System

Abstract

To provide a system that allows for easy checking of the health condition of an ovum and provides appropriate advice.SOLUTION: A system according to an embodiment comprises a reception unit, an analysis unit, and a provision unit. The reception unit receives responses from the user. The analysis unit analyzes the responses received by the reception unit and diagnoses the health condition of an ovum. The provision unit provides advice based on the diagnosis result obtained by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to easily check the health of eggs and provide appropriate advice.

[0005] The system according to the embodiment aims to easily check the health status of eggs and provide appropriate advice. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, an analysis unit, and a providing unit. The receiving unit receives a response from a user. The analysis unit analyzes the response received by the receiving unit and diagnoses a health condition of the egg. The providing unit provides advice based on the diagnosis result obtained by the analysis unit. Effect of the Invention

[0007] The system according to the embodiment can easily check the health status of eggs and provide appropriate advice. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

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

[0028] (Example 1) The system for checking the health of eggs according to the embodiment of the present invention is a system that allows a user to easily check the health of eggs on the chat screen of a communication app. In this system, when a user answers questions to check the health of eggs on the chat screen, the answers are sent to an AI, which analyzes the user's answers and diagnoses the health of the eggs. For example, when a user answers questions such as "Has your menstrual cycle been stable recently?" or "Is your diet balanced?", the answers are sent to the AI. Next, the AI ​​analyzes the user's answers and diagnoses the health of the eggs. For example, if a user answers "My menstrual cycle has been unstable recently," the AI ​​diagnoses that the health of the eggs may not be good based on that information. Based on the diagnosis result, the AI ​​provides advice to improve the health of the eggs. For example, advice such as "Try to eat a balanced diet" and "Get moderate exercise" is displayed. In addition, specific meal and exercise menus are also provided. For example, specific menus such as "Include yogurt and fruit in your breakfast" and "Walk for 30 minutes three times a week" are displayed. This system provides support for protecting the health of women's eggs, contributing to measures to combat the declining birthrate and to support health during menopause. The egg health check system allows users to easily diagnose the health of their eggs and receive appropriate advice.

[0029] The system for checking the health condition of an egg according to the embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives a user's answer. The user's answer includes, for example, multiple choice, free description, and questions about health, but is not limited to such examples. The reception unit can receive an answer by, for example, the user answering questions such as "Is your menstrual cycle stable recently?" and "Is your diet balanced?" on the chat screen. The analysis unit uses AI to analyze the answer received by the reception unit and diagnose the health condition of the egg. The analysis is performed based on, for example, the type of algorithm, the accuracy of the analysis, and the data used, but is not limited to such examples. For example, if the user answers "My menstrual cycle has been unstable recently," the analysis unit diagnoses that the health condition of the egg may not be good based on the information. The provision unit uses AI to provide advice based on the diagnosis result obtained by the analysis unit. The advice includes, for example, suggestions for improving lifestyle habits, diet and exercise guidance, but is not limited to such examples. For example, the providing unit displays advice such as "Try to eat a balanced diet" or "Get moderate exercise." The providing unit can also provide specific meal and exercise menus. For example, specific menus such as "Include yogurt and fruit in your breakfast" or "Walk for 30 minutes three times a week" are displayed. This allows the system for checking the health of eggs according to the embodiment to diagnose the health of eggs based on the user's answers and provide appropriate advice.

[0030] The reception unit receives the user's answer. The user's answer includes, for example, multiple choice, free description, and health-related questions, but is not limited to such examples. Specifically, the reception unit can receive the answer by the user answering questions such as "Is your menstrual cycle stable recently?" and "Is your diet balanced?" on the chat screen. These questions are designed to collect information about the user's health condition and lifestyle habits, and are devised so that the user can easily answer them. For example, multiple choice questions allow the user to answer by simply selecting the most appropriate answer from multiple options, and free description questions allow the user to provide detailed information in their own words. Furthermore, the reception unit has a function of receiving the user's answer in real time and storing it in a database. As a result, the information is reflected in the system at the same time as the user sends the answer, and the analysis unit can quickly start analysis. Furthermore, the reception unit also has a function of encrypting and storing the answer data in order to protect the user's privacy. As a result, the user can provide his or her health information with peace of mind. Furthermore, the reception unit can track changes in the user's health condition by managing the user's answer history and comparing it with past answers. For example, by comparing past answers with current answers, the reception unit can evaluate the fluctuation of the menstrual cycle or the improvement of dietary habits. This allows the reception unit to continuously monitor the user's health condition and provide basic data for more accurate diagnosis.

[0031] The analysis unit uses AI to analyze the answers received by the reception unit and diagnose the health of the eggs. The analysis is performed based on, for example, the type of algorithm, the accuracy of the analysis, the data used, and the like, but is not limited to such examples. Specifically, if the user answers "my menstrual cycle has been unstable recently," the analysis unit diagnoses that the health of the eggs may be poor based on that information. The AI ​​uses natural language processing technology to analyze the user's free-form answers and extracts important keywords and phrases. In addition, the AI ​​uses a machine learning algorithm to compare the current health condition with past data. For example, if the user answers "my menstrual cycle has been unstable recently," the AI ​​uses that information to compare with past data and evaluate the possibility that the health of the eggs is deteriorating. Furthermore, the analysis unit can integrate multiple data sources for analysis. For example, not only the user's answer data but also health data and lifestyle data provided by the user are used for analysis. This enables a more comprehensive diagnosis. The analysis unit can continuously update the AI's learning model and improve the analysis accuracy. For example, every time new data is added, the AI ​​learns from the data and improves the diagnostic algorithm. This allows the analysis unit to provide highly accurate diagnoses based on the latest information at all times. Furthermore, the analysis unit can use an anomaly detection algorithm to detect unusual patterns or abnormal data and issue an early warning. This allows the analysis unit to monitor the user's health condition in real time and respond quickly if an abnormality occurs.

[0032] The providing unit uses AI to provide advice based on the diagnosis results obtained by the analyzing unit. The advice includes, for example, suggestions for improving lifestyle habits, dietary and exercise guidance, but is not limited to such examples. Specifically, the providing unit displays advice such as "Try to eat a balanced diet" and "Get moderate exercise." The providing unit can also provide specific diet and exercise menus. For example, specific menus such as "Include yogurt and fruit in your breakfast" and "Walk for 30 minutes three times a week" are displayed. The providing unit uses AI to analyze the user's data and generate optimal advice in order to provide personalized advice according to the user's health condition and lifestyle habits. For example, if the user answers that "my menstrual cycle has been unstable recently," the providing unit provides dietary and exercise advice to balance hormones based on that information. The providing unit can also collect user feedback and continuously improve the accuracy and effectiveness of the advice. For example, by having the user report the results of following the advice provided, the AI ​​learns from the data and improves the accuracy of future advice. Furthermore, the providing unit can reliably transmit information using multiple communication means. For example, important information can be delivered reliably not only by smartphone notifications but also by voice calls, SMS, emails, etc. This allows the provider to provide advice to users quickly and reliably and support them in improving the health of their eggs.

[0033] A system comprising a questioning unit that displays specific questions for checking the health condition of an egg. The questioning unit displays specific questions for checking the health condition of an egg. The specific questions include, but are not limited to, questions about health conditions and questions about lifestyle habits. For example, the questioning unit can display questions such as "Has your menstrual cycle been stable recently?" and "Is your diet balanced?". This allows the user to check the health condition of the egg by answering the specific questions.

