system

Abstract

The system according to the embodiment aims to predict a user's menstrual cycle and provide health advice. According to an embodiment, the system includes a data input unit, a prediction unit, and an advice providing unit. The data input unit inputs data related to a user's menstrual cycle. The prediction unit analyzes the data input by the data input unit and predicts the user's menstrual cycle. The advice providing unit provides health advice based on the results predicted by the prediction unit.

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques do not adequately manage health based on a user's menstrual cycle, and there is room for improvement.

[0005] The system according to the embodiment aims to predict a user's menstrual cycle and provide health advice. [Means for solving the problem]

[0006] The system according to the embodiment includes a data input unit, a prediction unit, and an advice providing unit. The data input unit inputs data related to a user's menstrual cycle. The prediction unit analyzes the data input by the data input unit and predicts the user's menstrual cycle. The advice providing unit provides health advice based on the results predicted by the prediction unit. [Effects of the Invention]

[0007] The system according to the embodiment can predict a user's menstrual cycle and provide health advice. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

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

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

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A health management system according to an embodiment of the present invention utilizes a generative AI to predict a user's menstrual cycle and provide health advice tailored to the health needs of the femtech field. In the health management system, a user inputs data about their menstrual cycle, and a generative AI analyzes the data to predict the menstrual cycle and provides health advice based on the prediction results. For example, in a health management system, a user inputs data about their menstrual cycle through an application. The generative AI then analyzes the data and learns from past data and patterns to predict the start date of the next period and ovulation date. Furthermore, the generative AI provides health advice to the user based on the prediction results. For example, the advice may include advice on premenstrual health management and nutritional intake, and advice on stress management during ovulation. This allows the health management system to provide healthcare tailored to the user's individual needs and improve women's health. By predicting a user's menstrual cycle and providing health advice, the health management system can improve women's health. For example, users can understand their physical condition and manage their health in accordance with their menstrual cycle. This improves women's quality of life and enables them to live healthier lives.

[0029] A health management system according to an embodiment includes a data input unit, a prediction unit, and an advice providing unit. The data input unit allows a user to input data related to their menstrual cycle. The data input by the user includes, but is not limited to, the start and end dates of menstruation and details of symptoms. The data input unit can input data via, for example, a smartphone app or a web app. The data input unit can also estimate the user's emotions and adjust the timing of data input based on the estimated emotions. For example, if the user is feeling stressed, a notification can be sent encouraging the user to input data during a time when they are able to relax. The prediction unit uses a generation AI to analyze the data input by the data input unit and predict the user's menstrual cycle. For example, the prediction can learn from past data and patterns to predict the start date of the next menstrual period and the date of ovulation, but is not limited to such examples. For example, the generation AI can make predictions based on past menstrual cycle data and symptom records. The advice providing unit provides health advice based on the results predicted by the prediction unit. The advice can include, but is not limited to, advice on premenstrual health management, nutritional intake, and stress management during ovulation. For example, the advice providing unit can provide the user with specific advice on nutritional intake and rest based on the prediction results. As a result, the health management system according to the embodiment can predict the user's menstrual cycle and provide health advice, thereby improving the health status of women. For example, the user can understand the condition of her body and manage her health in accordance with her menstrual cycle. This improves the quality of life of women, enabling them to live healthier lives.

[0030] The data input unit allows a user to input data related to the menstrual cycle through an application. Examples of applications include, but are not limited to, smartphone applications and web applications. For example, the data input unit allows a user to input the start and end dates of menstruation and details of symptoms through a smartphone application. The data input unit also allows a user to input similar data through a web application. In this way, the system can obtain data related to the menstrual cycle by the user inputting data through the application. Some or all of the above-described processing in the data input unit may be performed using, or without, AI. For example, the data input unit may input the data entered by the user to a generation AI and have the generation AI analyze the data.

[0031] The prediction unit can learn from past data and patterns to predict the start date of the next period and the date of ovulation. The prediction unit makes predictions based on, for example, past menstrual cycle data and symptom records. For example, the prediction unit learns past menstrual start and end dates and detailed symptoms to predict the start date of the next period and the date of ovulation. The prediction unit can also use a generation AI to learn from past data and patterns to improve the accuracy of the prediction. For example, the prediction unit can analyze past data and predict the start date of the next period and the date of ovulation with high accuracy. Furthermore, the prediction unit can estimate the user's emotions and adjust the way the prediction is expressed based on the estimated emotions. For example, if the user is relaxed, a detailed prediction result is provided, and if the user is stressed, a concise prediction result is provided. This allows the prediction unit to accurately predict the start date of the next period and the date of ovulation by learning from past data and patterns. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the prediction unit can input past data into the generation AI and have the generation AI perform the prediction.

[0032] The advice providing unit can provide advice on premenstrual health management and nutritional intake based on the prediction results. The advice providing unit, for example, provides specific nutritional intake and rest advice to the user based on the prediction results. For example, the advice providing unit provides nutritional intake and rest advice tailored to a user who tends to feel unwell before their period to that user. The advice providing unit can also provide advice based on the prediction results using a generation AI. For example, the generation AI analyzes the prediction results and generates optimal advice for the user. Furthermore, the advice providing unit can estimate the user's emotions and adjust the way the advice is expressed based on the estimated emotions. For example, detailed advice is provided when the user is relaxed, and concise advice is provided when the user is stressed. This supports the user's health management by providing appropriate advice based on the prediction results. Some or all of the above-described processing in the advice providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the advice providing unit can input the prediction results to the generation AI and have the generation AI generate advice.

