System

Abstract

An object of the system according to the embodiment is to measure the basal body temperature of a woman with high accuracy and precisely track the menstrual cycle.SOLUTION: A system according to an embodiment includes a basal body temperature measuring section, a data analyzing section, and an advice providing section. The basal body temperature measuring section measures a basal body temperature. The data analyzing section analyzes basal body temperature data measured by the basal body temperature measuring section. The advice providing unit provides personalized advice based on the data analyzed by the data 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 an instruction sentence related to a description of 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology does not adequately measure a woman's basal body temperature with high accuracy and precisely tracks her menstrual cycle, and there is room for improvement.

[0005] The system according to the embodiment aims to measure a woman's basal body temperature with high accuracy and precisely track her menstrual cycle. [Means for solving the problem]

[0006] The system according to the embodiment includes a basal body temperature measurement unit, a data analysis unit, and an advice provision unit. The basal body temperature measurement unit measures basal body temperature. The data analysis unit analyzes basal body temperature data measured by the basal body temperature measurement unit. The advice provision unit provides personalized advice based on the data analyzed by the data analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can measure a woman's basal body temperature with high accuracy and precisely track her menstrual cycle. [Brief explanation 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. [Figure 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. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 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. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 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 INVENTION

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

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

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single 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 coded 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, 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 symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and 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. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "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, RAM 30, and 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 (for example, 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., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with 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 CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 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 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. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A healthcare platform according to an embodiment of the present invention is a system that uses a smartwatch or smart ring to accurately measure a woman's basal body temperature and precisely track her menstrual cycle. The system combines cutting-edge sensor technology with generative AI to detect subtle changes in a user's body temperature and provide real-time information on ovulation, menstruation, and other important health indicators. This enables the healthcare platform to support women's health management and daily well-being, providing insights and tools to help users better connect with their bodies and live healthier lives.

[0029] A healthcare platform according to an embodiment includes a basal body temperature measurement unit, a data analysis unit, and an advice provision unit. The basal body temperature measurement unit measures a user's basal body temperature. For example, the basal body temperature can be measured with high accuracy using sensor technology installed in a smart watch or smart ring. The basal body temperature measurement unit can measure the user's body temperature daily and accumulate the data. The data analysis unit analyzes the basal body temperature data measured by the basal body temperature measurement unit. For example, a generation AI analyzes the user's body temperature data and provides important health indicators such as ovulation and menstrual periods in real time. The generation AI receives the user's body temperature data as input and performs analysis based on the data. The advice provision unit provides personalized advice based on the data analyzed by the data analysis unit. For example, the generation AI generates advice to support healthy lifestyle choices based on the user's body temperature data and menstrual cycle information. This allows the user to live a smarter and healthier life. As a result, the healthcare platform according to an embodiment can support health management by measuring and analyzing the user's basal body temperature with high accuracy and providing personalized advice.

[0030] The basal body temperature measurement unit simultaneously measures skin humidity and blood flow velocity, enabling a detailed analysis of factors that cause basal body temperature fluctuations. For example, the basal body temperature measurement unit may improve sensor technology and add a function to measure skin humidity. This allows for an analysis of whether body temperature fluctuations are affected by humidity. For example, the difference in body temperature between high and low humidity may be compared. The basal body temperature measurement unit may also add a sensor that measures blood flow velocity to analyze the influence of blood flow as a factor in body temperature fluctuations. For example, the relationship between blood flow velocity and body temperature after exercise may be investigated. The basal body temperature measurement unit may also simultaneously measure skin humidity and blood flow velocity, and use this data to perform a detailed analysis of factors that cause body temperature fluctuations. For example, the change in body temperature when humidity is high and blood flow is fast may be analyzed. This allows for a more detailed analysis of factors that cause basal body temperature fluctuations, improving the accuracy of health management.

[0031] The basal body temperature measurement unit can simultaneously measure different body parts and obtain data that takes into account regional differences in body temperature. The basal body temperature measurement unit, for example, places sensors on the wrist and ankle and measures body temperature simultaneously to analyze regional differences in body temperature. For example, the difference in body temperature between the wrist and ankle can be compared. The basal body temperature measurement unit also optimizes sensor placement to measure body temperature at different body parts. For example, it simultaneously measures multiple body parts, such as the wrist, ankle, and forehead, to analyze regional differences in body temperature. The basal body temperature measurement unit also integrates body temperature measurement data from different body parts and performs data analysis that takes into account regional differences in body temperature. For example, it integrates body temperature data from the wrist and ankle to analyze overall body temperature fluctuations. This allows for more accurate health management by obtaining data that takes into account regional differences in body temperature.

[0032] In addition to smart watches and smart rings, the basal body temperature measurement unit will develop new wearable devices such as smart piercings and smart necklaces, thereby expanding the diversity of body temperature measurement. For example, the basal body temperature measurement unit will develop smart piercings and add a function to measure earlobe temperature. This will enable analysis of body temperature fluctuations based on earlobe temperature data. The basal body temperature measurement unit will also develop smart necklaces and add a function to measure neck temperature. This will enable analysis of body temperature fluctuations based on neck temperature data. In addition to smart watches and smart rings, the basal body temperature measurement unit will develop new wearable devices such as smart piercings and smart necklaces, thereby expanding the diversity of body temperature measurement. This will enable integration of body temperature data from multiple locations and more detailed body temperature analysis. This will expand the diversity of body temperature measurement, enabling more detailed health management.