[0034] The system is characterized by including a menu unit that provides a meal menu and an exercise menu. The menu unit provides a specific meal and exercise menu. The meal menu includes, but is not limited to, a nutritionally balanced meal and a menu corresponding to a specific health condition. For example, the menu unit can provide a specific meal menu such as "have yogurt and fruit for breakfast." The exercise menu includes, but is not limited to, the type, frequency, and intensity of exercise. For example, the menu unit can provide a specific exercise menu such as "walk for 30 minutes three times a week." This allows the user to receive a specific meal and exercise menu, thereby improving the health of the ovum.

[0035] The analysis unit can diagnose the health condition of the egg based on the user's answers. The analysis unit uses AI to diagnose the health condition of the egg based on the user's answers. The diagnosis is made based on, for example, the evaluation criteria for the health condition, the type of diagnosis result, etc., but is not limited to such examples. For example, if the user answers "my menstrual cycle has been unstable recently," the analysis unit diagnoses that the health condition of the egg may be poor based on that information. This makes it possible to accurately diagnose the health condition of the egg based on the user's answers.

[0036] The providing unit can provide advice to improve the health of the eggs based on the diagnosis results. The providing unit uses AI to provide advice to improve the health of the eggs based on the diagnosis results. The advice includes, but is not limited to, suggestions for improving lifestyle habits, dietary and exercise guidance, etc. For example, the providing unit can display advice such as "Try to eat a balanced diet" or "Get moderate exercise." This makes it possible to improve the health of the eggs by providing appropriate advice based on the diagnosis results.

[0037] The reception unit can analyze the user's past answer history and select the order of questions. The reception unit uses AI to analyze the user's past answer history and select the order of questions. The past answer history includes, for example, past question and answer data, analysis algorithms, and the like, but is not limited to such examples. For example, the reception unit analyzes the pattern of questions that the user has answered in the past and displays questions in the most effective order. In addition, the reception unit can preferentially display questions that the user found easy to answer in the past. In addition, the reception unit can select and display highly relevant questions from the user's past answer history. In this way, effective questions can be asked by selecting the optimal order of questions based on the user's past answer history.

[0038] The reception unit can perform filtering based on the user's current health condition and lifestyle habits when receiving a question. The reception unit uses AI to perform filtering based on the user's current health condition and lifestyle habits when receiving a question. The current health condition includes, for example, health check results, self-reported data, etc., but is not limited to such examples. The lifestyle habits include, for example, habits such as eating, exercise, and sleep, but is not limited to such examples. For example, when the user inputs the current health condition, the reception unit displays related questions based on the information. In addition, the reception unit can select appropriate questions by referring to the lifestyle habit data of the user. In addition, the reception unit can omit unnecessary questions based on the user's health condition and lifestyle habits. As a result, appropriate questions can be displayed by filtering questions based on the user's current health condition and lifestyle habits.

[0039] The reception unit can display highly relevant questions with priority in consideration of the geographical location information of the user when receiving a question. The reception unit uses AI to display highly relevant questions with priority in consideration of the geographical location information of the user when receiving a question. The geographical location information includes, but is not limited to, GPS data, health problems specific to a region, and the like. For example, when the user is in a specific region, the reception unit displays questions based on health information related to the region. In addition, the reception unit can display questions related to the climate and environment of the region based on the user's location information. In addition, when the user is traveling, the reception unit can also display questions related to health risks at the travel destination. In this way, by displaying highly relevant questions with consideration of the geographical location information of the user, more appropriate questions can be asked.

[0040] The reception unit can analyze the user's social media activity when receiving a question and display related questions. The reception unit uses AI to analyze the user's social media activity when receiving a question and display related questions. Social media activity includes, but is not limited to, for example, the content of posts and the reactions of followers. For example, when a user posts about health on social media, the reception unit displays questions related to the content. In addition, the reception unit can display questions related to health topics in which the user is interested from the user's social media activity. In addition, the reception unit can display questions based on health information shared by the user's social media friends. In this way, by analyzing the user's social media activity and displaying related questions, more effective questions can be asked.

[0041] The analysis unit can adjust the analysis algorithm by referring to the user's past health data during analysis. The analysis unit adjusts the analysis algorithm by referring to the user's past health data using AI during analysis. Past health data includes, but is not limited to, for example, health check results and self-reported data. For example, the analysis unit adjusts the analysis algorithm based on the user's past health data to improve accuracy. In addition, the analysis unit can extract a specific pattern from the user's past health data and reflect it in the analysis. In addition, the analysis unit can also perform an individually optimized analysis by referring to the user's past health data. Thereby, the analysis algorithm can be optimized by referring to the user's past health data, thereby improving the accuracy of the analysis.

[0042] The analysis unit can perform the analysis while taking into consideration the lifestyle habits and dietary history of the user. The analysis unit performs the analysis while taking into consideration the lifestyle habits and dietary history of the user using AI. The lifestyle habits include, for example, but are not limited to, habits such as eating, exercise, and sleep. The dietary history includes, for example, but are not limited to, past meal contents and nutritional balance. For example, the analysis unit adjusts the analysis algorithm based on the lifestyle habits data of the user to improve accuracy. In addition, the analysis unit can refer to the dietary history of the user and perform an analysis taking into consideration nutritional balance. In addition, the analysis unit can also comprehensively analyze the lifestyle habits and dietary history of the user and diagnose the health condition. As a result, a more accurate diagnosis can be made by performing the analysis while taking into consideration the lifestyle habits and dietary history of the user.

[0043] The analysis unit can perform the analysis while taking into consideration the geographical location information of the user. The analysis unit performs the analysis while taking into consideration the geographical location information of the user using AI. The geographical location information includes, for example, GPS data, health problems specific to the region, and the like, but is not limited to such examples. For example, when the user is in a specific region, the analysis unit performs the analysis while taking into consideration the health risks of the region. Furthermore, the analysis unit can perform an analysis related to the climate and environment of the region based on the user's location information. Furthermore, when the user is traveling, the analysis unit can also perform the analysis while taking into consideration the health risks of the travel destination. As a result, a more accurate diagnosis can be made by performing the analysis while taking into consideration the geographical location information of the user.

[0044] The analysis unit can improve the accuracy of the analysis by referring to the user's related literature during the analysis. The analysis unit uses AI to improve the accuracy of the analysis by referring to the user's related literature during the analysis. The related literature includes, but is not limited to, academic papers, specialized books, etc. For example, the analysis unit performs the analysis by referring to the latest research papers related to the user's health condition. In addition, the analysis unit can adjust the analysis algorithm based on the literature related to the user's symptoms. In addition, the analysis unit can also improve the accuracy of the analysis by referring to past research data on the user's health condition. As a result, a more accurate diagnosis can be made by improving the accuracy of the analysis by referring to the user's related literature.

[0045] The providing unit can select advice by referring to the user's past health data when providing advice. The providing unit uses AI to select advice by referring to the user's past health data when providing advice. Past health data includes, but is not limited to, health check results, self-reported data, and the like. For example, the providing unit provides optimal advice based on the user's past health data. In addition, the providing unit can extract a specific pattern from the user's past health data and reflect it in the advice. In addition, the providing unit can also provide individually optimized advice by referring to the user's past health data. As a result, more effective advice can be provided by selecting optimal advice by referring to the user's past health data.

