System

Abstract

To analyze human body information and dream content to enable the early prevention of stress conditions and diseases.SOLUTION: A system according to the embodiment comprises a reception unit, an analysis unit, and a provision unit. The reception unit accepts human body information and dream content from a user. The analysis unit analyzes the information received by the reception unit to estimate a stress condition or disease. The provision unit provides specific preventive measures based on the estimation result from the analysis unit.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 does not combine human body information with dream content to estimate stress levels or illnesses, and there is room for improvement.

[0005] The system according to the embodiment aims to prevent stress and illness in advance by analyzing human body information and the contents of dreams. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives human body information and dream contents from a user. The analysis unit analyzes the information received by the reception unit and estimates a stress state or illness. The provision unit provides specific preventive measures based on the results estimated by the analysis unit. Effect of the Invention

[0007] The system according to the embodiment can analyze human body information and dream content to prevent stress and illness in advance. [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 a 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) The health prevention system according to the embodiment of the present invention is a system that can prevent the stress state of the body and diseases that the user is likely to suffer from by adding the type of dream as a variable in addition to the user's human body information (heart rate, sleep time, body temperature, etc.). In this health prevention system, the user inputs human body information such as heart rate, sleep time, and body temperature, and also inputs the type of dream, and the generation AI analyzes this information, estimates the user's stress state and diseases that the user is likely to suffer from, and provides preventive measures. For example, when a user inputs information such as "I had a dream about falling from a high place" or "I had a dream about being chased," the generation AI combines data such as heart rate, sleep time, and body temperature with the content of the dream to perform an analysis. The generation AI estimates that if the heart rate is high, the sleep time is short, and the body temperature is high, the stress state is high, and if the content of a specific dream is repeated, the generation AI estimates that the user may be prone to a specific disease. Based on these estimation results, the generation AI provides the user with advice on how to relax and how to reduce stress, and suggests preventive measures such as recommending early medical examination by a doctor if the user is prone to a specific disease. This service allows users to understand their own physical condition and take preventive measures in advance, thereby maintaining their health and preventing the onset of illness. The health prevention system can analyze the user's body information and dream content, estimate stress levels and illnesses, and provide preventive measures.

[0029] The health prevention system according to the embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives human body information and dream contents from a user. The human body information includes, but is not limited to, for example, heart rate, blood pressure, and body temperature. The dream contents are input in text format, such as, for example, "I had a dream about falling from a high place" or "I had a dream about being chased." The reception unit receives these pieces of information and transmits them to the analysis unit. The analysis unit uses a generation AI to analyze the information received by the reception unit and estimates a stress state and illness. For example, if the heart rate is high, the sleep time is short, and the body temperature is high, the analysis unit estimates that the stress state is high. In addition, if the content of a specific dream is repeated, it estimates that there is a possibility that the person is prone to a specific disease. The generation AI uses natural language processing technology to analyze the content of the dream and combines it with data such as the heart rate, sleep time, and body temperature to perform analysis. For example, the generation AI uses a text generation AI (for example, LLM) to analyze the content of the dream and estimates a stress state and illness. The providing unit provides specific preventive measures based on the results estimated by the analyzing unit. For example, when stress is high, the providing unit provides a method for relaxing and advice for reducing stress. Also, when a user is prone to a particular disease, the providing unit suggests preventive measures such as recommending early medical examination by a doctor. In this way, the health prevention system according to the embodiment can analyze the user's body information and the content of the dream, estimate the stress state and disease, and provide preventive measures.

[0030] The reception unit receives human body information and the contents of the dream from the user. The human body information includes, but is not limited to, for example, heart rate, blood pressure, and body temperature. The user can input these pieces of information through a dedicated device or application. For example, heart rate and blood pressure are often automatically acquired from a wearable device such as a smart watch or a fitness tracker. Body temperature is measured using a dedicated thermometer, and the result is input into the application. The contents of the dream are input in text form, such as, for example, "I had a dream about falling from a high place" or "I had a dream about being chased." The user can freely input the contents of the dream through the interface of the application. The reception unit receives these pieces of information and transmits them to the analysis unit. The reception unit also has a check function for centrally managing the information input by the user and maintaining the consistency of the data. For example, if the data of the heart rate or blood pressure shows an abnormal value, a notification can be sent to the user to prompt the user to re-input. In addition, if the contents of the dream are vague or incomplete, additional questions can be asked to collect more detailed information. This allows the reception unit to efficiently collect various information from the user and provide accurate data to the analysis unit. Furthermore, the reception unit also has a security function for protecting the user's privacy, and can prevent information leaks by encrypting data and controlling access.

[0031] The analysis unit uses the generation AI to analyze the information received by the reception unit and estimates the stress state and illness. For example, if the heart rate is high, the sleep time is short, and the body temperature is high, the analysis unit estimates that the stress state is high. The generation AI uses natural language processing technology to analyze the content of the dream and performs analysis by combining it with data such as the heart rate, sleep time, and body temperature. Specifically, the generation AI analyzes the content of the dream entered by the user using a text generation AI (e.g., LLM) to extract the dream pattern and emotional tone. For example, it can be estimated that the emotion of fear or anxiety is strong from the content "I had a dream about falling from a high place." This allows the analysis unit to comprehensively analyze the content of the dream and the physiological data and estimate the user's stress state and risk of potential illness with high accuracy. Furthermore, the analysis unit can perform more accurate analysis by comparing it with past data and data of other users. For example, it can refer to the data of users who have similar dream content and find common patterns and trends. This allows the analysis unit to generate basic data for providing more appropriate advice and preventive measures to individual users.

[0032] The providing unit provides specific preventive measures based on the results estimated by the analysis unit. For example, when stress is high, the providing unit provides methods for relaxation and advice for reducing stress. Specifically, the providing unit suggests relaxation methods such as deep breathing, meditation, and light exercise, and guides the user to practice them. In addition, when the user is prone to a certain disease, the providing unit suggests preventive measures such as recommending early medical examination. For example, when it is estimated from the analysis result that the risk of high blood pressure is high, the providing unit recommends improving diet and regular exercise, and advises the user to see a doctor if necessary. The providing unit has an interactive interface to provide these advices to the user in an easy-to-understand manner. For example, the application can record the effects of the relaxation methods and preventive measures practiced by the user and receive feedback. In addition, the providing unit can continuously improve the content of the advice based on the user's feedback. As a result, the providing unit can provide individually customized preventive measures to the user and support the maintenance and improvement of health conditions. In addition, the providing unit also has a security function for protecting the user's privacy, and can prevent information leakage by encrypting data and controlling access.