[0033] The advice providing unit can provide advice on stress management during the ovulation period based on the prediction results. The advice providing unit can provide specific stress management advice to the user based on the prediction results. For example, the advice providing unit can provide relaxation methods and stress management advice tailored to a user who tends to experience increased stress during ovulation. The advice providing unit can also provide advice based on the prediction results using a generation AI. For example, the generation AI can analyze the prediction results and generate optimal stress management advice for the user. The advice providing unit can also estimate the user's emotions and adjust the way the advice is expressed based on the estimated emotions. For example, if the user is relaxed, detailed advice can be provided, and if the user is feeling stressed, concise advice can be provided. This can reduce the user's stress by providing advice on stress management during the ovulation period. Some or all of the above-described processing in the advice providing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the advice providing unit can input the prediction results to the generation AI and have the generation AI generate stress management advice.

[0034] The data input unit can analyze the user's past data input history and select the optimal input method. For example, the data input unit prioritizes and suggests input methods (such as voice and text) that the user has frequently used in the past. For example, the data input unit sends notifications avoiding time periods when the user tends to forget to input data in the past. The data input unit can also find specific patterns from the user's past input history and suggest the optimal input timing. In this way, by analyzing the past data input history, the optimal input method can be provided to the user. Some or all of the above-mentioned processing in the data input unit may be performed, for example, using AI or may be performed without using AI. For example, the data input unit can input the past data input history into a generation AI and have the generation AI select the optimal input method.

[0035] The data input unit can filter data based on the user's current health condition and lifestyle when inputting data. For example, if the user is in poor health, the data input unit simplifies the input items to reduce the burden. For example, if the user is busy, the data input unit only requires the minimum amount of data to be input. The data input unit can also prompt the user to input detailed data when the user is relaxed. This reduces the burden by adjusting the data input according to the user's health condition and lifestyle. Some or all of the above-mentioned processing in the data input unit may be performed using, for example, AI, or may be performed without using AI. For example, the data input unit can input data on the user's health condition and lifestyle into the generation AI and have the generation AI perform filtering.

[0036] The data input unit can select the optimal input means depending on the user's input method when inputting data. For example, if the user prefers voice input, the data input unit preferentially suggests voice input. For example, if the user prefers text input, the data input unit preferentially suggests text input. Furthermore, if the user prefers image input, the data input unit can also preferentially suggest image input. This improves input efficiency by selecting the optimal means depending on the user's input method. Some or all of the above-described processing in the data input unit may be performed using, for example, AI, or may be performed without using AI. For example, the data input unit can input data on the user's input method to a generation AI and have the generation AI select the optimal input means.

[0037] When inputting data, the data input unit can prioritize inputting highly relevant data taking into account the user's geographical location information. For example, when the user is at home, the data input unit prioritizes inputting health data from within the home. For example, when the user is out, the data input unit prioritizes inputting health data from the user's location. Furthermore, when the user is traveling, the data input unit can also prioritize inputting health data from the user's location. This increases the accuracy of the data by inputting highly relevant data based on the user's geographical location information. Some or all of the above-described processing in the data input unit may be performed using, or without, AI. For example, the data input unit can input the user's geographical location information to the generation AI and have the generation AI select highly relevant data.

[0038] The data input unit can analyze the user's social media activity and input relevant data when inputting data. The data input unit, for example, automatically inputs health information shared by the user on social media. For example, the data input unit inputs relevant health data from the user's social media activity. The data input unit can also input relevant health data by referring to the activity of the user's friends on social media. This allows for efficient input of relevant data by analyzing social media activity. Some or all of the above-described processing in the data input unit may be performed using, for example, AI, or may be performed without using AI. For example, the data input unit can input data on the user's social media activity to the generation AI and have the generation AI input relevant data.

[0039] The data input unit can customize the input method by reflecting the user's past feedback when inputting data. For example, the data input unit preferentially suggests methods that the user has found easy to use in the past. For example, the data input unit avoids methods that the user has found difficult to use in the past. The data input unit can also suggest the optimal input method based on the user's past feedback. This makes it possible to provide the user with the optimal input method by reflecting the past feedback. Some or all of the above-described processing in the data input unit may be performed using, for example, AI, or may be performed without using AI. For example, the data input unit can input the user's past feedback into a generation AI and have the generation AI customize the input method.

[0040] The prediction unit can adjust the level of detail of the prediction based on the importance of the data when making a prediction. For example, the prediction unit makes a detailed prediction based on important data. For example, the prediction unit makes a brief prediction based on less important data. The prediction unit can also dynamically adjust the level of detail of the prediction according to the importance of the data. This makes it possible to provide important information in detail by adjusting the level of detail of the prediction according to the importance of the data. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input the importance of the data to the generation AI and have the generation AI adjust the level of detail of the prediction.

[0041] The prediction unit can apply different prediction algorithms depending on the data category during prediction. For example, the prediction unit applies a specific algorithm to data related to menstrual cycles. For example, the prediction unit applies a different algorithm to data related to health conditions. The prediction unit can also select an optimal prediction algorithm depending on the data category. This improves the accuracy of prediction by applying the optimal prediction algorithm depending on the data category. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input the data category into a generation AI and have the generation AI select the optimal prediction algorithm.

[0042] The prediction unit can improve the accuracy of prediction by referring to the user's past prediction results when making predictions. The prediction unit, for example, adjusts the prediction algorithm based on the user's past prediction results. For example, the prediction unit finds a pattern for improving prediction accuracy from the user's past prediction results. The prediction unit can also dynamically improve prediction accuracy by referring to the user's past prediction results. In this way, prediction accuracy is improved by referring to past prediction results. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input past prediction results into a generation AI and have the generation AI adjust the prediction algorithm.

