system

Abstract

The system according to the embodiment aims to detect changes in a woman's physical condition based on data such as her basal body temperature and menstrual period, and provide appropriate advice. [Solution] A system according to an embodiment includes a reception unit, a detection unit, an advice unit, and a provision unit. The reception unit inputs the user's basal body temperature or menstrual period. The detection unit detects changes in physical condition based on the data input by the reception unit. The advice unit provides advice based on the changes in physical condition detected by the detection unit. The provision unit provides the data input by the reception unit to a doctor.

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 efficiently manage data such as a woman's basal body temperature and menstrual period, and do not adequately detect changes in her physical condition, so there is room for improvement.

[0005] The system according to the embodiment aims to detect changes in a woman's physical condition based on data such as her basal body temperature and menstrual period, and provide appropriate advice. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a detection unit, an advice unit, and a provision unit. The reception unit inputs the user's basal body temperature or menstrual period. The detection unit detects changes in physical condition based on the data input by the reception unit. The advice unit provides advice based on the changes in physical condition detected by the detection unit. The provision unit provides the data input by the reception unit to a doctor. [Effects of the Invention]

[0007] The system according to the embodiment can detect changes in a woman's physical condition based on data such as her basal body temperature and menstrual period, 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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A healthcare system according to an embodiment of the present invention is an app service specialized in women's healthcare that utilizes a generation AI. In this healthcare system, a user inputs their daily basal body temperature and menstrual cycle date. The generation AI analyzes the data, detects changes in their physical condition, and notifies the user. Furthermore, when a user consults about their physical concerns or anxieties via chat, the generation AI provides advice based on medical data and knowledge. Furthermore, in gynecology clinics, patients can share their basal body temperature and menstrual cycle data with hospitals, and the generation AI provides the patient data to doctors, improving the efficiency of consultations. For example, when a user inputs their daily basal body temperature and menstrual cycle date, the generation AI analyzes the data, detects changes in their physical condition, and notifies the user. Next, when a user consults about their physical concerns or anxieties via chat, the generation AI provides advice based on medical data and knowledge. Furthermore, in gynecology clinics, patients can share their basal body temperature and menstrual cycle data with hospitals, and the generation AI provides the patient data to doctors, improving the efficiency of consultations. This enables women's healthcare to be provided more efficiently and effectively. Thus, the healthcare system allows women's healthcare to be provided efficiently by inputting their basal body temperature and menstrual cycle date, detecting changes in their physical condition, providing advice, and providing the data to doctors.

[0029] The healthcare system according to the embodiment includes a reception unit, a detection unit, an advice unit, and a provision unit. The reception unit inputs the user's basal body temperature or menstrual period. The user's basal body temperature includes, for example, the measurement time and the measurement site, but is not limited to these examples. The reception unit, for example, prompts the user to measure their basal body temperature at the same time every morning and input the data. The reception unit can also prompt the user to record the start and end dates of their menstrual period when inputting their menstrual period. For example, when the reception unit inputs the start date of their menstrual period, the reception unit can predict and notify the user of the next expected menstrual period date. The detection unit detects changes in the user's physical condition based on the data input by the reception unit. Changes in the user's physical condition include, for example, fluctuations in body temperature and the appearance of symptoms, but are not limited to these examples. The detection unit, for example, analyzes the user's basal body temperature data and detects abnormal body temperature fluctuations. The detection unit can also analyze the user's menstrual cycle data and detect cycle disruptions. For example, if the user's basal body temperature is higher than normal, the detection unit notifies the user of the possibility of a fever. The advice unit provides advice based on the changes in the user's physical condition detected by the detection unit. The advice may include, but is not limited to, recommendations to improve lifestyle habits or to see a doctor. For example, if the user's body temperature is high, the advice unit may recommend hydration and rest. Furthermore, if the user's menstrual cycle is irregular, the advice unit may recommend stress management and improved nutritional balance. For example, the advice unit may suggest relaxation techniques and balanced meals to the user. The providing unit provides the data input by the reception unit to a doctor. The provided data may include, but is not limited to, basal body temperature trends and menstrual cycle data. For example, the providing unit may graph the user's basal body temperature data and provide it to the doctor. The providing unit may also compile the user's menstrual cycle data into a list and provide it to the doctor. For example, the providing unit may visually display fluctuations in the user's basal body temperature so that the doctor can refer to it during an examination. As a result, the healthcare system according to the embodiment can efficiently provide women's healthcare by inputting the user's basal body temperature and menstrual dates, detecting changes in physical condition, providing advice, and providing the data to the doctor.

[0030] The reception unit can analyze the user's past input history and select an input method. For example, the reception unit can prioritize and suggest input methods (such as voice input and text input) that the user has frequently used in the past. The reception unit can also analyze the user's past input history to determine input tendencies during specific time periods and send notifications during those time periods. For example, if the user has tended to input data in the morning in the past, the reception unit can send notifications during those time periods to prompt the user to input data. The reception unit can also identify days on which the user has tended to forget to input data in the past and reinforce reminders on those days. For example, the reception unit can identify days on which the user has forgotten to input the start date of their period in the past and send a reminder on that day. In this way, by analyzing the past input history, the optimal input method can be provided to the user. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's past input data into a generation AI and have the generation AI select the optimal input method.

[0031] The reception unit can filter the input of basal body temperature and menstrual period based on the user's current lifestyle and health condition. For example, if the user complains of poor health, the reception unit can simplify the input and request only the minimum necessary information. Furthermore, if the user is traveling, the reception unit can reduce notifications prompting input so that the user can input all the information once they return home. For example, the reception unit can detect that the user is traveling, reduce notifications while the user is traveling, and prompt the user to input the information once they return home. Furthermore, if the user has just exercised, the reception unit can delay notifications prompting input so that the user can input the information at a time when they are relaxed. For example, the reception unit can detect that the user has just exercised and send a notification during the relaxed time after exercise. This allows for more appropriate data input by filtering the input according to the user's lifestyle and health condition. Some or all of the above-described processing by the reception unit can be performed using, or without, AI. For example, the reception unit can input the user's lifestyle data into a generation AI and have the generation AI perform filtering.

[0032] When inputting basal body temperature or menstrual period, the reception unit can prioritize input of highly relevant data based on the user's geographical location information. For example, when the user is at home, the reception unit suggests a normal input method. Furthermore, when the user is out, the reception unit can also suggest a simplified input method. For example, the reception unit detects that the user is out and displays a simplified input form. Furthermore, when the user is traveling, the reception unit can also adjust the input based on the climate and environment of the travel destination. For example, the reception unit detects that the user is traveling and adjusts the input based on the climate and environment of the travel destination. This allows input data to be prioritized based on the user's geographical location information, thereby allowing more relevant data to be input. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's geographical location information data to a generation AI and have the generation AI prioritize highly relevant data.