[0033] The basal body temperature measurement unit can integrate the basal body temperature measurement data with other health data to evaluate the overall health condition. The basal body temperature measurement unit, for example, integrates the basal body temperature measurement data with heart rate data to evaluate the overall health condition. For example, it analyzes the relationship between body temperature and heart rate to evaluate the health condition. The basal body temperature measurement unit also integrates the basal body temperature measurement data with activity level data to evaluate the overall health condition. For example, it analyzes the relationship between body temperature and activity level to evaluate the health condition. The basal body temperature measurement unit also integrates the basal body temperature measurement data with other health data (for example, heart rate and activity level) to evaluate the overall health condition. This enables detailed health analysis based on multiple health indicators. This enables more detailed health management by evaluating the overall health condition.

[0034] The data analysis unit can use the generation AI to analyze long-term trends in basal body temperature data and predict future health risks. The data analysis unit, for example, uses the generation AI to analyze long-term trends in basal body temperature data and predict future health risks. For example, future health risks are predicted based on body temperature data from the past few months. The data analysis unit also uses the generation AI to analyze long-term trends in basal body temperature data and predict future health risks. For example, it analyzes body temperature fluctuation patterns and predicts future health risks. The data analysis unit also uses the generation AI to analyze long-term trends in basal body temperature data and predict future health risks. This allows the user to understand future health risks in advance and take preventive measures. This allows preventive measures to be taken by analyzing long-term trends and predicting future health risks.

[0035] The data analysis unit can use the generation AI to analyze the relationship between the user's lifestyle habits and body temperature fluctuations and make specific suggestions for improving the lifestyle habits. For example, the data analysis unit uses the generation AI to analyze the relationship between the user's lifestyle habits (e.g., diet and exercise) and body temperature fluctuations. For example, it analyzes the relationship between dietary content and body temperature and makes specific suggestions for improving the lifestyle habits. The data analysis unit also uses the generation AI to analyze the relationship between the user's lifestyle habits (e.g., diet and exercise) and body temperature fluctuations. For example, it analyzes the relationship between the amount of exercise and body temperature and makes specific suggestions for improving the lifestyle habits. The data analysis unit also uses the generation AI to analyze the relationship between the user's lifestyle habits (e.g., diet and exercise) and body temperature fluctuations and makes specific suggestions for improving the lifestyle habits. This allows the user to improve their lifestyle habits and live a healthy life. This supports a healthy lifestyle by analyzing the relationship between lifestyle habits and body temperature fluctuations and making specific suggestions for improvement.

[0036] The data analysis unit can use the generation AI to compare the user's body temperature data with the data of other users and discover common health patterns. For example, the data analysis unit can use the generation AI to compare the user's body temperature data with the data of other users and discover common health patterns. For example, it can compare the body temperature data of users in the same age group. The data analysis unit can also use the generation AI to compare the user's body temperature data with the data of other users and discover common health patterns. For example, it can compare the body temperature data of users with the same lifestyle habits. The data analysis unit can also use the generation AI to compare the user's body temperature data with the data of other users and discover common health patterns. This allows the user to compare their own health condition with other users and use it as reference. By discovering common health patterns, the user can compare their own health condition with other users and use it as reference.

[0037] The data analysis unit can use the generation AI to analyze the effects of different seasons and climatic conditions on body temperature and provide health management advice for each season. The data analysis unit, for example, uses the generation AI to analyze the effects of different seasons and climatic conditions on body temperature. For example, it compares body temperature data from summer and winter and provides health management advice for each season. The data analysis unit also uses the generation AI to analyze the effects of different climatic conditions on body temperature. For example, it analyzes the effects of changes in humidity and temperature on body temperature and provides health management advice. The data analysis unit also uses the generation AI to analyze the effects of different seasons and climatic conditions on body temperature and provides health management advice for each season. This allows the user to manage their health according to the season. By providing health management advice for each season, the user can manage their health according to the season.

[0038] The advice providing unit can use the generation AI to compare the user's past health data with current data and provide an individualized health improvement plan. The advice providing unit, for example, uses the generation AI to compare the user's past health data with current data and provide an individualized health improvement plan. For example, it compares past body temperature data with current data and makes specific suggestions for health improvement. The advice providing unit also uses the generation AI to compare the user's past health data with current data and provide an individualized health improvement plan. For example, it compares past lifestyle habit data with current data and makes specific suggestions for lifestyle improvement. The advice providing unit also uses the generation AI to compare the user's past health data with current data and provide an individualized health improvement plan. This allows the user to compare their health condition with the past and identify areas for improvement. This allows the user to compare past and current data and provide an individualized health improvement plan, allowing the user to compare their health condition with the past and identify areas for improvement.

[0039] The advice providing unit can use the generation AI to integrate the user's body temperature data with other vital data and provide comprehensive health advice. The advice providing unit, for example, uses the generation AI to integrate the user's body temperature data with other vital data (e.g., blood pressure and blood glucose level) and provide comprehensive health advice. For example, it analyzes the relationship between body temperature and blood pressure and provides health advice. The advice providing unit also uses the generation AI to integrate the user's body temperature data with other vital data (e.g., blood pressure and blood glucose level) and provide comprehensive health advice. For example, it analyzes the relationship between body temperature and blood glucose level and provides health advice. The advice providing unit also uses the generation AI to integrate the user's body temperature data with other vital data (e.g., blood pressure and blood glucose level) and provide comprehensive health advice. This allows the user to receive detailed health advice based on multiple health indicators. By integrating the body temperature data with other vital data and providing comprehensive health advice, the user can receive detailed health advice based on multiple health indicators.