[0043] The prediction unit can determine the priority of predictions based on the time of data submission when making predictions. For example, the prediction unit prioritizes predictions based on the most recent data. For example, the prediction unit postpones predictions based on data submitted earlier. The prediction unit can also dynamically adjust the priority of predictions according to the time of data submission. This allows the most recent information to be provided preferentially by determining the priority of predictions based on the time of data submission. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input the time of data submission to the generation AI and have the generation AI determine the priority of predictions.

[0044] The prediction unit can adjust the order of predictions based on the relevance of the data during prediction. For example, the prediction unit prioritizes predictions based on highly relevant data. For example, the prediction unit postpones predictions based on less relevant data. The prediction unit can also dynamically adjust the order of predictions according to the relevance of the data. This allows highly relevant information to be provided preferentially by adjusting the order of predictions based on the relevance of the data. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input the relevance of the data to the generation AI and have the generation AI adjust the order of predictions.

[0045] The prediction unit can adjust the use of technical terminology in the prediction according to the user's level of expertise when making a prediction. For example, if the user has technical expertise, the prediction unit uses detailed technical terminology. For example, if the user does not have technical expertise, the prediction unit explains the prediction result in simple terms. The prediction unit can also dynamically adjust the use of technical terminology in the prediction according to the user's level of expertise. This allows the prediction result to be more easily understood by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input the user's level of expertise into the generation AI and have the generation AI adjust the use of technical terminology.

[0046] When providing advice, the advice providing unit can analyze the user's past health data and select optimal advice. The advice providing unit, for example, provides advice on optimal nutritional intake based on the user's past health data. For example, the advice providing unit can suggest an optimal exercise method based on the user's past health data. The advice providing unit can also analyze the user's past health data and suggest an optimal stress management method. In this way, optimal advice can be provided to the user by analyzing past health data. Some or all of the above-mentioned processing in the advice providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice providing unit can input the user's past health data into a generation AI and have the generation AI select optimal advice.

[0047] When providing advice, the advice providing unit can customize the content of the advice based on the user's current living situation. For example, if the user is busy, the advice providing unit provides advice that is easy to implement. For example, if the user is relaxed, the advice providing unit provides detailed advice. The advice providing unit can also dynamically customize the content of the advice according to the user's current living situation. This makes it possible to provide advice that is easy to implement by customizing the content of the advice according to the current living situation. Some or all of the above-mentioned processing in the advice providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice providing unit can input the user's living situation data into a generation AI and have the generation AI customize the advice.

[0048] The advice providing unit can improve the advice method by reflecting user feedback when providing advice. For example, if a user provides feedback on advice provided in the past, the advice providing unit improves the advice method based on that feedback. For example, the advice providing unit analyzes the user's feedback and suggests an optimal advice method. The advice providing unit can also dynamically improve the content of the advice based on the user's feedback. In this way, the advice method can be continuously improved by reflecting the feedback. Some or all of the above-mentioned processing in the advice providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice providing unit can input user feedback data into a generation AI and have the generation AI improve the advice.

[0049] When providing advice, the advice providing unit can provide optimal advice by taking into account the user's geographical location information. For example, when the user is at home, the advice providing unit provides advice that can be implemented at home. For example, when the user is out, the advice providing unit provides advice that can be implemented while out. Furthermore, when the user is traveling, the advice providing unit can also provide advice that can be implemented while traveling. In this way, by providing optimal advice based on geographical location information, it is possible to provide advice that is easy to implement. Some or all of the above-mentioned processing in the advice providing unit may be performed using AI, for example, or may be performed without using AI. For example, the advice providing unit can input the user's geographical location information to the generation AI and have the generation AI select optimal advice.

[0050] When providing advice, the advice providing unit can analyze the user's social media activity and suggest the content of the advice. The advice providing unit can provide advice based on, for example, health information shared by the user on social media. For example, the advice providing unit can provide relevant advice based on the user's social media activity. The advice providing unit can also provide relevant advice by referring to the activity of the user's friends on social media. In this way, relevant advice can be provided by analyzing social media activity. Some or all of the above-mentioned processing in the advice providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the advice providing unit can input data on the user's social media activity into a generation AI and have the generation AI suggest the content of the advice.

[0051] When providing advice, the advice providing unit can customize the method of advice by reflecting the user's past feedback. For example, if the user provides feedback on advice provided in the past, the advice providing unit customizes the method of advice based on that feedback. For example, the advice providing unit analyzes the user's feedback and suggests an optimal method of advice. The advice providing unit can also dynamically customize the content of the advice based on the user's feedback. In this way, optimal advice can be provided to the user by reflecting past feedback. Some or all of the above-mentioned processing in the advice providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice providing unit can input the user's feedback data into a generation AI and have the generation AI customize the method of advice.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The health management system can further acquire the user's sleep data and analyze it in the prediction unit. For example, the user records sleep data using a smartwatch and inputs the data into the data input unit. The prediction unit then combines and analyzes the sleep data and menstrual cycle data to more accurately predict the user's physical condition and stress level. Furthermore, the advice providing unit can provide the user with appropriate sleep management advice based on the sleep data. This allows the user to improve not only their menstrual cycle but also the quality of their sleep.

[0054] The health management system can further acquire the user's dietary data and analyze it in the prediction unit. For example, the user inputs the details of their diet into an application and sends the data to the data input unit. The prediction unit then analyzes the dietary data in combination with the menstrual cycle data to more accurately predict the user's nutritional status and physical condition. Furthermore, the advice providing unit can provide the user with appropriate nutritional management advice based on the dietary data. This allows the user to improve not only their menstrual cycle but also the quality of their diet.