[0033] The reception unit can analyze the user's social media activity and input relevant data when entering basal body temperature or menstrual period. For example, if the user posts about their physical condition on social media, the reception unit complements the input based on that information. Furthermore, if the user is participating in a trip or event on social media, the reception unit can also adjust the input based on that information. For example, the reception unit can detect from social media posts that the user is traveling and adjust the input based on the environment of the travel destination. Furthermore, if the user posts about exercise or diet on social media, the reception unit can complement the input based on that information. For example, the reception unit can detect from social media posts that the user has just exercised and prompt the user to enter changes in their physical condition after exercise. This allows for complementary input of relevant data by analyzing social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can input the user's social media data into a generation AI and have the generation AI complement the relevant data.

[0034] The detection unit can predict changes in physical condition by referring to past data upon detection. The detection unit, for example, refers to the user's past basal body temperature data and predicts the date of the next menstrual period. The detection unit can also predict the next change in physical condition by referring to the user's past data on changes in physical condition. For example, the detection unit analyzes the user's past basal body temperature data and predicts the date of the next menstrual period. The detection unit can also predict changes in physical condition by referring to the user's past lifestyle habit data. For example, the detection unit analyzes the user's past lifestyle habit data and predicts the next change in physical condition. In this way, changes in physical condition can be predicted by referring to the past data. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input the user's past data into the generation AI and cause the generation AI to predict changes in physical condition.

[0035] The detection unit can detect changes in physical condition based on the user's lifestyle and diet during detection. For example, if the user is exercising, the detection unit detects changes in physical condition after exercise. Furthermore, if the user is eating a specific meal, the detection unit can also detect changes in physical condition based on the meal. For example, if the user is eating a specific meal, the detection unit can detect changes in physical condition based on the meal. Furthermore, if the user is living an irregular lifestyle, the detection unit can also detect changes in physical condition based on the user's lifestyle. For example, if the user is living an irregular lifestyle, the detection unit can detect changes in physical condition based on the user's lifestyle. This enables more accurate detection of changes in physical condition by detecting changes in physical condition based on the user's lifestyle and diet. Some or all of the above-described processing by the detection unit may be performed using, for example, AI, or may be performed without AI. For example, the detection unit can input the user's lifestyle data into the generation AI and cause the generation AI to detect changes in physical condition.

[0036] The detection unit can detect changes in physical condition based on the user's geographical location information during detection. For example, if the user is at a high altitude, the detection unit detects changes in physical condition specific to high altitudes. Furthermore, if the user is in an urban area, the detection unit can also detect changes in physical condition specific to urban areas. For example, if the user is at a high altitude, the detection unit detects changes in physical condition specific to high altitudes. Furthermore, if the user is traveling, the detection unit can also detect changes in physical condition based on the environment of the travel destination. For example, the detection unit detects that the user is traveling and detects changes in physical condition based on the environment of the travel destination. This allows for more relevant changes in physical condition to be detected by detecting changes in physical condition based on the user's geographical location information. Some or all of the above-described processing in the detection unit may be performed using, or without, AI. For example, the detection unit can input the user's geographical location information data to the generation AI and cause the generation AI to detect changes in physical condition.

[0037] The detection unit can detect changes in the user's physical condition by referring to related literature of the user during detection. For example, if the user is referring to literature about a specific disease, the detection unit detects changes in physical condition related to the disease. Furthermore, if the user is referring to literature about health, the detection unit can also detect changes in physical condition based on the literature. For example, if the user is referring to literature about a specific disease, the detection unit detects changes in physical condition related to the disease. Furthermore, if the user is referring to literature about a specific symptom, the detection unit can also detect changes in physical condition related to the symptom. For example, if the user is referring to literature about a specific symptom, the detection unit detects changes in physical condition related to the symptom. This allows changes in physical condition to be detected more accurately by referring to related literature. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without AI. For example, the detection unit can input literature data referenced by the user into a generation AI and cause the generation AI to detect changes in physical condition.

[0038] When providing advice, the advice unit can adjust the level of detail of the advice based on the importance of the change in physical condition. For example, the advice unit provides detailed advice for a change in physical condition that is highly important. The advice unit can also provide concise advice for a change in physical condition that is low in importance. For example, the advice unit provides detailed advice for a change in physical condition that is highly important. The advice unit can also provide balanced advice for a change in physical condition that is medium in importance. For example, the advice unit provides balanced advice for a change in physical condition that is medium in importance. In this way, by adjusting the level of detail of the advice according to the importance of the change in physical condition, more appropriate advice can be provided. Some or all of the above-mentioned processing in the advice unit may be performed using, or without using, AI, for example. For example, the advice unit can input data on the change in physical condition to a generation AI and cause the generation AI to adjust the level of detail of the advice.

[0039] When providing advice, the advice unit can apply different advice algorithms depending on the category of change in physical condition. For example, the advice unit can suggest relaxation methods for stress-related changes in physical condition. The advice unit can also suggest methods of rest and nutrition for fatigue-related changes in physical condition. For example, the advice unit can suggest methods of rest and nutrition for fatigue-related changes in physical condition. The advice unit can also provide advice on exercise and diet for health-maintenance-related changes in physical condition. For example, the advice unit provides exercise and diet advice for health-maintenance-related changes in physical condition. In this way, by applying different advice algorithms depending on the category of change in physical condition, more appropriate advice can be provided. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input category data of change in physical condition to the generation AI and cause the generation AI to apply the advice algorithm.

[0040] When giving advice, the advice unit can determine the priority of the advice based on the time when the change in physical condition was submitted. For example, the advice unit can prioritize providing advice for recent changes in physical condition. The advice unit can also postpone providing advice for past changes in physical condition. For example, the advice unit prioritizes providing advice for recent changes in physical condition. The advice unit can also prioritize providing advice for important changes in physical condition regardless of the time when the change was submitted. For example, the advice unit prioritizes providing advice for important changes in physical condition regardless of the time when the change was submitted. In this way, by determining the priority of advice based on the time when the change in physical condition was submitted, more appropriate advice can be provided. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input data on changes in physical condition to the generation AI and cause the generation AI to determine the priority of advice.

[0041] When providing advice, the advice unit can adjust the order of advice based on the relevance of changes in physical condition. For example, if the relevance of a change in physical condition is high, the advice unit provides the advice preferentially. Furthermore, if the relevance of a change in physical condition is low, the advice unit can also provide the advice later. For example, if the relevance of a change in physical condition is high, the advice unit provides the advice preferentially. Furthermore, if the relevance of a change in physical condition is medium, the advice unit can also provide the advice in a balanced order. For example, if the relevance of a change in physical condition is medium, the advice unit provides the advice in a balanced order. In this way, by adjusting the order of advice based on the relevance of changes in physical condition, more appropriate advice can be provided. Some or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI. For example, the advice unit can input data on changes in physical condition to a generation AI and cause the generation AI to adjust the order of advice.