[0046] The providing unit can customize the advice based on the user's current living situation when providing the advice. The providing unit uses AI to customize the advice based on the user's current living situation when providing the advice. The current living situation includes, for example, a work situation, a home environment, and the like, but is not limited to such examples. For example, the providing unit provides appropriate advice based on the user's current living situation. In addition, the providing unit can provide advice customized in accordance with the user's living habits and environment. In addition, the providing unit can also provide executable advice taking into account the user's current living situation. As a result, more executable advice can be provided by customizing the advice based on the user's current living situation.

[0047] The providing unit can select advice taking into consideration the geographical location information of the user when providing advice. The providing unit uses AI to select advice taking into consideration the geographical location information of the user when providing advice. The geographical location information includes, but is not limited to, for example, GPS data, health problems specific to a region, and the like. For example, when the user is in a specific region, the providing unit provides advice taking into consideration the health risks of the region. In addition, the providing unit can provide advice related to the climate and environment of the region based on the user's location information. In addition, when the user is traveling, the providing unit can also provide advice taking into consideration the health risks of the travel destination. This makes it possible to provide more effective advice by providing optimal advice taking into consideration the geographical location information of the user.

[0048] The providing unit can analyze the user's social media activity and propose advice when providing advice. The providing unit uses AI to analyze the user's social media activity and propose advice when providing advice. Social media activity includes, but is not limited to, for example, the content of posts and the reactions of followers. For example, when the user posts about health on social media, the providing unit provides advice related to the content. In addition, the providing unit can provide advice related to health topics in which the user is interested from the user's social media activity. In addition, the providing unit can provide advice based on health information shared by the user's social media friends. In this way, more effective advice can be provided by analyzing the user's social media activity and proposing advice.

[0049] The questioning unit can select the most appropriate question by referring to the user's past answer history when displaying a question. The questioning unit uses AI to select the most appropriate question by referring to the user's past answer history when displaying a question. The past answer history includes, for example, past question and answer data, analysis algorithms, etc., but is not limited to such examples. For example, the questioning unit analyzes the patterns of questions that the user has answered in the past and displays the most effective question. In addition, the questioning unit can preferentially display questions that the user found easy to answer in the past. In addition, the questioning unit can select and display highly relevant questions from the user's past answer history. In this way, by selecting the most appropriate question by referring to the user's past answer history, more effective questions can be asked.

[0050] The question unit can display an optimal question in consideration of the device information of the user when displaying a question. The question unit uses AI to display an optimal question in consideration of the device information of the user when displaying a question. The device information includes, but is not limited to, device information such as a smartphone, tablet, and PC. For example, when the user is using a smartphone, the question unit displays a question that matches the screen size. Also, when the user is using a tablet, the question unit can display a question that is optimized for a large screen. Also, when the user is using a smartwatch, the question unit can display a concise question with high visibility. This allows for more effective questioning by displaying an optimal question in consideration of the device information of the user.

[0051] The menu unit can select an optimal menu by referring to the user's past meal history when providing the menu. The menu unit uses AI to select an optimal menu by referring to the user's past meal history when providing the menu. The past meal history includes, for example, past meal contents, nutritional balance, etc., but is not limited to such examples. For example, the menu unit proposes a balanced meal menu based on the user's past meal history. In addition, the menu unit can propose a menu that supplements a specific nutrient when a specific nutrient is lacking from the user's past meal history. In addition, the menu unit can also propose an individually optimized meal menu by referring to the user's past meal history. As a result, a more effective menu can be provided by selecting an optimal menu by referring to the user's past meal history.

[0052] The menu unit can provide an optimal menu by considering the geographical location information of the user when providing the menu. The menu unit uses AI to provide an optimal menu by considering the geographical location information of the user when providing the menu. The geographical location information includes, but is not limited to, for example, GPS data, health issues specific to a region, and the like. For example, when the user is in a specific region, the menu unit can suggest a menu using ingredients from that region. Furthermore, the menu unit can suggest a menu suitable for the climate and environment of the region based on the user's location information. Furthermore, when the user is traveling, the menu unit can also suggest a menu using ingredients from the travel destination. In this way, a more effective menu can be provided by providing an optimal menu by considering the geographical location information of the user.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The reception unit not only receives the user's answers, but also analyzes the user's past answer history and selects the order of questions. For example, the pattern of questions answered by the user in the past is analyzed, and questions are displayed in the most effective order. Also, questions that the user found easy to answer in the past can be preferentially displayed. Furthermore, highly relevant questions can be selected and displayed from the user's past answer history. In this way, effective questions can be asked by selecting the optimal order of questions based on the user's past answer history.

[0055] During analysis, the analysis unit can adjust the analysis algorithm by referring to the user's past health data. For example, the analysis algorithm can be adjusted based on the user's past health data to improve accuracy. In addition, specific patterns can be extracted from the user's past health data and reflected in the analysis. Furthermore, the analysis unit can also perform an individually optimized analysis by referring to the user's past health data. This makes it possible to improve the accuracy of the analysis by optimizing the analysis algorithm by referring to the user's past health data.

[0056] When providing advice, the providing unit can select advice by referring to the user's past health data. For example, optimal advice can be provided based on the user's past health data. Also, specific patterns can be extracted from the user's past health data and reflected in the advice. Furthermore, individually optimized advice can be provided by referring to the user's past health data. In this way, more effective advice can be provided by selecting optimal advice by referring to the user's past health data.

[0057] The providing unit can customize the advice based on the user's current living situation when providing the advice. For example, appropriate advice can be provided based on the user's current living situation. Also, customized advice can be provided in accordance with the user's living habits and environment. Furthermore, executable advice can be provided taking into account the user's current living situation. In this way, by customizing the advice based on the user's current living situation, more executable advice can be provided.

[0058] When providing advice, the providing unit can select advice taking into consideration the geographical location information of the user. For example, when the user is in a specific area, advice taking into consideration the health risks of that area is provided. Also, based on the user's location information, advice related to the local climate and environment can be provided. Furthermore, when the user is traveling, advice taking into consideration the health risks of the travel destination can also be provided. In this way, more effective advice can be provided by providing optimal advice taking into consideration the user's geographical location information.

[0059] The process flow of the first embodiment will be briefly described below.

[0060] Step 1: The reception unit receives the user's answer. The user's answer may be multiple choice, free description, or a question about health. For example, the user can answer questions such as "Has your menstrual cycle been stable recently?" or "Is your diet balanced?" on the chat screen, and the answer can be received. Step 2: The analysis unit uses AI to analyze the responses received by the reception unit and diagnose the health of the eggs. The analysis is based on the type of algorithm, the accuracy of the analysis, the data used, etc. For example, if the user responds that "my menstrual cycle has been unstable recently," it will be diagnosed based on that information that the health of the eggs may not be good. Step 3: The provider uses AI to provide advice based on the diagnosis results obtained by the analyzer. The advice includes suggestions for improving lifestyle habits and diet and exercise guidance. For example, advice such as "Try to eat a balanced diet" or "Get moderate exercise" may be displayed. Specific diet and exercise menus can also be provided. For example, specific menus such as "Include yogurt and fruit in your breakfast" or "Walk for 30 minutes three times a week" are displayed.

