system

Abstract

The system according to the embodiment aims to analyze data on the user's body temperature and health condition and provide appropriate advice. [Solution] A system according to an embodiment includes a reception unit, a storage unit, an analysis unit, and a provision unit. The reception unit receives input of body temperature and health condition from a user. The storage unit stores the data received by the reception unit. The analysis unit analyzes the data stored by the storage unit. The provision unit provides advice based on the results of the analysis by the analysis unit.

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 technologies do not adequately manage users' body temperature and health condition data efficiently and provide appropriate advice, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze data on the user's body temperature and health condition and provide appropriate advice. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a storage unit, an analysis unit, and a provision unit. The reception unit receives input of body temperature and health condition from a user. The storage unit stores the data received by the reception unit. The analysis unit analyzes the data stored by the storage unit. The provision unit provides advice based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze data on the user's body temperature and health condition and provide appropriate advice. [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) In a health management system according to an embodiment of the present invention, a user inputs their body temperature and health status into a generating AI every day, and the data is stored. In this health management system, the generating AI analyzes the stored data and determines the user's health status. For example, if the user's body temperature is higher than normal or if an abnormality in their health status is detected, the generating AI provides appropriate advice. This allows the user to understand their health status on a daily basis and take necessary measures. For example, types of data input by the user may include body temperature, heart rate, blood pressure, sleep time, and dietary details. This data is input daily and stored in the generating AI. Possible analysis methods for the generating AI include comparison with past data, outlier detection, and trend analysis. For example, if the body temperature is higher than normal or the heart rate is abnormally high, the generating AI detects the abnormality and provides appropriate advice. Possible advice content includes specific instructions such as "Your body temperature is high today, so please get plenty of rest" or advice such as "Your heart rate is high, so we recommend that you consult a doctor." The advice is also provided via notifications to the user's smartphone or computer. Furthermore, a function will be added that visually displays the user's health condition in graphs and charts based on the data analyzed by the generative AI. This will allow users to understand their health condition at a glance. The data input by users can be entered via a smartphone app or website. This allows users to easily input data and have it stored in the generative AI. This will enable the health management system to efficiently manage the user's body temperature and health condition and provide appropriate advice.

[0029] A health management system according to an embodiment includes a reception unit, a storage unit, an analysis unit, and a provision unit. The reception unit receives input of a user's body temperature and health condition. Data input by the user includes, but is not limited to, body temperature, heart rate, blood pressure, sleep time, and dietary details. The reception unit can receive data, for example, through a smartphone app or a website. The reception unit can also receive data using voice input or sensor input. For example, the reception unit can receive body temperature data input by the user through a smartphone app. The reception unit can also receive heart rate data input by the user through a website. The reception unit can also convert a user's dictated health condition data into text data and accept the text data. The storage unit stores the data received by the reception unit. The storage unit can store data using, for example, a database. The storage unit can also store data using cloud storage. For example, the storage unit stores body temperature data in a database. The storage unit can also store heart rate data in cloud storage. The analysis unit analyzes the data stored by the storage unit. The analysis unit, for example, compares data with past data, detects outliers, and analyzes trends. For example, the analysis unit compares data with past body temperature data to detect outliers. The analysis unit can also analyze trends in heart rate data. The analysis unit can also analyze data using a generation AI. For example, the analysis unit inputs body temperature data into the generation AI to detect outliers. The analysis unit can also input heart rate data into the generation AI to analyze trends. The provision unit provides advice based on the results of the analysis by the analysis unit. For example, if the body temperature is higher than normal or the heart rate is abnormally high, the provision unit provides specific instructions or advice to consult a doctor. For example, the provision unit provides specific instructions such as, "Your body temperature is high today, so please take sufficient rest." The provision unit can also provide advice such as, "Your heart rate is high, so we recommend that you consult a doctor." The provision unit can also send notifications to the user's smartphone or computer. For example, the provision unit notifies the user of the advice through a smartphone app.The providing unit can also notify the user of advice via a website. This allows the health management system according to the embodiment to efficiently manage the user's body temperature and health condition and provide appropriate advice.

[0030] The reception unit can receive data on body temperature, heart rate, blood pressure, sleep time, and dietary details. The reception unit, for example, receives body temperature data. The body temperature data is generally input in degrees Celsius. For example, a user inputs their body temperature through a smartphone app. The reception unit can also receive heart rate data. The heart rate data is generally input in beats per minute. For example, a user inputs their heart rate through a website. The reception unit can also receive blood pressure data. The blood pressure data is generally input in mmHg. For example, a user inputs their blood pressure using voice input. The reception unit can also receive sleep time data. The sleep time data is generally input in hours. For example, a user inputs their sleep time through a smartphone app. The reception unit can also receive dietary details data. The dietary details data is generally input in calories. For example, a user inputs their dietary details through a website. By receiving a variety of health data, the user's health condition can be comprehensively managed. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit may input data entered by a user into the generation AI and automatically convert the data format.

[0031] The analysis unit can compare data with past data, detect outliers, and analyze trends. The analysis unit, for example, compares data with past data. Comparison with past data is typically performed by comparing with an average value over a certain period of time or calculating a standard deviation. For example, the analysis unit compares the current body temperature data with the average body temperature data over the past week. The analysis unit can also detect outliers. Detection of outliers is typically performed by detecting deviations from the normal range or by using a statistical outlier detection algorithm. For example, the analysis unit detects a body temperature of 37.5°C or higher as an abnormal value. The analysis unit can also analyze trends. Trend analysis is typically performed by moving averages, regression analysis, time series analysis, or other methods. For example, the analysis unit analyzes the trend of heart rate data over the past month. This enables detection of outliers and trend analysis by comparing with past data. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input past data into the generation AI and have the generation AI detect outliers and analyze trends.

[0032] The providing unit can provide specific instructions or advice to consult a doctor when the body temperature is higher than normal or the heart rate is abnormally high. For example, the providing unit provides specific instructions when the body temperature is higher than normal. The specific instructions may include, for example, "Your body temperature is high today, so please get plenty of rest." The providing unit can also provide advice to consult a doctor when the heart rate is abnormally high. The advice to consult a doctor may include, for example, "Your heart rate is high, so we recommend that you consult a doctor." This supports the user's health management by providing specific instructions or advice to consult a doctor when an abnormality is detected. Some or all of the above-described processing by the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the results of the analysis by the analysis unit into the generation AI and cause the generation AI to generate advice.