[0040] The advice providing unit can use the generation AI to provide personalized meal plans and exercise plans based on the user's health data. The advice providing unit, for example, uses the generation AI to provide personalized meal plans based on the user's health data. For example, it proposes an optimal meal plan based on body temperature data and lifestyle habit data. The advice providing unit also uses the generation AI to provide personalized exercise plans based on the user's health data. For example, it proposes an optimal exercise plan based on body temperature data and activity level data. The advice providing unit also uses the generation AI to provide personalized meal plans and exercise plans based on the user's health data. This allows the user to implement optimal plans according to their own health condition. By providing personalized meal plans and exercise plans based on health data, the user can implement optimal plans according to their own health condition.

[0041] The advice providing unit can use the generation AI to provide a personalized stress management plan based on the user's health data. The advice providing unit, for example, uses the generation AI to provide a personalized stress management plan based on the user's health data. For example, it proposes an optimal stress management plan based on body temperature data or emotional data. The advice providing unit also uses the generation AI to provide a personalized stress management plan based on the user's health data. For example, it proposes an optimal stress management plan based on lifestyle habit data or activity level data. The advice providing unit also uses the generation AI to provide a personalized stress management plan based on the user's health data. This allows the user to perform optimal stress management according to their own health condition. By providing a personalized stress management plan based on health data, the user can perform optimal stress management according to their own health condition.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The basal body temperature measurement unit measures the user's basal body temperature. For example, the sensor technology installed in a smart watch or smart ring can be used to measure the user's basal body temperature with high accuracy. The basal body temperature measurement unit can also measure the user's body temperature daily and accumulate the data. The data analysis unit analyzes the basal body temperature data measured by the basal body temperature measurement unit. For example, the generation AI analyzes the user's body temperature data and provides important health indicators such as ovulation and menstrual periods in real time. The generation AI receives the user's body temperature data as input and performs analysis based on that data. The advice provision unit provides personalized advice based on the data analyzed by the data analysis unit. For example, the generation AI generates advice to support healthy lifestyle choices based on the user's body temperature data and menstrual cycle information. This allows the user to live a smarter and healthier life. As a result, the healthcare platform according to the embodiment can support health management by measuring and analyzing the user's basal body temperature with high accuracy and providing personalized advice.

[0044] For example, the basal body temperature measurement unit may improve sensor technology and add a function to measure skin humidity. This allows for an analysis of whether body temperature fluctuations are affected by humidity. For example, the difference in body temperature when humidity is high and when it is low may be compared. The basal body temperature measurement unit may also add a sensor to measure blood flow velocity and analyze the influence of blood flow as a factor in body temperature fluctuations. For example, the relationship between blood flow velocity and body temperature after exercise may be investigated. The basal body temperature measurement unit may also simultaneously measure skin humidity and blood flow velocity and use this data to perform a detailed analysis of the factors behind body temperature fluctuations. For example, the fluctuations in body temperature when humidity is high and blood flow is fast may be analyzed. This allows for a more detailed analysis of the factors behind basal body temperature fluctuations, improving the accuracy of health management.

[0045] The basal body temperature measurement unit analyzes regional differences in body temperature by, for example, placing sensors on the wrist and ankle and measuring body temperature simultaneously. For example, the difference in body temperature between the wrist and ankle is compared. The basal body temperature measurement unit also optimizes the placement of the sensors to measure body temperature at different parts of the body. For example, simultaneous measurements are taken at multiple parts such as the wrist, ankle, and forehead to analyze regional differences in body temperature. The basal body temperature measurement unit also integrates body temperature measurement data from different parts of the body and performs data analysis taking regional differences in body temperature into account. For example, body temperature data from the wrist and ankle is integrated to analyze overall body temperature fluctuations. This allows for more accurate health management by obtaining data that takes regional differences in body temperature into account.

[0046] For example, the basal body temperature measurement unit may develop smart piercings and add a function to measure earlobe temperature. This will allow for analysis of body temperature fluctuations based on earlobe temperature data. The basal body temperature measurement unit may also develop smart necklaces and add a function to measure neck temperature. This will allow for analysis of body temperature fluctuations based on neck temperature data. The basal body temperature measurement unit may also develop new wearable devices such as smart piercings and smart necklaces in addition to smart watches and smart rings, thereby expanding the diversity of body temperature measurement. This will allow for more detailed body temperature analysis by integrating body temperature data from multiple locations. This will expand the diversity of body temperature measurement, enabling more detailed health management.

[0047] The basal body temperature measurement unit, for example, integrates basal body temperature measurement data and heart rate data to evaluate the overall health condition. For example, it analyzes the relationship between body temperature and heart rate to evaluate the health condition. The basal body temperature measurement unit also integrates basal body temperature measurement data and activity level data to evaluate the overall health condition. For example, it analyzes the relationship between body temperature and activity level to evaluate the health condition. The basal body temperature measurement unit also integrates basal body temperature measurement data with other health data (for example, heart rate and activity level) to evaluate the overall health condition. This enables detailed health analysis based on multiple health indicators. This enables more detailed health management by evaluating the overall health condition.

[0048] The data analysis unit, for example, uses the generation AI to analyze long-term trends in basal body temperature data and predict future health risks. For example, future health risks are predicted based on body temperature data from the past few months. The data analysis unit also uses the generation AI to analyze long-term trends in basal body temperature data and predict future health risks. For example, it analyzes body temperature fluctuation patterns and predicts future health risks. The data analysis unit also uses the generation AI to analyze long-term trends in basal body temperature data and predict future health risks. This allows the user to understand future health risks in advance and take preventive measures. This allows preventive measures to be taken by analyzing long-term trends and predicting future health risks.