[0033] The input unit provides a method for the user to input the content of the dream as text and a method for the user to select from options. The input unit provides a method for the user to input the content of the dream as text and a method for the user to select from options. For example, a field is provided in which the user can freely input text. Also, a drop-down menu or radio buttons is provided in which the user can select the content of the dream from options. This allows the user to input the content of the dream as text or select from options. For example, the user can input the content of the dream as text, such as "I had a dream about falling from a high place" or "I had a dream about being chased." Also, by allowing the user to select the content of the dream from options, the effort of inputting the content can be reduced. This allows the user to easily input the content of the dream, and the system can perform analysis based on this information.

[0034] The device includes an analysis unit that analyzes the content of a dream using natural language processing. The analysis unit uses a generation AI to analyze the content of the dream using natural language processing. For example, the generation AI analyzes the content of the dream using natural language processing techniques such as morphological analysis, grammatical analysis, and semantic analysis. The generation AI analyzes the content of the dream using a text generation AI (for example, LLM) to estimate the stress state and illness. For example, the generation AI analyzes the content of a dream such as "I had a dream about falling from a high place" or "I had a dream about being chased" and performs analysis in combination with data such as heart rate, sleep time, and body temperature. This makes it possible to analyze the content of the dream using natural language processing, and to accurately estimate the user's stress state and illness. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI.

[0035] The device includes an evaluation unit that evaluates the stress state based on data of the heart rate, sleep time, and body temperature. The evaluation unit evaluates the stress state based on data such as the heart rate, sleep time, and body temperature. For example, when the heart rate is high, the sleep time is short, and the body temperature is high, the stress state is evaluated to be high. The evaluation unit can use a heart rate monitor or a wearable device to measure the heart rate. The sleep time can be measured using a sleep tracker or self-reporting. The body temperature can be measured using a thermometer or a wearable device. For example, the evaluation unit measures the heart rate using a heart rate monitor, measures the sleep time using a sleep tracker, and measures the body temperature using a thermometer. This makes it possible to evaluate the stress state based on data such as the heart rate, sleep time, and body temperature. A part or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI.

[0036] The device includes an estimation unit that estimates the type of illness by combining the content of the dream with the data. The estimation unit estimates the type of illness by combining the content of the dream with data such as heart rate, sleep time, and body temperature. For example, if the content of a particular dream is repeated, it is estimated that there is a possibility that the person is prone to a particular illness. The estimation unit uses a generation AI to analyze the content of the dream and estimate the type of illness by combining it with data such as heart rate, sleep time, and body temperature. For example, the generation AI analyzes the content of a dream such as "I had a dream about falling from a high place" or "I had a dream about being chased" and estimates the type of illness by combining it with data such as heart rate, sleep time, and body temperature. This makes it possible to estimate the type of illness by combining the content of the dream with the data. A part or all of the above-mentioned processing in the estimation unit may be performed, for example, using AI, or may be performed without using AI.

[0037] The reception unit can analyze the user's past data submission history and select the optimal reception method. The reception unit can suggest the optimal reception time based on, for example, a time period during which the user frequently submitted data in the past. Also, the reception unit can preferentially suggest a submission method (manual input, voice input, etc.) that the user used in the past. Furthermore, the reception unit can select the optimal reception method for a specific day of the week or time period from the user's past data submission history. This makes it possible to select the optimal reception method based on the user's past data submission history. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, AI, or can be performed without using AI.

[0038] The reception unit can perform filtering based on the user's current living situation and areas of interest at the time of reception. For example, if the user is interested in health, the reception unit preferentially receives health-related information. In addition, if the user leads a busy life, the reception unit can preferentially receive simplified information. Furthermore, if the user is interested in a specific disease, the reception unit can preferentially receive information related to the disease. This makes it possible to filter information based on the user's living situation and areas of interest. A part or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI.

[0039] The reception unit can preferentially receive highly relevant information in consideration of the geographical location information of the user at the time of reception. For example, when the user is in a specific area, the reception unit preferentially receives health information related to the area. Also, when the user is traveling, the reception unit can preferentially receive health information of the travel destination. Furthermore, when the user is at home, the reception unit can preferentially receive health information around the home. This makes it possible to preferentially receive 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 reception unit may be performed, for example, using AI, or may be performed without using AI.

[0040] The reception unit can analyze the user's social media activity and receive related information at the time of reception. The reception unit receives related information based on, for example, health information shared by the user on social media. Also, the reception unit can receive information from health-related accounts that the user follows on social media. Furthermore, the reception unit can receive information from health-related groups that the user participates in on social media. This makes it possible to receive related information based on the user's social media activity. 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.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on important data. Also, the analysis unit can perform a simplified analysis on general data. Furthermore, the analysis unit can perform a detailed analysis on data in which the user is highly interested. This makes it possible to adjust the level of detail of the analysis based on the importance of the data. A part or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI.

[0042] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit can apply a heart rate analysis algorithm to heart rate data. Also, the analysis unit can apply a sleep analysis algorithm to sleep data. Furthermore, the analysis unit can apply a body temperature analysis algorithm to body temperature data. This makes it possible to apply different analysis algorithms depending on the category of data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI.

[0043] During analysis, the analysis unit can determine the priority of analysis based on the time of data submission. The analysis unit, for example, prioritizes analysis of the latest data. Also, the analysis unit can prioritize analysis of data from a period specified by the user. Furthermore, data related to a specific event can be prioritized for analysis. This makes it possible to determine the priority of analysis based on the time of data submission. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0044] The analysis unit can adjust the order of analysis based on the relevance of data during analysis. The analysis unit, for example, preferentially analyzes highly relevant data. Also, the analysis unit can preferentially analyze data in which the user is highly interested. Furthermore, the analysis unit can preferentially analyze data related to a specific category. This makes it possible to adjust the order of analysis based on the relevance of data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0045] The providing unit can adjust the level of detail of the provision based on the importance of the preventive measures at the time of provision. For example, the providing unit can provide a detailed explanation for important preventive measures. Also, a simplified explanation can be provided for general preventive measures. Furthermore, a detailed explanation can be provided for preventive measures in which the user is highly interested. This makes it possible to adjust the level of detail of the provision based on the importance of the preventive measures. A part or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI.

[0046] The providing unit can apply different providing algorithms depending on the category of the preventive measures at the time of providing. For example, the providing unit can apply a stress reduction algorithm to a stress reduction measure. Also, the providing unit can apply a health maintenance algorithm to a health maintenance measure. Furthermore, the providing unit can apply a disease prevention algorithm to a disease prevention measure. Thereby, different providing algorithms can be applied depending on the category of the preventive measures. A part or all of the above-mentioned processing in the providing unit can be performed using AI, for example, or can be performed without using AI.

[0047] The providing unit can determine the priority of provision based on the submission timing of the preventive measures at the time of provision. The providing unit can, for example, provide the latest preventive measures preferentially. Also, the providing unit can provide the preventive measures for a period specified by the user preferentially. Furthermore, the providing unit can provide the preventive measures related to a specific event preferentially. This makes it possible to determine the priority of provision based on the submission timing of the preventive measures. A part or all of the above-mentioned processing in the providing unit can be performed, for example, using AI or can be performed without using AI.