[0042] The providing unit can select a providing method by referring to the user's past data when providing the data. For example, the providing unit selects the optimal providing method based on data that the user frequently provided in the past. The providing unit can also analyze the user's tendency to provide data during a specific time period based on the user's past data provision history and provide the data during that time period. For example, if the user has tended to provide data during a specific time period in the past, the providing unit can provide the data during that time period. The providing unit can also identify data that the user tends to forget to provide in the past and provide that data preferentially. For example, the providing unit can identify data that the user tends to forget to provide in the past and provide that data preferentially. This allows the optimal data providing method to be selected by referring to the past data. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past data into a generation AI and have the generation AI select a providing method.

[0043] The providing unit can customize the data provision method based on the user's current health condition at the time of provision. For example, if the user complains of poor health, the providing unit selects a simplified data provision method. Furthermore, the providing unit can also select a detailed data provision method if the user is in good health. For example, if the user is in good health, the providing unit selects a detailed data provision method. Furthermore, if the user is in a specific health condition, the providing unit can also select a data provision method appropriate for that condition. For example, if the user is in a specific health condition, the providing unit selects a data provision method appropriate for that condition. This enables more appropriate data provision by customizing the data provision method according to the user's health condition. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's health condition data into the generation AI and cause the generation AI to customize the data provision method.

[0044] The providing unit can select a data provision method based on the user's geographical location information at the time of provision. For example, when the user is at home, the providing unit selects a normal data provision method. Furthermore, when the user is out, the providing unit can also select a simplified data provision method. For example, the providing unit detects that the user is out and selects a simplified data provision method. Furthermore, when the user is traveling, the providing unit can also adjust the data provision method based on the environment of the travel destination. For example, the providing unit detects that the user is traveling and adjusts the data provision method based on the environment of the travel destination. This enables more appropriate data provision by selecting the optimal data provision method based on the user's geographical location information. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the user's geographical location information data to the generation AI and cause the generation AI to select a data provision method.

[0045] The providing unit can analyze the user's social media activity and suggest a data provision method when providing data. For example, if the user posts about their physical condition on social media, the providing unit can suggest a data provision method based on that information. Furthermore, if the user is participating in a trip or event on social media, the providing unit can also adjust the data provision method based on that information. For example, the providing unit can detect from the social media post that the user is traveling and adjust the data provision method based on the environment of the travel destination. Furthermore, if the user posts about exercise or diet on social media, the providing unit can suggest a data provision method based on that information. For example, the providing unit can detect from the social media post that the user has just exercised and suggest a data provision method based on changes in physical condition after exercise. In this way, by analyzing social media activity, a more appropriate data provision method can be suggested. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the user's social media data into a generation AI and have the generation AI execute a data provision method suggestion.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The reception unit can provide feedback to improve input accuracy based on the user's past input data. For example, if the user has made an error in the data they have previously entered, the reception unit can point out the error and guide them on the correct input method. The reception unit can also send reminders for items that the user tends to forget to enter, helping them to make input a habit. For example, if the user forgets to enter the start date of their period, the reception unit can send a reminder on that day. Furthermore, the reception unit can analyze the user's input data and provide advice to improve input accuracy. For example, the reception unit can guide the user on points to note when measuring their basal body temperature, allowing them to enter accurate data. This improves the accuracy of the user's input data, enabling more accurate health care.

[0048] The reception unit can provide feedback to improve input accuracy based on the user's past input data. For example, if the user has made an error in the data they have previously entered, the reception unit can point out the error and guide them on the correct input method. The reception unit can also send reminders for items that the user tends to forget to enter, helping them to make input a habit. For example, if the user forgets to enter the start date of their period, the reception unit can send a reminder on that day. Furthermore, the reception unit can analyze the user's input data and provide advice to improve input accuracy. For example, the reception unit can guide the user on points to note when measuring their basal body temperature, allowing them to enter accurate data. This improves the accuracy of the user's input data, enabling more accurate health care.

[0049] The reception unit can determine the priority of input data based on the user's current living situation and health condition. For example, if the user complains of poor health, the reception unit can prioritize input of only important data, leaving detailed data for later. Also, if the user is relaxed, the reception unit can prompt the user to input detailed data as well, thereby improving the accuracy of the data. For example, a detailed input form can be displayed when the user is relaxed, improving the accuracy of the data. Also, if the user is busy, the reception unit can prompt the user to input only the most important data, allowing other data to be input later. For example, when the user is busy, the reception unit can prompt the user to input only important data, allowing other data to be input later. In this way, by determining the priority of input data according to the user's living situation and health condition, important data can be input preferentially.

[0050] The reception unit can determine the priority of input data based on the user's geographical location information. For example, when the user is at home, a normal input method is suggested. Also, when the user is out, a simplified input method can be suggested. For example, the reception unit detects that the user is out and displays a simplified input form. Also, when the user is traveling, the reception unit can adjust the input based on the climate and environment of the travel destination. For example, the reception unit detects that the user is traveling and adjusts the input based on the climate and environment of the travel destination. In this way, by prioritizing input data based on the user's geographical location information, more relevant data can be input.

[0051] The reception unit can analyze the user's social media activity and input relevant data. For example, if the user posts about their physical condition on social media, the input can be supplemented based on that information. Also, if the user posts on social media about traveling or participating in an event, the input can be adjusted based on that information. For example, the reception unit can detect from the social media post that the user is traveling and adjust the input based on the environment of the travel destination. Also, if the user posts on social media about exercise or diet, the input can be supplemented based on that information. For example, the reception unit can detect from the social media post that the user has just exercised and prompt the user to input changes in their physical condition after exercising. In this way, by analyzing social media activity, relevant data can be supplemented and input.

[0052] At the time of detection, the detection unit can predict changes in physical condition by referring to past data. For example, by referring to the user's past basal body temperature data, the detection unit predicts the date of the next menstrual period. The detection unit can also predict the next change in physical condition by referring to the user's past data on changes in physical condition. For example, the detection unit can analyze the user's past basal body temperature data and predict the date of the next menstrual period. The detection unit can also predict changes in physical condition by referring to the user's past lifestyle habit data. For example, the detection unit can analyze the user's past lifestyle habit data and predict the next change in physical condition. In this way, changes in physical condition can be predicted by referring to past data.