[0049] The data analysis unit, for example, uses a generating AI to analyze the relationship between the user's lifestyle habits (e.g., diet and exercise) and body temperature fluctuations. For example, it analyzes the relationship between dietary content and body temperature and makes specific suggestions for improving the lifestyle. The data analysis unit also uses a generating AI to analyze the relationship between the user's lifestyle habits (e.g., diet and exercise) and body temperature fluctuations. For example, it analyzes the relationship between the amount of exercise and body temperature and makes specific suggestions for improving the lifestyle. The data analysis unit also uses a generating AI to analyze the relationship between the user's lifestyle habits (e.g., diet and exercise) and body temperature fluctuations and makes specific suggestions for improving the lifestyle. This allows the user to improve their lifestyle and live a healthy life. This supports a healthy lifestyle by analyzing the relationship between lifestyle habits and body temperature fluctuations and making specific suggestions for improvement.

[0050] The data analysis unit, for example, uses the generation AI to compare the user's body temperature data with the data of other users to discover common health patterns. For example, it compares the body temperature data of users in the same age group. The data analysis unit also uses the generation AI to compare the user's body temperature data with the data of other users to discover common health patterns. For example, it compares the body temperature data of users with the same lifestyle habits. The data analysis unit also uses the generation AI to compare the user's body temperature data with the data of other users to discover common health patterns. This allows the user to compare their own health condition with other users and use it as reference. By discovering common health patterns, the user can compare their own health condition with other users and use it as reference.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The basal body temperature measurement unit measures the user's basal body temperature. For example, the sensor technology installed in a smart watch or smart ring can be used to measure the user's basal body temperature with high accuracy. The basal body temperature measurement unit can also measure the user's body temperature every day and accumulate that data. Step 2: The data analysis unit analyzes the basal body temperature data measured by the basal body temperature measurement unit. For example, the generation AI analyzes the user's body temperature data and provides important health indicators such as ovulation and menstruation in real time. The generation AI receives the user's body temperature data as input and performs analysis based on that data. Step 3: The advice provider provides personalized advice based on the data analyzed by the data analyzer. For example, the generator AI generates advice to support healthy lifestyle choices based on the user's body temperature data and menstrual cycle information. This allows the user to live a smarter, healthier life.

[0053] (Example 2) A healthcare platform according to an embodiment of the present invention is a system that uses a smartwatch or smart ring to accurately measure a woman's basal body temperature and precisely track her menstrual cycle. The system combines cutting-edge sensor technology with generative AI to detect subtle changes in a user's body temperature and provide real-time information on ovulation, menstruation, and other important health indicators. This enables the healthcare platform to support women's health management and daily well-being, providing insights and tools to help users better connect with their bodies and live healthier lives.

[0054] A healthcare platform according to an embodiment includes a basal body temperature measurement unit, a data analysis unit, and an advice provision unit. The basal body temperature measurement unit measures a user's basal body temperature. For example, the basal body temperature can be measured with high accuracy using sensor technology installed in a smart watch or smart ring. The basal body temperature measurement unit can measure the user's body temperature daily and accumulate the data. The data analysis unit analyzes the basal body temperature data measured by the basal body temperature measurement unit. For example, a generation AI analyzes the user's body temperature data and provides important health indicators such as ovulation and menstrual periods in real time. The generation AI receives the user's body temperature data as input and performs analysis based on the data. The advice provision unit provides personalized advice based on the data analyzed by the data analysis unit. For example, the generation AI generates advice to support healthy lifestyle choices based on the user's body temperature data and menstrual cycle information. This allows the user to live a smarter and healthier life. As a result, the healthcare platform according to an embodiment can support health management by measuring and analyzing the user's basal body temperature with high accuracy and providing personalized advice.

[0055] The basal body temperature measurement unit simultaneously measures skin humidity and blood flow velocity, enabling a detailed analysis of factors that cause basal body temperature fluctuations. For example, the basal body temperature measurement unit may improve sensor technology and add a function to measure skin humidity. This allows for an analysis of whether body temperature fluctuations are affected by humidity. For example, the difference in body temperature between high and low humidity may be compared. The basal body temperature measurement unit may also add a sensor that measures blood flow velocity to analyze the influence of blood flow as a factor in body temperature fluctuations. For example, the relationship between blood flow velocity and body temperature after exercise may be investigated. The basal body temperature measurement unit may also simultaneously measure skin humidity and blood flow velocity, and use this data to perform a detailed analysis of factors that cause body temperature fluctuations. For example, the change in body temperature when humidity is high and blood flow is fast may be analyzed. This allows for a more detailed analysis of factors that cause basal body temperature fluctuations, improving the accuracy of health management.

[0056] The basal body temperature measurement unit can simultaneously measure different body parts and obtain data that takes into account regional differences in body temperature. The basal body temperature measurement unit, for example, places sensors on the wrist and ankle and measures body temperature simultaneously to analyze regional differences in body temperature. For example, the difference in body temperature between the wrist and ankle can be compared. The basal body temperature measurement unit also optimizes sensor placement to measure body temperature at different body parts. For example, it simultaneously measures multiple body parts, such as the wrist, ankle, and forehead, to analyze regional differences in body temperature. The basal body temperature measurement unit also integrates body temperature measurement data from different body parts and performs data analysis that takes into account regional differences in body temperature. For example, it integrates body temperature data from the wrist and ankle to analyze overall body temperature fluctuations. This allows for more accurate health management by obtaining data that takes into account regional differences in body temperature.