[0048] The providing unit can adjust the order of provision based on the relevance of the preventive measures at the time of provision. The providing unit can, for example, provide highly relevant preventive measures preferentially. Also, the providing unit can provide preferentially preventive measures in which the user is highly interested. Furthermore, the providing unit can provide preferentially preventive measures related to a specific category. This makes it possible to adjust the order of provision based on the relevance of the preventive measures. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0049] The input unit can provide a method for inputting the content of the dream in text. The input unit can provide, for example, a field in which the user can freely input text. Also, the input unit can provide a text area in which the user can describe the content of the dream in detail. Furthermore, a text box can be provided in which the user can briefly input the content of the dream. This allows the user to input the content of the dream in text. A part or all of the above-mentioned processing in the input unit can be performed, for example, using AI or can be performed without using AI.

[0050] The input unit can provide a method for selecting the content of the dream from options. The input unit can provide, for example, a drop-down menu that allows the user to select the content of the dream from options. The input unit can also provide radio buttons that allow the user to select the content of the dream from options. Furthermore, the input unit can provide check boxes that allow the user to select the content of the dream from options. This allows the user to select the content of the dream from options. Some or all of the above-mentioned processing in the input unit can be performed, for example, using AI or can be performed without using AI.

[0051] The input unit can provide a method for providing the content of a dream by voice input. The input unit can provide, for example, a microphone function that allows the user to input the content of the dream by voice. Also, a function can be provided that allows the user to input the content of the dream by voice and convert it into text. Furthermore, a function can be provided that allows the user to input the content of the dream by voice and convert it into text in real time. This allows the user to input the content of the dream by voice. A part or all of the above-mentioned processing in the input unit can be performed, for example, using AI, or can be performed without using AI.

[0052] The input unit can provide a method for inputting the content of a dream as an image or a picture. The input unit can provide, for example, a sketchpad function that allows the user to draw the content of the dream. The input unit can also provide a function that allows the user to upload the content of the dream as an image. Furthermore, an illustration library can be provided that allows the user to select the content of the dream from a selection of options. This allows the user to input the content of the dream as an image or a picture. Some or all of the above-mentioned processing in the input unit can be performed, for example, using AI or without using AI.

[0053] The evaluation unit can select the optimal evaluation method by referring to the user's past data at the time of evaluation. The evaluation unit can select the optimal evaluation method based on the user's past heart rate data, for example. The evaluation unit can also select the optimal evaluation method based on the user's past sleep data. Furthermore, the evaluation unit can select the optimal evaluation method based on the user's past body temperature data. This makes it possible to select the optimal evaluation method based on the user's past data. A part or all of the above-mentioned processing in the evaluation unit can be performed, for example, using AI, or can be performed without using AI.

[0054] The evaluation unit can select the optimal evaluation method in consideration of the geographical location information of the user at the time of evaluation. For example, when the user is in a specific area, the evaluation unit performs a health evaluation related to the area. Also, when the user is traveling, the evaluation unit can perform a health evaluation of the travel destination. Furthermore, when the user is at home, the evaluation unit can perform a health evaluation of the area around the home. This makes it possible to select the optimal evaluation method based on the geographical location information of the user. A part or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI.

[0055] The estimation unit can select the optimal estimation method by referring to the user's past data at the time of estimation. The estimation unit selects the optimal estimation method based on the user's past heart rate data, for example. The estimation unit can also select the optimal estimation method based on the user's past sleep data. Furthermore, the estimation unit can select the optimal estimation method based on the user's past body temperature data. This makes it possible to select the optimal estimation method based on the user's past data. A part or all of the above-mentioned processing in the estimation unit may be performed, for example, using AI, or may be performed without using AI.

[0056] The estimation unit can select the optimal estimation method in consideration of the geographical location information of the user at the time of estimation. For example, when the user is in a specific area, the estimation unit performs health estimation related to the area. Also, when the user is traveling, the estimation unit can perform health estimation at the travel destination. Furthermore, when the user is at home, the estimation unit can perform health estimation around the home. This makes it possible to select the optimal estimation method based on the geographical location information of the user. A part or all of the above-mentioned processing in the estimation unit may be performed using AI, for example, or may be performed without using AI.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The reception unit can analyze the user's past data submission history and select the optimal reception method. For example, the optimal reception time can be suggested based on the time period during which the user frequently submitted data in the past. Also, the reception unit can preferentially suggest the submission method (manual input, voice input, etc.) that the user used in the past. Furthermore, the reception unit can select the optimal reception method for a specific day of the week or time period from the user's past data submission history. This makes it possible to select the optimal reception method based on the user's past data submission history. Some 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.

[0059] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, a detailed analysis can be performed on important data. Also, a simplified analysis can be performed on general data. Furthermore, a detailed analysis can be performed on data in which the user is highly interested. This makes it possible to adjust the level of detail of the analysis based on the importance of the data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0060] The providing unit can adjust the level of detail of the provision based on the importance of the preventive measures at the time of provision. For example, a detailed explanation can be provided for important preventive measures. Also, a simplified explanation can be provided for general preventive measures. Furthermore, a detailed explanation can be provided for preventive measures in which the user is highly interested. This makes it possible to adjust the level of detail of the provision based on the importance of the preventive measures. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI.

[0061] The providing unit can apply different providing algorithms depending on the category of the preventive measures at the time of providing. For example, a stress reduction algorithm can be applied to a stress reduction measure. Furthermore, a health maintenance algorithm can be applied to a health maintenance measure. Furthermore, a disease prevention algorithm can be applied to a disease prevention measure. Thereby, different providing algorithms can be applied depending on the category of the preventive measures. A part or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI.

[0062] The providing unit can determine the priority of provision based on the submission time of the preventive measures at the time of provision. For example, the latest preventive measures can be provided preferentially. Also, preventive measures for a period specified by a user can be provided preferentially. Furthermore, preventive measures related to a specific event can be provided preferentially. This makes it possible to determine the priority of provision based on the submission time of the preventive measures. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI.

[0063] The process flow of the first embodiment will be briefly described below.

[0064] Step 1: The reception unit receives the body information and dream content from the user. The body information includes heart rate, blood pressure, body temperature, etc., and the dream content is input in text format. The reception unit receives this information and sends it to the analysis unit. Step 2: The analysis unit uses the generation AI to analyze the information received by the reception unit and estimate the stress state and illness. For example, if the heart rate is high, the sleep time is short, and the body temperature is high, it is estimated that the person is in a high stress state. In addition, if the content of a particular dream is repeated, it is estimated that the person may be susceptible to a particular illness. The generation AI uses natural language processing technology to analyze the content of the dream and combines it with data such as heart rate, sleep time, and body temperature for analysis. Step 3: The provider provides specific preventive measures based on the results estimated by the analyzer. For example, if stress levels are high, the provider provides advice on how to relax or reduce stress, and if the user is prone to a particular illness, the provider suggests early medical treatment.