[0033] The providing unit can send notifications to the user's smartphone or computer. The providing unit, for example, sends the advice via a smartphone app. Notifications are generally sent by push notification, email notification, alert sound, or other methods. For example, the providing unit sends a push notification via a smartphone app. The providing unit can also send the advice via a website. For example, the providing unit sends an email notification via a website. This allows the user to receive the advice in real time. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can cause the generation AI to execute the advice notification.

[0034] The analysis unit can visually display the user's health condition in graphs or charts based on the analysis results. The analysis unit, for example, displays the analysis results in a line graph. Line graphs are generally used to visually show changes in data over time. For example, the analysis unit may display body temperature data for the past week in a line graph. The analysis unit can also display data in a bar graph. Bar graphs are generally used to visually show comparisons of data between different categories. For example, the analysis unit may display heart rate data for different dates in a bar graph. The analysis unit can also display data in a pie chart. Pie charts are generally used to visually show the proportion of each part to the whole. For example, the analysis unit may display the calorie percentage of each meal in a pie chart. This allows the user to understand their health condition at a glance. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit may input the analysis results to a generation AI and have the generation AI generate graphs or charts.

[0035] The reception unit can analyze the user's past health data input history and select the optimal input method. For example, the reception unit prioritizes suggesting input methods (such as voice and text) that the user has frequently used in the past. For example, the reception unit prioritizes suggesting voice input that the user has frequently used in the past. The reception unit can also send notifications avoiding time periods when the user tends to forget to input data in the past. For example, the reception unit sends notifications avoiding time periods when the user tends to forget to input data in the past. The reception unit can also suggest optimal input items based on the type of data the user has previously input. For example, the reception unit prioritizes suggesting body temperature input based on body temperature data the user has previously input. This allows the analysis of the past input history to suggest the optimal input method for the user. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's past input history data into the generation AI and have the generation AI select the optimal input method.

[0036] The reception unit can customize input items based on the user's current lifestyle and areas of interest when entering data. For example, if the user is interested in exercise, the reception unit prioritizes displaying input items related to exercise. For example, if the reception unit determines that the user is interested in exercise, the reception unit prioritizes displaying input items related to exercise. Furthermore, if the user is on a diet, the reception unit can emphasize input items related to dietary content. For example, if the reception unit determines that the user is on a diet, the reception unit emphasizes input items related to dietary content. Furthermore, if the user is interested in stress management, the reception unit can add an input item related to stress levels. For example, if the reception unit determines that the user is interested in stress management, the reception unit adds an input item related to stress levels. This allows for more appropriate data entry by providing input items according to the user's lifestyle and areas of interest. Some or all of the above-described processing by the reception unit may be performed using, or without, a generation AI. For example, the reception unit may input data related to the user's lifestyle and areas of interest into the generation AI and have the generation AI customize the input items.

[0037] When inputting data, the reception unit can prioritize inputting highly relevant data in consideration of the user's geographical location information. For example, if the user is at a high altitude, the reception unit prompts the user to prioritize inputting data related to oxygen concentration. For example, if the reception unit determines that the user is at a high altitude, the reception unit prompts the user to prioritize inputting data related to oxygen concentration. The reception unit can also prompt the user to prioritize inputting data related to air quality if the user is in an urban area. For example, if the reception unit determines that the user is in an urban area, the reception unit prompts the user to prioritize inputting data related to air quality. The reception unit can also prompt the user to prioritize inputting data related to distance traveled and number of steps if the user is traveling. For example, if the reception unit determines that the user is traveling, the reception unit prompts the user to prioritize inputting data related to distance traveled and number of steps. This enables more appropriate data input by inputting highly relevant data based on the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed, for example, using a generation AI or without using a generation AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to prioritize the input of highly relevant data.

[0038] The reception unit can analyze the user's social media activity and input relevant data when inputting data. For example, if the user posts about exercise on social media, the reception unit prompts the user to input data about exercise first. For example, if the reception unit determines that the user posts about exercise on social media, the reception unit prompts the user to input data about exercise first. Furthermore, if the user posts about food, the reception unit can also prompt the user to input data about the meal content first. For example, if the reception unit determines that the user posts about food, the reception unit prompts the user to input data about the meal content first. Furthermore, if the user posts about stress, the reception unit can also prompt the user to input data about stress levels first. For example, if the reception unit determines that the user posts about stress, the reception unit prompts the user to input data about stress levels first. This enables more appropriate data input by inputting relevant data based on the user's social media activity. Some or all of the above-described processing by the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's social media activity data into the generation AI and cause the generation AI to input relevant data.

[0039] When storing data, the storage unit can determine the priority of storage by taking into account the relevance to past data. For example, the storage unit prioritizes storage of data that is highly relevant to past data. For example, the storage unit prioritizes storage of new body temperature data that is highly relevant to past body temperature data. The storage unit can also postpone storage of data that is less relevant to past data. For example, the storage unit postpones storage of new data that is less relevant to past heart rate data. The storage unit can also group and store data that is relevant to past data. For example, the storage unit groups and stores new data that is relevant to past blood pressure data. This enables more efficient data management by taking into account the relevance to past data. Some or all of the above-described processing in the storage unit may be performed using, or without, a generation AI. For example, the storage unit can input past data into the generation AI and have the generation AI determine the priority of storage.

[0040] The storage unit can apply different storage algorithms depending on the type of data when storing data. For example, the storage unit stores body temperature data as time-series data. For example, the storage unit stores body temperature data as time-series data. The storage unit can also store heart rate data as real-time data. For example, the storage unit stores heart rate data as real-time data. The storage unit can also store meal content data by category. For example, the storage unit stores meal content data by category. This enables more efficient data management by applying a storage algorithm depending on the type of data. Some or all of the above-mentioned processing in the storage unit may be performed using, or without, a generation AI. For example, the storage unit can input the type of data to the generation AI and cause the generation AI to apply a storage algorithm.

[0041] When storing data, the storage unit can determine the priority of storage based on the time of data submission. For example, the storage unit prioritizes storing recently submitted data. For example, the storage unit prioritizes storing recently submitted body temperature data. The storage unit can also postpone storing older data. For example, the storage unit postpones storing older heart rate data. The storage unit can also group and store data based on the time of submission. For example, the storage unit groups and stores blood pressure data based on the time of submission. This enables more efficient data management by determining the priority of storage based on the time of data submission. Some or all of the above-mentioned processing in the storage unit may be performed using, or without, the generation AI. For example, the storage unit can input the time of data submission to the generation AI and have the generation AI determine the priority of storage.