[0053] During detection, the detection unit can detect changes in physical condition based on the user's lifestyle habits and dietary content. For example, if the user is exercising, the detection unit detects changes in physical condition after the exercise. Furthermore, if the user is eating a specific meal, the detection unit can also detect changes in physical condition based on the meal. For example, if the user is eating a specific meal, the detection unit detects changes in physical condition based on the meal. Furthermore, if the user is living an irregular lifestyle, the detection unit can also detect changes in physical condition based on the user's lifestyle habits. For example, if the user is living an irregular lifestyle, the detection unit detects changes in physical condition based on the user's lifestyle habits. This allows for more accurate detection of changes in physical condition by detecting changes in physical condition based on the user's lifestyle habits and dietary content.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The reception unit inputs the user's basal body temperature or menstrual period. The user's basal body temperature includes, for example, the measurement time and measurement site, but is not limited to these examples. The reception unit, for example, prompts the user to measure their basal body temperature at the same time every morning and input that data. The reception unit can also prompt the user to record the start and end dates of their menstrual period when inputting their menstrual period. For example, when the user inputs the start date of their menstrual period, the reception unit can predict and notify the user of the expected date of their next menstrual period. Step 2: The detection unit detects changes in physical condition based on the data input by the reception unit. Changes in physical condition include, but are not limited to, changes in body temperature and the appearance of symptoms. For example, the detection unit analyzes the user's basal body temperature data and detects abnormal changes in body temperature. The detection unit can also analyze the user's menstrual cycle data and detect disruptions to the cycle. For example, if the user's basal body temperature is higher than normal, the detection unit notifies the user of the possibility of a fever. Step 3: The advice unit provides advice based on the change in physical condition detected by the detection unit. Examples of advice include, but are not limited to, improving lifestyle habits and recommending a doctor's visit. For example, if the user's body temperature is high, the advice unit may recommend hydration and rest. Furthermore, if the user's menstrual cycle is irregular, the advice unit may recommend stress management and improved nutritional balance. For example, the advice unit may suggest relaxation techniques and a balanced diet to the user. Step 4: The providing unit provides the data input by the receiving unit to the doctor. The provided data includes, for example, but is not limited to, data on basal body temperature trends and menstrual cycles. For example, the providing unit graphs the user's basal body temperature data and provides it to the doctor. The providing unit can also compile the user's menstrual cycle data into a list and provide it to the doctor. For example, the providing unit visually displays fluctuations in the user's basal body temperature so that the doctor can refer to it during the examination.

[0056] (Example 2) A healthcare system according to an embodiment of the present invention is an app service specialized in women's healthcare that utilizes a generation AI. In this healthcare system, a user inputs their daily basal body temperature and menstrual cycle date. The generation AI analyzes the data, detects changes in their physical condition, and notifies the user. Furthermore, when a user consults about their physical concerns or anxieties via chat, the generation AI provides advice based on medical data and knowledge. Furthermore, in gynecology clinics, patients can share their basal body temperature and menstrual cycle data with hospitals, and the generation AI provides the patient data to doctors, improving the efficiency of consultations. For example, when a user inputs their daily basal body temperature and menstrual cycle date, the generation AI analyzes the data, detects changes in their physical condition, and notifies the user. Next, when a user consults about their physical concerns or anxieties via chat, the generation AI provides advice based on medical data and knowledge. Furthermore, in gynecology clinics, patients can share their basal body temperature and menstrual cycle data with hospitals, and the generation AI provides the patient data to doctors, improving the efficiency of consultations. This enables women's healthcare to be provided more efficiently and effectively. Thus, the healthcare system allows women's healthcare to be provided efficiently by inputting their basal body temperature and menstrual cycle date, detecting changes in their physical condition, providing advice, and providing the data to doctors.

[0057] The healthcare system according to the embodiment includes a reception unit, a detection unit, an advice unit, and a provision unit. The reception unit inputs the user's basal body temperature or menstrual period. The user's basal body temperature includes, for example, the measurement time and the measurement site, but is not limited to these examples. The reception unit, for example, prompts the user to measure their basal body temperature at the same time every morning and input the data. The reception unit can also prompt the user to record the start and end dates of their menstrual period when inputting their menstrual period. For example, when the reception unit inputs the start date of their menstrual period, the reception unit can predict and notify the user of the next expected menstrual period date. The detection unit detects changes in the user's physical condition based on the data input by the reception unit. Changes in the user's physical condition include, for example, fluctuations in body temperature and the appearance of symptoms, but are not limited to these examples. The detection unit, for example, analyzes the user's basal body temperature data and detects abnormal body temperature fluctuations. The detection unit can also analyze the user's menstrual cycle data and detect cycle disruptions. For example, if the user's basal body temperature is higher than normal, the detection unit notifies the user of the possibility of a fever. The advice unit provides advice based on the changes in the user's physical condition detected by the detection unit. The advice may include, but is not limited to, recommendations to improve lifestyle habits or to see a doctor. For example, if the user's body temperature is high, the advice unit may recommend hydration and rest. Furthermore, if the user's menstrual cycle is irregular, the advice unit may recommend stress management and improved nutritional balance. For example, the advice unit may suggest relaxation techniques and balanced meals to the user. The providing unit provides the data input by the reception unit to a doctor. The provided data may include, but is not limited to, basal body temperature trends and menstrual cycle data. For example, the providing unit may graph the user's basal body temperature data and provide it to the doctor. The providing unit may also compile the user's menstrual cycle data into a list and provide it to the doctor. For example, the providing unit may visually display fluctuations in the user's basal body temperature so that the doctor can refer to it during an examination. As a result, the healthcare system according to the embodiment can efficiently provide women's healthcare by inputting the user's basal body temperature and menstrual dates, detecting changes in physical condition, providing advice, and providing the data to the doctor.

[0058] The reception unit can estimate the user's emotions and adjust the timing of inputting the basal body temperature and menstrual period based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit reduces notifications prompting input and prompts input at a time when the user is relaxed. Furthermore, when the user is relaxed, the reception unit can actively send notifications prompting input, helping the user to develop an input habit. For example, the reception unit sends notifications to prompt input during times when the user is relaxed. Furthermore, when the user is busy, the reception unit can shorten the notifications prompting input and provide an interface that allows for easy input. For example, the reception unit displays a simplified input form during times when the user is busy, allowing the user to complete input in a short time. This allows data to be input at a more appropriate time by adjusting the input timing according to the user's emotions. Emotion estimation is realized using an emotion estimation function, such as 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 reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0059] The reception unit can analyze the user's past input history and select an input method. For example, the reception unit can prioritize and suggest input methods (such as voice input and text input) that the user has frequently used in the past. The reception unit can also analyze the user's past input history to determine input tendencies during specific time periods and send notifications during those time periods. For example, if the user has tended to input data in the morning in the past, the reception unit can send notifications during those time periods to prompt the user to input data. The reception unit can also identify days on which the user has tended to forget to input data in the past and reinforce reminders on those days. For example, the reception unit can identify days on which the user has forgotten to input the start date of their period in the past and send a reminder on that day. In this way, by analyzing the past input history, the optimal input method can be provided to the user. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's past input data into a generation AI and have the generation AI select the optimal input method.