[0065] (Example 2) The health prevention system according to the embodiment of the present invention is a system that can prevent the stress state of the body and diseases that the user is likely to suffer from by adding the type of dream as a variable in addition to the user's human body information (heart rate, sleep time, body temperature, etc.). In this health prevention system, the user inputs human body information such as heart rate, sleep time, and body temperature, and also inputs the type of dream, and the generation AI analyzes this information, estimates the user's stress state and diseases that the user is likely to suffer from, and provides preventive measures. For example, when a user inputs information such as "I had a dream about falling from a high place" or "I had a dream about being chased," the generation AI combines data such as heart rate, sleep time, and body temperature with the content of the dream to perform an analysis. The generation AI estimates that if the heart rate is high, the sleep time is short, and the body temperature is high, the stress state is high, and if the content of a specific dream is repeated, the generation AI estimates that the user may be prone to a specific disease. Based on these estimation results, the generation AI provides the user with advice on how to relax and how to reduce stress, and suggests preventive measures such as recommending early medical examination by a doctor if the user is prone to a specific disease. This service allows users to understand their own physical condition and take preventive measures in advance, thereby maintaining their health and preventing the onset of illness. The health prevention system can analyze the user's body information and dream content, estimate stress levels and illnesses, and provide preventive measures.

[0066] The health prevention system according to the embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives human body information and dream contents from a user. The human body information includes, but is not limited to, for example, heart rate, blood pressure, and body temperature. The dream contents are input in text format, such as, for example, "I had a dream about falling from a high place" or "I had a dream about being chased." The reception unit receives these pieces of information and transmits them to the analysis unit. The analysis unit uses a generation AI to analyze the information received by the reception unit and estimates a stress state and illness. For example, if the heart rate is high, the sleep time is short, and the body temperature is high, the analysis unit estimates that the stress state is high. In addition, if the content of a specific dream is repeated, it estimates that there is a possibility that the person is prone to a specific disease. The generation AI uses natural language processing technology to analyze the content of the dream and combines it with data such as the heart rate, sleep time, and body temperature to perform analysis. For example, the generation AI uses a text generation AI (for example, LLM) to analyze the content of the dream and estimates a stress state and illness. The providing unit provides specific preventive measures based on the results estimated by the analyzing unit. For example, when stress is high, the providing unit provides a method for relaxing and advice for reducing stress. Also, when a user is prone to a particular disease, the providing unit suggests preventive measures such as recommending early medical examination by a doctor. In this way, the health prevention system according to the embodiment can analyze the user's body information and the content of the dream, estimate the stress state and disease, and provide preventive measures.

[0067] The reception unit receives human body information and the contents of the dream from the user. The human body information includes, but is not limited to, for example, heart rate, blood pressure, and body temperature. The user can input these pieces of information through a dedicated device or application. For example, heart rate and blood pressure are often automatically acquired from a wearable device such as a smart watch or a fitness tracker. Body temperature is measured using a dedicated thermometer, and the result is input into the application. The contents of the dream are input in text form, such as, for example, "I had a dream about falling from a high place" or "I had a dream about being chased." The user can freely input the contents of the dream through the interface of the application. The reception unit receives these pieces of information and transmits them to the analysis unit. The reception unit also has a check function for centrally managing the information input by the user and maintaining the consistency of the data. For example, if the data of the heart rate or blood pressure shows an abnormal value, a notification can be sent to the user to prompt the user to re-input. In addition, if the contents of the dream are vague or incomplete, additional questions can be asked to collect more detailed information. This allows the reception unit to efficiently collect various information from the user and provide accurate data to the analysis unit. Furthermore, the reception unit also has a security function for protecting the user's privacy, and can prevent information leaks by encrypting data and controlling access.

[0068] The analysis unit uses the generation AI to analyze the information received by the reception unit and estimates the stress state and illness. For example, if the heart rate is high, the sleep time is short, and the body temperature is high, the analysis unit estimates that the stress state is high. The generation AI uses natural language processing technology to analyze the content of the dream and performs analysis by combining it with data such as the heart rate, sleep time, and body temperature. Specifically, the generation AI analyzes the content of the dream entered by the user using a text generation AI (e.g., LLM) to extract the dream pattern and emotional tone. For example, it can be estimated that the emotion of fear or anxiety is strong from the content "I had a dream about falling from a high place." This allows the analysis unit to comprehensively analyze the content of the dream and the physiological data and estimate the user's stress state and risk of potential illness with high accuracy. Furthermore, the analysis unit can perform more accurate analysis by comparing it with past data and data of other users. For example, it can refer to the data of users who have similar dream content and find common patterns and trends. This allows the analysis unit to generate basic data for providing more appropriate advice and preventive measures to individual users.

[0069] The providing unit provides specific preventive measures based on the results estimated by the analysis unit. For example, when stress is high, the providing unit provides methods for relaxation and advice for reducing stress. Specifically, the providing unit suggests relaxation methods such as deep breathing, meditation, and light exercise, and guides the user to practice them. In addition, when the user is prone to a certain disease, the providing unit suggests preventive measures such as recommending early medical examination. For example, when it is estimated from the analysis result that the risk of high blood pressure is high, the providing unit recommends improving diet and regular exercise, and advises the user to see a doctor if necessary. The providing unit has an interactive interface to provide these advices to the user in an easy-to-understand manner. For example, the application can record the effects of the relaxation methods and preventive measures practiced by the user and receive feedback. In addition, the providing unit can continuously improve the content of the advice based on the user's feedback. As a result, the providing unit can provide individually customized preventive measures to the user and support the maintenance and improvement of health conditions. In addition, the providing unit also has a security function for protecting the user's privacy, and can prevent information leakage by encrypting data and controlling access.

[0070] The input unit provides a method for the user to input the content of the dream as text and a method for the user to select from options. The input unit provides a method for the user to input the content of the dream as text and a method for the user to select from options. For example, a field is provided in which the user can freely input text. Also, a drop-down menu or radio buttons is provided in which the user can select the content of the dream from options. This allows the user to input the content of the dream as text or select from options. For example, the user can input the content of the dream as text, such as "I had a dream about falling from a high place" or "I had a dream about being chased." Also, by allowing the user to select the content of the dream from options, the effort of inputting the content can be reduced. This allows the user to easily input the content of the dream, and the system can perform analysis based on this information.