[0042] When storing data, the storage unit can adjust the order of storage based on the relevance of the data. For example, the storage unit prioritizes storage of highly relevant data. For example, the storage unit prioritizes storage of highly relevant body temperature data. The storage unit can also postpone storage of less relevant data. For example, the storage unit postpones storage of less relevant heart rate data. The storage unit can also group and store data based on relevance. For example, the storage unit groups and stores highly relevant blood pressure data. This enables more efficient data management by adjusting the order of storage based on the relevance of the data. Some or all of the above-described processing in the storage unit may be performed using, or without, a generation AI. For example, the storage unit can input the relevance of the data into the generation AI and have the generation AI adjust the order of storage.

[0043] The analysis unit can improve the accuracy of the analysis by taking into account the correlation between data during analysis. The analysis unit, for example, analyzes the correlation between body temperature and heart rate. For example, the analysis unit analyzes the correlation between body temperature and heart rate. The analysis unit can also analyze the correlation between sleep time and stress level. For example, the analysis unit analyzes the correlation between sleep time and stress level. The analysis unit can also analyze the correlation between dietary content and body weight. For example, the analysis unit analyzes the correlation between dietary content and body weight. In this way, by taking into account the correlation between data, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit may input the correlation between data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0044] The analysis unit can perform the analysis while taking into account the attribute information of the data submitter. The analysis unit, for example, adjusts the analysis criteria based on the user's age. For example, the analysis unit adjusts the analysis criteria based on the user's age. The analysis unit can also adjust the analysis criteria based on the user's gender. For example, the analysis unit adjusts the analysis criteria based on the user's gender. The analysis unit can also adjust the analysis criteria based on the user's lifestyle. For example, the analysis unit adjusts the analysis criteria based on the user's lifestyle. This enables more appropriate analysis by taking into account the attribute information of the data submitter. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the user's attribute information into the generation AI and have the generation AI adjust the analysis criteria.

[0045] The analysis unit can take the geographical distribution of data into consideration when performing the analysis. For example, if the user lives in a high altitude, the analysis unit prioritizes data related to oxygen concentration. For example, if the analysis unit determines that the user lives in a high altitude, the analysis prioritizes data related to oxygen concentration. Furthermore, if the user lives in an urban area, the analysis can also prioritize data related to air quality. For example, if the analysis unit determines that the user lives in an urban area, the analysis prioritizes data related to air quality. Furthermore, if the user is traveling, the analysis can also prioritize data related to distance traveled and number of steps. For example, if the analysis unit determines that the user is traveling, the analysis prioritizes data related to distance traveled and number of steps. This enables more appropriate analysis by taking the geographical distribution of data into consideration. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input the user's geographical distribution data into the generation AI and have the generation AI perform the analysis.

[0046] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to the data. The analysis unit, for example, performs the analysis by referring to the latest research paper on body temperature. For example, the analysis unit performs the analysis by referring to the latest research paper on body temperature. The analysis unit can also perform the analysis by referring to the latest research paper on heart rate. For example, the analysis unit performs the analysis by referring to the latest research paper on heart rate. The analysis unit can also perform the analysis by referring to the latest research paper on sleep duration. For example, the analysis unit performs the analysis by referring to the latest research paper on sleep duration. In this way, by referring to literature related to the data, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input related literature into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0047] When providing advice, the providing unit can adjust the level of detail of the advice based on the importance of the data. For example, if the body temperature is higher than normal, the providing unit provides detailed advice on how to rest. For example, if the providing unit determines that the body temperature is higher than normal, the providing unit provides detailed advice on how to rest. Furthermore, if the heart rate is abnormally high, the providing unit can advise the user to consult a doctor. For example, if the providing unit determines that the heart rate is abnormally high, the providing unit advises the user to consult a doctor. Furthermore, if the user's sleep time is insufficient, the providing unit can advise specific ways to improve sleep. For example, if the providing unit determines that the user's sleep time is insufficient, the providing unit advises specific ways to improve sleep. In this way, by adjusting the level of detail of the advice based on the importance of the data, more appropriate advice can be provided. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit may input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the advice.

[0048] When providing advice, the providing unit can apply different advice algorithms depending on the data category. The providing unit provides advice regarding body temperature management based on, for example, body temperature data. For example, the providing unit provides advice regarding body temperature management based on body temperature data. The providing unit can also provide advice regarding heart rate management based on heart rate data. For example, the providing unit provides advice regarding heart rate management based on heart rate data. The providing unit can also provide advice regarding nutritional balance based on dietary content data. For example, the providing unit provides advice regarding nutritional balance based on dietary content data. In this way, more appropriate advice can be provided by applying an advice algorithm according to the data category. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the data category to the generation AI and cause the generation AI to apply the advice algorithm.

[0049] When providing advice, the providing unit can determine the priority of advice based on the time of data submission. The providing unit, for example, prioritizes providing advice based on recently submitted data. For example, the providing unit prioritizes providing advice based on recently submitted body temperature data. The providing unit can also provide advice based on older data at a later date. For example, the providing unit can provide advice based on older heart rate data at a later date. The providing unit can also adjust the order of advice based on the time of submission. For example, the providing unit adjusts the order of advice based on the time of submission. In this way, more appropriate advice is provided by determining the priority of advice based on the time of data submission. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can input the time of data submission to the generation AI and cause the generation AI to determine the priority of advice.

[0050] When providing advice, the providing unit can adjust the order of advice based on the relevance of data. The providing unit, for example, provides advice preferentially based on highly relevant data. For example, the providing unit provides advice preferentially based on highly relevant body temperature data. The providing unit can also provide advice later based on less relevant data. For example, the providing unit provides advice later based on less relevant heart rate data. The providing unit can also adjust the order of advice based on relevance. For example, the providing unit adjusts the order of advice based on highly relevant data. As a result, more appropriate advice is provided by adjusting the order of advice based on the relevance of data. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit may input the relevance of data to the generation AI and cause the generation AI to adjust the order of advice.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] When accepting the user's health data, the accepting unit can analyze the user's past input history and suggest the optimal input method. For example, it can prioritize and suggest input methods (voice, text, etc.) that the user has frequently used in the past. It can also send notifications that avoid time periods when the user tends to forget to enter data in the past. Furthermore, it can also suggest the optimal input items based on the types of data the user has entered in the past. In this way, it is possible to suggest the optimal input method for the user by analyzing the past input history.