[0061] (Example 2) The system for checking the health of eggs according to the embodiment of the present invention is a system that allows a user to easily check the health of eggs on the chat screen of a communication app. In this system, when a user answers questions to check the health of eggs on the chat screen, the answers are sent to an AI, which analyzes the user's answers and diagnoses the health of the eggs. For example, when a user answers questions such as "Has your menstrual cycle been stable recently?" or "Is your diet balanced?", the answers are sent to the AI. Next, the AI ​​analyzes the user's answers and diagnoses the health of the eggs. For example, if a user answers "My menstrual cycle has been unstable recently," the AI ​​diagnoses that the health of the eggs may not be good based on that information. Based on the diagnosis result, the AI ​​provides advice to improve the health of the eggs. For example, advice such as "Try to eat a balanced diet" and "Get moderate exercise" is displayed. In addition, specific meal and exercise menus are also provided. For example, specific menus such as "Include yogurt and fruit in your breakfast" and "Walk for 30 minutes three times a week" are displayed. This system provides support for protecting the health of women's eggs, contributing to measures to combat the declining birthrate and to support health during menopause. The egg health check system allows users to easily diagnose the health of their eggs and receive appropriate advice.

[0062] The system for checking the health condition of an egg according to the embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives a user's answer. The user's answer includes, for example, multiple choice, free description, and questions about health, but is not limited to such examples. The reception unit can receive an answer by, for example, the user answering questions such as "Is your menstrual cycle stable recently?" and "Is your diet balanced?" on the chat screen. The analysis unit uses AI to analyze the answer received by the reception unit and diagnose the health condition of the egg. The analysis is performed based on, for example, the type of algorithm, the accuracy of the analysis, and the data used, but is not limited to such examples. For example, if the user answers "My menstrual cycle has been unstable recently," the analysis unit diagnoses that the health condition of the egg may not be good based on the information. The provision unit uses AI to provide advice based on the diagnosis result obtained by the analysis unit. The advice includes, for example, suggestions for improving lifestyle habits, diet and exercise guidance, but is not limited to such examples. For example, the providing unit displays advice such as "Try to eat a balanced diet" or "Get moderate exercise." The providing unit can also provide specific meal and exercise menus. For example, specific menus such as "Include yogurt and fruit in your breakfast" or "Walk for 30 minutes three times a week" are displayed. This allows the system for checking the health of eggs according to the embodiment to diagnose the health of eggs based on the user's answers and provide appropriate advice.

[0063] The reception unit receives the user's answer. The user's answer includes, for example, multiple choice, free description, and health-related questions, but is not limited to such examples. Specifically, the reception unit can receive the answer by the user answering questions such as "Is your menstrual cycle stable recently?" and "Is your diet balanced?" on the chat screen. These questions are designed to collect information about the user's health condition and lifestyle habits, and are devised so that the user can easily answer them. For example, multiple choice questions allow the user to answer by simply selecting the most appropriate answer from multiple options, and free description questions allow the user to provide detailed information in their own words. Furthermore, the reception unit has a function of receiving the user's answer in real time and storing it in a database. As a result, the information is reflected in the system at the same time as the user sends the answer, and the analysis unit can quickly start analysis. Furthermore, the reception unit also has a function of encrypting and storing the answer data in order to protect the user's privacy. As a result, the user can provide his or her health information with peace of mind. Furthermore, the reception unit can track changes in the user's health condition by managing the user's answer history and comparing it with past answers. For example, by comparing past responses with current responses, the reception unit can evaluate the fluctuation of the menstrual cycle or the improvement of dietary habits. This allows the reception unit to continuously monitor the user's health condition and provide basic data for more accurate diagnosis.

[0064] The analysis unit uses AI to analyze the answers received by the reception unit and diagnose the health of the eggs. The analysis is performed based on, for example, the type of algorithm, the accuracy of the analysis, the data used, and the like, but is not limited to such examples. Specifically, if the user answers "my menstrual cycle has been unstable recently," the analysis unit diagnoses that the health of the eggs may be poor based on that information. The AI ​​uses natural language processing technology to analyze the user's free-form answers and extracts important keywords and phrases. In addition, the AI ​​uses a machine learning algorithm to compare the current health condition with past data. For example, if the user answers "my menstrual cycle has been unstable recently," the AI ​​uses that information to compare with past data and evaluate the possibility that the health of the eggs is deteriorating. Furthermore, the analysis unit can integrate multiple data sources for analysis. For example, not only the user's answer data but also health data and lifestyle data provided by the user are used for analysis. This enables a more comprehensive diagnosis. The analysis unit can continuously update the AI's learning model and improve the analysis accuracy. For example, every time new data is added, the AI ​​learns from the data and improves the diagnostic algorithm. This allows the analysis unit to provide highly accurate diagnoses based on the latest information at all times. Furthermore, the analysis unit can use an anomaly detection algorithm to detect unusual patterns or abnormal data and issue an early warning. This allows the analysis unit to monitor the user's health condition in real time and respond quickly if an abnormality occurs.

[0065] The providing unit uses AI to provide advice based on the diagnosis results obtained by the analyzing unit. The advice includes, for example, suggestions for improving lifestyle habits, dietary and exercise guidance, but is not limited to such examples. Specifically, the providing unit displays advice such as "Try to eat a balanced diet" and "Get moderate exercise." The providing unit can also provide specific diet and exercise menus. For example, specific menus such as "Include yogurt and fruit in your breakfast" and "Walk for 30 minutes three times a week" are displayed. The providing unit uses AI to analyze the user's data and generate optimal advice in order to provide personalized advice according to the user's health condition and lifestyle habits. For example, if the user answers that "my menstrual cycle has been unstable recently," the providing unit provides dietary and exercise advice to balance hormones based on that information. The providing unit can also collect user feedback and continuously improve the accuracy and effectiveness of the advice. For example, by having the user report the results of following the advice provided, the AI ​​learns from the data and improves the accuracy of future advice. Furthermore, the providing unit can reliably transmit information using multiple communication means. For example, important information can be delivered reliably not only by smartphone notifications but also by voice calls, SMS, emails, etc. This allows the provider to provide advice to users quickly and reliably and support them in improving the health of their eggs.

[0066] A system comprising a questioning unit that displays specific questions for checking the health condition of an egg. The questioning unit displays specific questions for checking the health condition of an egg. The specific questions include, but are not limited to, questions about health conditions and questions about lifestyle habits. For example, the questioning unit can display questions such as "Has your menstrual cycle been stable recently?" and "Is your diet balanced?". This allows the user to check the health condition of the egg by answering the specific questions.

[0067] The system is characterized by including a menu unit that provides a meal menu and an exercise menu. The menu unit provides a specific meal and exercise menu. The meal menu includes, but is not limited to, a nutritionally balanced meal and a menu corresponding to a specific health condition. For example, the menu unit can provide a specific meal menu such as "have yogurt and fruit for breakfast." The exercise menu includes, but is not limited to, the type, frequency, and intensity of exercise. For example, the menu unit can provide a specific exercise menu such as "walk for 30 minutes three times a week." This allows the user to receive a specific meal and exercise menu, thereby improving the health of the ovum.