[0071] The device includes an analysis unit that analyzes the content of a dream using natural language processing. The analysis unit uses a generation AI to analyze the content of the dream using natural language processing. For example, the generation AI analyzes the content of the dream using natural language processing techniques such as morphological analysis, grammatical analysis, and semantic analysis. The generation AI analyzes the content of the dream using a text generation AI (for example, LLM) to estimate the stress state and illness. For example, the generation AI analyzes the content of a dream such as "I had a dream about falling from a high place" or "I had a dream about being chased" and performs analysis in combination with data such as heart rate, sleep time, and body temperature. This makes it possible to analyze the content of the dream using natural language processing, and to accurately estimate the user's stress state and illness. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI.

[0072] The device includes an evaluation unit that evaluates the stress state based on data of the heart rate, sleep time, and body temperature. The evaluation unit evaluates the stress state based on data such as the heart rate, sleep time, and body temperature. For example, when the heart rate is high, the sleep time is short, and the body temperature is high, the stress state is evaluated to be high. The evaluation unit can use a heart rate monitor or a wearable device to measure the heart rate. The sleep time can be measured using a sleep tracker or self-reporting. The body temperature can be measured using a thermometer or a wearable device. For example, the evaluation unit measures the heart rate using a heart rate monitor, measures the sleep time using a sleep tracker, and measures the body temperature using a thermometer. This makes it possible to evaluate the stress state based on data such as the heart rate, sleep time, and body temperature. A part or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI.

[0073] The device includes an estimation unit that estimates the type of illness by combining the content of the dream with the data. The estimation unit estimates the type of illness by combining the content of the dream with data such as heart rate, sleep time, and body temperature. For example, if the content of a particular dream is repeated, it is estimated that there is a possibility that the person is prone to a particular illness. The estimation unit uses a generation AI to analyze the content of the dream and estimate the type of illness by combining it with data such as heart rate, sleep time, and body temperature. For example, the generation AI analyzes the content of a dream such as "I had a dream about falling from a high place" or "I had a dream about being chased" and estimates the type of illness by combining it with data such as heart rate, sleep time, and body temperature. This makes it possible to estimate the type of illness by combining the content of the dream with the data. A part or all of the above-mentioned processing in the estimation unit may be performed, for example, using AI, or may be performed without using AI.

[0074] The reception unit can estimate the user's emotion and adjust the timing of receiving the human body information based on the estimated emotion of the user. For example, when the user is relaxed, the reception unit receives the human body information at regular intervals. Also, when the user is stressed, the reception unit can receive the human body information at a timing when the stress is reduced. Furthermore, when the user is in a hurry, the reception unit can quickly receive the human body information with a simplified procedure. 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. This makes it possible to adjust the timing of receiving the human body information based on the user's emotion. 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.

[0075] The reception unit can analyze the user's past data submission history and select the optimal reception method. The reception unit can suggest the optimal reception time based on, for example, a time period during which the user frequently submitted data in the past. Also, the reception unit can preferentially suggest a submission method (manual input, voice input, etc.) that the user used in the past. Furthermore, the reception unit can select the optimal reception method for a specific day of the week or time period from the user's past data submission history. This makes it possible to select the optimal reception method based on the user's past data submission history. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, AI, or can be performed without using AI.

[0076] The reception unit can perform filtering based on the user's current living situation and areas of interest at the time of reception. For example, if the user is interested in health, the reception unit preferentially receives health-related information. In addition, if the user leads a busy life, the reception unit can preferentially receive simplified information. Furthermore, if the user is interested in a specific disease, the reception unit can preferentially receive information related to the disease. This makes it possible to filter information based on the user's living situation and areas of interest. A part or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI.

[0077] The reception unit can estimate the user's emotion and determine the priority of information to be received based on the estimated user's emotion. For example, when the user is feeling stressed, the reception unit preferentially receives stress-related information. Also, when the user is relaxed, the reception unit can preferentially receive general health information. Furthermore, when the user is in a hurry, the reception unit can preferentially receive important information. 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. This makes it possible to determine the priority of information based on the user's emotion. 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.

[0078] The reception unit can preferentially receive highly relevant information in consideration of the geographical location information of the user at the time of reception. For example, when the user is in a specific area, the reception unit preferentially receives health information related to the area. Also, when the user is traveling, the reception unit can preferentially receive health information of the travel destination. Furthermore, when the user is at home, the reception unit can preferentially receive health information around the home. This makes it possible to preferentially receive 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 reception unit may be performed, for example, using AI, or may be performed without using AI.

[0079] The reception unit can analyze the user's social media activity and receive related information at the time of reception. The reception unit receives related information based on, for example, health information shared by the user on social media. Also, the reception unit can receive information from health-related accounts that the user follows on social media. Furthermore, the reception unit can receive information from health-related groups that the user participates in on social media. This makes it possible to receive related information based on the user's social media activity. 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.

[0080] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. For example, when the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, when the user is stressed, the analysis unit can provide a concise analysis result. Furthermore, when the user is in a hurry, the analysis unit can provide an analysis result that focuses on the main points. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. This makes it possible to adjust the expression method of the analysis based on the user's emotion. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using an AI, or may be performed without using an AI.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on important data. Also, the analysis unit can perform a simplified analysis on general data. Furthermore, the analysis unit can perform a detailed analysis on data in which the user is highly interested. This makes it possible to adjust the level of detail of the analysis based on the importance of the data. A part or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI.

[0082] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit can apply a heart rate analysis algorithm to heart rate data. Also, the analysis unit can apply a sleep analysis algorithm to sleep data. Furthermore, the analysis unit can apply a body temperature analysis algorithm to body temperature data. This makes it possible to apply different analysis algorithms depending on the category of data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI.

[0083] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. For example, when the user is relaxed, the analysis unit performs a detailed analysis. Also, when the user is stressed, the analysis unit can perform a concise analysis. Furthermore, when the user is in a hurry, the analysis unit can perform an analysis that focuses on the main points. The estimation of emotion is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. This makes it possible to adjust the length of the analysis based on the user's emotion. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI.

[0084] During analysis, the analysis unit can determine the priority of analysis based on the time of data submission. The analysis unit, for example, prioritizes analysis of the latest data. Also, the analysis unit can prioritize analysis of data from a period specified by the user. Furthermore, data related to a specific event can be prioritized for analysis. This makes it possible to determine the priority of analysis based on the time of data submission. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0085] The analysis unit can adjust the order of analysis based on the relevance of data during analysis. The analysis unit, for example, preferentially analyzes highly relevant data. Also, the analysis unit can preferentially analyze data in which the user is highly interested. Furthermore, the analysis unit can preferentially analyze data related to a specific category. This makes it possible to adjust the order of analysis based on the relevance of data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0086] The providing unit can estimate the user's emotion and adjust the presentation method based on the estimated user's emotion. For example, when the user is relaxed, the providing unit can provide detailed preventive measures. Also, when the user is stressed, the providing unit can provide concise preventive measures. Furthermore, when the user is in a hurry, the providing unit can provide preventive measures that focus on the main points. 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. This makes it possible to adjust the presentation method based on the user's emotion. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI.