[0057] The basal body temperature measurement unit uses the emotion estimation function to analyze the effect of the user's emotional state on body temperature and clarify the correlation between emotion and body temperature. The basal body temperature measurement unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and analyze body temperature fluctuations based on that data. For example, it compares the difference in body temperature between a stressed state and a relaxed state. The basal body temperature measurement unit also uses the emotion estimation function to analyze the effect of the user's emotional state on body temperature. For example, it investigates how emotions such as joy and sadness affect body temperature. The basal body temperature measurement unit also integrates the emotion estimation data with the body temperature data to clarify the correlation between emotion and body temperature. For example, it analyzes body temperature fluctuations when the emotion score is high. This clarifies the correlation between emotion and body temperature, enabling more detailed health management.

[0058] In addition to smart watches and smart rings, the basal body temperature measurement unit will develop new wearable devices such as smart piercings and smart necklaces, thereby expanding the diversity of body temperature measurement. For example, the basal body temperature measurement unit will develop smart piercings and add a function to measure earlobe temperature. This will enable analysis of body temperature fluctuations based on earlobe temperature data. The basal body temperature measurement unit will also develop smart necklaces and add a function to measure neck temperature. This will enable analysis of body temperature fluctuations based on neck temperature data. In addition to smart watches and smart rings, the basal body temperature measurement unit will develop new wearable devices such as smart piercings and smart necklaces, thereby expanding the diversity of body temperature measurement. This will enable integration of body temperature data from multiple locations and more detailed body temperature analysis. This will expand the diversity of body temperature measurement, enabling more detailed health management.

[0059] The basal body temperature measurement unit can integrate the basal body temperature measurement data with other health data to evaluate the overall health condition. The basal body temperature measurement unit, for example, integrates the basal body temperature measurement data with heart rate data to evaluate the overall health condition. For example, it analyzes the relationship between body temperature and heart rate to evaluate the health condition. The basal body temperature measurement unit also integrates the basal body temperature measurement data with activity level data to evaluate the overall health condition. For example, it analyzes the relationship between body temperature and activity level to evaluate the health condition. The basal body temperature measurement unit also integrates the basal body temperature measurement data with other health data (for example, heart rate and activity level) to evaluate the overall health condition. This enables detailed health analysis based on multiple health indicators. This enables more detailed health management by evaluating the overall health condition.

[0060] The basal body temperature measurement unit develops an app equipped with an emotion estimation function, which can record in real time the stress and relaxation state felt by the user when measuring body temperature. The basal body temperature measurement unit, for example, develops an app equipped with an emotion estimation function, which records in real time the stress and relaxation state felt by the user when measuring body temperature. For example, the emotional state at the time of measurement is input into the app. The basal body temperature measurement unit also uses the emotion estimation function to analyze the user's emotional state in real time and records the emotional state at the time of body temperature measurement based on the data. For example, it automatically distinguishes between a stress state and a relaxation state. The basal body temperature measurement unit also develops an app equipped with an emotion estimation function, which records in real time the stress and relaxation state felt by the user when measuring body temperature. This allows for a detailed analysis of the relationship between the emotional state and body temperature. This allows for a detailed analysis of the relationship between the emotion and body temperature by recording the emotional state at the time of body temperature measurement.

[0061] The data analysis unit can use the generation AI to analyze long-term trends in basal body temperature data and predict future health risks. The data analysis unit, for example, uses the generation AI to analyze long-term trends in basal body temperature data and predict future health risks. For example, future health risks are predicted based on body temperature data from the past few months. The data analysis unit also uses the generation AI to analyze long-term trends in basal body temperature data and predict future health risks. For example, it analyzes body temperature fluctuation patterns and predicts future health risks. The data analysis unit also uses the generation AI to analyze long-term trends in basal body temperature data and predict future health risks. This allows the user to understand future health risks in advance and take preventive measures. This allows preventive measures to be taken by analyzing long-term trends and predicting future health risks.

[0062] The data analysis unit can use the generation AI to analyze the relationship between the user's lifestyle habits and body temperature fluctuations and make specific suggestions for improving the lifestyle habits. For example, the data analysis unit uses the generation AI to analyze the relationship between the user's lifestyle habits (e.g., diet and exercise) and body temperature fluctuations. For example, it analyzes the relationship between dietary content and body temperature and makes specific suggestions for improving the lifestyle habits. The data analysis unit also uses the generation AI to analyze the relationship between the user's lifestyle habits (e.g., diet and exercise) and body temperature fluctuations. For example, it analyzes the relationship between the amount of exercise and body temperature and makes specific suggestions for improving the lifestyle habits. The data analysis unit also uses the generation AI to analyze the relationship between the user's lifestyle habits (e.g., diet and exercise) and body temperature fluctuations and makes specific suggestions for improving the lifestyle habits. This allows the user to improve their lifestyle habits and live a healthy life. This supports a healthy lifestyle by analyzing the relationship between lifestyle habits and body temperature fluctuations and making specific suggestions for improvement.

[0063] The data analysis unit can use the generation AI to compare the user's body temperature data with the data of other users and discover common health patterns. For example, the data analysis unit can use the generation AI to compare the user's body temperature data with the data of other users and discover common health patterns. For example, it can compare the body temperature data of users in the same age group. The data analysis unit can also use the generation AI to compare the user's body temperature data with the data of other users and discover common health patterns. For example, it can compare the body temperature data of users with the same lifestyle habits. The data analysis unit can also use the generation AI to compare the user's body temperature data with the data of other users and discover common health patterns. This allows the user to compare their own health condition with other users and use it as reference. By discovering common health patterns, the user can compare their own health condition with other users and use it as reference.