[0053] The analysis unit can improve the accuracy of the analysis by taking into account the correlations between data during analysis. For example, it can analyze the correlation between body temperature and heart rate. It can also analyze the correlation between sleep time and stress level. It can also analyze the correlation between dietary content and weight. In this way, the accuracy of the analysis can be improved by taking into account the correlations between data.

[0054] When providing advice, the providing unit can adjust the level of detail of the advice based on the importance of the data. For example, if the body temperature is higher than normal, detailed advice on how to rest can be provided. Also, if the heart rate is abnormally high, advice can be given to consult a doctor. Furthermore, if the amount of sleep is insufficient, specific advice on how to improve sleep can be provided. In this way, by adjusting the level of detail of the advice based on the importance of the data, more appropriate advice can be provided.

[0055] When storing data, the storage unit can determine the priority of storage by taking into account the relevance to past data. For example, data that is highly relevant to past data can be stored preferentially. Data that is less relevant to past data can also be stored later. Furthermore, data that is relevant to past data can be grouped and stored. This allows for more efficient data management by taking into account the relevance to past data.

[0056] When providing advice, the providing unit can apply different advice algorithms depending on the data category. For example, advice on body temperature management can be provided based on body temperature data. Advice on heart rate management can also be provided based on heart rate data. Furthermore, advice on nutritional balance can be provided based on dietary content data. In this way, more appropriate advice can be provided by applying an advice algorithm depending on the data category.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The reception unit accepts input of body temperature and health conditions from the user. Data entered by the user includes body temperature, heart rate, blood pressure, sleep time, dietary details, etc. The reception unit can accept data via a smartphone app or website, and can also accept data using voice input or sensor input. Step 2: The storage unit stores the data accepted by the acceptance unit. The storage unit can store the data using a database or cloud storage. Step 3: The analysis unit analyzes the data stored by the storage unit. The analysis unit can compare data with past data, detect outliers, and analyze trends. It can also analyze the data using generative AI. Step 4: The provider provides advice based on the results of the analysis by the analyzer. If the body temperature is higher than normal or the heart rate is abnormally high, the provider provides specific instructions or advice to consult a doctor. The provider can also send a notification to the user's smartphone or computer.

[0059] (Example 2) In a health management system according to an embodiment of the present invention, a user inputs their body temperature and health status into a generating AI every day, and the data is stored. In this health management system, the generating AI analyzes the stored data and determines the user's health status. For example, if the user's body temperature is higher than normal or if an abnormality in their health status is detected, the generating AI provides appropriate advice. This allows the user to understand their health status on a daily basis and take necessary measures. For example, types of data input by the user may include body temperature, heart rate, blood pressure, sleep time, and dietary details. This data is input daily and stored in the generating AI. Possible analysis methods for the generating AI include comparison with past data, outlier detection, and trend analysis. For example, if the body temperature is higher than normal or the heart rate is abnormally high, the generating AI detects the abnormality and provides appropriate advice. Possible advice content includes specific instructions such as "Your body temperature is high today, so please get plenty of rest" or advice such as "Your heart rate is high, so we recommend that you consult a doctor." The advice is also provided via notifications to the user's smartphone or computer. Furthermore, a function will be added that visually displays the user's health condition in graphs and charts based on the data analyzed by the generative AI. This will allow users to understand their health condition at a glance. The data input by users can be entered via a smartphone app or website. This allows users to easily input data and have it stored in the generative AI. This will enable the health management system to efficiently manage the user's body temperature and health condition and provide appropriate advice.

[0060] A health management system according to an embodiment includes a reception unit, a storage unit, an analysis unit, and a provision unit. The reception unit receives input of a user's body temperature and health condition. Data input by the user includes, but is not limited to, body temperature, heart rate, blood pressure, sleep time, and dietary details. The reception unit can receive data, for example, through a smartphone app or a website. The reception unit can also receive data using voice input or sensor input. For example, the reception unit can receive body temperature data input by the user through a smartphone app. The reception unit can also receive heart rate data input by the user through a website. The reception unit can also convert a user's dictated health condition data into text data and accept the text data. The storage unit stores the data received by the reception unit. The storage unit can store data using, for example, a database. The storage unit can also store data using cloud storage. For example, the storage unit stores body temperature data in a database. The storage unit can also store heart rate data in cloud storage. The analysis unit analyzes the data stored by the storage unit. The analysis unit, for example, compares data with past data, detects outliers, and analyzes trends. For example, the analysis unit compares data with past body temperature data to detect outliers. The analysis unit can also analyze trends in heart rate data. The analysis unit can also analyze data using a generation AI. For example, the analysis unit inputs body temperature data into the generation AI to detect outliers. The analysis unit can also input heart rate data into the generation AI to analyze trends. The provision unit provides advice based on the results of the analysis by the analysis unit. For example, if the body temperature is higher than normal or the heart rate is abnormally high, the provision unit provides specific instructions or advice to consult a doctor. For example, the provision unit provides specific instructions such as, "Your body temperature is high today, so please take sufficient rest." The provision unit can also provide advice such as, "Your heart rate is high, so we recommend that you consult a doctor." The provision unit can also send notifications to the user's smartphone or computer. For example, the provision unit notifies the user of the advice through a smartphone app.The providing unit can also notify the user of advice via a website. This allows the health management system according to the embodiment to efficiently manage the user's body temperature and health condition and provide appropriate advice.

[0061] The reception unit can receive data on body temperature, heart rate, blood pressure, sleep time, and dietary details. The reception unit, for example, receives body temperature data. The body temperature data is generally input in degrees Celsius. For example, a user inputs their body temperature through a smartphone app. The reception unit can also receive heart rate data. The heart rate data is generally input in beats per minute. For example, a user inputs their heart rate through a website. The reception unit can also receive blood pressure data. The blood pressure data is generally input in mmHg. For example, a user inputs their blood pressure using voice input. The reception unit can also receive sleep time data. The sleep time data is generally input in hours. For example, a user inputs their sleep time through a smartphone app. The reception unit can also receive dietary details data. The dietary details data is generally input in calories. For example, a user inputs their dietary details through a website. By receiving a variety of health data, the user's health condition can be comprehensively managed. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit may input data entered by a user into the generation AI and automatically convert the data format.

[0062] The analysis unit can compare data with past data, detect outliers, and analyze trends. The analysis unit, for example, compares data with past data. Comparison with past data is typically performed by comparing with an average value over a certain period of time or calculating a standard deviation. For example, the analysis unit compares the current body temperature data with the average body temperature data over the past week. The analysis unit can also detect outliers. Detection of outliers is typically performed by detecting deviations from the normal range or by using a statistical outlier detection algorithm. For example, the analysis unit detects a body temperature of 37.5°C or higher as an abnormal value. The analysis unit can also analyze trends. Trend analysis is typically performed by moving averages, regression analysis, time series analysis, or other methods. For example, the analysis unit analyzes the trend of heart rate data over the past month. This enables detection of outliers and trend analysis by comparing with past data. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input past data into the generation AI and have the generation AI detect outliers and analyze trends.

