System

Abstract

An object of a system according to an embodiment is to provide accurate advice and continuous support to a user who receives infertility treatment.SOLUTION: A system includes a collection unit, an analysis unit, a proposal unit, and a support unit. The collection unit collects health data of a user. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes a pregnancy timing and a lifestyle on the basis of an analysis result obtained by the analysis unit. The support unit provides support based on the individual treatment plan on the basis of the content proposed by the proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately provide accurate advice and ongoing support to users undergoing infertility treatment, and there is room for improvement.

[0005] The system according to the embodiment aims to provide accurate advice and continuous support to users undergoing infertility treatment. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, and a support unit. The collection unit collects health data of a user. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes pregnancy timing and lifestyle based on the analysis results obtained by the analysis unit. The support unit provides support based on an individual treatment plan based on the content proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide accurate advice and continuous support to users undergoing infertility treatment. [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) An infertility treatment support system according to an embodiment of the present invention collects a user's health data, analyzes it using AI, suggests optimal pregnancy timing and lifestyle, and provides support based on an individualized treatment plan. The infertility treatment support system collects a user's health data, analyzes it using AI, and suggests optimal pregnancy timing and lifestyle, thereby providing precise support based on an individualized treatment plan. For example, the infertility treatment support system allows a user to input information about their own health data and lifestyle. For example, the infertility treatment support system inputs data such as menstrual cycle, body temperature, diet, and exercise habits. This information is analyzed by AI. The infertility treatment support system then uses AI to suggest optimal pregnancy timing and lifestyle based on the input data. For example, the AI ​​analyzes fluctuations in menstrual cycle and body temperature to predict ovulation. The AI ​​also analyzes diet and exercise habits to suggest lifestyles suitable for pregnancy. Furthermore, the infertility treatment support system uses AI to provide precise support based on an individualized treatment plan. For example, the AI ​​analyzes the treatment the user is receiving and its progress, and suggests next steps and precautions. The AI ​​also notifies the user of the timing of necessary tests and examinations based on the progress of treatment. This allows the infertility treatment support system to reduce the stress of infertility treatment for users and provide a healthy and peaceful pregnancy experience. This allows the infertility treatment support system to reduce the stress of infertility treatment for users and provide a healthy and peaceful pregnancy experience. For example, by receiving accurate advice from AI, users can understand their own condition and take appropriate measures. In addition, by receiving continuous support, they can progress smoothly through treatment. Furthermore, by following suggestions from AI, users can maintain a lifestyle that is suitable for pregnancy. In addition, by receiving support based on an individual treatment plan, they can proceed with treatment with peace of mind. In this way, an infertility treatment support system that provides accurate advice and continuous support from AI becomes a reliable partner for users, enabling them to achieve a hopeful infertility treatment experience.

[0029] The infertility treatment support system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, and a support unit. The collection unit collects health data of a user. The health data of the user includes, but is not limited to, for example, menstrual cycle, body temperature, dietary details, and exercise habits. For example, the collection unit collects body temperature and heart rate using a wearable device. The collection unit can also collect data manually entered by the user. For example, the user inputs their menstrual cycle and dietary details through an app. The collection unit can also record exercise habits using a smartphone sensor. For example, the collection unit records the user's step count using an acceleration sensor in the smartphone. The analysis unit analyzes the data collected by the collection unit. The analysis can be performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to, for example. For example, the analysis unit can analyze menstrual cycle patterns using statistical analysis. The analysis unit can also analyze body temperature fluctuations using a machine learning algorithm. The analysis unit can also analyze dietary details and exercise habits using AI. For example, the analysis unit can evaluate the nutritional balance of dietary details using AI. The suggestion unit suggests optimal pregnancy timing and lifestyle based on the analysis results obtained by the analysis unit. Suggestions include, but are not limited to, recommendations for improving diet and exercise. For example, the suggestion unit suggests to the user to consume specific ingredients based on the analysis results. The suggestion unit can also suggest to the user to perform specific exercises based on the analysis results. Furthermore, the suggestion unit can also suggest stress management methods to the user based on the analysis results. For example, the suggestion unit suggests relaxation techniques to the user. The support unit provides support based on an individual treatment plan based on the content suggested by the suggestion unit. Support includes, but is not limited to, online counseling and referrals to medical institutions. For example, the support unit provides online counseling to the user. The support unit can also refer the user to an appropriate medical institution. Furthermore, the support unit can notify the user of the timing of necessary tests and examinations according to the progress of treatment.For example, the support unit notifies the user of the date of the next medical examination. As a result, the infertility treatment support system according to the embodiment can reduce the stress of infertility treatment and provide a healthy and peaceful pregnancy experience by collecting, analyzing, suggesting, and supporting the user's health data.

[0030] The collection unit can collect data on the user's menstrual cycle, body temperature, dietary details, and exercise habits. For example, the collection unit records the user's menstrual cycle using an app. For example, the user records the menstrual cycle by entering the start and end dates of their period. The collection unit can also measure body temperature using a basal thermometer and collect data. For example, the user measures their temperature using a basal thermometer every morning and enters the data into the app. The collection unit can also record dietary details in a food diary and collect data. For example, the user enters their daily dietary details into the app. The collection unit can also record exercise habits using a fitness tracker and collect data. For example, the user wears a fitness tracker and records the amount of exercise. This allows for more precise analysis and recommendations by collecting data such as the user's menstrual cycle, body temperature, dietary details, and exercise habits. Some or all of the above-described processing by the collection unit can be performed using, or without, AI. For example, the collection unit can input the menstrual cycle data entered by the user into AI and have the AI ​​verify the consistency of the data.

[0031] The analysis unit can analyze the collected data and predict the date of ovulation. The analysis unit, for example, analyzes changes in basal body temperature and predicts the date of ovulation. For example, it analyzes the timing of basal body temperature rise and predicts the date of ovulation. The analysis unit can also analyze hormone level measurement data and predict the date of ovulation. For example, it analyzes fluctuations in hormone levels and predicts the date of ovulation. Furthermore, the analysis unit can analyze menstrual cycle patterns and predict the date of ovulation. For example, it predicts the next date of ovulation based on past menstrual cycle data. In this way, by analyzing the collected data and predicting the date of ovulation, it is possible to propose the optimal timing for pregnancy. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input basal body temperature data into AI and have the AI ​​predict the date of ovulation.

[0032] The suggestion unit can suggest a lifestyle suitable for pregnancy based on the analysis results. The suggestion unit, for example, suggests improvements to diet. For example, based on the analysis results, the suggestion unit may suggest to the user that they consume ingredients that are rich in specific nutrients. The suggestion unit can also suggest exercise recommendations. For example, based on the analysis results, the suggestion unit may suggest to the user that they perform a specific exercise. The suggestion unit can also suggest stress management methods. For example, based on the analysis results, the suggestion unit may suggest relaxation techniques to the user. By doing so, by suggesting a lifestyle suitable for pregnancy based on the analysis results, the user's lifestyle is improved and the possibility of pregnancy is increased. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the analysis results into AI and cause the AI ​​to suggest a lifestyle suitable for pregnancy.

[0033] The support unit can analyze the content and progress of the treatment the user is receiving and suggest the next action to be taken or points to be careful about. For example, the support unit can analyze the content of the treatment the user is receiving and suggest the next action to be taken. For example, the support unit can analyze the progress of the drug therapy the user is receiving and suggest the next timing of the medication to be taken. The support unit can also analyze the progress of the treatment and suggest points to be careful about. For example, the support unit can suggest specific points to be careful about to the user depending on the stage of the treatment. Furthermore, the support unit can notify the timing of necessary tests and examinations depending on the progress of the treatment. For example, the support unit can analyze the progress of the treatment and notify the user of the next test date. In this way, by analyzing the content and progress of the treatment the user is receiving and suggesting the next action to be taken or points to be careful about, the progress of the treatment can be smoothly advanced. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without AI. For example, the support unit can input data on the content and progress of the treatment into AI and have the AI ​​execute suggestions on the next action to be taken or points to be careful about.