[0055] The health management system can further acquire the user's exercise data and analyze it in the prediction unit. For example, the user inputs details of their exercise into an application and sends the data to the data input unit. The prediction unit then combines and analyzes the exercise data and menstrual cycle data to more accurately predict the user's physical condition and stress level. Furthermore, the advice providing unit can provide the user with appropriate exercise management advice based on the exercise data. This allows the user to improve not only their menstrual cycle but also the quality of their exercise.

[0056] The health management system can further acquire the user's fluid intake data and analyze it in the prediction unit. For example, the user inputs details of fluid intake into an application and sends the data to the data input unit. The prediction unit then combines and analyzes the fluid intake data and menstrual cycle data to more accurately predict the user's physical condition and fluid balance. Furthermore, the advice providing unit can provide the user with appropriate fluid management advice based on the fluid intake data. This allows the user to improve not only their menstrual cycle but also the quality of their fluid intake.

[0057] The health management system can further measure the user's stress level and analyze it in the prediction unit. For example, the user uses a device to measure their stress level and sends the data to the data input unit. The prediction unit then combines and analyzes the stress level data and menstrual cycle data to more accurately predict the user's physical condition and stress level. Furthermore, the advice providing unit can provide the user with appropriate stress management advice based on the stress level data. This allows the user to manage not only their menstrual cycle but also their stress level.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The data input unit allows the user to input data about their menstrual cycle. The data input by the user includes the start and end dates of menstruation, details of symptoms, etc. The data input unit can input data through a smartphone app or a web app. The data input unit can also estimate the user's emotions and adjust the timing of data input based on the estimated emotions. For example, if the user is feeling stressed, it can send a notification to prompt the user to input data at a time when they are able to relax. Step 2: The prediction unit uses the generation AI to analyze the data entered by the data input unit and predict the user's menstrual cycle. The prediction learns from past data and patterns and predicts the start date of the next period and ovulation date. For example, the generation AI can make predictions based on past menstrual cycle data and symptom records. Step 3: The advice providing unit provides health advice based on the results predicted by the prediction unit. The advice includes advice on premenstrual health management and nutritional intake, advice on stress management during ovulation, etc. For example, the advice providing unit can provide the user with specific advice on nutritional intake and rest based on the prediction results.

[0060] (Example 2) A health management system according to an embodiment of the present invention utilizes a generative AI to predict a user's menstrual cycle and provide health advice tailored to the health needs of the femtech field. In the health management system, a user inputs data about their menstrual cycle, and a generative AI analyzes the data to predict the menstrual cycle and provides health advice based on the prediction results. For example, in a health management system, a user inputs data about their menstrual cycle through an application. The generative AI then analyzes the data and learns from past data and patterns to predict the start date of the next period and ovulation date. Furthermore, the generative AI provides health advice to the user based on the prediction results. For example, the advice may include advice on premenstrual health management and nutritional intake, and advice on stress management during ovulation. This allows the health management system to provide healthcare tailored to the user's individual needs and improve women's health. By predicting a user's menstrual cycle and providing health advice, the health management system can improve women's health. For example, users can understand their physical condition and manage their health in accordance with their menstrual cycle. This improves women's quality of life and enables them to live healthier lives.

[0061] A health management system according to an embodiment includes a data input unit, a prediction unit, and an advice providing unit. The data input unit allows a user to input data related to their menstrual cycle. The data input by the user includes, but is not limited to, the start and end dates of menstruation and details of symptoms. The data input unit can input data via, for example, a smartphone app or a web app. The data input unit can also estimate the user's emotions and adjust the timing of data input based on the estimated emotions. For example, if the user is feeling stressed, a notification can be sent encouraging the user to input data during a time when they are able to relax. The prediction unit uses a generation AI to analyze the data input by the data input unit and predict the user's menstrual cycle. For example, the prediction can learn from past data and patterns to predict the start date of the next menstrual period and the date of ovulation, but is not limited to such examples. For example, the generation AI can make predictions based on past menstrual cycle data and symptom records. The advice providing unit provides health advice based on the results predicted by the prediction unit. The advice can include, but is not limited to, advice on premenstrual health management, nutritional intake, and stress management during ovulation. For example, the advice providing unit can provide the user with specific advice on nutritional intake and rest based on the prediction results. As a result, the health management system according to the embodiment can predict the user's menstrual cycle and provide health advice, thereby improving the health status of women. For example, the user can understand the condition of her body and manage her health in accordance with her menstrual cycle. This improves the quality of life of women, enabling them to live healthier lives.

[0062] The data input unit allows a user to input data related to the menstrual cycle through an application. Examples of applications include, but are not limited to, smartphone applications and web applications. For example, the data input unit allows a user to input the start and end dates of menstruation and details of symptoms through a smartphone application. The data input unit also allows a user to input similar data through a web application. In this way, the system can obtain data related to the menstrual cycle by the user inputting data through the application. Some or all of the above-described processing in the data input unit may be performed using, or without, AI. For example, the data input unit may input the data entered by the user to a generation AI and have the generation AI analyze the data.