[0063] The providing unit can provide specific instructions or advice to consult a doctor when the body temperature is higher than normal or the heart rate is abnormally high. For example, the providing unit provides specific instructions when the body temperature is higher than normal. The specific instructions may include, for example, "Your body temperature is high today, so please get plenty of rest." The providing unit can also provide advice to consult a doctor when the heart rate is abnormally high. The advice to consult a doctor may include, for example, "Your heart rate is high, so we recommend that you consult a doctor." This supports the user's health management by providing specific instructions or advice to consult a doctor when an abnormality is detected. Some or all of the above-described processing by the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the results of the analysis by the analysis unit into the generation AI and cause the generation AI to generate advice.

[0064] The providing unit can send notifications to the user's smartphone or computer. The providing unit, for example, sends the advice via a smartphone app. Notifications are generally sent by push notification, email notification, alert sound, or other methods. For example, the providing unit sends a push notification via a smartphone app. The providing unit can also send the advice via a website. For example, the providing unit sends an email notification via a website. This allows the user to receive the advice in real time. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can cause the generation AI to execute the advice notification.

[0065] The analysis unit can visually display the user's health condition in graphs or charts based on the analysis results. The analysis unit, for example, displays the analysis results in a line graph. Line graphs are generally used to visually show changes in data over time. For example, the analysis unit may display body temperature data for the past week in a line graph. The analysis unit can also display data in a bar graph. Bar graphs are generally used to visually show comparisons of data between different categories. For example, the analysis unit may display heart rate data for different dates in a bar graph. The analysis unit can also display data in a pie chart. Pie charts are generally used to visually show the proportion of each part to the whole. For example, the analysis unit may display the calorie percentage of each meal in a pie chart. This allows the user to understand their health condition at a glance. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit may input the analysis results to a generation AI and have the generation AI generate graphs or charts.

[0066] The reception unit can estimate the user's emotions and adjust the timing of data input based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit sends a notification prompting the user to enter data during a time when the user can relax. For example, if the reception unit estimates that the user is feeling stressed, the reception unit sends a notification prompting the user to enter data during a time when the user can relax at night. The reception unit can also immediately send a notification prompting the user to enter data when the user is relaxed. For example, if the reception unit estimates that the user is relaxed, the reception unit immediately sends a notification prompting the user to enter data. The reception unit can also send a notification to postpone data input when the user is busy. For example, if the reception unit estimates that the user is busy, the reception unit sends a notification to postpone data input. This allows for more appropriate data input by adjusting the timing of data input according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit may input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of data input.

[0067] The reception unit can analyze the user's past health data input history and select the optimal input method. For example, the reception unit prioritizes suggesting input methods (such as voice and text) that the user has frequently used in the past. For example, the reception unit prioritizes suggesting voice input that the user has frequently used in the past. The reception unit can also send notifications avoiding time periods when the user tends to forget to input data in the past. For example, the reception unit sends notifications avoiding time periods when the user tends to forget to input data in the past. The reception unit can also suggest optimal input items based on the type of data the user has previously input. For example, the reception unit prioritizes suggesting body temperature input based on body temperature data the user has previously input. This allows the analysis of the past input history to suggest the optimal input method for the user. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's past input history data into the generation AI and have the generation AI select the optimal input method.

[0068] The reception unit can customize input items based on the user's current lifestyle and areas of interest when entering data. For example, if the user is interested in exercise, the reception unit prioritizes displaying input items related to exercise. For example, if the reception unit determines that the user is interested in exercise, the reception unit prioritizes displaying input items related to exercise. Furthermore, if the user is on a diet, the reception unit can emphasize input items related to dietary content. For example, if the reception unit determines that the user is on a diet, the reception unit emphasizes input items related to dietary content. Furthermore, if the user is interested in stress management, the reception unit can add an input item related to stress levels. For example, if the reception unit determines that the user is interested in stress management, the reception unit adds an input item related to stress levels. This allows for more appropriate data entry by providing input items according to the user's lifestyle and areas of interest. Some or all of the above-described processing by the reception unit may be performed using, or without, a generation AI. For example, the reception unit may input data related to the user's lifestyle and areas of interest into the generation AI and have the generation AI customize the input items.

[0069] The reception unit can estimate the user's emotions and prioritize input data based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit prompts the user to input data related to the stress level first. For example, if the reception unit estimates that the user is feeling stressed, the reception unit prompts the user to input data related to the stress level first. Furthermore, if the user is relaxed, the reception unit can prompt the user to input all data equally. For example, if the reception unit estimates that the user is relaxed, the reception unit prompts the user to input all data equally. Furthermore, if the user is tired, the reception unit can prompt the user to input data related to body temperature and sleep time first. For example, if the reception unit estimates that the user is tired, the reception unit prompts the user to input data related to body temperature and sleep time first. This enables more appropriate data input by prioritizing input data according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit may input user emotion data to the generation AI and have the generation AI determine the priority of the input data.

[0070] When inputting data, the reception unit can prioritize inputting highly relevant data in consideration of the user's geographical location information. For example, if the user is at a high altitude, the reception unit prompts the user to prioritize inputting data related to oxygen concentration. For example, if the reception unit determines that the user is at a high altitude, the reception unit prompts the user to prioritize inputting data related to oxygen concentration. The reception unit can also prompt the user to prioritize inputting data related to air quality if the user is in an urban area. For example, if the reception unit determines that the user is in an urban area, the reception unit prompts the user to prioritize inputting data related to air quality. The reception unit can also prompt the user to prioritize inputting data related to distance traveled and number of steps if the user is traveling. For example, if the reception unit determines that the user is traveling, the reception unit prompts the user to prioritize inputting data related to distance traveled and number of steps. This enables more appropriate data input by inputting highly relevant data based on the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed, for example, using a generation AI or without using a generation AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to prioritize the input of highly relevant data.