[0068] The analysis unit can diagnose the health condition of the egg based on the user's answers. The analysis unit uses AI to diagnose the health condition of the egg based on the user's answers. The diagnosis is made based on, for example, the evaluation criteria for the health condition, the type of diagnosis result, etc., but is not limited to such examples. For example, if the user answers "my menstrual cycle has been unstable recently," the analysis unit diagnoses that the health condition of the egg may be poor based on that information. This makes it possible to accurately diagnose the health condition of the egg based on the user's answers.

[0069] The providing unit can provide advice to improve the health of the eggs based on the diagnosis results. The providing unit uses AI to provide advice to improve the health of the eggs based on the diagnosis results. The advice includes, but is not limited to, suggestions for improving lifestyle habits, dietary and exercise guidance, etc. For example, the providing unit can display advice such as "Try to eat a balanced diet" or "Get moderate exercise." This makes it possible to improve the health of the eggs by providing appropriate advice based on the diagnosis results.

[0070] The reception unit can estimate the user's emotion and adjust the timing of displaying the question based on the estimated user's emotion. The reception unit uses AI to estimate the user's emotion and adjusts the timing of displaying the question based on the estimated user's emotion. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user is feeling stressed, the reception unit displays the question during a time period when the user can relax. Also, when the user is relaxed, the reception unit can display the question immediately to prompt the user to answer. Also, when the user is busy, the reception unit can delay the notification and display the question later. Thereby, the timing of displaying the question can be adjusted according to the user's emotion, so that the question can be displayed at a more appropriate timing.

[0071] The reception unit can analyze the user's past answer history and select the order of questions. The reception unit uses AI to analyze the user's past answer history and select the order of questions. The past answer history includes, for example, past question and answer data, analysis algorithms, and the like, but is not limited to such examples. For example, the reception unit analyzes the pattern of questions that the user has answered in the past and displays questions in the most effective order. In addition, the reception unit can preferentially display questions that the user found easy to answer in the past. In addition, the reception unit can select and display highly relevant questions from the user's past answer history. In this way, effective questions can be asked by selecting the optimal order of questions based on the user's past answer history.

[0072] The reception unit can perform filtering based on the user's current health condition and lifestyle habits when receiving a question. The reception unit uses AI to perform filtering based on the user's current health condition and lifestyle habits when receiving a question. The current health condition includes, for example, health check results, self-reported data, etc., but is not limited to such examples. The lifestyle habits include, for example, habits such as eating, exercise, and sleep, but is not limited to such examples. For example, when the user inputs the current health condition, the reception unit displays related questions based on the information. In addition, the reception unit can select appropriate questions by referring to the lifestyle habit data of the user. In addition, the reception unit can omit unnecessary questions based on the user's health condition and lifestyle habits. As a result, appropriate questions can be displayed by filtering questions based on the user's current health condition and lifestyle habits.

[0073] The reception unit can estimate the user's emotion and determine the priority of questions based on the estimated user's emotion. The reception unit uses AI to estimate the user's emotion and determine the priority of questions based on the estimated user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the reception unit preferentially displays simple questions when the user is stressed. Also, the reception unit can preferentially display detailed questions when the user is relaxed. Also, the reception unit can preferentially display important questions when the user is in a hurry. This allows more effective questions to be asked by determining the priority of questions according to the user's emotion.

[0074] The reception unit can display highly relevant questions with priority in consideration of the geographical location information of the user when receiving a question. The reception unit uses AI to display highly relevant questions with priority in consideration of the geographical location information of the user when receiving a question. The geographical location information includes, but is not limited to, GPS data, health problems specific to a region, and the like. For example, when the user is in a specific region, the reception unit displays questions based on health information related to the region. In addition, the reception unit can display questions related to the climate and environment of the region based on the user's location information. In addition, when the user is traveling, the reception unit can also display questions related to health risks at the travel destination. In this way, by displaying highly relevant questions with consideration of the geographical location information of the user, more appropriate questions can be asked.

[0075] The reception unit can analyze the user's social media activity when receiving a question and display related questions. The reception unit uses AI to analyze the user's social media activity when receiving a question and display related questions. Social media activity includes, but is not limited to, for example, the content of posts and the reactions of followers. For example, when a user posts about health on social media, the reception unit displays questions related to the content. In addition, the reception unit can display questions related to health topics in which the user is interested from the user's social media activity. In addition, the reception unit can display questions based on health information shared by the user's social media friends. In this way, by analyzing the user's social media activity and displaying related questions, more effective questions can be asked.

[0076] The analysis unit can estimate the user's emotion and adjust the accuracy of the analysis based on the estimated user's emotion. The analysis unit uses AI to estimate the user's emotion and adjust the accuracy of the analysis based on the estimated user's emotion. The emotion estimation is realized by using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user is stressed, the analysis unit increases the accuracy of the analysis to perform a detailed diagnosis. In addition, when the user is relaxed, the analysis unit can adjust the accuracy of the analysis to perform a simple diagnosis. In addition, when the user is in a hurry, the analysis unit can also perform a quick analysis and provide a concise diagnosis result. As a result, a more accurate diagnosis can be performed by adjusting the accuracy of the analysis according to the user's emotion.

[0077] The analysis unit can adjust the analysis algorithm by referring to the user's past health data during analysis. The analysis unit adjusts the analysis algorithm by referring to the user's past health data using AI during analysis. Past health data includes, but is not limited to, for example, health check results and self-reported data. For example, the analysis unit adjusts the analysis algorithm based on the user's past health data to improve accuracy. In addition, the analysis unit can extract a specific pattern from the user's past health data and reflect it in the analysis. In addition, the analysis unit can also perform an individually optimized analysis by referring to the user's past health data. Thereby, the analysis algorithm can be optimized by referring to the user's past health data, thereby improving the accuracy of the analysis.

[0078] The analysis unit can perform the analysis while taking into consideration the lifestyle habits and dietary history of the user. The analysis unit performs the analysis while taking into consideration the lifestyle habits and dietary history of the user using AI. The lifestyle habits include, for example, but are not limited to, habits such as eating, exercise, and sleep. The dietary history includes, for example, but are not limited to, past meal contents and nutritional balance. For example, the analysis unit adjusts the analysis algorithm based on the lifestyle habits data of the user to improve accuracy. In addition, the analysis unit can refer to the dietary history of the user and perform an analysis taking into consideration nutritional balance. In addition, the analysis unit can also comprehensively analyze the lifestyle habits and dietary history of the user and diagnose the health condition. As a result, a more accurate diagnosis can be made by performing the analysis while taking into consideration the lifestyle habits and dietary history of the user.

[0079] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. The analysis unit uses AI to estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the analysis unit provides a simple and highly visible display method when the user is nervous. In addition, the analysis unit can provide a display method including detailed information when the user is relaxed. In addition, the analysis unit can also provide a display method that focuses on the main points when the user is in a hurry. This makes it possible to provide a more appropriate display method by adjusting the display method of the analysis result according to the user's emotion.

[0080] The analysis unit can perform the analysis while taking into consideration the geographical location information of the user. The analysis unit performs the analysis while taking into consideration the geographical location information of the user using AI. The geographical location information includes, for example, GPS data, health problems specific to the region, and the like, but is not limited to such examples. For example, when the user is in a specific region, the analysis unit performs the analysis while taking into consideration the health risks of the region. Furthermore, the analysis unit can perform an analysis related to the climate and environment of the region based on the user's location information. Furthermore, when the user is traveling, the analysis unit can also perform the analysis while taking into consideration the health risks of the travel destination. As a result, a more accurate diagnosis can be made by performing the analysis while taking into consideration the geographical location information of the user.