[0063] The prediction unit can learn from past data and patterns to predict the start date of the next period and the date of ovulation. The prediction unit makes predictions based on, for example, past menstrual cycle data and symptom records. For example, the prediction unit learns past menstrual start and end dates and detailed symptoms to predict the start date of the next period and the date of ovulation. The prediction unit can also use a generation AI to learn from past data and patterns to improve the accuracy of the prediction. For example, the prediction unit can analyze past data and predict the start date of the next period and the date of ovulation with high accuracy. Furthermore, the prediction unit can estimate the user's emotions and adjust the way the prediction is expressed based on the estimated emotions. For example, if the user is relaxed, a detailed prediction result is provided, and if the user is stressed, a concise prediction result is provided. This allows the prediction unit to accurately predict the start date of the next period and the date of ovulation by learning from past data and patterns. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the prediction unit can input past data into the generation AI and have the generation AI perform the prediction.

[0064] The advice providing unit can provide advice on premenstrual health management and nutritional intake based on the prediction results. The advice providing unit, for example, provides specific nutritional intake and rest advice to the user based on the prediction results. For example, the advice providing unit provides nutritional intake and rest advice tailored to a user who tends to feel unwell before their period to that user. The advice providing unit can also provide advice based on the prediction results using a generation AI. For example, the generation AI analyzes the prediction results and generates optimal advice for the user. Furthermore, the advice providing unit can estimate the user's emotions and adjust the way the advice is expressed based on the estimated emotions. For example, detailed advice is provided when the user is relaxed, and concise advice is provided when the user is stressed. This supports the user's health management by providing appropriate advice based on the prediction results. Some or all of the above-described processing in the advice providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the advice providing unit can input the prediction results to the generation AI and have the generation AI generate advice.

[0065] The advice providing unit can provide advice on stress management during the ovulation period based on the prediction results. The advice providing unit can provide specific stress management advice to the user based on the prediction results. For example, the advice providing unit can provide relaxation methods and stress management advice tailored to a user who tends to experience increased stress during ovulation. The advice providing unit can also provide advice based on the prediction results using a generation AI. For example, the generation AI can analyze the prediction results and generate optimal stress management advice for the user. The advice providing unit can also estimate the user's emotions and adjust the way the advice is expressed based on the estimated emotions. For example, if the user is relaxed, detailed advice can be provided, and if the user is feeling stressed, concise advice can be provided. This can reduce the user's stress by providing advice on stress management during the ovulation period. Some or all of the above-described processing in the advice providing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the advice providing unit can input the prediction results to the generation AI and have the generation AI generate stress management advice.

[0066] The data input unit can estimate the user's emotions and adjust the timing of data input based on the estimated user emotions. For example, if the user is feeling stressed, the data input unit can send a notification prompting the user to input data during a time when the user is able to relax. For example, if the user is relaxed, the data input unit can send a notification prompting the user to input data, thereby improving the accuracy of input. Furthermore, if the user is busy, the data input unit can suggest postponing data input and remind the user later. This improves the accuracy of input by adjusting the timing of data input according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the data input unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the data input unit can input the user's emotion data into a generative AI and have the generative AI adjust the timing of data input.

[0067] The data input unit can analyze the user's past data input history and select the optimal input method. For example, the data input unit prioritizes and suggests input methods (such as voice and text) that the user has frequently used in the past. For example, the data input unit sends notifications avoiding time periods when the user tends to forget to input data in the past. The data input unit can also find specific patterns from the user's past input history and suggest the optimal input timing. In this way, by analyzing the past data input history, the optimal input method can be provided to the user. Some or all of the above-mentioned processing in the data input unit may be performed, for example, using AI or may be performed without using AI. For example, the data input unit can input the past data input history into a generation AI and have the generation AI select the optimal input method.

[0068] The data input unit can filter data based on the user's current health condition and lifestyle when inputting data. For example, if the user is in poor health, the data input unit simplifies the input items to reduce the burden. For example, if the user is busy, the data input unit only requires the minimum amount of data to be input. The data input unit can also prompt the user to input detailed data when the user is relaxed. This reduces the burden by adjusting the data input according to the user's health condition and lifestyle. Some or all of the above-mentioned processing in the data input unit may be performed using, for example, AI, or may be performed without using AI. For example, the data input unit can input data on the user's health condition and lifestyle into the generation AI and have the generation AI perform filtering.

[0069] The data input unit can select the optimal input means depending on the user's input method when inputting data. For example, if the user prefers voice input, the data input unit preferentially suggests voice input. For example, if the user prefers text input, the data input unit preferentially suggests text input. Furthermore, if the user prefers image input, the data input unit can also preferentially suggest image input. This improves input efficiency by selecting the optimal means depending on the user's input method. Some or all of the above-described processing in the data input unit may be performed using, for example, AI, or may be performed without using AI. For example, the data input unit can input data on the user's input method to a generation AI and have the generation AI select the optimal input means.

[0070] The data input unit can estimate the user's emotions and determine the priority of data to be input based on the estimated user emotions. For example, if the user is feeling stressed, the data input unit prompts the user to input only important data. For example, if the user is relaxed, the data input unit prompts the user to input detailed data. The data input unit can also remind the user to input data later if the user is busy. This allows important data to be input preferentially by determining the priority of data according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI 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 data input unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the data input unit can input the user's emotion data into a generation AI and have the generation AI determine the priority of the data.

[0071] When inputting data, the data input unit can prioritize inputting highly relevant data taking into account the user's geographical location information. For example, when the user is at home, the data input unit prioritizes inputting health data from within the home. For example, when the user is out, the data input unit prioritizes inputting health data from the user's location. Furthermore, when the user is traveling, the data input unit can also prioritize inputting health data from the user's location. This increases the accuracy of the data by inputting highly relevant data based on the user's geographical location information. Some or all of the above-described processing in the data input unit may be performed using, or without, AI. For example, the data input unit can input the user's geographical location information to the generation AI and have the generation AI select highly relevant data.