[0064] The data analysis unit can use the generation AI to analyze the effects of different seasons and climatic conditions on body temperature and provide health management advice for each season. The data analysis unit, for example, uses the generation AI to analyze the effects of different seasons and climatic conditions on body temperature. For example, it compares body temperature data from summer and winter and provides health management advice for each season. The data analysis unit also uses the generation AI to analyze the effects of different climatic conditions on body temperature. For example, it analyzes the effects of changes in humidity and temperature on body temperature and provides health management advice. The data analysis unit also uses the generation AI to analyze the effects of different seasons and climatic conditions on body temperature and provides health management advice for each season. This allows the user to manage their health according to the season. By providing health management advice for each season, the user can manage their health according to the season.

[0065] The data analysis unit uses the emotion estimation function to provide an analysis result of the body temperature data based on the user's emotional state, and can provide health management advice according to the emotion. The data analysis unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and provides an analysis result of the body temperature data based on the data. For example, it analyzes the difference in body temperature between a stressed state and a relaxed state. The data analysis unit also uses the emotion estimation function to provide an analysis result of the body temperature data based on the user's emotional state. For example, it investigates how emotions such as joy and sadness affect body temperature. The data analysis unit also integrates the emotion estimation data with the body temperature data to provide health management advice according to the emotion. For example, it analyzes fluctuations in body temperature when the emotion score is high and provides health management advice. In this way, health management advice according to the emotion can be provided, allowing the user to manage their health according to their emotional state.

[0066] The advice providing unit can use the generation AI to compare the user's past health data with current data and provide an individualized health improvement plan. The advice providing unit, for example, uses the generation AI to compare the user's past health data with current data and provide an individualized health improvement plan. For example, it compares past body temperature data with current data and makes specific suggestions for health improvement. The advice providing unit also uses the generation AI to compare the user's past health data with current data and provide an individualized health improvement plan. For example, it compares past lifestyle habit data with current data and makes specific suggestions for lifestyle improvement. The advice providing unit also uses the generation AI to compare the user's past health data with current data and provide an individualized health improvement plan. This allows the user to compare their health condition with the past and identify areas for improvement. This allows the user to compare past and current data and provide an individualized health improvement plan, allowing the user to compare their health condition with the past and identify areas for improvement.

[0067] The advice providing unit can use the generation AI to integrate the user's body temperature data with other vital data and provide comprehensive health advice. The advice providing unit, for example, uses the generation AI to integrate the user's body temperature data with other vital data (e.g., blood pressure and blood glucose level) and provide comprehensive health advice. For example, it analyzes the relationship between body temperature and blood pressure and provides health advice. The advice providing unit also uses the generation AI to integrate the user's body temperature data with other vital data (e.g., blood pressure and blood glucose level) and provide comprehensive health advice. For example, it analyzes the relationship between body temperature and blood glucose level and provides health advice. The advice providing unit also uses the generation AI to integrate the user's body temperature data with other vital data (e.g., blood pressure and blood glucose level) and provide comprehensive health advice. This allows the user to receive detailed health advice based on multiple health indicators. By integrating the body temperature data with other vital data and providing comprehensive health advice, the user can receive detailed health advice based on multiple health indicators.

[0068] The advice providing unit uses the emotion estimation function to provide personalized health advice based on the user's emotional state and make lifestyle improvement suggestions based on the emotions. The advice providing unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and provide personalized health advice based on the data. For example, lifestyle improvement suggestions are made based on stress and relaxation levels. The advice providing unit also uses the emotion estimation function to provide personalized health advice based on the user's emotional state. For example, lifestyle improvement suggestions are made based on emotions such as joy and sadness. The advice providing unit also integrates the emotion estimation data and health data to make lifestyle improvement suggestions based on the emotions. For example, health advice is provided when the emotion score is high, supporting lifestyle improvements. In this way, by providing health advice based on the emotional state, the user can make lifestyle improvements based on their emotions.

[0069] The advice providing unit can use the generation AI to provide personalized meal plans and exercise plans based on the user's health data. The advice providing unit, for example, uses the generation AI to provide personalized meal plans based on the user's health data. For example, it proposes an optimal meal plan based on body temperature data and lifestyle habit data. The advice providing unit also uses the generation AI to provide personalized exercise plans based on the user's health data. For example, it proposes an optimal exercise plan based on body temperature data and activity level data. The advice providing unit also uses the generation AI to provide personalized meal plans and exercise plans based on the user's health data. This allows the user to implement optimal plans according to their own health condition. By providing personalized meal plans and exercise plans based on health data, the user can implement optimal plans according to their own health condition.

[0070] The advice providing unit can use the generation AI to provide a personalized stress management plan based on the user's health data. The advice providing unit, for example, uses the generation AI to provide a personalized stress management plan based on the user's health data. For example, it proposes an optimal stress management plan based on body temperature data or emotional data. The advice providing unit also uses the generation AI to provide a personalized stress management plan based on the user's health data. For example, it proposes an optimal stress management plan based on lifestyle habit data or activity level data. The advice providing unit also uses the generation AI to provide a personalized stress management plan based on the user's health data. This allows the user to perform optimal stress management according to their own health condition. By providing a personalized stress management plan based on health data, the user can perform optimal stress management according to their own health condition.