[0034] The support unit can notify the user of the timing of necessary tests and examinations depending on the progress of treatment. For example, the support unit analyzes the progress of treatment and notifies the user of the date of the next test. For example, based on the progress of treatment, the support unit notifies the user of the date of the next blood test. The support unit can also notify the user of the timing of examinations depending on the progress of treatment. For example, based on the stage of treatment, the support unit can notify the user of the date of the next examination. Furthermore, the support unit can notify the user of the need for specific tests or examinations depending on the progress of treatment. For example, based on the progress of treatment, the support unit notifies the user of the timing of necessary tests and examinations depending on the progress of treatment, allowing the user to receive treatment at the appropriate time. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input treatment progress data into AI and have the AI ​​notify the user of the timing of tests and examinations.

[0035] The collection unit can analyze the user's past health data and select the optimal data collection method. The collection unit, for example, analyzes the user's past menstrual cycle data and determines the optimal collection timing. For example, the collection unit predicts the start date of the next menstruation based on the past menstrual cycle data and determines the optimal data collection timing. The collection unit can also analyze the user's past body temperature data and select the optimal collection method. For example, the collection unit determines the optimal temperature measurement timing based on the past body temperature data. The collection unit can also analyze the user's past diet data and select the optimal collection method. For example, the collection unit determines the optimal diet recording method based on the past diet data. In this way, by analyzing the user's past health data, the optimal data collection method can be selected and more accurate data can be collected. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the past health data into AI and have the AI ​​select the optimal data collection method.

[0036] The collection unit can filter data based on the user's current lifestyle and stress level when collecting data. For example, the collection unit temporarily suspends data collection when the user is in a high-stress state. For example, the collection unit captures the user's facial expressions with a camera and estimates the stress level using an emotion estimation algorithm. The collection unit can also collect detailed data when the user is relaxed. For example, the collection unit records the user's voice and estimates the stress level using voice analysis technology. Furthermore, the collection unit can adjust the type of data to be collected depending on the user's lifestyle. For example, the collection unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the stress level using an emotion estimation algorithm. This allows for more appropriate data to be collected by filtering data collection based on the user's lifestyle and stress level. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's living situation data into AI and have the AI ​​perform filtering of the data collection.

[0037] When collecting data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user prefers voice input, the collection unit collects data via voice. For example, the collection unit records the user's voice with a microphone and collects data using voice recognition technology. Furthermore, if the user prefers text input, the collection unit can also collect data via text. For example, the collection unit allows the user to input text data through an app. Furthermore, if the user prefers image input, the collection unit can also collect data via images. For example, the collection unit analyzes images taken by the user with a smartphone camera and collects data. This improves user convenience by selecting the optimal collection means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the user's input data into AI and have the AI ​​select the optimal collection means.

[0038] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting health data related to that area. For example, the collection unit acquires the user's geographical location information using GPS data and collects health data related to the area. Furthermore, when the user is traveling, the collection unit can prioritize collecting data related to the environment of the travel destination. For example, the collection unit collects data related to the environment of the travel destination based on the user's geographical location information. Furthermore, when the user is at home, the collection unit can prioritize collecting data related to the home environment. For example, the collection unit collects data related to the home environment based on the user's geographical location information. This allows for more useful data to be collected by prioritizing the collection of highly relevant data by taking into account the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input the user's geographical location information into AI and cause the AI ​​to collect highly relevant data.

[0039] The collection unit may analyze the user's social media activities and collect related data during data collection. The collection unit may, for example, collect health information shared by the user on social media. For example, the collection unit may analyze the user's social media accounts to collect health information. The collection unit may also analyze the user's social media activities and collect related data. For example, the collection unit may analyze the user's posts and the number of likes to collect related data. Furthermore, the collection unit may also collect related data by referring to the activities of the user's friends on social media. For example, the collection unit may analyze the posts of the user's friends to collect related data. This allows for the collection of related data by analyzing the user's social media activities, enabling more precise analysis. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input the user's social media data into AI and have the AI ​​collect related data.

[0040] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. The collection unit adjusts the collection method based on, for example, feedback provided by the user in the past. For example, the collection unit analyzes the user's past survey results and adjusts the collection method. The collection unit can also select the optimal collection method based on the user's past feedback. For example, the collection unit analyzes the user's comments and selects the optimal collection method. The collection unit can also adjust the collection timing by reflecting the user's past feedback. For example, the collection unit determines the optimal data collection timing based on the user's past feedback. This enables more appropriate data collection by customizing the collection method by reflecting the user's past feedback. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into AI and have the AI ​​customize the collection method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on important data. For example, the analysis unit selects particularly important data from the user's health data and performs a detailed analysis. The analysis unit can also perform a simplified analysis on less important data. For example, the analysis unit selects less important data from the user's health data and performs a simplified analysis. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the data. For example, the analysis unit determines the priority of the analysis based on the importance of the user's health data. In this way, by adjusting the level of detail of the analysis based on the importance of the data, more important data can be analyzed in detail. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to AI and have the AI ​​adjust the level of detail of the analysis.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies a specific algorithm to menstrual cycle data. For example, the analysis unit applies a specific statistical analysis algorithm to analyze menstrual cycle data. The analysis unit can also apply a different algorithm to body temperature data. For example, the analysis unit applies a machine learning algorithm to analyze body temperature data. The analysis unit can also apply a different algorithm to dietary content data. For example, the analysis unit applies a natural language processing algorithm to analyze dietary content data. This allows for more precise analysis by applying different analysis algorithms depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the data category into AI and have the AI ​​apply different analysis algorithms.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, improves the current analysis accuracy based on the user's past analysis results. For example, the analysis unit analyzes the user's past diagnosis results and improves the current analysis accuracy. The analysis unit can also adjust the analysis algorithm by referring to the user's past analysis results. For example, the analysis unit adjusts the analysis algorithm based on the user's past analysis reports. Furthermore, the analysis unit can adjust the level of detail of the analysis by reflecting the user's past analysis results. For example, the analysis unit adjusts the level of detail of the analysis based on the user's past analysis results. In this way, the current analysis accuracy can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis results into AI and have the AI ​​improve the analysis accuracy.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the time when the data was collected. The analysis unit, for example, prioritizes analysis of the most recent data. For example, the analysis unit selects the most recent data from the user's health data and prioritizes analysis. The analysis unit can also determine the priority of analysis by referring to past data. For example, the analysis unit determines the priority of analysis based on the user's past health data. Furthermore, the analysis unit can adjust the level of detail of the analysis depending on the time when the data was collected. For example, the analysis unit adjusts the level of detail of the analysis based on the time when the user's health data was collected. In this way, by determining the priority of analysis based on the time when the data was collected, the most recent data can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the data was collected into AI and have the AI ​​determine the priority of analysis.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. For example, the analysis unit selects highly relevant data from the user's health data and prioritizes analysis. The analysis unit can also postpone less relevant data. For example, the analysis unit selects less relevant data from the user's health data and postpones it. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the data. For example, the analysis unit adjusts the order of analysis based on the relevance of the user's health data. In this way, by adjusting the order of analysis based on the relevance of the data, more relevant data can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data into AI and have the AI ​​adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit uses a lot of technical terms. For example, the analysis unit determines the user's level of expertise based on the user's self-assessment or test results and uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can provide analysis results in simple language. For example, the analysis unit provides analysis results in simple language according to the user's level of expertise. Furthermore, the analysis unit can adjust the level of detail of the analysis according to the user's level of expertise. For example, the analysis unit adjusts the level of detail of the analysis based on the user's level of expertise. This allows the analysis results to be easily understood by the user by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the user's level of expertise into AI and have the AI ​​adjust the use of technical terms in the analysis.

[0047] When making a proposal, the proposal unit can adjust the level of detail of the proposal based on the importance of the pregnancy timing. For example, the proposal unit makes a detailed proposal for important pregnancy timing. For example, the proposal unit determines the importance of the pregnancy timing based on a doctor's evaluation or the user's wishes and makes a detailed proposal. The proposal unit can also make a simplified proposal for less important pregnancy timing. For example, the proposal unit makes a simplified proposal based on the importance of the pregnancy timing. Furthermore, the proposal unit can also determine the priority of the proposal according to the importance of the pregnancy timing. For example, the proposal unit determines the priority of the proposal based on the importance of the pregnancy timing. As a result, by adjusting the level of detail of the proposal based on the importance of the pregnancy timing, detailed proposals can be made for more important timing. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input the importance of the pregnancy timing to AI and cause the AI ​​to adjust the level of detail of the proposal.