[0081] The analysis unit can improve the accuracy of the analysis by referring to the user's related literature during the analysis. The analysis unit uses AI to improve the accuracy of the analysis by referring to the user's related literature during the analysis. The related literature includes, but is not limited to, academic papers, specialized books, etc. For example, the analysis unit performs the analysis by referring to the latest research papers related to the user's health condition. In addition, the analysis unit can adjust the analysis algorithm based on the literature related to the user's symptoms. In addition, the analysis unit can also improve the accuracy of the analysis by referring to past research data on the user's health condition. As a result, a more accurate diagnosis can be made by improving the accuracy of the analysis by referring to the user's related literature.

[0082] The providing unit can estimate the user's emotion and adjust the way of expressing the advice based on the estimated user's emotion. The providing unit uses AI to estimate the user's emotion and adjust the way of expressing the advice based on the estimated user's emotion. The estimation of the emotion is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the providing unit provides advice in gentle words when the user is feeling stressed. In addition, the providing unit can provide detailed advice when the user is relaxed. In addition, the providing unit can provide concise and to the point advice when the user is in a hurry. In this way, more appropriate advice can be provided by adjusting the way of expressing the advice according to the user's emotion.

[0083] The providing unit can select advice by referring to the user's past health data when providing advice. The providing unit uses AI to select advice by referring to the user's past health data when providing advice. Past health data includes, but is not limited to, health check results, self-reported data, and the like. For example, the providing unit provides optimal advice based on the user's past health data. In addition, the providing unit can extract a specific pattern from the user's past health data and reflect it in the advice. In addition, the providing unit can also provide individually optimized advice by referring to the user's past health data. As a result, more effective advice can be provided by selecting optimal advice by referring to the user's past health data.

[0084] The providing unit can customize the advice based on the user's current living situation when providing the advice. The providing unit uses AI to customize the advice based on the user's current living situation when providing the advice. The current living situation includes, for example, a work situation, a home environment, and the like, but is not limited to such examples. For example, the providing unit provides appropriate advice based on the user's current living situation. In addition, the providing unit can provide advice customized in accordance with the user's living habits and environment. In addition, the providing unit can also provide executable advice taking into account the user's current living situation. As a result, more executable advice can be provided by customizing the advice based on the user's current living situation.

[0085] The providing unit can estimate the user's emotion and determine the priority of advice based on the estimated user's emotion. The providing unit uses AI to estimate the user's emotion and determine the priority of advice based on the estimated user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user is feeling stressed, the providing unit can provide advice to relax preferentially. In addition, when the user is relaxed, the providing unit can provide detailed advice for improving health. In addition, when the user is in a hurry, the providing unit can also provide important advice preferentially. In this way, more effective advice can be provided by determining the priority of advice according to the user's emotion.

[0086] The providing unit can select advice taking into consideration the geographical location information of the user when providing advice. The providing unit uses AI to select advice taking into consideration the geographical location information of the user when providing advice. The geographical location information includes, but is not limited to, for example, GPS data, health problems specific to a region, and the like. For example, when the user is in a specific region, the providing unit provides advice taking into consideration the health risks of the region. In addition, the providing unit can provide advice related to the climate and environment of the region based on the user's location information. In addition, when the user is traveling, the providing unit can also provide advice taking into consideration the health risks of the travel destination. This makes it possible to provide more effective advice by providing optimal advice taking into consideration the geographical location information of the user.

[0087] The providing unit can analyze the user's social media activity and propose advice when providing advice. The providing unit uses AI to analyze the user's social media activity and propose advice when providing advice. Social media activity includes, but is not limited to, for example, the content of posts and the reactions of followers. For example, when the user posts about health on social media, the providing unit provides advice related to the content. In addition, the providing unit can provide advice related to health topics in which the user is interested from the user's social media activity. In addition, the providing unit can provide advice based on health information shared by the user's social media friends. In this way, more effective advice can be provided by analyzing the user's social media activity and proposing advice.

[0088] The questioning unit can estimate the user's emotion and adjust the content of the question based on the estimated user's emotion. The questioning unit can estimate the user's emotion using AI and adjust the content of the question based on the estimated user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the questioning unit displays a simple and easy-to-answer question when the user is stressed. Also, the questioning unit can display a detailed question when the user is relaxed. Also, the questioning unit can display important questions preferentially when the user is in a hurry. This allows the content of the question to be adjusted according to the user's emotion, making it possible to ask more appropriate questions.

[0089] The questioning unit can select the most appropriate question by referring to the user's past answer history when displaying a question. The questioning unit uses AI to select the most appropriate question by referring to the user's past answer history when displaying a question. The past answer history includes, for example, past question and answer data, analysis algorithms, etc., but is not limited to such examples. For example, the questioning unit analyzes the patterns of questions that the user has answered in the past and displays the most effective question. In addition, the questioning unit can preferentially display questions that the user found easy to answer in the past. In addition, the questioning unit can select and display highly relevant questions from the user's past answer history. In this way, by selecting the most appropriate question by referring to the user's past answer history, more effective questions can be asked.

[0090] The questioning unit can estimate the user's emotion and adjust the order of questions based on the estimated user's emotion. The questioning unit can estimate the user's emotion using AI and adjust the order of questions based on the estimated user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the questioning unit can display simple questions first when the user is stressed. Also, the questioning unit can display detailed questions first when the user is relaxed. Also, the questioning unit can display important questions first when the user is in a hurry. This allows more effective questioning by adjusting the order of questions according to the user's emotion.

[0091] The question unit can display an optimal question in consideration of the device information of the user when displaying a question. The question unit uses AI to display an optimal question in consideration of the device information of the user when displaying a question. The device information includes, but is not limited to, device information such as a smartphone, tablet, and PC. For example, when the user is using a smartphone, the question unit displays a question that matches the screen size. Also, when the user is using a tablet, the question unit can display a question that is optimized for a large screen. Also, when the user is using a smartwatch, the question unit can display a concise question with high visibility. This allows for more effective questioning by displaying an optimal question in consideration of the device information of the user.

[0092] The menu unit can estimate the user's emotion and adjust the menu contents based on the estimated user's emotion. The menu unit uses AI to estimate the user's emotion and adjust the menu contents based on the estimated user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the menu unit can suggest a meal menu that can relax the user when the user is feeling stressed. Also, the menu unit can suggest a healthy meal menu when the user is relaxed. Also, the menu unit can suggest a meal menu that can be easily prepared when the user is in a hurry. This makes it possible to provide a more appropriate menu by adjusting the menu contents according to the user's emotion.

[0093] The menu unit can select an optimal menu by referring to the user's past meal history when providing the menu. The menu unit uses AI to select an optimal menu by referring to the user's past meal history when providing the menu. The past meal history includes, for example, past meal contents, nutritional balance, etc., but is not limited to such examples. For example, the menu unit proposes a balanced meal menu based on the user's past meal history. In addition, the menu unit can propose a menu that supplements a specific nutrient when a specific nutrient is lacking from the user's past meal history. In addition, the menu unit can also propose an individually optimized meal menu by referring to the user's past meal history. As a result, a more effective menu can be provided by selecting an optimal menu by referring to the user's past meal history.