[0072] The data input unit can analyze the user's social media activity and input relevant data when inputting data. The data input unit, for example, automatically inputs health information shared by the user on social media. For example, the data input unit inputs relevant health data from the user's social media activity. The data input unit can also input relevant health data by referring to the activity of the user's friends on social media. This allows for efficient input of relevant data by analyzing social media activity. Some or all of the above-described processing in the data input unit may be performed using, for example, AI, or may be performed without using AI. For example, the data input unit can input data on the user's social media activity to the generation AI and have the generation AI input relevant data.

[0073] The data input unit can customize the input method by reflecting the user's past feedback when inputting data. For example, the data input unit preferentially suggests methods that the user has found easy to use in the past. For example, the data input unit avoids methods that the user has found difficult to use in the past. The data input unit can also suggest the optimal input method based on the user's past feedback. This makes it possible to provide the user with the optimal input method by reflecting the past feedback. Some or all of the above-described processing in the data input unit may be performed using, for example, AI, or may be performed without using AI. For example, the data input unit can input the user's past feedback into a generation AI and have the generation AI customize the input method.

[0074] The prediction unit can estimate the user's emotions and adjust the way the prediction is expressed based on the estimated user's emotions. For example, if the user is relaxed, the prediction unit provides a detailed prediction result. For example, if the user is stressed, the prediction unit can provide a concise prediction result. Furthermore, if the user is busy, the prediction unit can provide a prediction result that focuses on the main points. This allows the prediction result to be better understood by adjusting the way the prediction is expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 prediction unit can be performed using an AI, for example, or without an AI. For example, the prediction unit can input the user's emotion data into the generation AI and have the generation AI adjust the way the prediction is expressed.

[0075] The prediction unit can adjust the level of detail of the prediction based on the importance of the data when making a prediction. For example, the prediction unit makes a detailed prediction based on important data. For example, the prediction unit makes a brief prediction based on less important data. The prediction unit can also dynamically adjust the level of detail of the prediction according to the importance of the data. This makes it possible to provide important information in detail by adjusting the level of detail of the prediction according to the importance of the data. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input the importance of the data to the generation AI and have the generation AI adjust the level of detail of the prediction.

[0076] The prediction unit can apply different prediction algorithms depending on the data category during prediction. For example, the prediction unit applies a specific algorithm to data related to menstrual cycles. For example, the prediction unit applies a different algorithm to data related to health conditions. The prediction unit can also select an optimal prediction algorithm depending on the data category. This improves the accuracy of prediction by applying the optimal prediction algorithm depending on the data category. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input the data category into a generation AI and have the generation AI select the optimal prediction algorithm.

[0077] The prediction unit can improve the accuracy of prediction by referring to the user's past prediction results when making predictions. The prediction unit, for example, adjusts the prediction algorithm based on the user's past prediction results. For example, the prediction unit finds a pattern for improving prediction accuracy from the user's past prediction results. The prediction unit can also dynamically improve prediction accuracy by referring to the user's past prediction results. In this way, prediction accuracy is improved by referring to past prediction results. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input past prediction results into a generation AI and have the generation AI adjust the prediction algorithm.

[0078] The prediction unit can estimate the user's emotion and adjust the length of the prediction based on the estimated user emotion. For example, the prediction unit provides a detailed prediction when the user is relaxed. For example, the prediction unit provides a concise prediction when the user is stressed. The prediction unit can also provide a short prediction that covers the main points when the user is busy. This allows the length of the prediction to be adjusted according to the user's emotion, thereby deepening understanding of the prediction result. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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-mentioned processing in the prediction unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the prediction unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the prediction.

[0079] The prediction unit can determine the priority of predictions based on the time of data submission when making predictions. For example, the prediction unit prioritizes predictions based on the most recent data. For example, the prediction unit postpones predictions based on data submitted earlier. The prediction unit can also dynamically adjust the priority of predictions according to the time of data submission. This allows the most recent information to be provided preferentially by determining the priority of predictions based on the time of data submission. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input the time of data submission to the generation AI and have the generation AI determine the priority of predictions.

[0080] The prediction unit can adjust the order of predictions based on the relevance of the data during prediction. For example, the prediction unit prioritizes predictions based on highly relevant data. For example, the prediction unit postpones predictions based on less relevant data. The prediction unit can also dynamically adjust the order of predictions according to the relevance of the data. This allows highly relevant information to be provided preferentially by adjusting the order of predictions based on the relevance of the data. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input the relevance of the data to the generation AI and have the generation AI adjust the order of predictions.

[0081] The prediction unit can adjust the use of technical terminology in the prediction according to the user's level of expertise when making a prediction. For example, if the user has technical expertise, the prediction unit uses detailed technical terminology. For example, if the user does not have technical expertise, the prediction unit explains the prediction result in simple terms. The prediction unit can also dynamically adjust the use of technical terminology in the prediction according to the user's level of expertise. This allows the prediction result to be more easily understood by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input the user's level of expertise into the generation AI and have the generation AI adjust the use of technical terminology.

[0082] The advice providing 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 relaxed, the advice providing unit can provide detailed advice. For example, if the user is stressed, the advice providing unit can provide concise advice. Furthermore, if the user is busy, the advice providing unit can also provide advice that focuses on the main points. This adjusts the way the advice is expressed according to the user's emotions, thereby improving the ease with which the advice is accepted. Emotion estimation is achieved using an emotion estimation function, for example, using 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 advice providing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the advice providing unit can input the user's emotion data into the generation AI and have the generation AI adjust the way the advice is expressed.