[0048] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the lifestyle category. For example, the suggestion unit applies a specific algorithm to suggestions related to meal content. For example, the suggestion unit applies a specific nutrition analysis algorithm to analyze meal content data. The suggestion unit can also apply a different algorithm to suggestions related to exercise habits. For example, the suggestion unit applies a specific exercise analysis algorithm to analyze exercise habit data. The suggestion unit can also apply a different algorithm to suggestions related to lifestyle rhythms. For example, the suggestion unit applies a specific rhythm analysis algorithm to analyze lifestyle rhythm data. This allows for more appropriate suggestions to be made by applying different suggestion algorithms depending on the lifestyle category. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input lifestyle categories into AI and cause the AI ​​to apply different suggestion algorithms.

[0049] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. The suggestion unit, for example, improves the accuracy of the current proposal based on the user's past proposal results. For example, the suggestion unit analyzes the user's past proposal history and improves the accuracy of the current proposal. The suggestion unit can also adjust the proposal algorithm by referring to the user's past proposal results. For example, the suggestion unit adjusts the proposal algorithm based on the user's past feedback. Furthermore, the suggestion unit can adjust the level of detail of the proposal by reflecting the user's past proposal results. For example, the suggestion unit adjusts the level of detail of the proposal based on the user's past proposal results. In this way, the accuracy of the current proposal can be improved by referring to the user's past proposal results. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's past proposal results into AI and cause the AI ​​to improve the accuracy of the proposal.

[0050] When making a proposal, the proposal unit can determine the priority of the proposal based on the submission time of the pregnancy timing. For example, the proposal unit prioritizes proposals for pregnancy timings whose submission time is close. For example, the proposal unit prioritizes proposals based on the submission time of the pregnancy timing. The proposal unit can also postpone pregnancy timings whose submission time is far away. For example, the proposal unit postpones proposals based on the submission time of the pregnancy timing. Furthermore, the proposal unit can adjust the level of detail of the proposal depending on the submission time. For example, the proposal unit adjusts the level of detail of the proposal based on the submission time of the pregnancy timing. This allows proposals to be made at more appropriate times by determining the priority of proposals based on the submission time of the pregnancy timing. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input the submission time of the pregnancy timing into AI and have the AI ​​determine the priority of proposals.

[0051] When making a proposal, the suggestion unit can adjust the order of proposals based on the relevance of the lifestyles. For example, the suggestion unit prioritizes proposals for highly relevant lifestyles. For example, the suggestion unit analyzes the user's lifestyle data and prioritizes proposing highly relevant data. The suggestion unit can also postpone less relevant lifestyles. For example, the suggestion unit analyzes the user's lifestyle data and postpones less relevant data. Furthermore, the suggestion unit can also adjust the order of proposals according to the relevance of the lifestyles. For example, the suggestion unit adjusts the order of proposals based on the relevance of the user's lifestyle data. In this way, by adjusting the order of proposals based on the relevance of the lifestyles, more relevant proposals can be prioritized. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit may input the relevance of the lifestyles into AI and cause the AI ​​to adjust the order of proposals.

[0052] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal depending on the user's level of expertise. For example, if the user has technical expertise, the suggestion unit uses a lot of technical terminology. For example, the suggestion unit determines the user's level of expertise based on the user's self-assessment or test results and uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the suggestion unit can provide the proposal in simple language. For example, the suggestion unit provides the proposal in simple language depending on the user's level of expertise. Furthermore, the suggestion unit can adjust the level of detail of the proposal depending on the user's level of expertise. For example, the suggestion unit adjusts the level of detail of the proposal based on the user's level of expertise. This allows the proposal to be easily understood by the user by adjusting the use of technical terminology in the proposal depending on the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's level of expertise into AI and cause the AI ​​to adjust the use of technical terminology in the proposal.

[0053] During support, the support unit can analyze the user's past treatment behavior and select the optimal support method. The support unit selects the optimal support method, for example, based on the user's past treatment behavior. For example, the support unit analyzes the user's treatment history and selects the optimal support method. The support unit can also adjust the level of detail of the support by referring to the user's past treatment behavior. For example, the support unit adjusts the level of detail of the support based on the user's medical records. Furthermore, the support unit can determine the priority of support by reflecting the user's past treatment behavior. For example, the support unit determines the priority of support based on the user's treatment history. This allows the user's past treatment behavior to be analyzed, thereby selecting the optimal support method and providing more effective support. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without AI. For example, the support unit can input the user's past treatment behavior data into AI and have the AI ​​select the optimal support method.

[0054] The support unit can customize the support means based on the user's current living situation when providing support. The support unit, for example, adjusts the support means according to the user's current living situation. For example, the support unit analyzes the user's daily activity level and work stress and adjusts the support means. The support unit can also select the optimal support method based on the user's current living situation. For example, the support unit selects the optimal support method based on the user's living situation data. Furthermore, the support unit can adjust the level of detail of the support to reflect the user's current living situation. For example, the support unit adjusts the level of detail of the support based on the user's living situation data. This allows the support means to be customized based on the user's current living situation, thereby providing more appropriate support. Some or all of the above-described processing in the support unit may be performed using, or without, AI. For example, the support unit may input the user's living situation data into AI and have the AI ​​customize the support means.

[0055] The support unit can improve the support method by reflecting user feedback when providing support. The support unit, for example, improves the support method based on user feedback. For example, the support unit analyzes the results of a user survey and improves the support method. The support unit can also adjust the level of detail of the support by referring to the user feedback. For example, the support unit adjusts the level of detail of the support based on user comments. Furthermore, the support unit can also determine support priorities by reflecting user feedback. For example, the support unit determines support priorities based on user feedback. In this way, more effective support can be provided by improving the support method by reflecting user feedback. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input user feedback data into AI and have the AI ​​improve the support method.

[0056] When providing support, the support unit can select the optimal support method by taking into account the user's geographical location information. For example, if the user is in a specific area, the support unit provides support related to that area. For example, the support unit acquires the user's geographical location information using GPS data and provides support related to the area. Furthermore, if the user is traveling, the support unit can provide support related to the environment of the travel destination. For example, the support unit provides support related to the environment of the travel destination based on the user's geographical location information. Furthermore, if the user is at home, the support unit can provide support related to the home environment. For example, the support unit provides support related to the home environment based on the user's geographical location information. This allows for more relevant support to be provided by selecting the optimal support method by taking into account the user's geographical location information. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input the user's geographical location information into AI and have the AI ​​select the optimal support method.

[0057] When providing support, the support unit can analyze the user's social media activity and suggest support methods. The support unit can provide support based on, for example, health information shared by the user on social media. For example, the support unit can analyze the user's social media account and provide support based on the health information. The support unit can also analyze the user's social media activity and provide relevant support. For example, the support unit can analyze the user's posts and the number of likes to provide relevant support. Furthermore, the support unit can provide relevant support based on the activities of the user's friends on social media. For example, the support unit can analyze the posts of the user's friends and provide relevant support. In this way, by analyzing the user's social media activity, relevant support can be provided and more effective support can be achieved. Some or all of the above-described processing in the support unit can be performed using, for example, AI, or without AI. For example, the support unit can input the user's social media data into AI and have the AI ​​execute the suggestion of support methods.

[0058] When providing support, the support unit can customize the support method by reflecting the user's past feedback. The support unit, for example, adjusts the support method based on the user's past feedback. For example, the support unit analyzes the user's survey results and adjusts the support method. The support unit can also select the optimal support method based on the user's past feedback. For example, the support unit analyzes the user's comments and selects the optimal support method. The support unit can also adjust the level of detail of the support by reflecting the user's past feedback. For example, the support unit adjusts the level of detail of the support based on the user's past feedback. This allows the support method to be customized by reflecting the user's past feedback, thereby providing more appropriate support. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input the user's past feedback data into AI and have the AI ​​customize the support method.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The infertility treatment support system can also collect and analyze the user's sleep data to suggest sleep patterns suitable for pregnancy. For example, the collection unit records the user's sleep time and sleep quality using a wearable device. The analysis unit can analyze the user's sleep patterns based on the collected sleep data and suggest optimal sleep time and sleep environment. Furthermore, the suggestion unit can suggest specific methods to improve sleep to the user based on the analysis results. For example, the suggestion unit can suggest to the user adjusting the temperature and lighting in the bedroom and practicing relaxation techniques. This can improve the user's sleep quality and increase the possibility of pregnancy.