[0071] The advice providing unit can use the emotion estimation function to provide personalized sleep improvement advice based on the user's emotional state and suggest a better sleeping environment. The advice providing unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and provide personalized sleep improvement advice based on the data. For example, it makes sleep improvement suggestions based on stress and relaxation levels. The advice providing unit also uses the emotion estimation function to provide personalized sleep improvement advice based on the user's emotional state. For example, it makes sleep improvement suggestions based on emotions such as joy and sadness. The advice providing unit also integrates the emotion estimation data and sleep data to make sleep improvement suggestions based on emotions. For example, it provides sleep improvement advice when the emotion score is high and suggests a better sleeping environment. In this way, by providing sleep improvement advice based on the emotional state, the user can achieve a better sleeping environment.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The basal body temperature measurement unit measures the user's basal body temperature. For example, the sensor technology installed in a smart watch or smart ring can be used to measure the user's basal body temperature with high accuracy. The basal body temperature measurement unit can also measure the user's body temperature daily and accumulate the data. The data analysis unit analyzes the basal body temperature data measured by the basal body temperature measurement unit. For example, the generation AI analyzes the user's body temperature data and provides important health indicators such as ovulation and menstrual periods in real time. The generation AI receives the user's body temperature data as input and performs analysis based on that data. The advice provision unit provides personalized advice based on the data analyzed by the data analysis unit. For example, the generation AI generates advice to support healthy lifestyle choices based on the user's body temperature data and menstrual cycle information. This allows the user to live a smarter and healthier life. As a result, the healthcare platform according to the embodiment can support health management by measuring and analyzing the user's basal body temperature with high accuracy and providing personalized advice.

[0074] For example, the basal body temperature measurement unit may improve sensor technology and add a function to measure skin humidity. This allows for an analysis of whether body temperature fluctuations are affected by humidity. For example, the difference in body temperature when humidity is high and when it is low may be compared. The basal body temperature measurement unit may also add a sensor to measure blood flow velocity and analyze the influence of blood flow as a factor in body temperature fluctuations. For example, the relationship between blood flow velocity and body temperature after exercise may be investigated. The basal body temperature measurement unit may also simultaneously measure skin humidity and blood flow velocity and use this data to perform a detailed analysis of the factors behind body temperature fluctuations. For example, the fluctuations in body temperature when humidity is high and blood flow is fast may be analyzed. This allows for a more detailed analysis of the factors behind basal body temperature fluctuations, improving the accuracy of health management.

[0075] The basal body temperature measurement unit analyzes regional differences in body temperature by, for example, placing sensors on the wrist and ankle and measuring body temperature simultaneously. For example, the difference in body temperature between the wrist and ankle is compared. The basal body temperature measurement unit also optimizes the placement of the sensors to measure body temperature at different parts of the body. For example, simultaneous measurements are taken at multiple parts such as the wrist, ankle, and forehead to analyze regional differences in body temperature. The basal body temperature measurement unit also integrates body temperature measurement data from different parts of the body and performs data analysis taking regional differences in body temperature into account. For example, body temperature data from the wrist and ankle is integrated to analyze overall body temperature fluctuations. This allows for more accurate health management by obtaining data that takes regional differences in body temperature into account.

[0076] The basal body temperature measurement unit, for example, uses an emotion estimation function to analyze the user's emotional state in real time and analyzes body temperature fluctuations based on that data. For example, it compares the difference in body temperature between a stressed state and a relaxed state. The basal body temperature measurement unit also uses the emotion estimation function to analyze the effect of the user's emotional state on body temperature. For example, it investigates how emotions such as joy and sadness affect body temperature. The basal body temperature measurement unit also integrates the emotion estimation data with the body temperature data to clarify the correlation between emotion and body temperature. For example, it analyzes body temperature fluctuations when the emotion score is high. This clarifies the correlation between emotion and body temperature, enabling more detailed health management.

[0077] For example, the basal body temperature measurement unit may develop smart piercings and add a function to measure earlobe temperature. This will allow for analysis of body temperature fluctuations based on earlobe temperature data. The basal body temperature measurement unit may also develop smart necklaces and add a function to measure neck temperature. This will allow for analysis of body temperature fluctuations based on neck temperature data. The basal body temperature measurement unit may also develop new wearable devices such as smart piercings and smart necklaces in addition to smart watches and smart rings, thereby expanding the diversity of body temperature measurement. This will allow for more detailed body temperature analysis by integrating body temperature data from multiple locations. This will expand the diversity of body temperature measurement, enabling more detailed health management.

[0078] The basal body temperature measurement unit, for example, integrates basal body temperature measurement data and heart rate data to evaluate the overall health condition. For example, it analyzes the relationship between body temperature and heart rate to evaluate the health condition. The basal body temperature measurement unit also integrates basal body temperature measurement data and activity level data to evaluate the overall health condition. For example, it analyzes the relationship between body temperature and activity level to evaluate the health condition. The basal body temperature measurement unit also integrates basal body temperature measurement data with other health data (for example, heart rate and activity level) to evaluate the overall health condition. This enables detailed health analysis based on multiple health indicators. This enables more detailed health management by evaluating the overall health condition.

[0079] For example, the basal body temperature measurement unit may develop an app equipped with an emotion estimation function, which records the stress and relaxation state felt by the user when measuring body temperature in real time. For example, the emotional state at the time of measurement is input into the app. The basal body temperature measurement unit may also use the emotion estimation function to analyze the user's emotional state in real time and record the emotional state at the time of body temperature measurement based on that data. For example, it may automatically distinguish between a stress state and a relaxation state. The basal body temperature measurement unit may also develop an app equipped with an emotion estimation function, which records the stress and relaxation state felt by the user when measuring body temperature in real time. This allows for a detailed analysis of the relationship between the emotional state and body temperature. This allows for a detailed analysis of the relationship between the emotion and body temperature by recording the emotional state at the time of body temperature measurement.