[0071] The reception unit can analyze the user's social media activity and input relevant data when inputting data. For example, if the user posts about exercise on social media, the reception unit prompts the user to input data about exercise first. For example, if the reception unit determines that the user posts about exercise on social media, the reception unit prompts the user to input data about exercise first. Furthermore, if the user posts about food, the reception unit can also prompt the user to input data about the meal content first. For example, if the reception unit determines that the user posts about food, the reception unit prompts the user to input data about the meal content first. Furthermore, if the user posts about stress, the reception unit can also prompt the user to input data about stress levels first. For example, if the reception unit determines that the user posts about stress, the reception unit prompts the user to input data about stress levels first. This enables more appropriate data input by inputting relevant data based on the user's social media activity. Some or all of the above-described processing by the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's social media activity data into the generation AI and cause the generation AI to input relevant data.

[0072] The storage unit can estimate the user's emotions and adjust the data storage method based on the estimated user emotions. For example, if the user is feeling stressed, the storage unit compresses and stores the data to reduce the storage burden. For example, if the storage unit estimates that the user is feeling stressed, the storage unit compresses and stores the data to reduce the storage burden. Furthermore, if the user is relaxed, the storage unit can store the data in detail so that it can be analyzed in detail later. For example, if the storage unit estimates that the user is relaxed, the storage unit can store the data in detail so that it can be analyzed in detail later. Furthermore, if the user is busy, the storage unit can temporarily store the data in a cache and then store it in detail later. For example, if the storage unit estimates that the user is busy, the storage unit can temporarily store the data in a cache and then store it in detail later. This enables more efficient data management by adjusting the data storage method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the storage unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the storage unit may input user emotion data into the generation AI and have the generation AI adjust the data storage method.

[0073] When storing data, the storage unit can determine the priority of storage by taking into account the relevance to past data. For example, the storage unit prioritizes storage of data that is highly relevant to past data. For example, the storage unit prioritizes storage of new body temperature data that is highly relevant to past body temperature data. The storage unit can also postpone storage of data that is less relevant to past data. For example, the storage unit postpones storage of new data that is less relevant to past heart rate data. The storage unit can also group and store data that is relevant to past data. For example, the storage unit groups and stores new data that is relevant to past blood pressure data. This enables more efficient data management by taking into account the relevance to past data. Some or all of the above-described processing in the storage unit may be performed using, or without, a generation AI. For example, the storage unit can input past data into the generation AI and have the generation AI determine the priority of storage.

[0074] The storage unit can apply different storage algorithms depending on the type of data when storing data. For example, the storage unit stores body temperature data as time-series data. For example, the storage unit stores body temperature data as time-series data. The storage unit can also store heart rate data as real-time data. For example, the storage unit stores heart rate data as real-time data. The storage unit can also store meal content data by category. For example, the storage unit stores meal content data by category. This enables more efficient data management by applying a storage algorithm depending on the type of data. Some or all of the above-mentioned processing in the storage unit may be performed using, or without, a generation AI. For example, the storage unit can input the type of data to the generation AI and cause the generation AI to apply a storage algorithm.

[0075] The storage unit can estimate the user's emotions and adjust the data storage frequency based on the estimated user emotions. For example, if the user is feeling stressed, the storage unit reduces the data storage frequency to reduce the storage burden. For example, if the storage unit estimates that the user is feeling stressed, the storage unit reduces the data storage frequency to reduce the storage burden. The storage unit can also increase the data storage frequency to store more detailed data if the user is relaxed. For example, if the storage unit estimates that the user is relaxed, the storage unit increases the data storage frequency to store more detailed data. The storage unit can also temporarily reduce the data storage frequency if the user is busy and store the data in more detail later. For example, if the storage unit estimates that the user is busy, the storage unit temporarily reduces the data storage frequency and stores the data in more detail later. This enables more efficient data management by adjusting the data storage frequency according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the storage unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the storage unit may input user emotion data into the generation AI and have the generation AI adjust the frequency of saving the data.

[0076] When storing data, the storage unit can determine the priority of storage based on the time of data submission. For example, the storage unit prioritizes storing recently submitted data. For example, the storage unit prioritizes storing recently submitted body temperature data. The storage unit can also postpone storing older data. For example, the storage unit postpones storing older heart rate data. The storage unit can also group and store data based on the time of submission. For example, the storage unit groups and stores blood pressure data based on the time of submission. This enables more efficient data management by determining the priority of storage based on the time of data submission. Some or all of the above-mentioned processing in the storage unit may be performed using, or without, the generation AI. For example, the storage unit can input the time of data submission to the generation AI and have the generation AI determine the priority of storage.

[0077] When storing data, the storage unit can adjust the order of storage based on the relevance of the data. For example, the storage unit prioritizes storage of highly relevant data. For example, the storage unit prioritizes storage of highly relevant body temperature data. The storage unit can also postpone storage of less relevant data. For example, the storage unit postpones storage of less relevant heart rate data. The storage unit can also group and store data based on relevance. For example, the storage unit groups and stores highly relevant blood pressure data. This enables more efficient data management by adjusting the order of storage based on the relevance of the data. Some or all of the above-described processing in the storage unit may be performed using, or without, a generation AI. For example, the storage unit can input the relevance of the data into the generation AI and have the generation AI adjust the order of storage.

[0078] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit prioritizes data related to the stress level. For example, if the analysis unit estimates that the user is feeling stressed, the analysis prioritizes data related to the stress level. Furthermore, if the user is relaxed, the analysis unit can analyze all data equally. For example, if the analysis unit estimates that the user is relaxed, the analysis unit analyzes all data equally. Furthermore, if the user is tired, the analysis unit can prioritize data related to body temperature and sleep time. For example, if the analysis unit estimates that the user is tired, the analysis prioritizes data related to body temperature and sleep time. This allows for more appropriate analysis by adjusting the analysis criteria according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can input the user's emotional data into the generation AI and have the generation AI adjust the analysis criteria.

[0079] The analysis unit can improve the accuracy of the analysis by taking into account the correlation between data during analysis. The analysis unit, for example, analyzes the correlation between body temperature and heart rate. For example, the analysis unit analyzes the correlation between body temperature and heart rate. The analysis unit can also analyze the correlation between sleep time and stress level. For example, the analysis unit analyzes the correlation between sleep time and stress level. The analysis unit can also analyze the correlation between dietary content and body weight. For example, the analysis unit analyzes the correlation between dietary content and body weight. In this way, by taking into account the correlation between data, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit may input the correlation between data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0080] The analysis unit can perform the analysis while taking into account the attribute information of the data submitter. The analysis unit, for example, adjusts the analysis criteria based on the user's age. For example, the analysis unit adjusts the analysis criteria based on the user's age. The analysis unit can also adjust the analysis criteria based on the user's gender. For example, the analysis unit adjusts the analysis criteria based on the user's gender. The analysis unit can also adjust the analysis criteria based on the user's lifestyle. For example, the analysis unit adjusts the analysis criteria based on the user's lifestyle. This enables more appropriate analysis by taking into account the attribute information of the data submitter. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the user's attribute information into the generation AI and have the generation AI adjust the analysis criteria.