[0061] The infertility treatment support system can also monitor the user's nutritional status and suggest ways to improve nutritional balance. For example, the collection unit records the user's diet using an app and calculates the amount of nutrients consumed. The analysis unit can analyze the user's nutritional status based on the collected data and identify any imbalances in nutritional balance. Furthermore, the suggestion unit can suggest specific ways to improve the user's diet based on the analysis results. For example, the suggestion unit can suggest to the user that they consume ingredients that are rich in specific nutrients. This can improve the user's nutritional status and increase the chances of pregnancy.

[0062] The infertility treatment support system can also collect and analyze the user's exercise data to propose an exercise plan suitable for pregnancy. For example, the collection unit records the user's exercise amount and type using a fitness tracker. The analysis unit can analyze the user's exercise patterns based on the collected exercise data and propose an optimal exercise plan. Furthermore, the suggestion unit can suggest specific exercise methods to the user based on the analysis results. For example, the suggestion unit can suggest light exercise such as yoga or walking to the user. This can improve the user's exercise habits and increase the possibility of pregnancy.

[0063] The infertility treatment support system can also analyze the user's lifestyle rhythm and suggest a lifestyle rhythm that is suitable for pregnancy. For example, the collection unit records the user's wake-up time and bedtime to understand the lifestyle rhythm. The analysis unit can also analyze the user's lifestyle rhythm based on the collected data and suggest an optimal lifestyle rhythm. Furthermore, the suggestion unit can suggest to the user specific ways to improve the lifestyle rhythm based on the analysis results. For example, the suggestion unit can suggest to the user that they go to bed at a certain time and wake up at a certain time. This can help regulate the user's lifestyle rhythm and increase the chances of pregnancy.

[0064] The infertility treatment support system can also analyze the user's social support network and suggest support according to the progress of treatment. For example, the collection unit collects communication data with the user's family and friends to understand the status of social support. The analysis unit can also analyze the user's social support network based on the collected data and determine whether support is needed according to the progress of treatment. Furthermore, the support unit can suggest specific support methods to the user based on the analysis results. For example, the support unit can suggest to the user that they increase communication with family and friends. This can strengthen the user's social support network and improve the effectiveness of treatment.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The collection unit collects the user's health data. The user's health data includes information such as the menstrual cycle, body temperature, dietary details, and exercise habits. The collection unit uses a wearable device to collect body temperature and heart rate data, and also collects data manually entered by the user. For example, the user may enter their menstrual cycle and dietary details through an app. The collection unit can also record exercise habits using a smartphone sensor. For example, the smartphone's acceleration sensor can be used to record the user's number of steps. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using statistical analysis and machine learning algorithms. For example, statistical analysis is used to analyze menstrual cycle patterns, and machine learning algorithms are used to analyze body temperature fluctuations. Furthermore, AI is used to analyze dietary content and exercise habits, and to evaluate the nutritional balance of the diet. Step 3: The suggestion unit makes suggestions based on the analysis results obtained by the analysis unit regarding optimal pregnancy timing and lifestyle. These suggestions include dietary improvements, recommended exercise, and stress management methods. For example, it may suggest consuming certain foods or engaging in certain exercises. It may also suggest relaxation techniques. Step 4: The support department provides support based on the individual treatment plan proposed by the proposal department. This support includes online counseling, referrals to medical institutions, and notifications of examination and consultation timing according to the progress of treatment. For example, the support department notifies the patient of the date of the next consultation.

[0067] (Example 2) An infertility treatment support system according to an embodiment of the present invention collects a user's health data, analyzes it using AI, suggests optimal pregnancy timing and lifestyle, and provides support based on an individualized treatment plan. The infertility treatment support system collects a user's health data, analyzes it using AI, and suggests optimal pregnancy timing and lifestyle, thereby providing precise support based on an individualized treatment plan. For example, the infertility treatment support system allows a user to input information about their own health data and lifestyle. For example, the infertility treatment support system inputs data such as menstrual cycle, body temperature, diet, and exercise habits. This information is analyzed by AI. The infertility treatment support system then uses AI to suggest optimal pregnancy timing and lifestyle based on the input data. For example, the AI ​​analyzes fluctuations in menstrual cycle and body temperature to predict ovulation. The AI ​​also analyzes diet and exercise habits to suggest lifestyles suitable for pregnancy. Furthermore, the infertility treatment support system uses AI to provide precise support based on an individualized treatment plan. For example, the AI ​​analyzes the treatment the user is receiving and its progress, and suggests next steps and precautions. The AI ​​also notifies the user of the timing of necessary tests and examinations based on the progress of treatment. This allows the infertility treatment support system to reduce the stress of infertility treatment for users and provide a healthy and peaceful pregnancy experience. This allows the infertility treatment support system to reduce the stress of infertility treatment for users and provide a healthy and peaceful pregnancy experience. For example, by receiving accurate advice from AI, users can understand their own condition and take appropriate measures. In addition, by receiving continuous support, they can progress smoothly through treatment. Furthermore, by following suggestions from AI, users can maintain a lifestyle that is suitable for pregnancy. In addition, by receiving support based on an individual treatment plan, they can proceed with treatment with peace of mind. In this way, an infertility treatment support system that provides accurate advice and continuous support from AI becomes a reliable partner for users, enabling them to achieve a hopeful infertility treatment experience.

[0068] The infertility treatment support system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, and a support unit. The collection unit collects health data of a user. The health data of the user includes, but is not limited to, for example, menstrual cycle, body temperature, dietary details, and exercise habits. For example, the collection unit collects body temperature and heart rate using a wearable device. The collection unit can also collect data manually entered by the user. For example, the user inputs their menstrual cycle and dietary details through an app. The collection unit can also record exercise habits using a smartphone sensor. For example, the collection unit records the user's step count using an acceleration sensor in the smartphone. The analysis unit analyzes the data collected by the collection unit. The analysis can be performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to, for example. For example, the analysis unit can analyze menstrual cycle patterns using statistical analysis. The analysis unit can also analyze body temperature fluctuations using a machine learning algorithm. The analysis unit can also analyze dietary details and exercise habits using AI. For example, the analysis unit can evaluate the nutritional balance of dietary details using AI. The suggestion unit suggests optimal pregnancy timing and lifestyle based on the analysis results obtained by the analysis unit. Suggestions include, but are not limited to, recommendations for improving diet and exercise. For example, the suggestion unit suggests to the user to consume specific ingredients based on the analysis results. The suggestion unit can also suggest to the user to perform specific exercises based on the analysis results. Furthermore, the suggestion unit can also suggest stress management methods to the user based on the analysis results. For example, the suggestion unit suggests relaxation techniques to the user. The support unit provides support based on an individual treatment plan based on the content suggested by the suggestion unit. Support includes, but is not limited to, online counseling and referrals to medical institutions. For example, the support unit provides online counseling to the user. The support unit can also refer the user to an appropriate medical institution. Furthermore, the support unit can notify the user of the timing of necessary tests and examinations according to the progress of treatment.For example, the support unit notifies the user of the date of the next medical examination. As a result, the infertility treatment support system according to the embodiment can reduce the stress of infertility treatment and provide a healthy and peaceful pregnancy experience by collecting, analyzing, suggesting, and supporting the user's health data.

[0069] The collection unit can collect data on the user's menstrual cycle, body temperature, dietary details, and exercise habits. For example, the collection unit records the user's menstrual cycle using an app. For example, the user records the menstrual cycle by entering the start and end dates of their period. The collection unit can also measure body temperature using a basal thermometer and collect data. For example, the user measures their temperature using a basal thermometer every morning and enters the data into the app. The collection unit can also record dietary details in a food diary and collect data. For example, the user enters their daily dietary details into the app. The collection unit can also record exercise habits using a fitness tracker and collect data. For example, the user wears a fitness tracker and records the amount of exercise. This allows for more precise analysis and recommendations by collecting data such as the user's menstrual cycle, body temperature, dietary details, and exercise habits. Some or all of the above-described processing by the collection unit can be performed using, or without, AI. For example, the collection unit can input the menstrual cycle data entered by the user into AI and have the AI ​​verify the consistency of the data.