[0087] The providing unit can adjust the level of detail of the provision based on the importance of the preventive measures at the time of provision. For example, the providing unit can provide a detailed explanation for important preventive measures. Also, a simplified explanation can be provided for general preventive measures. Furthermore, a detailed explanation can be provided for preventive measures in which the user is highly interested. This makes it possible to adjust the level of detail of the provision based on the importance of the preventive measures. A part or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI.

[0088] The providing unit can apply different providing algorithms depending on the category of the preventive measures at the time of providing. For example, the providing unit can apply a stress reduction algorithm to a stress reduction measure. Also, the providing unit can apply a health maintenance algorithm to a health maintenance measure. Furthermore, the providing unit can apply a disease prevention algorithm to a disease prevention measure. Thereby, different providing algorithms can be applied depending on the category of the preventive measures. A part or all of the above-mentioned processing in the providing unit can be performed using AI, for example, or can be performed without using AI.

[0089] The providing unit can estimate the user's emotion and adjust the length of the provision based on the estimated user's emotion. For example, when the user is relaxed, the providing unit can provide detailed preventive measures. Also, when the user is stressed, the providing unit can provide concise preventive measures. Furthermore, when the user is in a hurry, the providing unit can provide preventive measures that focus on the main points. The estimation of the emotion is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. This makes it possible to adjust the length of the provision based on the user's emotion. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using an AI, or may be performed without using an AI.

[0090] The providing unit can determine the priority of provision based on the submission timing of the preventive measures at the time of provision. The providing unit can, for example, provide the latest preventive measures preferentially. Also, the providing unit can provide the preventive measures for a period specified by the user preferentially. Furthermore, the providing unit can provide the preventive measures related to a specific event preferentially. This makes it possible to determine the priority of provision based on the submission timing of the preventive measures. A part or all of the above-mentioned processing in the providing unit can be performed, for example, using AI or can be performed without using AI.

[0091] The providing unit can adjust the order of provision based on the relevance of the preventive measures at the time of provision. The providing unit can, for example, provide highly relevant preventive measures preferentially. Also, the providing unit can provide preferentially preventive measures in which the user is highly interested. Furthermore, the providing unit can provide preferentially preventive measures related to a specific category. This makes it possible to adjust the order of provision based on the relevance of the preventive measures. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0092] The input unit can provide a method for inputting the content of the dream in text. The input unit can provide, for example, a field in which the user can freely input text. Also, the input unit can provide a text area in which the user can describe the content of the dream in detail. Furthermore, a text box can be provided in which the user can briefly input the content of the dream. This allows the user to input the content of the dream in text. A part or all of the above-mentioned processing in the input unit can be performed, for example, using AI or can be performed without using AI.

[0093] The input unit can provide a method for selecting the content of the dream from options. The input unit can provide, for example, a drop-down menu that allows the user to select the content of the dream from options. The input unit can also provide radio buttons that allow the user to select the content of the dream from options. Furthermore, the input unit can provide check boxes that allow the user to select the content of the dream from options. This allows the user to select the content of the dream from options. Some or all of the above-mentioned processing in the input unit can be performed, for example, using AI or can be performed without using AI.

[0094] The input unit can provide a method for providing the content of a dream by voice input. The input unit can provide, for example, a microphone function that allows the user to input the content of the dream by voice. Also, a function can be provided that allows the user to input the content of the dream by voice and convert it into text. Furthermore, a function can be provided that allows the user to input the content of the dream by voice and convert it into text in real time. This allows the user to input the content of the dream by voice. A part or all of the above-mentioned processing in the input unit can be performed, for example, using AI, or can be performed without using AI.

[0095] The input unit can provide a method for inputting the content of a dream as an image or a picture. The input unit can provide, for example, a sketchpad function that allows the user to draw the content of the dream. The input unit can also provide a function that allows the user to upload the content of the dream as an image. Furthermore, an illustration library can be provided that allows the user to select the content of the dream from a selection of options. This allows the user to input the content of the dream as an image or a picture. Some or all of the above-mentioned processing in the input unit can be performed, for example, using AI or without using AI.

[0096] The evaluation unit can estimate the user's emotion and adjust the evaluation method based on the estimated user's emotion. For example, when the user is relaxed, the evaluation unit performs a detailed evaluation. Also, when the user is stressed, the evaluation unit can perform a concise evaluation. Furthermore, when the user is in a hurry, the evaluation unit can perform an evaluation that focuses on the main points. 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. This makes it possible to adjust the evaluation method based on the user's emotion. A part or all of the above-mentioned processing in the evaluation unit may be performed using, for example, an AI, or may be performed without using an AI.

[0097] The evaluation unit can select the optimal evaluation method by referring to the user's past data at the time of evaluation. The evaluation unit can select the optimal evaluation method based on the user's past heart rate data, for example. The evaluation unit can also select the optimal evaluation method based on the user's past sleep data. Furthermore, the evaluation unit can select the optimal evaluation method based on the user's past body temperature data. This makes it possible to select the optimal evaluation method based on the user's past data. A part or all of the above-mentioned processing in the evaluation unit can be performed, for example, using AI, or can be performed without using AI.

[0098] The evaluation unit can estimate the user's emotion and determine the priority of the evaluation based on the estimated user's emotion. For example, when the user is stressed, the evaluation unit can prioritize stress-related evaluation. Also, when the user is relaxed, the evaluation unit can prioritize general health evaluation. Furthermore, when the user is in a hurry, the evaluation unit can prioritize important evaluation. 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. This makes it possible to determine the priority of the evaluation based on the user's emotion. A part or all of the above-mentioned processing in the evaluation unit may be performed using, for example, an AI, or may be performed without using an AI.

[0099] The evaluation unit can select the optimal evaluation method in consideration of the geographical location information of the user at the time of evaluation. For example, when the user is in a specific area, the evaluation unit performs a health evaluation related to the area. Also, when the user is traveling, the evaluation unit can perform a health evaluation of the travel destination. Furthermore, when the user is at home, the evaluation unit can perform a health evaluation of the area around the home. This makes it possible to select the optimal evaluation method based on the geographical location information of the user. A part or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI.

[0100] The estimation unit can estimate the user's emotion and adjust the estimation method based on the estimated user's emotion. For example, when the user is relaxed, the estimation unit can perform a detailed estimation. Also, when the user is stressed, the estimation unit can perform a concise estimation. Furthermore, when the user is in a hurry, the estimation unit can perform an estimation that focuses on the main points. 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. This makes it possible to adjust the estimation method based on the user's emotion. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, an AI, or may be performed without using an AI.

