and notifications at the time of specific events. The notification unit provides information to the user through emails and app notifications.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] For example, the reception unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the collection unit can collect information on the user's health condition and lifestyle habits using the camera 42 and microphone 38B of the smart device 14. The determination unit is realized by the specific processing unit 290 of the data processing device 12, for example, and analyzes genetic information and questionnaire information using a generation AI to determine the risk of developing a disease. The notification unit can notify the user of measures through the output device 40 of the smart device 14, for example. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] For example, the reception unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the collection unit can collect information on the user's health condition and lifestyle habits using the camera 42 and microphone 238 of the smart glasses 214. The determination unit is realized by the specific processing unit 290 of the data processing device 12, for example, and analyzes genetic information and questionnaire information using a generation AI to determine the risk of developing a disease. The notification unit can notify the user of measures through the speaker 240 of the smart glasses 214, for example. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the collection unit can collect information on the user's health condition and lifestyle habits using the camera 42 and microphone 238 of the headset type terminal 314. The determination unit is realized by the specific processing unit 290 of the data processing device 12, for example, and analyzes genetic information and questionnaire information using a generation AI to determine the risk of developing a disease. The notification unit can notify the user of measures through the display 343 of the headset type terminal 314, for example. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] For example, the reception unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the collection unit can collect information on the user's health condition and lifestyle habits using the camera 42 and microphone 238 of the robot 414. The determination unit is realized by the specific processing unit 290 of the data processing device 12, for example, and analyzes genetic information and questionnaire information using a generation AI to determine the risk of developing a disease. The notification unit can notify the user of measures through the speaker 240 of the robot 414, for example. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] (Appendix 1) A reception unit that receives genetic information of a user; A collection unit for collecting questionnaire information on the health condition and lifestyle habits of the user; a determination unit that determines a risk of developing a disease at a life stage of the user based on the genetic information accepted by the acceptance unit and the questionnaire information collected by the collection unit; and a notification unit that notifies the user of the risk of developing the disease determined by the determination unit and measures to deal with the disease at a timing appropriate to the user's life stage. A system characterized by: (Appendix 2) The reception unit is The system estimates the user's emotions and adjusts the timing of receiving genetic information based on the estimated user emotions. 2. The system of claim 1, (Appendix 3) The reception unit is Analyze the user's past genetic testing history and select the appropriate reception method 2. The system of claim 1, (Appendix 4) The reception unit is When genetic information is received, it is filtered based on the user's current health status and lifestyle habits. 2. The system of claim 1, (Appendix 5) The reception unit is The system estimates the user's emotions and prioritizes the genetic information to be accepted based on the estimated user emotions. 2. The system of claim 1, (Appendix 6) The reception unit is When receiving genetic information, the system prioritizes relevant results based on the user's geographic location. 2. The system of claim 1, (Appendix 7) The reception unit is Upon receipt of genetic information, analyze the user's social media activity and receive relevant results. 2. The system of claim 1, (Appendix 8) The collecting unit includes: Infer user sentiment and adjust survey collection methods based on the inferred user sentiment 2. The system of claim 1, (Appendix 9) The collecting unit includes: When collecting surveys, customize the questions based on the user’s past health and lifestyle habits 2. The system of claim 1, (Appendix 10) The collecting unit includes: When collecting surveys, adjust the timing based on the user's current life situation 2. The system of claim 1, (Appendix 11) The collecting unit includes: Estimate user sentiment and prioritize surveys to be collected based on the estimated user sentiment 2. The system of claim 1, (Appendix 12) The collecting unit includes: When collecting survey data, prioritize collecting relevant information by considering the user's geographic location. 2. The system of claim 1, (Appendix 13) The collecting unit includes: When collecting surveys, analyze users' social media activity and collect relevant information 2. The system of claim 1, (Appendix 14) The determination unit is The user's emotions are estimated, and a criterion for determining the risk of onset is adjusted based on the estimated user's emotions. 2. The system of claim 1, (Appendix 15) The determination unit is Improve the accuracy of risk assessment by considering the correlation between genetic information and questionnaire information 2. The system of claim 1, (Appendix 16) The determination unit is When assessing the risk of developing the disease, different assessment algorithms are applied depending on the user's life stage. 2. The system of claim 1, (Appendix 17) The determination unit is The user's emotions are estimated, and the order in which the results of the risk of onset are displayed is adjusted based on the estimated user's emotions. 2. The system of claim 1, (Appendix 18) The determination unit is When assessing the risk of developing the disease, the geographical distribution of users is taken into account. 2. The system of claim 1, (Appendix 19) The determination unit is When assessing risk of developing a disease, refer to related literature to improve the accuracy of the assessment. 2. The system of claim 1, (Appendix 20) The notification unit is Infer user emotions and adjust notification presentation based on the inferred user emotions 2. The system of claim 1, (Appendix 21) The notification unit is When receiving notifications, the content of the notifications will be customized based on the user's past health conditions and lifestyle habits. 2. The system of claim 1, (Appendix 22) The notification unit is Adjust the timing of notifications according to the user's life stage 2. The system of claim 1, (Appendix 23) The notification unit is Infer user emotions and prioritize notifications based on the inferred user emotions 2. The system of claim 1, (Appendix 24) The notification unit is When sending notifications, the system takes into account the user's geographic location information and prioritizes notifications of the most relevant information. 2. The system of claim 1, (Appendix 25) The notification unit is When you notify us, we will analyze your social media activity and notify you of relevant information. 2. The system of claim 1, [Explanation of symbols]

[0173] 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 that receives genetic information of a user; A collection unit for collecting questionnaire information on the health condition and lifestyle habits of the user; a determination unit that determines a risk of developing a disease at a life stage of the user based on the genetic information accepted by the acceptance unit and the questionnaire information collected by the collection unit; a notification unit that notifies the user of the risk of developing a disease and measures to be taken against the disease determined by the determination unit at a timing according to the user's life stage. A system characterized by:

2. The reception unit is Estimating the user's emotion and adjusting the timing of receiving the genetic information based on the estimated user's emotion.

2. The system of claim 1 .

3. The reception unit is Analyzing the user's past genetic testing history and selecting an appropriate reception method 2. The system of claim 1 .

4. The reception unit is When receiving the genetic information, filtering is performed based on the user's current health condition and lifestyle habits.

2. The system of claim 1 .

5. The reception unit is A feeling of the user is estimated, and a priority order of the genetic information to be received is determined based on the estimated feeling of the user.

2. The system of claim 1 .

6. The reception unit is When receiving the genetic information, a result having a high relevance is preferentially received based on the geographic location information of the user.

2. The system of claim 1 .

7. The reception unit is Upon receipt of the genetic information, analyzing social media activity of the user and receiving associated results.

2. The system of claim 1 .

8. The collecting unit includes: The user's feelings are estimated, and a survey collection method is adjusted based on the estimated user's feelings.

2. The system of claim 1 .

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