[0070] The analysis unit can analyze the collected data and predict the date of ovulation. The analysis unit, for example, analyzes changes in basal body temperature and predicts the date of ovulation. For example, it analyzes the timing of basal body temperature rise and predicts the date of ovulation. The analysis unit can also analyze hormone level measurement data and predict the date of ovulation. For example, it analyzes fluctuations in hormone levels and predicts the date of ovulation. Furthermore, the analysis unit can analyze menstrual cycle patterns and predict the date of ovulation. For example, it predicts the next date of ovulation based on past menstrual cycle data. In this way, by analyzing the collected data and predicting the date of ovulation, it is possible to propose the optimal timing for pregnancy. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input basal body temperature data into AI and have the AI ​​predict the date of ovulation.

[0071] The suggestion unit can suggest a lifestyle suitable for pregnancy based on the analysis results. The suggestion unit, for example, suggests improvements to diet. For example, based on the analysis results, the suggestion unit may suggest to the user that they consume ingredients that are rich in specific nutrients. The suggestion unit can also suggest exercise recommendations. For example, based on the analysis results, the suggestion unit may suggest to the user that they perform a specific exercise. The suggestion unit can also suggest stress management methods. For example, based on the analysis results, the suggestion unit may suggest relaxation techniques to the user. By doing so, by suggesting a lifestyle suitable for pregnancy based on the analysis results, the user's lifestyle is improved and the possibility of pregnancy is increased. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the analysis results into AI and cause the AI ​​to suggest a lifestyle suitable for pregnancy.

[0072] The support unit can analyze the content and progress of the treatment the user is receiving and suggest the next action to be taken or points to be careful about. For example, the support unit can analyze the content of the treatment the user is receiving and suggest the next action to be taken. For example, the support unit can analyze the progress of the drug therapy the user is receiving and suggest the next timing of the medication to be taken. The support unit can also analyze the progress of the treatment and suggest points to be careful about. For example, the support unit can suggest specific points to be careful about to the user depending on the stage of the treatment. Furthermore, the support unit can notify the timing of necessary tests and examinations depending on the progress of the treatment. For example, the support unit can analyze the progress of the treatment and notify the user of the next test date. In this way, by analyzing the content and progress of the treatment the user is receiving and suggesting the next action to be taken or points to be careful about, the progress of the treatment can be smoothly advanced. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without AI. For example, the support unit can input data on the content and progress of the treatment into AI and have the AI ​​execute suggestions on the next action to be taken or points to be careful about.

[0073] The support unit can notify the user of the timing of necessary tests and examinations depending on the progress of treatment. For example, the support unit analyzes the progress of treatment and notifies the user of the date of the next test. For example, based on the progress of treatment, the support unit notifies the user of the date of the next blood test. The support unit can also notify the user of the timing of examinations depending on the progress of treatment. For example, based on the stage of treatment, the support unit can notify the user of the date of the next examination. Furthermore, the support unit can notify the user of the need for specific tests or examinations depending on the progress of treatment. For example, based on the progress of treatment, the support unit notifies the user of the timing of necessary tests and examinations depending on the progress of treatment, allowing the user to receive treatment at the appropriate time. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input treatment progress data into AI and have the AI ​​notify the user of the timing of tests and examinations.

[0074] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit collects data during a time when the user is able to relax. For example, the collection unit captures the user's facial expressions with a camera and estimates the user's emotions using an emotion estimation algorithm. The collection unit can also collect detailed data when the user is relaxed. For example, the collection unit records the user's voice and estimates the user's emotions using voice analysis technology. Furthermore, the collection unit can collect simplified data when the user is in a hurry. For example, the collection unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the user's emotions using an emotion estimation algorithm. This allows the timing of data collection to be adjusted based on the user's emotions, thereby reducing the user's stress and collecting more accurate data. Emotion estimation is achieved 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 collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input user emotion data into AI and have the AI ​​adjust the timing of data collection.

[0075] The collection unit can analyze the user's past health data and select the optimal data collection method. The collection unit, for example, analyzes the user's past menstrual cycle data and determines the optimal collection timing. For example, the collection unit predicts the start date of the next menstruation based on the past menstrual cycle data and determines the optimal data collection timing. The collection unit can also analyze the user's past body temperature data and select the optimal collection method. For example, the collection unit determines the optimal temperature measurement timing based on the past body temperature data. The collection unit can also analyze the user's past diet data and select the optimal collection method. For example, the collection unit determines the optimal diet recording method based on the past diet data. In this way, by analyzing the user's past health data, the optimal data collection method can be selected and more accurate data can be collected. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the past health data into AI and have the AI ​​select the optimal data collection method.

[0076] The collection unit can filter data based on the user's current lifestyle and stress level when collecting data. For example, the collection unit temporarily suspends data collection when the user is in a high-stress state. For example, the collection unit captures the user's facial expressions with a camera and estimates the stress level using an emotion estimation algorithm. The collection unit can also collect detailed data when the user is relaxed. For example, the collection unit records the user's voice and estimates the stress level using voice analysis technology. Furthermore, the collection unit can adjust the type of data to be collected depending on the user's lifestyle. For example, the collection unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the stress level using an emotion estimation algorithm. This allows for more appropriate data to be collected by filtering data collection based on the user's lifestyle and stress level. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's living situation data into AI and have the AI ​​perform filtering of the data collection.

[0077] When collecting data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user prefers voice input, the collection unit collects data via voice. For example, the collection unit records the user's voice with a microphone and collects data using voice recognition technology. Furthermore, if the user prefers text input, the collection unit can also collect data via text. For example, the collection unit allows the user to input text data through an app. Furthermore, if the user prefers image input, the collection unit can also collect data via images. For example, the collection unit analyzes images taken by the user with a smartphone camera and collects data. This improves user convenience by selecting the optimal collection means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the user's input data into AI and have the AI ​​select the optimal collection means.

[0078] The collection unit can estimate the user's emotions and prioritize the data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit prioritizes collecting stress-related data. For example, the collection unit captures the user's facial expressions with a camera and estimates the user's emotions using an emotion estimation algorithm. The collection unit can also prioritize collecting health data if the user is relaxed. For example, the collection unit records the user's voice and estimates the user's emotions using voice analysis technology. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting only important data. For example, the collection unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the user's emotions using an emotion estimation algorithm. By prioritizing the data to be collected based on the user's emotions, more important data can be prioritized. Emotion estimation is achieved using an emotion estimation function, such as 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 collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input user emotion data into AI and have the AI ​​determine the priority of the data.

[0079] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting health data related to that area. For example, the collection unit acquires the user's geographical location information using GPS data and collects health data related to the area. Furthermore, when the user is traveling, the collection unit can prioritize collecting data related to the environment of the travel destination. For example, the collection unit collects data related to the environment of the travel destination based on the user's geographical location information. Furthermore, when the user is at home, the collection unit can prioritize collecting data related to the home environment. For example, the collection unit collects data related to the home environment based on the user's geographical location information. This allows for more useful data to be collected by prioritizing the collection of highly relevant data by taking into account the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input the user's geographical location information into AI and cause the AI ​​to collect highly relevant data.

[0080] The collection unit may analyze the user's social media activities and collect related data during data collection. The collection unit may, for example, collect health information shared by the user on social media. For example, the collection unit may analyze the user's social media accounts to collect health information. The collection unit may also analyze the user's social media activities and collect related data. For example, the collection unit may analyze the user's posts and the number of likes to collect related data. Furthermore, the collection unit may also collect related data by referring to the activities of the user's friends on social media. For example, the collection unit may analyze the posts of the user's friends to collect related data. This allows for the collection of related data by analyzing the user's social media activities, enabling more precise analysis. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input the user's social media data into AI and have the AI ​​collect related data.

[0081] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. The collection unit adjusts the collection method based on, for example, feedback provided by the user in the past. For example, the collection unit analyzes the user's past survey results and adjusts the collection method. The collection unit can also select the optimal collection method based on the user's past feedback. For example, the collection unit analyzes the user's comments and selects the optimal collection method. The collection unit can also adjust the collection timing by reflecting the user's past feedback. For example, the collection unit determines the optimal data collection timing based on the user's past feedback. This enables more appropriate data collection by customizing the collection method by reflecting the user's past feedback. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into AI and have the AI ​​customize the collection method.