[0081] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit provides a simple, highly visible display method. For example, if the analysis unit estimates that the user is feeling stressed, the analysis unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method including detailed information. For example, if the analysis unit estimates that the user is relaxed, the analysis unit provides a display method including detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. For example, if the analysis unit estimates that the user is in a hurry, the analysis unit provides a display method that focuses on the main points. This enables more appropriate information to be provided by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or without the generation AI. For example, the analysis unit can input the user's emotional data into the generation AI and have the generation AI adjust the way the analysis results are displayed.

[0082] The analysis unit can take the geographical distribution of data into consideration when performing the analysis. For example, if the user lives in a high altitude, the analysis unit prioritizes data related to oxygen concentration. For example, if the analysis unit determines that the user lives in a high altitude, the analysis prioritizes data related to oxygen concentration. Furthermore, if the user lives in an urban area, the analysis can also prioritize data related to air quality. For example, if the analysis unit determines that the user lives in an urban area, the analysis prioritizes data related to air quality. Furthermore, if the user is traveling, the analysis can also prioritize data related to distance traveled and number of steps. For example, if the analysis unit determines that the user is traveling, the analysis prioritizes data related to distance traveled and number of steps. This enables more appropriate analysis by taking the geographical distribution of data into consideration. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input the user's geographical distribution data into the generation AI and have the generation AI perform the analysis.

[0083] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to the data. The analysis unit, for example, performs the analysis by referring to the latest research paper on body temperature. For example, the analysis unit performs the analysis by referring to the latest research paper on body temperature. The analysis unit can also perform the analysis by referring to the latest research paper on heart rate. For example, the analysis unit performs the analysis by referring to the latest research paper on heart rate. The analysis unit can also perform the analysis by referring to the latest research paper on sleep duration. For example, the analysis unit performs the analysis by referring to the latest research paper on sleep duration. In this way, by referring to literature related to the data, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input related literature into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0084] The providing unit can estimate the user's emotions and adjust the way in which advice is expressed based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit provides advice in gentle terms. For example, if the providing unit estimates that the user is feeling stressed, the providing unit provides advice in gentle terms. The providing unit can also provide detailed advice if the user is relaxed. For example, if the providing unit estimates that the user is relaxed, the providing unit provides detailed advice. The providing unit can also provide concise and to-the-point advice if the user is in a hurry. For example, if the providing unit estimates that the user is in a hurry, the providing unit provides concise and to-the-point advice. In this way, by adjusting the way in which advice is expressed according to the user's emotions, more appropriate advice is provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the providing unit can input the user's emotional data into the generating AI and cause the generating AI to adjust the way the advice is expressed.

[0085] When providing advice, the providing unit can adjust the level of detail of the advice based on the importance of the data. For example, if the body temperature is higher than normal, the providing unit provides detailed advice on how to rest. For example, if the providing unit determines that the body temperature is higher than normal, the providing unit provides detailed advice on how to rest. Furthermore, if the heart rate is abnormally high, the providing unit can advise the user to consult a doctor. For example, if the providing unit determines that the heart rate is abnormally high, the providing unit advises the user to consult a doctor. Furthermore, if the user's sleep time is insufficient, the providing unit can advise specific ways to improve sleep. For example, if the providing unit determines that the user's sleep time is insufficient, the providing unit advises specific ways to improve sleep. In this way, by adjusting the level of detail of the advice based on the importance of the data, more appropriate advice can be provided. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit may input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the advice.

[0086] When providing advice, the providing unit can apply different advice algorithms depending on the data category. The providing unit provides advice regarding body temperature management based on, for example, body temperature data. For example, the providing unit provides advice regarding body temperature management based on body temperature data. The providing unit can also provide advice regarding heart rate management based on heart rate data. For example, the providing unit provides advice regarding heart rate management based on heart rate data. The providing unit can also provide advice regarding nutritional balance based on dietary content data. For example, the providing unit provides advice regarding nutritional balance based on dietary content data. In this way, more appropriate advice can be provided by applying an advice algorithm according to the data category. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the data category to the generation AI and cause the generation AI to apply the advice algorithm.

[0087] The providing unit can estimate the user's emotions and adjust the length of advice based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit provides short, concise advice to the point. For example, if the providing unit estimates that the user is feeling stressed, the providing unit provides short, concise advice to the point. The providing unit can also provide detailed advice if the user is relaxed. For example, if the providing unit estimates that the user is relaxed, the providing unit provides detailed advice. The providing unit can also provide concise, quick advice if the user is in a hurry. For example, if the providing unit estimates that the user is in a hurry, the providing unit provides concise, quick advice. In this way, by adjusting the length of advice according to the user's emotions, more appropriate advice is provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the providing unit can input the user's emotional data into the generating AI and cause the generating AI to adjust the length of the advice.

[0088] When providing advice, the providing unit can determine the priority of advice based on the time of data submission. The providing unit, for example, prioritizes providing advice based on recently submitted data. For example, the providing unit prioritizes providing advice based on recently submitted body temperature data. The providing unit can also provide advice based on older data at a later date. For example, the providing unit can provide advice based on older heart rate data at a later date. The providing unit can also adjust the order of advice based on the time of submission. For example, the providing unit adjusts the order of advice based on the time of submission. In this way, more appropriate advice is provided by determining the priority of advice based on the time of data submission. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can input the time of data submission to the generation AI and cause the generation AI to determine the priority of advice.