[0101] The estimation unit can select the optimal estimation method by referring to the user's past data at the time of estimation. The estimation unit selects the optimal estimation method based on the user's past heart rate data, for example. The estimation unit can also select the optimal estimation method based on the user's past sleep data. Furthermore, the estimation unit can select the optimal estimation method based on the user's past body temperature data. This makes it possible to select the optimal estimation method based on the user's past data. A part or all of the above-mentioned processing in the estimation unit may be performed, for example, using AI, or may be performed without using AI.

[0102] The estimation unit can estimate the user's emotion and determine the priority of the estimation based on the estimated user's emotion. For example, when the user is stressed, the estimation unit can prioritize stress-related estimation. Also, when the user is relaxed, the estimation unit can prioritize general health estimation. Furthermore, when the user is in a hurry, the estimation unit can prioritize important estimation. 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. This makes it possible to determine the priority of the estimation based on the user's emotion. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, an AI, or may be performed without using an AI.

[0103] The estimation unit can select the optimal estimation method in consideration of the geographical location information of the user at the time of estimation. For example, when the user is in a specific area, the estimation unit performs health estimation related to the area. Also, when the user is traveling, the estimation unit can perform health estimation at the travel destination. Furthermore, when the user is at home, the estimation unit can perform health estimation around the home. This makes it possible to select the optimal estimation method based on the geographical location information of the user. A part or all of the above-mentioned processing in the estimation unit may be performed using AI, for example, or may be performed without using AI.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The reception unit can analyze the user's past data submission history and select the optimal reception method. For example, the optimal reception time can be suggested based on the time period during which the user frequently submitted data in the past. Also, the reception unit can preferentially suggest the submission method (manual input, voice input, etc.) that the user used in the past. Furthermore, the reception unit can select the optimal reception method for a specific day of the week or time period from the user's past data submission history. This makes it possible to select the optimal reception method based on the user's past data submission history. Some 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.

[0106] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, a detailed analysis can be performed on important data. Also, a simplified analysis can be performed on general data. Furthermore, a detailed analysis can be performed on data in which the user is highly interested. This makes it possible to adjust the level of detail of the analysis based on the importance of the data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0107] The providing unit can adjust the level of detail of the provision based on the importance of the preventive measures at the time of provision. For example, a detailed explanation can be provided for important preventive measures. Also, a simplified explanation can be provided for general preventive measures. Furthermore, a detailed explanation can be provided for preventive measures in which the user is highly interested. This makes it possible to adjust the level of detail of the provision based on the importance of the preventive measures. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI.

[0108] The providing unit can apply different providing algorithms depending on the category of the preventive measures at the time of providing. For example, a stress reduction algorithm can be applied to a stress reduction measure. Furthermore, a health maintenance algorithm can be applied to a health maintenance measure. Furthermore, a disease prevention algorithm can be applied to a disease prevention measure. Thereby, different providing algorithms can be applied depending on the category of the preventive measures. A part or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI.

[0109] The providing unit can determine the priority of provision based on the submission time of the preventive measures at the time of provision. For example, the latest preventive measures can be provided preferentially. Also, preventive measures for a period specified by a user can be provided preferentially. Furthermore, preventive measures related to a specific event can be provided preferentially. This makes it possible to determine the priority of provision based on the submission time of the preventive measures. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI.

[0110] The reception unit can estimate the user's emotion and adjust the timing of receiving the human body information based on the estimated emotion of the user. For example, when the user is relaxed, the reception of the human body information is performed at regular intervals. When the user is stressed, the reception of the human body information can be performed at a timing when the stress is reduced. When the user is in a hurry, the reception of the human body information can be performed quickly with a simplified procedure. 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. This makes it possible to adjust the timing of receiving the human body information based on the user's emotion. 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.

[0111] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. For example, when the user is relaxed, a detailed analysis result can be provided. Also, when the user is stressed, a concise analysis result can be provided. Furthermore, when the user is in a hurry, an analysis result that focuses on the main points can be provided. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. This makes it possible to adjust the expression method of the analysis based on the user's emotion. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using an AI, or may be performed without using an AI.

[0112] The providing unit can estimate the user's emotion and adjust the presentation method based on the estimated user's emotion. For example, when the user is relaxed, detailed preventive measures can be provided. Also, when the user is stressed, concise preventive measures can be provided. Furthermore, when the user is in a hurry, preventive measures that focus on the main points can be provided. 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. This makes it possible to adjust the presentation method based on the user's emotion. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI.

[0113] The providing unit can estimate the user's emotion and adjust the length of the provision based on the estimated user's emotion. For example, when the user is relaxed, detailed preventive measures can be provided. Also, when the user is stressed, concise preventive measures can be provided. Furthermore, when the user is in a hurry, preventive measures that focus on the main points can be provided. The estimation of the emotion is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. This makes it possible to adjust the length of the provision based on the user's emotion. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using an AI, or may be performed without using an AI.

[0114] The estimation unit can estimate the user's emotion and adjust the estimation method based on the estimated user's emotion. For example, when the user is relaxed, a detailed estimation can be performed. Also, when the user is stressed, a concise estimation can be performed. Furthermore, when the user is in a hurry, an estimation that focuses on the main points can be performed. The estimation of the emotion is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. This makes it possible to adjust the estimation method based on the user's emotion. A part or all of the above-mentioned processing in the estimation unit may be performed using an AI, for example, or may be performed without using an AI.

[0115] The process flow of the second embodiment will be briefly described below.

[0116] Step 1: The reception unit receives the body information and dream content from the user. The body information includes heart rate, blood pressure, body temperature, etc., and the dream content is input in text format. The reception unit receives this information and sends it to the analysis unit. Step 2: The analysis unit uses the generation AI to analyze the information received by the reception unit and estimate the stress state and illness. For example, if the heart rate is high, the sleep time is short, and the body temperature is high, it is estimated that the person is in a high stress state. In addition, if the content of a particular dream is repeated, it is estimated that the person may be susceptible to a particular illness. The generation AI uses natural language processing technology to analyze the content of the dream and combines it with data such as heart rate, sleep time, and body temperature for analysis. Step 3: The provider provides specific preventive measures based on the results estimated by the analyzer. For example, if stress levels are high, the provider provides advice on how to relax or reduce stress, and if the user is prone to a particular illness, the provider suggests early medical treatment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, input unit, evaluation unit, and estimation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14, and receives human body information and dream contents from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes information using a generation AI. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and provides preventive measures based on the analysis results. The input unit is realized, for example, by the control unit 46A of the smart device 14, and provides a method for inputting the dream contents from text or options. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and evaluates the stress state based on data such as heart rate, sleep time, and body temperature. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and combines the dream contents and data to estimate the type of illness. The correspondence between each part and the device or control part is not limited to the above-mentioned example, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 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.

[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 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.

[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. 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.

[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 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.

[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 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.

[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 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.