[0082] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides simple, highly visible analysis results. For example, the analysis unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, the analysis unit records the user's voice and estimates the emotions using voice analysis technology. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results that focus on the key points. For example, the analysis unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotions using an emotion estimation algorithm. This allows the analysis results to be easily understood by adjusting the presentation method of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data into AI and have the AI ​​adjust the way the analysis is expressed.

[0083] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on important data. For example, the analysis unit selects particularly important data from the user's health data and performs a detailed analysis. The analysis unit can also perform a simplified analysis on less important data. For example, the analysis unit selects less important data from the user's health data and performs a simplified analysis. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the data. For example, the analysis unit determines the priority of the analysis based on the importance of the user's health data. In this way, by adjusting the level of detail of the analysis based on the importance of the data, more important data can be analyzed in detail. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to AI and have the AI ​​adjust the level of detail of the analysis.

[0084] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies a specific algorithm to menstrual cycle data. For example, the analysis unit applies a specific statistical analysis algorithm to analyze menstrual cycle data. The analysis unit can also apply a different algorithm to body temperature data. For example, the analysis unit applies a machine learning algorithm to analyze body temperature data. The analysis unit can also apply a different algorithm to dietary content data. For example, the analysis unit applies a natural language processing algorithm to analyze dietary content data. This allows for more precise analysis by applying different analysis algorithms depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the data category into AI and have the AI ​​apply different analysis algorithms.

[0085] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, improves the current analysis accuracy based on the user's past analysis results. For example, the analysis unit analyzes the user's past diagnosis results and improves the current analysis accuracy. The analysis unit can also adjust the analysis algorithm by referring to the user's past analysis results. For example, the analysis unit adjusts the analysis algorithm based on the user's past analysis reports. Furthermore, the analysis unit can adjust the level of detail of the analysis by reflecting the user's past analysis results. For example, the analysis unit adjusts the level of detail of the analysis based on the user's past analysis results. In this way, the current analysis accuracy can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis results into AI and have the AI ​​improve the analysis accuracy.

[0086] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, the analysis unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis result. For example, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows the analysis unit to adjust the length of the analysis based on the user's emotions, thereby providing the optimal analysis result for the user. Emotion estimation is achieved 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 analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data into AI and have the AI ​​adjust the length of analysis.

[0087] During analysis, the analysis unit can determine the priority of analysis based on the time when the data was collected. The analysis unit, for example, prioritizes analysis of the most recent data. For example, the analysis unit selects the most recent data from the user's health data and prioritizes analysis. The analysis unit can also determine the priority of analysis by referring to past data. For example, the analysis unit determines the priority of analysis based on the user's past health data. Furthermore, the analysis unit can adjust the level of detail of the analysis depending on the time when the data was collected. For example, the analysis unit adjusts the level of detail of the analysis based on the time when the user's health data was collected. In this way, by determining the priority of analysis based on the time when the data was collected, the most recent data can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the data was collected into AI and have the AI ​​determine the priority of analysis.

[0088] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. For example, the analysis unit selects highly relevant data from the user's health data and prioritizes analysis. The analysis unit can also postpone less relevant data. For example, the analysis unit selects less relevant data from the user's health data and postpones it. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the data. For example, the analysis unit adjusts the order of analysis based on the relevance of the user's health data. In this way, by adjusting the order of analysis based on the relevance of the data, more relevant data can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data into AI and have the AI ​​adjust the order of analysis.

[0089] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit uses a lot of technical terms. For example, the analysis unit determines the user's level of expertise based on the user's self-assessment or test results and uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can provide analysis results in simple language. For example, the analysis unit provides analysis results in simple language according to the user's level of expertise. Furthermore, the analysis unit can adjust the level of detail of the analysis according to the user's level of expertise. For example, the analysis unit adjusts the level of detail of the analysis based on the user's level of expertise. This allows the analysis results to be easily understood by the user by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the user's level of expertise into AI and have the AI ​​adjust the use of technical terms in the analysis.

[0090] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on the estimated user emotions. For example, if the user is nervous, the suggestion unit provides simple, highly visible suggestions. For example, the suggestion unit captures the user's facial expressions with a camera and estimates the user's emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the suggestion unit can provide detailed suggestions. For example, the suggestion unit records the user's voice and estimates the user's emotions using voice analysis technology. Furthermore, if the user is in a hurry, the suggestion unit can provide suggestions that focus on the key points. For example, the suggestion unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the user's emotions using an emotion estimation algorithm. This allows the suggestion unit to adjust the way suggestions are presented based on the user's emotions, thereby providing suggestions that are easy for the user to understand. Emotion estimation is achieved 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 suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input user emotion data into AI and have the AI ​​adjust the way the suggestion is expressed.

[0091] When making a proposal, the proposal unit can adjust the level of detail of the proposal based on the importance of the pregnancy timing. For example, the proposal unit makes a detailed proposal for important pregnancy timing. For example, the proposal unit determines the importance of the pregnancy timing based on a doctor's evaluation or the user's wishes and makes a detailed proposal. The proposal unit can also make a simplified proposal for less important pregnancy timing. For example, the proposal unit makes a simplified proposal based on the importance of the pregnancy timing. Furthermore, the proposal unit can also determine the priority of the proposal according to the importance of the pregnancy timing. For example, the proposal unit determines the priority of the proposal based on the importance of the pregnancy timing. As a result, by adjusting the level of detail of the proposal based on the importance of the pregnancy timing, detailed proposals can be made for more important timing. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input the importance of the pregnancy timing to AI and cause the AI ​​to adjust the level of detail of the proposal.

[0092] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the lifestyle category. For example, the suggestion unit applies a specific algorithm to suggestions related to meal content. For example, the suggestion unit applies a specific nutrition analysis algorithm to analyze meal content data. The suggestion unit can also apply a different algorithm to suggestions related to exercise habits. For example, the suggestion unit applies a specific exercise analysis algorithm to analyze exercise habit data. The suggestion unit can also apply a different algorithm to suggestions related to lifestyle rhythms. For example, the suggestion unit applies a specific rhythm analysis algorithm to analyze lifestyle rhythm data. This allows for more appropriate suggestions to be made by applying different suggestion algorithms depending on the lifestyle category. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input lifestyle categories into AI and cause the AI ​​to apply different suggestion algorithms.

[0093] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. The suggestion unit, for example, improves the accuracy of the current proposal based on the user's past proposal results. For example, the suggestion unit analyzes the user's past proposal history and improves the accuracy of the current proposal. The suggestion unit can also adjust the proposal algorithm by referring to the user's past proposal results. For example, the suggestion unit adjusts the proposal algorithm based on the user's past feedback. Furthermore, the suggestion unit can adjust the level of detail of the proposal by reflecting the user's past proposal results. For example, the suggestion unit adjusts the level of detail of the proposal based on the user's past proposal results. In this way, the accuracy of the current proposal can be improved by referring to the user's past proposal results. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's past proposal results into AI and cause the AI ​​to improve the accuracy of the proposal.

[0094] The suggestion unit can estimate the user's emotions and adjust the length of suggestions based on the estimated user emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. For example, the suggestion unit can capture the user's facial expressions with a camera and estimate the user's emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the suggestion unit can provide detailed suggestions. For example, the suggestion unit can record the user's voice and estimate the user's emotions using voice analysis technology. Furthermore, if the user is excited, the suggestion unit can provide visually stimulating suggestions. For example, the suggestion unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the user's emotions using an emotion estimation algorithm. This allows the length of suggestions to be adjusted based on the user's emotions, thereby providing optimal suggestions for the user. Emotion estimation is achieved 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 suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input user emotion data into AI and have the AI ​​adjust the length of the suggestions.

[0095] When making a proposal, the proposal unit can determine the priority of the proposal based on the submission time of the pregnancy timing. For example, the proposal unit prioritizes proposals for pregnancy timings whose submission time is close. For example, the proposal unit prioritizes proposals based on the submission time of the pregnancy timing. The proposal unit can also postpone pregnancy timings whose submission time is far away. For example, the proposal unit postpones proposals based on the submission time of the pregnancy timing. Furthermore, the proposal unit can adjust the level of detail of the proposal depending on the submission time. For example, the proposal unit adjusts the level of detail of the proposal based on the submission time of the pregnancy timing. This allows proposals to be made at more appropriate times by determining the priority of proposals based on the submission time of the pregnancy timing. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input the submission time of the pregnancy timing into AI and have the AI ​​determine the priority of proposals.