[0060] The reception unit can filter the input of basal body temperature and menstrual period based on the user's current lifestyle and health condition. For example, if the user complains of poor health, the reception unit can simplify the input and request only the minimum necessary information. Furthermore, if the user is traveling, the reception unit can reduce notifications prompting input so that the user can input all the information once they return home. For example, the reception unit can detect that the user is traveling, reduce notifications while the user is traveling, and prompt the user to input the information once they return home. Furthermore, if the user has just exercised, the reception unit can delay notifications prompting input so that the user can input the information at a time when they are relaxed. For example, the reception unit can detect that the user has just exercised and send a notification during the relaxed time after exercise. This allows for more appropriate data input by filtering the input according to the user's lifestyle and health condition. Some or all of the above-described processing by the reception unit can be performed using, or without, AI. For example, the reception unit can input the user's lifestyle data into a generation AI and have the generation AI perform filtering.

[0061] The reception unit can estimate the user's emotions and determine the priority of data to be input based on the estimated user emotions. For example, when the user is stressed, the reception unit prioritizes input of only important data and postpones input of detailed data. Furthermore, when the user is relaxed, the reception unit can also encourage input of detailed data, thereby improving data accuracy. For example, the reception unit displays a detailed input form during times when the user is relaxed, thereby improving data accuracy. Furthermore, when the user is busy, the reception unit can also encourage input of only the most important data and allow other data to be input later. For example, the reception unit can encourage input of only important data during times when the user is busy and allow other data to be input later. Thus, by determining the priority of input data according to the user's emotions, important data can be input preferentially. 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 reception unit can be performed using, for example, an AI, or without an AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0062] When inputting basal body temperature or menstrual period, the reception unit can prioritize input of highly relevant data based on the user's geographical location information. For example, when the user is at home, the reception unit suggests a normal input method. Furthermore, when the user is out, the reception unit can also suggest a simplified input method. For example, the reception unit detects that the user is out and displays a simplified input form. Furthermore, when the user is traveling, the reception unit can also adjust the input based on the climate and environment of the travel destination. For example, the reception unit detects that the user is traveling and adjusts the input based on the climate and environment of the travel destination. This allows input data to be prioritized based on the user's geographical location information, thereby allowing more relevant data to be input. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's geographical location information data to a generation AI and have the generation AI prioritize highly relevant data.

[0063] The reception unit can analyze the user's social media activity and input relevant data when entering basal body temperature or menstrual period. For example, if the user posts about their physical condition on social media, the reception unit complements the input based on that information. Furthermore, if the user is participating in a trip or event on social media, the reception unit can also adjust the input based on that information. For example, the reception unit can detect from social media posts that the user is traveling and adjust the input based on the environment of the travel destination. Furthermore, if the user posts about exercise or diet on social media, the reception unit can complement the input based on that information. For example, the reception unit can detect from social media posts that the user has just exercised and prompt the user to enter changes in their physical condition after exercise. This allows for complementary input of relevant data by analyzing social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can input the user's social media data into a generation AI and have the generation AI complement the relevant data.

[0064] The detection unit can estimate the user's emotions and adjust the detection method for changes in physical condition based on the estimated user emotions. For example, if the user is feeling stressed, the detection unit prioritizes detecting stress-related changes in physical condition. Furthermore, if the user is relaxed, the detection unit can also detect overall changes in physical condition in a balanced manner. For example, the detection unit detects overall changes in physical condition during times when the user is relaxed. Furthermore, if the user is tired, the detection unit can prioritize detecting fatigue-related changes in physical condition. For example, the detection unit detects fatigue-related changes in physical condition during times when the user is tired. This allows for more appropriate detection of changes in physical condition by adjusting the detection method for changes in physical condition according to the user's emotions. The estimation of emotions is achieved using an emotion estimation function, such as 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 detection unit may be performed using, for example, AI, or without AI. For example, the detection unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0065] The detection unit can predict changes in physical condition by referring to past data upon detection. The detection unit, for example, refers to the user's past basal body temperature data and predicts the date of the next menstrual period. The detection unit can also predict the next change in physical condition by referring to the user's past data on changes in physical condition. For example, the detection unit analyzes the user's past basal body temperature data and predicts the date of the next menstrual period. The detection unit can also predict changes in physical condition by referring to the user's past lifestyle habit data. For example, the detection unit analyzes the user's past lifestyle habit data and predicts the next change in physical condition. In this way, changes in physical condition can be predicted by referring to the past data. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input the user's past data into the generation AI and cause the generation AI to predict changes in physical condition.

[0066] The detection unit can detect changes in physical condition based on the user's lifestyle and diet during detection. For example, if the user is exercising, the detection unit detects changes in physical condition after exercise. Furthermore, if the user is eating a specific meal, the detection unit can also detect changes in physical condition based on the meal. For example, if the user is eating a specific meal, the detection unit can detect changes in physical condition based on the meal. Furthermore, if the user is living an irregular lifestyle, the detection unit can also detect changes in physical condition based on the user's lifestyle. For example, if the user is living an irregular lifestyle, the detection unit can detect changes in physical condition based on the user's lifestyle. This enables more accurate detection of changes in physical condition by detecting changes in physical condition based on the user's lifestyle and diet. Some or all of the above-described processing by the detection unit may be performed using, for example, AI, or may be performed without AI. For example, the detection unit can input the user's lifestyle data into the generation AI and cause the generation AI to detect changes in physical condition.

[0067] The detection unit can estimate the user's emotions and adjust the importance of changes in physical condition based on the estimated user emotions. For example, if the user is feeling stressed, the detection unit can detect stress-related physical condition changes with a high importance. Furthermore, if the user is relaxed, the detection unit can also detect overall physical condition changes in a balanced manner. For example, the detection unit detects overall physical condition changes during times when the user is relaxed. Furthermore, if the user is tired, the detection unit can also detect fatigue-related physical condition changes with a high importance. For example, the detection unit detects fatigue-related physical condition changes during times when the user is tired. In this way, by adjusting the importance of physical condition changes according to the user's emotions, important physical condition changes can be detected preferentially. 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 detection unit may be performed using, for example, AI, or without AI. For example, the detection unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0068] The detection unit can detect changes in physical condition based on the user's geographical location information during detection. For example, if the user is at a high altitude, the detection unit detects changes in physical condition specific to high altitudes. Furthermore, if the user is in an urban area, the detection unit can also detect changes in physical condition specific to urban areas. For example, if the user is at a high altitude, the detection unit detects changes in physical condition specific to high altitudes. Furthermore, if the user is traveling, the detection unit can also detect changes in physical condition based on the environment of the travel destination. For example, the detection unit detects that the user is traveling and detects changes in physical condition based on the environment of the travel destination. This allows for more relevant changes in physical condition to be detected by detecting changes in physical condition based on the user's geographical location information. Some or all of the above-described processing in the detection unit may be performed using, or without, AI. For example, the detection unit can input the user's geographical location information data to the generation AI and cause the generation AI to detect changes in physical condition.