[0083] When providing advice, the advice providing unit can analyze the user's past health data and select optimal advice. The advice providing unit, for example, provides advice on optimal nutritional intake based on the user's past health data. For example, the advice providing unit can suggest an optimal exercise method based on the user's past health data. The advice providing unit can also analyze the user's past health data and suggest an optimal stress management method. In this way, optimal advice can be provided to the user by analyzing past health data. Some or all of the above-mentioned processing in the advice providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice providing unit can input the user's past health data into a generation AI and have the generation AI select optimal advice.

[0084] When providing advice, the advice providing unit can customize the content of the advice based on the user's current living situation. For example, if the user is busy, the advice providing unit provides advice that is easy to implement. For example, if the user is relaxed, the advice providing unit provides detailed advice. The advice providing unit can also dynamically customize the content of the advice according to the user's current living situation. This makes it possible to provide advice that is easy to implement by customizing the content of the advice according to the current living situation. Some or all of the above-mentioned processing in the advice providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice providing unit can input the user's living situation data into a generation AI and have the generation AI customize the advice.

[0085] The advice providing unit can improve the advice method by reflecting user feedback when providing advice. For example, if a user provides feedback on advice provided in the past, the advice providing unit improves the advice method based on that feedback. For example, the advice providing unit analyzes the user's feedback and suggests an optimal advice method. The advice providing unit can also dynamically improve the content of the advice based on the user's feedback. In this way, the advice method can be continuously improved by reflecting the feedback. Some or all of the above-mentioned processing in the advice providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice providing unit can input user feedback data into a generation AI and have the generation AI improve the advice.

[0086] The advice providing unit can estimate the user's emotions and determine the priority of advice based on the estimated user emotions. For example, if the user is feeling stressed, the advice providing unit can prioritize providing advice on stress management. For example, if the user is relaxed, the advice providing unit can prioritize providing advice on nutritional intake and exercise. Furthermore, if the user is busy, the advice providing unit can prioritize providing advice that is easy to implement. In this way, by determining the priority of advice according to the user's emotions, important advice can be provided 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 advice providing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the advice providing unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of advice.

[0087] When providing advice, the advice providing unit can provide optimal advice by taking into account the user's geographical location information. For example, when the user is at home, the advice providing unit provides advice that can be implemented at home. For example, when the user is out, the advice providing unit provides advice that can be implemented while out. Furthermore, when the user is traveling, the advice providing unit can also provide advice that can be implemented while traveling. In this way, by providing optimal advice based on geographical location information, it is possible to provide advice that is easy to implement. Some or all of the above-mentioned processing in the advice providing unit may be performed using AI, for example, or may be performed without using AI. For example, the advice providing unit can input the user's geographical location information to the generation AI and have the generation AI select optimal advice.

[0088] When providing advice, the advice providing unit can analyze the user's social media activity and suggest the content of the advice. The advice providing unit can provide advice based on, for example, health information shared by the user on social media. For example, the advice providing unit can provide relevant advice based on the user's social media activity. The advice providing unit can also provide relevant advice by referring to the activity of the user's friends on social media. In this way, relevant advice can be provided by analyzing social media activity. Some or all of the above-mentioned processing in the advice providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the advice providing unit can input data on the user's social media activity into a generation AI and have the generation AI suggest the content of the advice.

[0089] When providing advice, the advice providing unit can customize the method of advice by reflecting the user's past feedback. For example, if the user provides feedback on advice provided in the past, the advice providing unit customizes the method of advice based on that feedback. For example, the advice providing unit analyzes the user's feedback and suggests an optimal method of advice. The advice providing unit can also dynamically customize the content of the advice based on the user's feedback. In this way, optimal advice can be provided to the user by reflecting past feedback. Some or all of the above-mentioned processing in the advice providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice providing unit can input the user's feedback data into a generation AI and have the generation AI customize the method of advice. === Hard Collateral 1-1 === Each of the multiple elements including the data input unit, prediction unit, and advice providing unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the data input unit allows a user to input data related to their menstrual cycle through the reception device 38 of the smart device 14. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input data using a generation AI to predict the user's menstrual cycle. The advice providing unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates health advice based on the prediction result and provides it to the user through the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned data input unit, prediction unit, and advice providing unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the data input unit allows the user to voice-input data related to their menstrual cycle through the microphone 238 of the smart glasses 214. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input data using a generation AI to predict the user's menstrual cycle. The advice providing unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates health advice based on the prediction result and provides it to the user through the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned data input unit, prediction unit, and advice providing unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the data input unit allows the user to voice-input data related to their menstrual cycle through the microphone 238 of the headset-type terminal 314. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input data using a generation AI to predict the user's menstrual cycle. The advice providing unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates health advice based on the prediction result and provides it to the user through the speaker 240 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned data input unit, prediction unit, and advice providing unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the data input unit allows the user to input data related to their menstrual cycle by voice through the microphone 238 of the robot 414. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input data using a generation AI to predict the user's menstrual cycle. The advice providing unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates health advice based on the prediction result and provides it to the user through the speaker 240 of the robot 414.

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

[0091] The health management system can further acquire the user's sleep data and analyze it in the prediction unit. For example, the user records sleep data using a smartwatch and inputs the data into the data input unit. The prediction unit then combines and analyzes the sleep data and menstrual cycle data to more accurately predict the user's physical condition and stress level. Furthermore, the advice providing unit can provide the user with appropriate sleep management advice based on the sleep data. This allows the user to improve not only their menstrual cycle but also the quality of their sleep.