[0096] When making a proposal, the suggestion unit can adjust the order of proposals based on the relevance of the lifestyles. For example, the suggestion unit prioritizes proposals for highly relevant lifestyles. For example, the suggestion unit analyzes the user's lifestyle data and prioritizes proposing highly relevant data. The suggestion unit can also postpone less relevant lifestyles. For example, the suggestion unit analyzes the user's lifestyle data and postpones less relevant data. Furthermore, the suggestion unit can also adjust the order of proposals according to the relevance of the lifestyles. For example, the suggestion unit adjusts the order of proposals based on the relevance of the user's lifestyle data. In this way, by adjusting the order of proposals based on the relevance of the lifestyles, more relevant proposals can be prioritized. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit may input the relevance of the lifestyles into AI and cause the AI ​​to adjust the order of proposals.

[0097] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal depending on the user's level of expertise. For example, if the user has technical expertise, the suggestion unit uses a lot of technical terminology. For example, the suggestion unit determines the user's level of expertise based on the user's self-assessment or test results and uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the suggestion unit can provide the proposal in simple language. For example, the suggestion unit provides the proposal in simple language depending on the user's level of expertise. Furthermore, the suggestion unit can adjust the level of detail of the proposal depending on the user's level of expertise. For example, the suggestion unit adjusts the level of detail of the proposal based on the user's level of expertise. This allows the proposal to be easily understood by the user by adjusting the use of technical terminology in the proposal depending on the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's level of expertise into AI and cause the AI ​​to adjust the use of technical terminology in the proposal.

[0098] The support unit can estimate the user's emotions and adjust the support method based on the estimated user emotions. For example, if the user is nervous, the support unit provides a support method that helps the user relax. For example, the support unit captures the user's facial expression with a camera and estimates the user's emotions using an emotion estimation algorithm. The support unit can also provide detailed support when the user is relaxed. For example, the support unit records the user's voice and estimates the user's emotions using voice analysis technology. Furthermore, the support unit can provide simplified support when the user is in a hurry. For example, the support unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the user's emotions using an emotion estimation algorithm. This allows the support method to be adjusted based on the user's emotions, thereby providing optimal support for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit may input the user's emotional data into AI and have the AI ​​adjust the support method.

[0099] During support, the support unit can analyze the user's past treatment behavior and select the optimal support method. The support unit selects the optimal support method, for example, based on the user's past treatment behavior. For example, the support unit analyzes the user's treatment history and selects the optimal support method. The support unit can also adjust the level of detail of the support by referring to the user's past treatment behavior. For example, the support unit adjusts the level of detail of the support based on the user's medical records. Furthermore, the support unit can determine the priority of support by reflecting the user's past treatment behavior. For example, the support unit determines the priority of support based on the user's treatment history. This allows the user's past treatment behavior to be analyzed, thereby selecting the optimal support method and providing more effective support. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without AI. For example, the support unit can input the user's past treatment behavior data into AI and have the AI ​​select the optimal support method.

[0100] The support unit can customize the support means based on the user's current living situation when providing support. The support unit, for example, adjusts the support means according to the user's current living situation. For example, the support unit analyzes the user's daily activity level and work stress and adjusts the support means. The support unit can also select the optimal support method based on the user's current living situation. For example, the support unit selects the optimal support method based on the user's living situation data. Furthermore, the support unit can adjust the level of detail of the support to reflect the user's current living situation. For example, the support unit adjusts the level of detail of the support based on the user's living situation data. This allows the support means to be customized based on the user's current living situation, thereby providing more appropriate support. Some or all of the above-described processing in the support unit may be performed using, or without, AI. For example, the support unit may input the user's living situation data into AI and have the AI ​​customize the support means.

[0101] The support unit can improve the support method by reflecting user feedback when providing support. The support unit, for example, improves the support method based on user feedback. For example, the support unit analyzes the results of a user survey and improves the support method. The support unit can also adjust the level of detail of the support by referring to the user feedback. For example, the support unit adjusts the level of detail of the support based on user comments. Furthermore, the support unit can also determine support priorities by reflecting user feedback. For example, the support unit determines support priorities based on user feedback. In this way, more effective support can be provided by improving the support method by reflecting user feedback. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input user feedback data into AI and have the AI ​​improve the support method.

[0102] The support unit can estimate the user's emotions and determine the priority of support based on the estimated user emotions. For example, if the user is feeling stressed, the support unit prioritizes support for stress reduction. For example, the support unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. Furthermore, if the user is relaxed, the support unit can prioritize support for maintaining health. For example, the support unit records the user's voice and estimates the emotion using voice analysis technology. Furthermore, if the user is in a hurry, the support unit can prioritize important support. For example, the support unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. This allows the system to prioritize support based on the user's emotions, thereby providing more important support first. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. 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 support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit may input user emotion data into AI and have the AI ​​determine the priority of support.

[0103] When providing support, the support unit can select the optimal support method by taking into account the user's geographical location information. For example, if the user is in a specific area, the support unit provides support related to that area. For example, the support unit acquires the user's geographical location information using GPS data and provides support related to the area. Furthermore, if the user is traveling, the support unit can provide support related to the environment of the travel destination. For example, the support unit provides support related to the environment of the travel destination based on the user's geographical location information. Furthermore, if the user is at home, the support unit can provide support related to the home environment. For example, the support unit provides support related to the home environment based on the user's geographical location information. This allows for more relevant support to be provided by selecting the optimal support method by taking into account the user's geographical location information. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input the user's geographical location information into AI and have the AI ​​select the optimal support method.

[0104] When providing support, the support unit can analyze the user's social media activity and suggest support methods. The support unit can provide support based on, for example, health information shared by the user on social media. For example, the support unit can analyze the user's social media account and provide support based on the health information. The support unit can also analyze the user's social media activity and provide relevant support. For example, the support unit can analyze the user's posts and the number of likes to provide relevant support. Furthermore, the support unit can provide relevant support based on the activities of the user's friends on social media. For example, the support unit can analyze the posts of the user's friends and provide relevant support. In this way, by analyzing the user's social media activity, relevant support can be provided and more effective support can be achieved. Some or all of the above-described processing in the support unit can be performed using, for example, AI, or without AI. For example, the support unit can input the user's social media data into AI and have the AI ​​execute the suggestion of support methods.

[0105] When providing support, the support unit can customize the support method by reflecting the user's past feedback. The support unit, for example, adjusts the support method based on the user's past feedback. For example, the support unit analyzes the user's survey results and adjusts the support method. The support unit can also select the optimal support method based on the user's past feedback. For example, the support unit analyzes the user's comments and selects the optimal support method. The support unit can also adjust the level of detail of the support by reflecting the user's past feedback. For example, the support unit adjusts the level of detail of the support based on the user's past feedback. This allows the support method to be customized by reflecting the user's past feedback, thereby providing more appropriate support. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input the user's past feedback data into AI and have the AI ​​customize the support method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, and support unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects the user's health data using sensors in the wearable device or smartphone of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using statistical analysis or machine learning algorithms. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests optimal pregnancy timing and lifestyle based on the analysis results. The support unit is realized, for example, by the control unit 46A of the smart device 14 and provides support based on an individual treatment plan based on the suggested content. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, and support unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects the user's health data using sensors in the wearable device of the smart glasses 214 or a smartphone. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using statistical analysis or machine learning algorithms. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests optimal pregnancy timing and lifestyle based on the analysis results. The support unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides support based on an individual treatment plan based on the suggested content. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, and support unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects the user's health data using sensors in the wearable device of the headset-type terminal 314 or a smartphone. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using statistical analysis or machine learning algorithms. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests optimal pregnancy timing and lifestyle based on the analysis results. The support unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and provides support based on an individual treatment plan based on the suggested content. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, and support unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects health data of the user using sensors in a wearable device or smartphone of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using statistical analysis or machine learning algorithms. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests optimal pregnancy timing and lifestyle based on the analysis results. The support unit is realized, for example, by the control unit 46A of the robot 414 and provides support based on an individual treatment plan based on the suggested content.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The infertility treatment support system can also collect and analyze the user's sleep data to suggest sleep patterns suitable for pregnancy. For example, the collection unit records the user's sleep time and sleep quality using a wearable device. The analysis unit can analyze the user's sleep patterns based on the collected sleep data and suggest optimal sleep time and sleep environment. Furthermore, the suggestion unit can suggest specific methods to improve sleep to the user based on the analysis results. For example, the suggestion unit can suggest to the user adjusting the temperature and lighting in the bedroom and practicing relaxation techniques. This can improve the user's sleep quality and increase the possibility of pregnancy.