[0069] The detection unit can detect changes in the user's physical condition by referring to related literature of the user during detection. For example, if the user is referring to literature about a specific disease, the detection unit detects changes in physical condition related to the disease. Furthermore, if the user is referring to literature about health, the detection unit can also detect changes in physical condition based on the literature. For example, if the user is referring to literature about a specific disease, the detection unit detects changes in physical condition related to the disease. Furthermore, if the user is referring to literature about a specific symptom, the detection unit can also detect changes in physical condition related to the symptom. For example, if the user is referring to literature about a specific symptom, the detection unit detects changes in physical condition related to the symptom. This allows changes in physical condition to be detected more accurately by referring to related literature. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without AI. For example, the detection unit can input literature data referenced by the user into a generation AI and cause the generation AI to detect changes in physical condition.

[0070] The advice unit can estimate the user's emotions and adjust the way the advice is expressed based on the estimated user's emotions. For example, if the user is feeling stressed, the advice unit can provide gentle advice. The advice unit can also provide detailed advice if the user is relaxed. For example, the advice unit can provide detailed advice when the user is relaxed. The advice unit can also provide concise, to-the-point advice when the user is in a hurry. For example, the advice unit can provide concise advice when the user is in a hurry. This allows the user to adjust the way the advice is expressed based on the user's emotions, thereby providing more appropriate advice. The emotion estimation is achieved 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 these examples. Some or all of the above-described processing in the advice unit can be performed using an AI, for example, or without an AI. For example, the advice unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0071] When providing advice, the advice unit can adjust the level of detail of the advice based on the importance of the change in physical condition. For example, the advice unit provides detailed advice for a change in physical condition that is highly important. The advice unit can also provide concise advice for a change in physical condition that is low in importance. For example, the advice unit provides detailed advice for a change in physical condition that is highly important. The advice unit can also provide balanced advice for a change in physical condition that is medium in importance. For example, the advice unit provides balanced advice for a change in physical condition that is medium in importance. In this way, by adjusting the level of detail of the advice according to the importance of the change in physical condition, more appropriate advice can be provided. Some or all of the above-mentioned processing in the advice unit may be performed using, or without using, AI, for example. For example, the advice unit can input data on the change in physical condition to a generation AI and cause the generation AI to adjust the level of detail of the advice.

[0072] When providing advice, the advice unit can apply different advice algorithms depending on the category of change in physical condition. For example, the advice unit can suggest relaxation methods for stress-related changes in physical condition. The advice unit can also suggest methods of rest and nutrition for fatigue-related changes in physical condition. For example, the advice unit can suggest methods of rest and nutrition for fatigue-related changes in physical condition. The advice unit can also provide advice on exercise and diet for health-maintenance-related changes in physical condition. For example, the advice unit provides exercise and diet advice for health-maintenance-related changes in physical condition. In this way, by applying different advice algorithms depending on the category of change in physical condition, more appropriate advice can be provided. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input category data of change in physical condition to the generation AI and cause the generation AI to apply the advice algorithm.

[0073] The advice unit can estimate the user's emotions and adjust the length of the advice based on the estimated user emotions. For example, when the user is stressed, the advice unit provides short, to-the-point advice. The advice unit can also provide detailed advice when the user is relaxed. For example, the advice unit provides detailed advice when the user is relaxed. The advice unit can also provide concise, quick advice when the user is in a hurry. For example, the advice unit provides concise advice when the user is in a hurry. This allows the length of advice to be adjusted according to the user's emotions, thereby providing more appropriate advice. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the advice unit may be performed using, for example, an AI. For example, the advice unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0074] When giving advice, the advice unit can determine the priority of the advice based on the time when the change in physical condition was submitted. For example, the advice unit can prioritize providing advice for recent changes in physical condition. The advice unit can also postpone providing advice for past changes in physical condition. For example, the advice unit prioritizes providing advice for recent changes in physical condition. The advice unit can also prioritize providing advice for important changes in physical condition regardless of the time when the change was submitted. For example, the advice unit prioritizes providing advice for important changes in physical condition regardless of the time when the change was submitted. In this way, by determining the priority of advice based on the time when the change in physical condition was submitted, more appropriate advice can be provided. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input data on changes in physical condition to the generation AI and cause the generation AI to determine the priority of advice.

[0075] When providing advice, the advice unit can adjust the order of advice based on the relevance of changes in physical condition. For example, if the relevance of a change in physical condition is high, the advice unit provides the advice preferentially. Furthermore, if the relevance of a change in physical condition is low, the advice unit can also provide the advice later. For example, if the relevance of a change in physical condition is high, the advice unit provides the advice preferentially. Furthermore, if the relevance of a change in physical condition is medium, the advice unit can also provide the advice in a balanced order. For example, if the relevance of a change in physical condition is medium, the advice unit provides the advice in a balanced order. In this way, by adjusting the order of advice based on the relevance of changes in physical condition, more appropriate advice can be provided. Some or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI. For example, the advice unit can input data on changes in physical condition to a generation AI and cause the generation AI to adjust the order of advice.

[0076] The providing unit can estimate the user's emotions and determine the priority of data to be provided to a doctor based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can prioritize providing stress-related data. Furthermore, if the user is relaxed, the providing unit can also provide overall data in a balanced manner. For example, the providing unit can provide overall data in a balanced manner during times when the user is relaxed. Furthermore, if the user is tired, the providing unit can prioritize providing fatigue-related data. For example, the providing unit can prioritize fatigue-related data during times when the user is tired. This enables more appropriate data to be provided by determining the priority of data to be provided to a doctor based on 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 providing unit can be performed using, for example, an AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0077] The providing unit can select a providing method by referring to the user's past data when providing the data. For example, the providing unit selects the optimal providing method based on data that the user frequently provided in the past. The providing unit can also analyze the user's tendency to provide data during a specific time period based on the user's past data provision history and provide the data during that time period. For example, if the user has tended to provide data during a specific time period in the past, the providing unit can provide the data during that time period. The providing unit can also identify data that the user tends to forget to provide in the past and provide that data preferentially. For example, the providing unit can identify data that the user tends to forget to provide in the past and provide that data preferentially. This allows the optimal data providing method to be selected by referring to the past data. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past data into a generation AI and have the generation AI select a providing method.