[0089] When providing advice, the providing unit can adjust the order of advice based on the relevance of data. The providing unit, for example, provides advice preferentially based on highly relevant data. For example, the providing unit provides advice preferentially based on highly relevant body temperature data. The providing unit can also provide advice later based on less relevant data. For example, the providing unit provides advice later based on less relevant heart rate data. The providing unit can also adjust the order of advice based on relevance. For example, the providing unit adjusts the order of advice based on highly relevant data. As a result, more appropriate advice is provided by adjusting the order of advice based on the relevance of data. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit may input the relevance of data to the generation AI and cause the generation AI to adjust the order of advice. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, storage unit, analysis unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives input of the user's body temperature and health condition through the reception device 38 of the smart device 14. The storage unit stores the data in the database 24 of the data processing device 12. The analysis unit analyzes the data using the specific processing unit 290 of the data processing device 12. The provision unit provides advice through the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, storage unit, analysis unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives input of the user's body temperature and health condition through the microphone 238 of the smart glasses 214. The storage unit stores the data in the database 24 of the data processing device 12. The analysis unit analyzes the data using the specific processing unit 290 of the data processing device 12. The provision unit provides advice through the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, storage unit, analysis unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit receives input of the user's body temperature and health condition through the microphone 238 of the headset type terminal 314. The storage unit stores the data in the database 24 of the data processing device 12. The analysis unit analyzes the data using the specific processing unit 290 of the data processing device 12. The provision unit provides advice through the speaker 240 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, storage unit, analysis unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives input of the user's body temperature and health condition through the microphone 238 of the robot 414. The storage unit stores the data in the database 24 of the data processing device 12. The analysis unit analyzes the data using the specific processing unit 290 of the data processing device 12. The provision unit provides advice through the speaker 240 of the robot 414.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] When accepting the user's health data, the accepting unit can analyze the user's past input history and suggest the optimal input method. For example, it can prioritize and suggest input methods (voice, text, etc.) that the user has frequently used in the past. It can also send notifications that avoid time periods when the user tends to forget to enter data in the past. Furthermore, it can also suggest the optimal input items based on the types of data the user has entered in the past. In this way, it is possible to suggest the optimal input method for the user by analyzing the past input history.

[0092] The reception unit can estimate the user's emotions and adjust the timing of data input based on the estimated user emotions. For example, if the user is feeling stressed, a notification urging the user to input data during a time when the user is able to relax can be sent. Also, if the user is relaxed, a notification urging the user to input data can be sent immediately. Furthermore, if the user is busy, a notification can be sent to postpone data input. This allows for more appropriate data input by adjusting the timing of data input according to the user's emotions.

[0093] The analysis unit can improve the accuracy of the analysis by taking into account the correlations between data during analysis. For example, it can analyze the correlation between body temperature and heart rate. It can also analyze the correlation between sleep time and stress level. It can also analyze the correlation between dietary content and weight. In this way, the accuracy of the analysis can be improved by taking into account the correlations between data.

[0094] The providing unit can estimate the user's emotions and adjust the way in which advice is expressed based on the estimated user's emotions. For example, if the user is feeling stressed, the advice can be provided in gentle words. If the user is relaxed, detailed advice can be provided. Furthermore, if the user is in a hurry, concise advice that gets to the point can be provided. In this way, by adjusting the way in which advice is expressed according to the user's emotions, more appropriate advice can be provided.

[0095] When providing advice, the providing unit can adjust the level of detail of the advice based on the importance of the data. For example, if the body temperature is higher than normal, detailed advice on how to rest can be provided. Also, if the heart rate is abnormally high, advice can be given to consult a doctor. Furthermore, if the amount of sleep is insufficient, specific advice on how to improve sleep can be provided. In this way, by adjusting the level of detail of the advice based on the importance of the data, more appropriate advice can be provided.

[0096] The storage unit can estimate the user's emotions and adjust the data storage method based on the estimated user emotions. For example, if the user is feeling stressed, the data can be compressed and stored to reduce storage load. Alternatively, if the user is relaxed, the data can be stored in detail for later detailed analysis. Furthermore, if the user is busy, the data can be temporarily cached and then stored in detail later. This allows for more efficient data management by adjusting the data storage method according to the user's emotions.

[0097] When storing data, the storage unit can determine the priority of storage by taking into account the relevance to past data. For example, data that is highly relevant to past data can be stored preferentially. Data that is less relevant to past data can also be stored later. Furthermore, data that is relevant to past data can be grouped and stored. This allows for more efficient data management by taking into account the relevance to past data.

[0098] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. For example, if the user is feeling stressed, the analysis can prioritize data related to stress levels. Alternatively, if the user is relaxed, all data can be analyzed equally. Furthermore, if the user is tired, the analysis can prioritize data related to body temperature and sleep time. This allows for more appropriate analysis by adjusting the analysis criteria according to the user's emotions.

[0099] When providing advice, the providing unit can apply different advice algorithms depending on the data category. For example, advice on body temperature management can be provided based on body temperature data. Advice on heart rate management can also be provided based on heart rate data. Furthermore, advice on nutritional balance can be provided based on dietary content data. In this way, more appropriate advice can be provided by applying an advice algorithm depending on the data category.

[0100] The providing unit can estimate the user's emotions and adjust the length of advice based on the estimated user's emotions. For example, if the user is feeling stressed, short and to the point advice can be provided. If the user is relaxed, detailed advice can be provided. Furthermore, if the user is in a hurry, concise and quick advice can be provided. In this way, more appropriate advice can be provided by adjusting the length of advice according to the user's emotions.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The reception unit accepts input of body temperature and health conditions from the user. Data entered by the user includes body temperature, heart rate, blood pressure, sleep time, dietary details, etc. The reception unit can accept data via a smartphone app or website, and can also accept data using voice input or sensor input. Step 2: The storage unit stores the data accepted by the acceptance unit. The storage unit can store the data using a database or cloud storage. Step 3: The analysis unit analyzes the data stored by the storage unit. The analysis unit can compare data with past data, detect outliers, and analyze trends. It can also analyze the data using generative AI. Step 4: The provider provides advice based on the results of the analysis by the analyzer. If the body temperature is higher than normal or the heart rate is abnormally high, the provider provides specific instructions or advice to consult a doctor. The provider can also send a notification to the user's smartphone or computer.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0151] 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.

[0152] 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.

[0153] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input of body temperature and health condition from a user; a storage unit that stores the data accepted by the accepting unit; an analysis unit that analyzes the data stored by the storage unit; a providing unit that provides advice based on the results of the analysis by the analyzing unit. A system characterized by:

2. The reception unit Accepts data on body temperature, heart rate, blood pressure, sleep time, and dietary details 2. The system of claim 1.

3. The analysis unit Compare with historical data, detect outliers, and analyze trends 2. The system of claim 1.

4. The providing unit Providing specific instructions and advice to consult a doctor if your body temperature is higher than normal or your heart rate is abnormally high 2. The system of claim 1.

5. The providing unit Send notifications to users' smartphones and computers 2. The system of claim 1.

6. The analysis unit Visually display the user's health status in graphs and charts based on the analysis results 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the timing of data input based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past health data entry history and select the entry method 2. The system of claim 1.

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