[0108] The infertility treatment support system can also monitor the user's stress level in real time and provide advice for stress reduction. For example, the collection unit collects the user's heart rate and electrodermal activity using a sensor to estimate the stress level. The analysis unit can analyze the user's stress level based on the collected data and identify the cause of the stress. Furthermore, the suggestion unit can suggest specific methods for stress reduction to the user based on the analysis results. For example, the suggestion unit can suggest deep breathing, meditation, or light exercise to the user. This can reduce the user's stress and increase the chances of pregnancy.

[0109] The infertility treatment support system can also monitor the user's nutritional status and suggest ways to improve nutritional balance. For example, the collection unit records the user's diet using an app and calculates the amount of nutrients consumed. The analysis unit can analyze the user's nutritional status based on the collected data and identify any imbalances in nutritional balance. Furthermore, the suggestion unit can suggest specific ways to improve the user's diet based on the analysis results. For example, the suggestion unit can suggest to the user that they consume ingredients that are rich in specific nutrients. This can improve the user's nutritional status and increase the chances of pregnancy.

[0110] The infertility treatment support system can also collect and analyze the user's exercise data to propose an exercise plan suitable for pregnancy. For example, the collection unit records the user's exercise amount and type using a fitness tracker. The analysis unit can analyze the user's exercise patterns based on the collected exercise data and propose an optimal exercise plan. Furthermore, the suggestion unit can suggest specific exercise methods to the user based on the analysis results. For example, the suggestion unit can suggest light exercise such as yoga or walking to the user. This can improve the user's exercise habits and increase the possibility of pregnancy.

[0111] The infertility treatment support system can also estimate the user's emotions and adjust the treatment plan based on the estimated emotions. For example, the collection unit analyzes the user's facial expressions and voice to estimate emotions. The analysis unit can also analyze the user's emotional fluctuations based on the collected emotional data and determine whether adjustments to the treatment plan are necessary. Furthermore, the support unit can propose a treatment plan to the user that takes emotions into consideration based on the analysis results. For example, if the user is feeling stressed, the support unit can suggest temporarily easing the progress of treatment. This can provide a treatment plan that takes the user's emotions into consideration and improve the effectiveness of the treatment.

[0112] The infertility treatment support system can also analyze the user's lifestyle rhythm and suggest a lifestyle rhythm that is suitable for pregnancy. For example, the collection unit records the user's wake-up time and bedtime to understand the lifestyle rhythm. The analysis unit can also analyze the user's lifestyle rhythm based on the collected data and suggest an optimal lifestyle rhythm. Furthermore, the suggestion unit can suggest to the user specific ways to improve the lifestyle rhythm based on the analysis results. For example, the suggestion unit can suggest to the user that they go to bed at a certain time and wake up at a certain time. This can help regulate the user's lifestyle rhythm and increase the chances of pregnancy.

[0113] The infertility treatment support system can also estimate the user's emotions and adjust the support content based on the estimated emotions. For example, the collection unit analyzes the user's facial expressions and voice to estimate emotions. The analysis unit can also analyze the user's emotional fluctuations based on the collected emotional data and determine whether adjustments to the support content are necessary. Furthermore, the support unit can provide the user with support that takes emotions into consideration based on the analysis results. For example, if the user is feeling stressed, the support unit can suggest relaxation techniques or counseling. This can provide support that takes the user's emotions into consideration and improve the effectiveness of treatment.

[0114] The infertility treatment support system can also analyze the user's social support network and suggest support according to the progress of treatment. For example, the collection unit collects communication data with the user's family and friends to understand the status of social support. The analysis unit can also analyze the user's social support network based on the collected data and determine whether support is needed according to the progress of treatment. Furthermore, the support unit can suggest specific support methods to the user based on the analysis results. For example, the support unit can suggest to the user that they increase communication with family and friends. This can strengthen the user's social support network and improve the effectiveness of treatment.

[0115] The infertility treatment support system can also estimate the user's emotions and adjust the progress of treatment based on the estimated emotions. For example, the collection unit analyzes the user's facial expressions and voice to estimate emotions. The analysis unit can also analyze the user's emotional fluctuations based on the collected emotional data and determine whether the progress of treatment is appropriate. Furthermore, the support unit can suggest to the user a treatment progress that takes emotions into consideration based on the analysis results. For example, if the user is feeling stressed, the support unit can suggest temporarily easing the progress of treatment. This can provide a treatment progress that takes the user's emotions into consideration and improve the effectiveness of the treatment.

[0116] The infertility treatment support system can further estimate the user's emotions and determine the priority of treatments based on the estimated emotions. For example, the collection unit analyzes the user's facial expressions and voice to estimate emotions. The analysis unit can also analyze the user's emotional fluctuations based on the collected emotional data and determine the priority of treatments. Furthermore, the support unit can suggest to the user a treatment priority that takes emotions into consideration based on the analysis results. For example, if the user is feeling stressed, the support unit can suggest that treatments that reduce stress be prioritized. This can provide a treatment priority that takes the user's emotions into consideration and improve the effectiveness of treatment.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The collection unit collects the user's health data. The user's health data includes information such as the menstrual cycle, body temperature, dietary details, and exercise habits. The collection unit uses a wearable device to collect body temperature and heart rate data, and also collects data manually entered by the user. For example, the user may enter their menstrual cycle and dietary details through an app. The collection unit can also record exercise habits using a smartphone sensor. For example, the smartphone's acceleration sensor can be used to record the user's number of steps. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using statistical analysis and machine learning algorithms. For example, statistical analysis is used to analyze menstrual cycle patterns, and machine learning algorithms are used to analyze body temperature fluctuations. Furthermore, AI is used to analyze dietary content and exercise habits, and to evaluate the nutritional balance of the diet. Step 3: The suggestion unit makes suggestions based on the analysis results obtained by the analysis unit regarding optimal pregnancy timing and lifestyle. These suggestions include dietary improvements, recommended exercise, and stress management methods. For example, it may suggest consuming certain foods or engaging in certain exercises. It may also suggest relaxation techniques. Step 4: The support department provides support based on the individual treatment plan proposed by the proposal department. This support includes online counseling, referrals to medical institutions, and notifications of examination and consultation timing according to the progress of treatment. For example, the support department notifies the patient of the date of the next consultation.

[0119] 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.

[0120] 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.

[0121] 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.

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

[0124] 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.

[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 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.

[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. 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.

[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 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.

[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 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.

[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 AI 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 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.

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

[0140] 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.

[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 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.

[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 (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).

[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] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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 AI 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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 AI 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.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 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 collection unit that collects health data of a user; an analysis unit that analyzes the data collected by the collection unit; a suggestion unit that suggests pregnancy timing and lifestyle based on the analysis results obtained by the analysis unit; a support unit that provides support based on an individual treatment plan based on the content proposed by the proposal unit. A system characterized by:

2. The collecting unit Collect data on users' menstrual cycles, body temperature, dietary habits, and exercise habits 2. The system of claim 1.

3. The analysis unit Analyze the collected data and predict the day of ovulation 2. The system of claim 1.

4. The proposal unit Based on the analysis results, we suggest lifestyles suitable for pregnancy.

2. The system of claim 1.

5. The support portion is Analyzes the user's treatment and progress, and suggests next steps and precautions 2. The system of claim 1.

6. The support portion is Notify the timing of necessary tests and examinations according to the progress of treatment 2. The system of claim 1.

7. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

8. The collecting unit Analyze users' past health data and select the optimal data collection method 2. The system of claim 1.

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