[0094] The menu unit can estimate the user's emotion and adjust the order of the menu based on the estimated user's emotion. The menu unit can estimate the user's emotion using AI and adjust the order of the menu based on the estimated user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the menu unit can display a relaxing menu first when the user is feeling stressed. Also, the menu unit can display a healthy menu first when the user is relaxed. Also, the menu unit can display an easy-to-prepare menu first when the user is in a hurry. This makes it possible to provide a more effective menu by adjusting the order of the menu according to the user's emotion.

[0095] The menu unit can provide an optimal menu by considering the geographical location information of the user when providing the menu. The menu unit uses AI to provide an optimal menu by considering the geographical location information of the user when providing the menu. The geographical location information includes, but is not limited to, for example, GPS data, health issues specific to a region, and the like. For example, when the user is in a specific region, the menu unit can suggest a menu using ingredients from that region. Furthermore, the menu unit can suggest a menu suitable for the climate and environment of the region based on the user's location information. Furthermore, when the user is traveling, the menu unit can also suggest a menu using ingredients from the travel destination. In this way, a more effective menu can be provided by providing an optimal menu by considering the geographical location information of the user.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The reception unit not only receives the user's answers, but also analyzes the user's past answer history and selects the order of questions. For example, the pattern of questions answered by the user in the past is analyzed, and questions are displayed in the most effective order. Also, questions that the user found easy to answer in the past can be preferentially displayed. Furthermore, highly relevant questions can be selected and displayed from the user's past answer history. In this way, effective questions can be asked by selecting the optimal order of questions based on the user's past answer history.

[0098] The questioning unit can estimate the user's emotions and adjust the content of the questions based on the estimated user's emotions. For example, when the user is stressed, simple and easy-to-answer questions can be displayed. When the user is relaxed, detailed questions can be displayed. Furthermore, when the user is in a hurry, important questions can be displayed with priority. In this way, more appropriate questions can be asked by adjusting the content of the questions according to the user's emotions.

[0099] The menu unit can estimate the user's emotions and adjust the menu contents based on the estimated user's emotions. For example, if the user is feeling stressed, a relaxing meal menu can be suggested. Also, if the user is relaxed, a healthy meal menu can be suggested. Furthermore, if the user is in a hurry, an easy-to-prepare meal menu can be suggested. In this way, a more appropriate menu can be provided by adjusting the menu contents according to the user's emotions.

[0100] During analysis, the analysis unit can adjust the analysis algorithm by referring to the user's past health data. For example, the analysis algorithm can be adjusted based on the user's past health data to improve accuracy. In addition, specific patterns can be extracted from the user's past health data and reflected in the analysis. Furthermore, the analysis unit can also perform an individually optimized analysis by referring to the user's past health data. This makes it possible to improve the accuracy of the analysis by optimizing the analysis algorithm by referring to the user's past health data.

[0101] When providing advice, the providing unit can select advice by referring to the user's past health data. For example, optimal advice can be provided based on the user's past health data. Also, specific patterns can be extracted from the user's past health data and reflected in the advice. Furthermore, individually optimized advice can be provided by referring to the user's past health data. In this way, more effective advice can be provided by selecting optimal advice by referring to the user's past health data.

[0102] The reception unit can estimate the user's emotions and adjust the timing of displaying the questions based on the estimated user's emotions. For example, if the user is feeling stressed, the questions can be displayed during a time when the user is able to relax. Also, if the user is relaxed, the questions can be displayed immediately to prompt the user to answer. Furthermore, if the user is busy, the notification can be delayed to display the questions later. In this way, the timing of displaying the questions can be adjusted according to the user's emotions, so that the questions can be displayed at a more appropriate timing.

[0103] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user's emotions. For example, when the user is stressed, the analysis accuracy can be increased to perform a detailed diagnosis. When the user is relaxed, the analysis accuracy can be adjusted to perform a simple diagnosis. Furthermore, when the user is in a hurry, the analysis can be performed quickly to provide a concise diagnosis result. In this way, a more accurate diagnosis can be performed by adjusting the analysis accuracy according to the user's emotions.

[0104] The providing unit can estimate the user's emotion and adjust the way in which advice is expressed based on the estimated user's emotion. For example, when the user is feeling stressed, the advice can be provided in gentle words. When the user is relaxed, detailed advice can be provided. Furthermore, when the user is in a hurry, concise advice that gets to the point can be provided. In this way, by adjusting the way in which advice is expressed depending on the user's emotion, more appropriate advice can be provided.

[0105] The providing unit can customize the advice based on the user's current living situation when providing the advice. For example, appropriate advice can be provided based on the user's current living situation. Also, customized advice can be provided in accordance with the user's living habits and environment. Furthermore, executable advice can be provided taking into account the user's current living situation. In this way, by customizing the advice based on the user's current living situation, more executable advice can be provided.

[0106] When providing advice, the providing unit can select advice taking into consideration the geographical location information of the user. For example, when the user is in a specific area, advice taking into consideration the health risks of that area is provided. Also, based on the user's location information, advice related to the local climate and environment can be provided. Furthermore, when the user is traveling, advice taking into consideration the health risks of the travel destination can also be provided. In this way, more effective advice can be provided by providing optimal advice taking into consideration the user's geographical location information.

[0107] The process flow of the second embodiment will be briefly described below.

[0108] Step 1: The reception unit receives the user's answer. The user's answer may be multiple choice, free description, or a question about health. For example, the user can answer questions such as "Has your menstrual cycle been stable recently?" or "Is your diet balanced?" on the chat screen, and the answer can be received. Step 2: The analysis unit uses AI to analyze the responses received by the reception unit and diagnose the health of the eggs. The analysis is based on the type of algorithm, the accuracy of the analysis, the data used, etc. For example, if the user responds that "my menstrual cycle has been unstable recently," it will be diagnosed based on that information that the health of the eggs may not be good. Step 3: The provider uses AI to provide advice based on the diagnosis results obtained by the analyzer. The advice includes suggestions for improving lifestyle habits and diet and exercise guidance. For example, advice such as "Try to eat a balanced diet" or "Get moderate exercise" may be displayed. Specific diet and exercise menus can also be provided. For example, specific menus such as "Include yogurt and fruit in your breakfast" or "Walk for 30 minutes three times a week" are displayed.

[0109] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0110] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0111] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, question unit, and menu unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives the user's answer. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's answer using AI to diagnose the health condition of the egg. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides advice based on the diagnosis result. The question unit is realized, for example, by the control unit 46A of the smart device 14 and displays specific question content. The menu unit is realized, for example, by the control unit 46A of the smart device 14 and provides specific meal and exercise menus. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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, the RAM 30, and the 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.

[0116] 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.

[0117] 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 the voice according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0119] 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 performed in a secure state.

[0120] Fig. 4 shows an example of 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.

[0121] 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.

[0122] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0123] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0124] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0125] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0126] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0127] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, question unit, and menu unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives the user's answer. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's answer using AI to diagnose the health condition of the egg. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides advice based on the diagnosis result. The question unit is realized, for example, by the control unit 46A of the smart glasses 214 and displays specific question content. The menu unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides specific meal and exercise menus. The correspondence between each unit and the device and the control unit is not limited to the above-mentioned example, and various changes are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0131] 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, the RAM 30, and the 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.