[0080] The data analysis unit, for example, uses the generation AI to analyze long-term trends in basal body temperature data and predict future health risks. For example, future health risks are predicted based on body temperature data from the past few months. The data analysis unit also uses the generation AI to analyze long-term trends in basal body temperature data and predict future health risks. For example, it analyzes body temperature fluctuation patterns and predicts future health risks. The data analysis unit also uses the generation AI to analyze long-term trends in basal body temperature data and predict future health risks. This allows the user to understand future health risks in advance and take preventive measures. This allows preventive measures to be taken by analyzing long-term trends and predicting future health risks.

[0081] The data analysis unit, for example, uses a generating AI to analyze the relationship between the user's lifestyle habits (e.g., diet and exercise) and body temperature fluctuations. For example, it analyzes the relationship between dietary content and body temperature and makes specific suggestions for improving the lifestyle. The data analysis unit also uses a generating AI to analyze the relationship between the user's lifestyle habits (e.g., diet and exercise) and body temperature fluctuations. For example, it analyzes the relationship between the amount of exercise and body temperature and makes specific suggestions for improving the lifestyle. The data analysis unit also uses a generating AI to analyze the relationship between the user's lifestyle habits (e.g., diet and exercise) and body temperature fluctuations and makes specific suggestions for improving the lifestyle. This allows the user to improve their lifestyle and live a healthy life. This supports a healthy lifestyle by analyzing the relationship between lifestyle habits and body temperature fluctuations and making specific suggestions for improvement.

[0082] The data analysis unit, for example, uses the generation AI to compare the user's body temperature data with the data of other users to discover common health patterns. For example, it compares the body temperature data of users in the same age group. The data analysis unit also uses the generation AI to compare the user's body temperature data with the data of other users to discover common health patterns. For example, it compares the body temperature data of users with the same lifestyle habits. The data analysis unit also uses the generation AI to compare the user's body temperature data with the data of other users to discover common health patterns. This allows the user to compare their own health condition with other users and use it as reference. By discovering common health patterns, the user can compare their own health condition with other users and use it as reference.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The basal body temperature measurement unit measures the user's basal body temperature. For example, the sensor technology installed in a smart watch or smart ring can be used to measure the user's basal body temperature with high accuracy. The basal body temperature measurement unit can also measure the user's body temperature every day and accumulate that data. Step 2: The data analysis unit analyzes the basal body temperature data measured by the basal body temperature measurement unit. For example, the generation AI analyzes the user's body temperature data and provides important health indicators such as ovulation and menstruation in real time. The generation AI receives the user's body temperature data as input and performs analysis based on that data. Step 3: The advice provider provides personalized advice based on the data analyzed by the data analyzer. For example, the generator AI generates advice to support healthy lifestyle choices based on the user's body temperature data and menstrual cycle information. This allows the user to live a smarter, healthier life.

[0085] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0086] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0087] Furthermore, the processing by the data processing system 10 described above 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 necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 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.

[0090] 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, RAM 30, and 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.

[0091] 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.

[0092] 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 audio according to instructions from the processor 46.

[0093] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0094] 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 carried out in a secure state.

[0095] Fig. 4 shows an example of the 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.

[0096] 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.

[0097] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0098] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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. The smart glasses 214 also 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 perform processing similar to that of the specific processing unit 290 using these models.

[0099] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0101] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, 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, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0105] 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, RAM 30, and 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.

[0106] 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.

[0107] 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 audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] 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 carried out in a secure state.

[0110] 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.

[0111] 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.

[0112] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 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 specific processing unit 290 using these models.

[0114] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] 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 audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0116] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] 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 also 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. Furthermore, 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, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The 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.

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

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

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

[0125] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the 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.

[0126] 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.

[0127] 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.

[0128] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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. 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.

[0130] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] 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 voice indicating a user input regarding 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.

[0132] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, 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.

[0134] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0135] FIG. 9 illustrates 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. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0136] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically 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.

[0137] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0138] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0139] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0140] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of 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, "relieved," "calm," and "reassuring," have similar emotion values.

[0141] 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.

[0142] 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 USB (Universal Serial Bus) 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.

[0143] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0144] 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.

[0145] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0146] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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.

[0147] As an example of a system configured with a single 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 system that uses a processor that realizes the functions of an 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.

[0148] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0149] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0150] The above-described description and illustrations are a detailed explanation 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, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects 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 replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0151] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0152] 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 basal body temperature measurement unit for measuring basal body temperature; a data analysis unit that analyzes the basal body temperature data measured by the basal body temperature measurement unit; an advice providing unit that provides personalized advice based on the data analyzed by the data analysis unit; A system characterized by:

2. The basal body temperature measuring unit Simultaneously measure skin humidity and blood flow rate to analyze in detail the factors that cause fluctuations in basal body temperature 2. The system of claim 1.

3. The basal body temperature measuring unit Simultaneous measurements are taken at different body parts to obtain data that takes into account regional differences in body temperature 2. The system of claim 1.

4. The basal body temperature measuring unit Analyzing the effect of a user's emotional state on body temperature and clarifying the correlation between emotions and body temperature 2. The system of claim 1.

5. The basal body temperature measuring unit In addition to smart watches and smart rings, we will develop new wearable devices such as smart earrings and smart necklaces to expand the variety of body temperature measurement methods.

2. The system of claim 1.

6. The basal body temperature measuring unit Integrate your basal body temperature data with other health data to assess your overall health 2. The system of claim 1.

7. The basal body temperature measuring unit Develop an app with emotion estimation functionality to record the stress and relaxation state felt by users in real time when measuring their body temperature.

2. The system of claim 1.

8. The data analysis unit Generative AI is used to analyze long-term trends in the basal body temperature data and predict future health risks.

2. The system of claim 1.

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