[0078] The providing unit can customize the data provision method based on the user's current health condition at the time of provision. For example, if the user complains of poor health, the providing unit selects a simplified data provision method. Furthermore, the providing unit can also select a detailed data provision method if the user is in good health. For example, if the user is in good health, the providing unit selects a detailed data provision method. Furthermore, if the user is in a specific health condition, the providing unit can also select a data provision method appropriate for that condition. For example, if the user is in a specific health condition, the providing unit selects a data provision method appropriate for that condition. This enables more appropriate data provision by customizing the data provision method according to the user's health condition. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's health condition data into the generation AI and cause the generation AI to customize the data provision method.

[0079] The providing unit can estimate the user's emotions and adjust the display method of the data to be provided based on the estimated user's emotions. For example, when the user is feeling stressed, the providing unit provides a simple, highly visible display method. Furthermore, when the user is relaxed, the providing unit can also provide a display method including detailed information. For example, the providing unit provides a display method including detailed information for a time period when the user is relaxed. Furthermore, when the user is in a hurry, the providing unit can also provide a display method that focuses on the main points. For example, the providing unit provides a display method that focuses on the main points for a time period when the user is in a hurry. This enables more appropriate data display by adjusting the data display method 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 may 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 may be performed using, for example, an AI. For example, the providing unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0080] The providing unit can select a data provision method based on the user's geographical location information at the time of provision. For example, when the user is at home, the providing unit selects a normal data provision method. Furthermore, when the user is out, the providing unit can also select a simplified data provision method. For example, the providing unit detects that the user is out and selects a simplified data provision method. Furthermore, when the user is traveling, the providing unit can also adjust the data provision method based on the environment of the travel destination. For example, the providing unit detects that the user is traveling and adjusts the data provision method based on the environment of the travel destination. This enables more appropriate data provision by selecting the optimal data provision method based on the user's geographical location information. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the user's geographical location information data to the generation AI and cause the generation AI to select a data provision method.

[0081] The providing unit can analyze the user's social media activity and suggest a data provision method when providing data. For example, if the user posts about their physical condition on social media, the providing unit can suggest a data provision method based on that information. Furthermore, if the user is participating in a trip or event on social media, the providing unit can also adjust the data provision method based on that information. For example, the providing unit can detect from the social media post that the user is traveling and adjust the data provision method based on the environment of the travel destination. Furthermore, if the user posts about exercise or diet on social media, the providing unit can suggest a data provision method based on that information. For example, the providing unit can detect from the social media post that the user has just exercised and suggest a data provision method based on changes in physical condition after exercise. In this way, by analyzing social media activity, a more appropriate data provision method can be suggested. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the user's social media data into a generation AI and have the generation AI execute a data provision method suggestion. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, detection unit, advice 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 is realized by the control unit 46A of the smart device 14 and prompts the user to input their basal body temperature and menstrual period. The detection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and detects changes in physical condition based on the input data. The advice unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides advice based on the detected changes in physical condition. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides the input data to a doctor. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, detection unit, advice 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 is realized by the control unit 46A of the smart glasses 214 and prompts the user to input their basal body temperature and menstrual period. The detection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and detects changes in physical condition based on the input data. The advice unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides advice based on the detected changes in physical condition. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the input data to a doctor. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, detection unit, advice 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 is realized by the control unit 46A of the headset type terminal 314 and prompts the user to input their basal body temperature and menstrual period. The detection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and detects changes in physical condition based on the input data. The advice unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides advice based on the detected changes in physical condition. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314 and provides the input data to a doctor. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, detection unit, advice 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 is realized by the control unit 46A of the robot 414 and prompts the user to input their basal body temperature and menstrual period. The detection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and detects changes in physical condition based on the input data. The advice unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides advice based on the detected changes in physical condition. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the input data to a doctor.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The reception unit can provide feedback to improve input accuracy based on the user's past input data. For example, if the user has made an error in the data they have previously entered, the reception unit can point out the error and guide them on the correct input method. The reception unit can also send reminders for items that the user tends to forget to enter, helping them to make input a habit. For example, if the user forgets to enter the start date of their period, the reception unit can send a reminder on that day. Furthermore, the reception unit can analyze the user's input data and provide advice to improve input accuracy. For example, the reception unit can guide the user on points to note when measuring their basal body temperature, allowing them to enter accurate data. This improves the accuracy of the user's input data, enabling more accurate health care.

[0084] The reception unit can estimate the user's emotions and change the design of the input interface based on the estimated user emotions. For example, if the user is feeling stressed, a simple, highly visible interface can be provided to reduce the burden of input. Also, if the user is relaxed, an interface including detailed input items can be provided to improve data accuracy. For example, a detailed input form can be displayed when the user is relaxed. Also, if the user is in a hurry, a simple interface that allows quick input can be provided. For example, a simplified input form can be displayed when the user is in a hurry. In this way, adjusting the input interface according to the user's emotions enables more appropriate data input.

[0085] The reception unit can provide feedback to improve input accuracy based on the user's past input data. For example, if the user has made an error in the data they have previously entered, the reception unit can point out the error and guide them on the correct input method. The reception unit can also send reminders for items that the user tends to forget to enter, helping them to make input a habit. For example, if the user forgets to enter the start date of their period, the reception unit can send a reminder on that day. Furthermore, the reception unit can analyze the user's input data and provide advice to improve input accuracy. For example, the reception unit can guide the user on points to note when measuring their basal body temperature, allowing them to enter accurate data. This improves the accuracy of the user's input data, enabling more accurate health care.

[0086] The reception unit can determine the priority of input data based on the user's current living situation and health condition. For example, if the user complains of poor health, the reception unit can prioritize input of only important data, leaving detailed data for later. Also, if the user is relaxed, the reception unit can prompt the user to input detailed data as well, thereby improving the accuracy of the data. For example, a detailed input form can be displayed when the user is relaxed, improving the accuracy of the data. Also, if the user is busy, the reception unit can prompt the user to input only the most important data, allowing other data to be input later. For example, when the user is busy, the reception unit can prompt the user to input only important data, allowing other data to be input later. In this way, by determining the priority of input data according to the user's living situation and health condition, important data can be input preferentially.

[0087] The reception unit can estimate the user's emotions and determine the priority of data to be input based on the estimated user's emotions. For example, when the user is feeling stressed, the reception unit can prioritize input of only important data and postpone input of detailed data. Furthermore, when the user is relaxed, the reception unit can also prompt the user to input detailed data as well, thereby improving data accuracy. For example, the reception unit can display a detailed input form during times when the user is relaxed, thereby improving data accuracy. Furthermore, when the user is busy, the reception unit can prompt the user to input only the most important data and allow other data to be input later. For example, when the user is busy, the reception unit can prompt the user to input only important data and allow other data to be input later. In this way, by determining the priority of input data according to the user's emotions, important data can be input preferentially.