[0092] The health management system can further acquire the user's dietary data and analyze it in the prediction unit. For example, the user inputs the details of their diet into an application and sends the data to the data input unit. The prediction unit then analyzes the dietary data in combination with the menstrual cycle data to more accurately predict the user's nutritional status and physical condition. Furthermore, the advice providing unit can provide the user with appropriate nutritional management advice based on the dietary data. This allows the user to improve not only their menstrual cycle but also the quality of their diet.

[0093] The health management system can further acquire the user's exercise data and analyze it in the prediction unit. For example, the user inputs details of their exercise into an application and sends the data to the data input unit. The prediction unit then combines and analyzes the exercise data and menstrual cycle data to more accurately predict the user's physical condition and stress level. Furthermore, the advice providing unit can provide the user with appropriate exercise management advice based on the exercise data. This allows the user to improve not only their menstrual cycle but also the quality of their exercise.

[0094] The health management system can further acquire the user's fluid intake data and analyze it in the prediction unit. For example, the user inputs details of fluid intake into an application and sends the data to the data input unit. The prediction unit then combines and analyzes the fluid intake data and menstrual cycle data to more accurately predict the user's physical condition and fluid balance. Furthermore, the advice providing unit can provide the user with appropriate fluid management advice based on the fluid intake data. This allows the user to improve not only their menstrual cycle but also the quality of their fluid intake.

[0095] The health management system can further measure the user's stress level and analyze it in the prediction unit. For example, the user uses a device to measure their stress level and sends the data to the data input unit. The prediction unit then combines and analyzes the stress level data and menstrual cycle data to more accurately predict the user's physical condition and stress level. Furthermore, the advice providing unit can provide the user with appropriate stress management advice based on the stress level data. This allows the user to manage not only their menstrual cycle but also their stress level.

[0096] The health management system can estimate the user's emotions and customize the content of advice based on the estimated emotions. For example, if the user is feeling stressed, it can provide relaxation methods and stress reduction advice. If the user is relaxed, it can provide more detailed health management advice. Also, if the user is busy, it can provide advice that is easy to implement. In this way, customizing the content of advice according to the user's emotions enables more effective health management.

[0097] The health management system can estimate the user's emotions and adjust the data entry interface based on the estimated emotions. For example, if the user is feeling stressed, a simple and intuitive interface can be provided. If the user is relaxed, an interface that allows detailed data entry can be provided. Also, if the user is busy, voice input or simple options can be provided. In this way, the burden of input can be reduced by adjusting the data entry interface according to the user's emotions.

[0098] The health management system can estimate the user's emotions and adjust the timing of notifications based on the estimated emotions. For example, if the user is feeling stressed, the system can send a notification at a time when the user is able to relax. If the user is relaxed, the system can send a notification immediately. If the user is busy, the system can also send a notification later. In this way, adjusting the timing of notifications according to the user's emotions improves the user's acceptance of notifications.

[0099] The health management system can estimate the user's emotions and prioritize data based on the estimated emotions. For example, if the user is feeling stressed, the system will prompt the user to input only important data. If the user is relaxed, the system will prompt the user to input detailed data. If the user is busy, the system can also remind the user to input data later. This allows the system to prioritize data based on the user's emotions, allowing important data to be input preferentially.

[0100] The health management system can estimate the user's emotions and adjust the way advice is presented based on the estimated emotions. For example, if the user is relaxed, detailed advice can be provided. If the user is stressed, concise advice can be provided. If the user is busy, advice that focuses on the main points can be provided. In this way, adjusting the way advice is presented according to the user's emotions increases the ease with which the advice is accepted.

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

[0102] Step 1: The data input unit allows the user to input data about their menstrual cycle. The data input by the user includes the start and end dates of menstruation, details of symptoms, etc. The data input unit can input data through a smartphone app or a web app. The data input unit can also estimate the user's emotions and adjust the timing of data input based on the estimated emotions. For example, if the user is feeling stressed, it can send a notification to prompt the user to input data at a time when they are able to relax. Step 2: The prediction unit uses the generation AI to analyze the data entered by the data input unit and predict the user's menstrual cycle. The prediction learns from past data and patterns and predicts the start date of the next period and ovulation date. For example, the generation AI can make predictions based on past menstrual cycle data and symptom records. Step 3: The advice providing unit provides health advice based on the results predicted by the prediction unit. The advice includes advice on premenstrual health management and nutritional intake, advice on stress management during ovulation, etc. For example, the advice providing unit can provide the user with specific advice on nutritional intake and rest based on the prediction results.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

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

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0147] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0156] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0158] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0159] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0160] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0164] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0165] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0166] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0167] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0168] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0169] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0171] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0172] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0173] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0174] [Explanation of symbols]

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

Claims

1. a data input unit for inputting data relating to the user's menstrual cycle; a prediction unit that analyzes the data input by the data input unit and predicts the menstrual cycle of the user; an advice providing unit that provides health advice based on the results predicted by the prediction unit; A system characterized by:

2. The data input unit The user enters data about their menstrual cycle through the application.

2. The system of claim 1.

3. The prediction unit Learns past data and patterns to predict the start date of your next period and ovulation date 2. The system of claim 1.

4. The advice providing unit Providing advice on premenstrual health management and nutritional intake based on the prediction results 2. The system of claim 1.

5. The advice providing unit Providing advice on stress management during ovulation based on prediction results 2. The system of claim 1.

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

2. The system of claim 1.

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

8. The data input unit Filtering data entry based on the user's current health and lifestyle status 2. The system of claim 1.

Images