[0132] 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.

[0133] 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 the voice according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0135] 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 performed in a secure state.

[0136] 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.

[0137] 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.

[0138] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0139] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 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.

[0140] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0141] 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 voice indicating a user input for 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.

[0142] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0143] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, question unit, and menu unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and receives the user's answer. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's answer using AI to diagnose the health condition of the egg. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides advice based on the diagnosis result. The question unit is realized, for example, by the control unit 46A of the headset type terminal 314 and displays the specific question content. The menu unit is realized, for example, by the control unit 46A of the headset type terminal 314 and provides a specific meal and exercise menu. The correspondence between each unit and the device and the control unit is not limited to the above-mentioned example, and various changes are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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, the RAM 30, and the 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.

[0148] 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.

[0149] 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 the voice according to instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0151] 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 performed in a secure state.

[0152] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0153] 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.

[0154] 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.

[0155] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0156] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0157] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0158] 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 a voice indicating a user input for 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.

[0159] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0160] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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.

[0161] Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, question unit, and menu unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives the user's answer. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's answer using AI to diagnose the health condition of the egg. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides advice based on the diagnosis result. The question unit is realized, for example, by the control unit 46A of the robot 414 and displays the specific question content. The menu unit is realized, for example, by the control unit 46A of the robot 414 and provides a specific meal and exercise menu. The correspondence between each unit and the device and the control unit is not limited to the above-mentioned example, and various changes are possible.

[0162] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0163] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0164] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0165] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0166] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0167] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0169] 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.

[0170] 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 Universal Serial Bus (USB) 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.

[0171] In addition, 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 upon request from the data processing device 12.

[0172] 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.

[0173] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0174] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0175] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0176] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0177] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0178] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0179] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0180] (Appendix 1) a reception unit for receiving an answer from a user; an analysis unit that analyzes the answer received by the reception unit and diagnoses the health condition of the egg; A providing unit that provides advice based on the diagnosis result obtained by the analysis unit. A system characterized by: (Appendix 2) Equipped with a question section that displays specific questions to check the health of eggs 2. The system of claim 1, (Appendix 3) Equipped with a menu section that offers meal and exercise menus 2. The system of claim 1, (Appendix 4) The analysis unit is Diagnose egg health based on user answers 2. The system of claim 1, (Appendix 5) The providing unit is Providing advice on how to improve egg health based on diagnostic results 2. The system of claim 1, (Appendix 6) The reception unit is Estimate the user's emotions and adjust the timing of question display based on the estimated user's emotions (e.g., joy, sadness, anger). 2. The system of claim 1, (Appendix 7) The reception unit is Analyze the user's past answer history and choose the order of questions 2. The system of claim 1, (Appendix 8) The reception unit is When receiving questions, filtering is performed based on the user's current health status and lifestyle habits. 2. The system of claim 1, (Appendix 9) The reception unit is Infer user emotions and prioritize questions based on the inferred user emotions (e.g., happy, sad, anger) 2. The system of claim 1, (Appendix 10) The reception unit is When accepting questions, the system takes into account the user's geographic location to prioritize relevant questions. 2. The system of claim 1, (Appendix 11) The reception unit is When a question is submitted, the system analyzes the user's social media activity and displays related questions. 2. The system of claim 1, (Appendix 12) The analysis unit is Estimate the user's emotion and adjust the accuracy of analysis based on the estimated user emotion (e.g., joy, sadness, anger) 2. The system of claim 1, (Appendix 13) The analysis unit is During analysis, the analysis algorithm is adjusted by referencing the user's past health data. 2. The system of claim 1, (Appendix 14) The analysis unit is When analyzing, the user's lifestyle and dietary history are taken into consideration. 2. The system of claim 1, (Appendix 15) The analysis unit is Infer the user's emotions and adjust the way the analysis results are displayed based on the estimated user's emotions (e.g., happiness, sadness, anger) 2. The system of claim 1, (Appendix 16) The analysis unit is When analyzing, the geographic location of the user is taken into account. 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, the accuracy of the analysis is improved by referring to the user's related literature. 2. The system of claim 1, (Appendix 18) The providing unit is Inferring the user's emotions and adjusting the way advice is presented based on the estimated user's emotions (e.g., happiness, sadness, anger) 2. The system of claim 1, (Appendix 19) The providing unit is When providing advice, the advice is selected by referring to the user's past health data. 2. The system of claim 1, (Appendix 20) The providing unit is When providing advice, tailor it to the user's current life situation 2. The system of claim 1, (Appendix 21) The providing unit is Inferring the user's emotions and prioritizing advice based on the estimated user's emotions (e.g., happiness, sadness, anger) 2. The system of claim 1, (Appendix 22) The providing unit is When providing advice, the advice is selected taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 23) The providing unit is When providing advice, the system analyzes the user's social media activity to provide advice. 2. The system of claim 1, (Appendix 24) The interrogation unit is Inferring the user's emotions and tailoring the questions asked based on the estimated user's emotions (e.g., happiness, sadness, anger) 2. The system of claim 1, (Appendix 25) The interrogation unit is When displaying a question, the most appropriate question is selected by referring to the user's past answer history. 2. The system of claim 1, (Appendix 26) The interrogation unit is Infer the user's emotions and adjust the order of questions based on the estimated user emotions (e.g., happy, sad, anger) 2. The system of claim 1, (Appendix 27) The interrogation unit is When displaying questions, the most appropriate questions are displayed taking into account the user's device information. 2. The system of claim 1, (Appendix 28) The menu section includes: Estimating a user's emotions and adjusting menu contents based on the estimated user's emotions (e.g., happiness, sadness, anger) 2. The system of claim 1, (Appendix 29) The menu section includes: When providing a menu, the most suitable menu is selected by referring to the user's past meal history. 2. The system of claim 1, (Appendix 30) The menu section includes: Inferring user emotions and adjusting menu order based on the estimated user emotions (e.g., happy, sad, angry) 2. The system of claim 1, (Appendix 31) The menu section includes: When providing a menu, provide the most suitable menu considering the user's geographic location information. 2. The system of claim 1, [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for receiving an answer from a user; an analysis unit that analyzes the answer received by the reception unit and diagnoses the health condition of the egg; A providing unit that provides advice based on the diagnosis result obtained by the analysis unit. A system characterized by:

2. Equipped with a question section that displays specific questions to check the health of eggs 2. The system of claim 1 .

3. Equipped with a menu section that offers meal and exercise menus 2. The system of claim 1 .

4. The providing unit is Providing advice on how to improve egg health based on diagnostic results 2. The system of claim 1 .

5. The reception unit is The system estimates the user's emotions and adjusts the timing of displaying questions based on the estimated user emotions.

2. The system of claim 1 .

6. The reception unit is Analyze the user's past answer history and choose the order of questions 2. The system of claim 1 .

7. The reception unit is When receiving questions, filtering is performed based on the user's current health status and lifestyle habits.

2. The system of claim 1 .

8. The reception unit is Estimate user sentiment and prioritize questions based on the estimated user sentiment 2. The system of claim 1 .

9. The reception unit is When accepting questions, the system takes into account the user's geographic location to prioritize relevant questions.

2. The system of claim 1 .

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