[0088] The reception unit can determine the priority of input data based on the user's geographical location information. For example, when the user is at home, a normal input method is suggested. Also, when the user is out, a simplified input method can be suggested. For example, the reception unit detects that the user is out and displays a simplified input form. Also, when the user is traveling, the reception unit can adjust the input based on the climate and environment of the travel destination. For example, the reception unit detects that the user is traveling and adjusts the input based on the climate and environment of the travel destination. In this way, by prioritizing input data based on the user's geographical location information, more relevant data can be input.

[0089] The reception unit can analyze the user's social media activity and input relevant data. For example, if the user posts about their physical condition on social media, the input can be supplemented based on that information. Also, if the user posts on social media about traveling or participating in an event, the input can be adjusted based on that information. For example, the reception unit can detect from the social media post that the user is traveling and adjust the input based on the environment of the travel destination. Also, if the user posts on social media about exercise or diet, the input can be supplemented based on that information. For example, the reception unit can detect from the social media post that the user has just exercised and prompt the user to input changes in their physical condition after exercising. In this way, by analyzing social media activity, relevant data can be supplemented and input.

[0090] The detection unit can estimate the user's emotions and adjust the detection method for changes in physical condition based on the estimated user emotions. For example, if the user is feeling stressed, the detection unit prioritizes detecting stress-related changes in physical condition. Furthermore, if the user is relaxed, the detection unit can also detect overall changes in physical condition in a balanced manner. For example, the detection unit detects overall changes in physical condition during times when the user is relaxed. Furthermore, if the user is tired, the detection unit can also prioritize detecting fatigue-related changes in physical condition. For example, the detection unit detects fatigue-related changes in physical condition during times when the user is tired. This allows the detection method for changes in physical condition to be adjusted according to the user's emotions, making it possible to detect changes in physical condition more appropriately.

[0091] At the time of detection, the detection unit can predict changes in physical condition by referring to past data. For example, by referring to the user's past basal body temperature data, the detection unit predicts the date of the next menstrual period. The detection unit can also predict the next change in physical condition by referring to the user's past data on changes in physical condition. For example, the detection unit can analyze the user's past basal body temperature data and predict the date of the next menstrual period. The detection unit can also predict changes in physical condition by referring to the user's past lifestyle habit data. For example, the detection unit can analyze the user's past lifestyle habit data and predict the next change in physical condition. In this way, changes in physical condition can be predicted by referring to past data.

[0092] During detection, the detection unit can detect changes in physical condition based on the user's lifestyle habits and dietary content. For example, if the user is exercising, the detection unit detects changes in physical condition after the exercise. Furthermore, if the user is eating a specific meal, the detection unit can also detect changes in physical condition based on the meal. For example, if the user is eating a specific meal, the detection unit detects changes in physical condition based on the meal. Furthermore, if the user is living an irregular lifestyle, the detection unit can also detect changes in physical condition based on the user's lifestyle habits. For example, if the user is living an irregular lifestyle, the detection unit detects changes in physical condition based on the user's lifestyle habits. This allows for more accurate detection of changes in physical condition by detecting changes in physical condition based on the user's lifestyle habits and dietary content.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The reception unit inputs the user's basal body temperature or menstrual period. The user's basal body temperature includes, for example, the measurement time and measurement site, but is not limited to these examples. The reception unit, for example, prompts the user to measure their basal body temperature at the same time every morning and input that data. The reception unit can also prompt the user to record the start and end dates of their menstrual period when inputting their menstrual period. For example, when the user inputs the start date of their menstrual period, the reception unit can predict and notify the user of the expected date of their next menstrual period. Step 2: The detection unit detects changes in physical condition based on the data input by the reception unit. Changes in physical condition include, but are not limited to, changes in body temperature and the appearance of symptoms. For example, the detection unit analyzes the user's basal body temperature data and detects abnormal changes in body temperature. The detection unit can also analyze the user's menstrual cycle data and detect disruptions to the cycle. For example, if the user's basal body temperature is higher than normal, the detection unit notifies the user of the possibility of a fever. Step 3: The advice unit provides advice based on the change in physical condition detected by the detection unit. Examples of advice include, but are not limited to, improving lifestyle habits and recommending a doctor's visit. For example, if the user's body temperature is high, the advice unit may recommend hydration and rest. Furthermore, if the user's menstrual cycle is irregular, the advice unit may recommend stress management and improved nutritional balance. For example, the advice unit may suggest relaxation techniques and a balanced diet to the user. Step 4: The providing unit provides the data input by the receiving unit to the doctor. The provided data includes, for example, but is not limited to, data on basal body temperature trends and menstrual cycles. For example, the providing unit graphs the user's basal body temperature data and provides it to the doctor. The providing unit can also compile the user's menstrual cycle data into a list and provide it to the doctor. For example, the providing unit visually displays fluctuations in the user's basal body temperature so that the doctor can refer to it during the examination.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0099] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0115] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0116] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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."

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] [Explanation of symbols]

[0167] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for inputting a user's basal body temperature or menstrual period; a detection unit that detects a change in physical condition based on the data input by the reception unit; an advice unit that gives advice based on the change in physical condition detected by the detection unit; a providing unit that provides the data input by the receiving unit to a doctor. A system characterized by:

2. The reception unit Estimates the user's emotions and adjusts the timing of inputting basal body temperature and menstruation based on the estimated user emotions.

2. The system of claim 1.

3. The reception unit Analyze the user's past input history and select the input method 2. The system of claim 1.

4. The reception unit When entering basal body temperature or menstrual period, filtering is performed based on the user's current living situation and health condition.

2. The system of claim 1.

5. The reception unit Estimate the user's emotions and prioritize the data to be input based on the estimated user emotions.

2. The system of claim 1.

6. The reception unit When entering basal body temperature or menstruation date, the system prioritizes entering relevant data based on the user's geographic location.

2. The system of claim 1.

7. The reception unit When entering basal body temperature or menstrual period, analyze the user's social media activity and enter related data.

2. The system of claim 1.

8. The detection unit Estimate the user's emotions and adjust the method for detecting changes in physical condition based on the estimated user emotions.

2. The system of claim 1.

9. The detection unit When a condition is detected, past data is referenced to predict changes in physical condition.

2. The system of claim 1.

10. The detection unit At the time of detection, changes in the user's physical condition are detected based on the user's lifestyle and dietary habits.

2. The system of claim 1.

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