[0136] Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, input unit, evaluation unit, and estimation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, and receives human body information and dream content from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes information using a generation AI. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides preventive measures based on the analysis results. The input unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides a method for inputting the dream content from text or options. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and evaluates the stress state based on data such as heart rate, sleep time, and body temperature. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and combines the dream content and data to estimate the type of illness. The correspondence between each part and the device or control part is not limited to the above-mentioned example, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 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.

[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 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.

[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 (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).

[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] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, input unit, evaluation unit, and estimation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314, and receives human body information and dream contents from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes information using a generation AI. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides preventive measures based on the analysis results. The input unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides a method for inputting the dream contents from text or options. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and evaluates the stress state based on data such as heart rate, sleep time, and body temperature. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and estimates the type of illness by combining the dream contents and data. The correspondence between each part and the device or control part is not limited to the above-mentioned example, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, input unit, evaluation unit, and estimation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and receives human body information and the content of the dream from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the information using a generation AI. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides preventive measures based on the analysis results. The input unit is realized, for example, by the control unit 46A of the robot 414, and provides a method for inputting the content of the dream from text or options. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and evaluates the stress state based on data such as heart rate, sleep time, and body temperature. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and estimates the type of illness by combining the content of the dream and the data. The correspondence between each part and the device or control part is not limited to the above-mentioned example, and various modifications are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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 in which a processor is used 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] (Appendix 1) A reception unit that receives body information and dream contents from a user; an analysis unit that analyzes the information received by the reception unit and infers a stress state or an illness; A provision unit that provides specific preventive measures based on the results estimated by the analysis unit. A system characterized by: (Appendix 2) It has an input section that provides a way to input the content of dreams as text and a way to select from options. 2. The system of claim 1, (Appendix 3) Equipped with an analysis unit that uses natural language processing to analyze the content of dreams 2. The system of claim 1, (Appendix 4) Equipped with an evaluation unit that evaluates stress state based on heart rate, sleep time, and body temperature data 2. The system of claim 1, (Appendix 5) It is equipped with an estimation unit that estimates the type of illness by combining the content of dreams and this data. 2. The system of claim 1, (Appendix 6) The reception unit is To estimate a user's emotion and adjust a timing of receiving human body information based on the estimated user's emotion. 2. The system of claim 1, (Appendix 7) The reception unit is Analyze the user's past data submission history and select the optimal reception method 2. The system of claim 1, (Appendix 8) The reception unit is At check-in, filtering is done based on the user's current life situation and interests 2. The system of claim 1, (Appendix 9) The reception unit is Estimate the user's emotions and determine the priority of information to be received based on the estimated user's emotions. 2. The system of claim 1, (Appendix 10) The reception unit is When accepting a request, the system takes into account the user's geographic location information and prioritizes the acceptance of information that is more relevant to the request. 2. The system of claim 1, (Appendix 11) The reception unit is Upon reception, analyze the user's social media activity and receive relevant information 2. The system of claim 1, (Appendix 12) The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred user emotions 2. The system of claim 1, (Appendix 13) The analysis unit is During analysis, adjust the level of detail based on the importance of the data 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, apply different analysis algorithms depending on the category of data 2. The system of claim 1, (Appendix 15) The analysis unit is Infer user sentiment and adjust the length of the analysis based on the inferred user sentiment 2. The system of claim 1, (Appendix 16) The analysis unit is During analysis, prioritize analysis based on when data were submitted 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, adjust the order of analysis based on data relevance 2. The system of claim 1, (Appendix 18) The providing unit is To estimate a user's emotion and adjust a presentation method of the offering based on the estimated user's emotion. 2. The system of claim 1, (Appendix 19) The providing unit is When providing, adjust the level of detail of the provision based on the importance of the preventive measures 2. The system of claim 1, (Appendix 20) The providing unit is During delivery, different delivery algorithms are applied depending on the category of preventive measures. 2. The system of claim 1, (Appendix 21) The providing unit is Inferring user emotion and adjusting the length of the offering based on the estimated user emotion 2. The system of claim 1, (Appendix 22) The providing unit is At the time of delivery, priority will be given to deliveries based on the timing of delivery of precautions. 2. The system of claim 1, (Appendix 23) The providing unit is At the time of delivery, adjust the order of delivery based on the relevance of precautions 2. The system of claim 1, (Appendix 24) The input unit is Provide a way to enter the content of your dreams as text 2. The system of claim 1, (Appendix 25) The input unit is Offering a way to choose from options for dream content 2. The system of claim 1, (Appendix 26) The input unit is Provide a way to provide dream content via voice input 2. The system of claim 1, (Appendix 27) The input unit is Provide a way to input the content of dreams using images or pictures 2. The system of claim 1, (Appendix 28) The evaluation unit is Inferring user emotions and adjusting the evaluation method based on the estimated user emotions 2. The system of claim 1, (Appendix 29) The evaluation unit is When evaluating, the most appropriate evaluation method is selected by referring to the user's past data. 2. The system of claim 1, (Appendix 30) The evaluation unit is Estimate user sentiment and prioritize ratings based on the estimated user sentiment 2. The system of claim 1, (Appendix 31) The evaluation unit is During evaluation, the optimal evaluation method is selected taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 32) The estimation unit is Estimate a user's emotion and adjust the estimation method based on the estimated user's emotion. 2. The system of claim 1, (Appendix 33) The estimation unit is When making an estimation, the optimal estimation method is selected by referring to the user's past data. 2. The system of claim 1, (Appendix 34) The estimation unit is To estimate a user's emotion and determine a priority of estimations based on the estimated user's emotion. 2. The system of claim 1, (Appendix 35) The estimation unit is When estimating, the optimal estimation method is selected taking into account the user's geographic location information. 2. The system of claim 1, [Explanation of symbols]

[0189] 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 body information and dream contents from a user; an analysis unit that analyzes the information received by the reception unit and infers a stress state or an illness; A provision unit that provides specific preventive measures based on the results estimated by the analysis unit. A system characterized by:

2. It has an input section that provides a way to input the content of dreams as text and a way to select from options.

2. The system of claim 1 .

3. Equipped with an analysis unit that uses natural language processing to analyze the content of dreams 2. The system of claim 1 .

4. Equipped with an evaluation unit that evaluates stress state based on heart rate, sleep time, and body temperature data 2. The system of claim 1 .

5. It is equipped with an estimation unit that estimates the type of illness by combining the content of dreams and this data.

2. The system of claim 1 .

6. The reception unit is To estimate a user's emotion and adjust a timing of receiving human body information based on the estimated user's emotion.

2. The system of claim 1 .

7. The reception unit is Analyze the user's past data submission history and select the optimal reception method 2. The system of claim 1 .

8. The reception unit is At check-in, filtering is done based on the user's current life situation and interests 2. The system of claim 1 .

9. The reception unit is Estimate the user's emotions and determine the priority of information to be received based on the estimated user's emotions.

2. The system of claim 1 .

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