System

Abstract

To provide a system according to an embodiment for diagnosing a newborn to enhance the future happiness of the newborn.SOLUTION: The system according to an embodiment includes a collection unit, an analysis unit, a diagnosis unit, and a provision unit. The collection unit collects information. The analysis unit analyzes the information collected by the collection unit. The diagnosis unit performs a diagnosis on the basis of the result of the analysis obtained by the analysis unit. The provision unit provides the result of the diagnosis obtained by the diagnosis 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] Conventional technology does not provide specific diagnostics to improve the future well-being of newborns, leaving room for improvement.

[0005] The system according to the embodiment aims to perform diagnosis to improve the future well-being of newborn babies. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a diagnosis unit, and a provision unit. The collection unit collects information. The analysis unit analyzes the information collected by the collection unit. The diagnosis unit performs diagnosis based on the analysis result obtained by the analysis unit. The provision unit provides the diagnosis result obtained by the diagnosis unit. Effect of the Invention

[0007] The system according to the embodiment can provide a diagnosis to improve the future well-being of the newborn baby. [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 a 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. 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 happiness diagnosis system according to the embodiment of the present invention is a system that reads information such as the DNA information of the newborn, the DNA information of the parents, the background, the environment in which the newborn was born and raised, the academic background, the occupation, and the hobbies into a generation AI, and diagnoses what kind of education the newborn will receive, how they will grow up, which school they will go to, what kind of member of society they will become, and what kind of old age they will have in order to live the happiest life. This system aims to reduce the suicide rate and improve happiness. First, the DNA information of the newborn and the DNA information of the parents are collected. Next, information such as the background, the environment in which the newborn was born and raised, the academic background, the occupation, and the hobbies are collected. These pieces of information are read into the generation AI. The generation AI analyzes these pieces of information and diagnoses what kind of education the newborn will receive, how they will grow up, which school they will go to, what kind of member of society they will become, and what kind of old age they will have in order to feel the happiest. For example, the generation AI analyzes the genetic characteristics from the DNA information of the newborn and considers the genetic influence from the DNA information of the parents. In addition, it determines what kind of environment is best for the newborn from the information on the background and the environment in which the newborn was born and raised. Furthermore, it suggests what kind of education and occupation are best for the newborn from the information on the academic background and occupation. From the hobby information, it is possible to diagnose what hobbies a newborn should have in order to improve their happiness. This system can provide specific advice to help the newborn live the life they feel happiest in. For example, advice can be given on which school to go to, what career to pursue, and what hobbies to take up. This is expected to lead to a reduction in suicide rates and an increase in happiness. This allows the happiness diagnosis system to provide specific advice to help the newborn feel the happiest in life.

[0029] The happiness diagnosis system according to the embodiment includes a collection unit, an analysis unit, a diagnosis unit, and a provision unit. The collection unit collects information such as DNA information of a newborn, DNA information of a parent, background, environment where the person was born and raised, academic background, occupation, and hobbies. For example, the collection unit collects blood samples and saliva samples and performs genetic analysis in order to collect DNA information. The collection unit can also conduct questionnaires and interviews in order to collect information on background and environment where the person was born and raised. Furthermore, the collection unit can also acquire data from schools and workplaces in order to collect information on educational background and occupation. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit uses a genetic analysis algorithm in order to analyze genetic characteristics. Furthermore, the analysis unit can also use data mining technology and machine learning algorithms in order to analyze background and environment information. Furthermore, the analysis unit can use statistical analysis and prediction models in order to analyze information on educational background and occupation. The diagnosis unit makes a diagnosis based on the analysis results obtained by the analysis unit. The diagnosis unit, for example, suggests what kind of education the newborn should receive, what kind of occupation the newborn should have, and what hobbies the newborn should have. The diagnosis unit can generate an optimal education plan or occupation plan using a generation AI. The provision unit provides the diagnosis result obtained by the diagnosis unit. The provision unit can provide the diagnosis result in a report format, for example. The provision unit can also provide the diagnosis result through an application. In this way, the happiness diagnosis system according to the embodiment can provide specific advice for the newborn to feel the happiest in his or her life. A part or all of the above-mentioned processing in the diagnosis unit may be performed, for example, using AI, or may be performed without using AI. For example, the diagnosis unit can perform a diagnosis using an AI model that inputs the analysis result obtained by the analysis unit and outputs a diagnosis result.

[0030] The collection department collects information such as the newborn's DNA information, parent's DNA information, upbringing, birth and upbringing environment, educational background, occupation, and hobbies. Specifically, to collect DNA information, blood samples and saliva samples are taken, and these samples are put into a genetic analyzer to obtain detailed genetic information. The genetic analyzer can analyze millions of DNA fragments at high speed and with high accuracy using next-generation sequencing technology. In addition, the collection department conducts questionnaires and interviews to collect information on upbringing and birth and upbringing environment. The questionnaires are provided in online forms or paper media, and the interviews are conducted face-to-face or through video calls. This allows detailed background information of each subject to be collected. In addition, the collection department obtains data from schools and workplaces to collect educational background and occupational information. From schools, official documents such as report cards and graduation certificates can be obtained, and from workplaces, official documents such as resumes and evaluation reports can be obtained. This information is collected in digital format and stored in a central database. Information on hobbies can be collected from questionnaires filled out by the subjects themselves and from social media activity histories. For example, to analyze what hobbies the subject has and what activities they participate in, the content of their social media posts and the history of events they have participated in are analyzed. This allows the collection unit to comprehensively collect a wide range of information and get a complete picture of the subject.

[0031] The analysis unit analyzes the information collected by the collection unit. Specifically, a genetic analysis algorithm is used to analyze genetic characteristics. The genetic analysis algorithm analyzes DNA sequence data and identifies specific gene mutations and polymorphisms. This makes it possible to clarify the genetic risks and characteristics of the subject. In addition, the analysis unit uses data mining technology and machine learning algorithms to analyze background and environmental information. Data mining technology is used to extract useful patterns and relationships from large amounts of data. For example, the background and environmental information of the subject can be analyzed to evaluate the impact of specific environmental factors on happiness. The machine learning algorithm is used to build a model based on the collected data and predict the subject's future happiness. Furthermore, the analysis unit uses statistical analysis and predictive models to analyze educational background and occupational information. Statistical analysis is used to analyze educational background and occupational data and evaluate the impact of specific educational background and occupation on happiness. The predictive model is used to predict the future career path and job satisfaction based on the educational background and occupational information of the subject. This makes it possible for the analysis unit to analyze the collected information from multiple angles and clarify factors that affect the subject's happiness.

[0032] The diagnostic unit performs a diagnosis based on the analysis results obtained by the analysis unit. Specifically, it suggests what kind of education the newborn should receive, what kind of occupation the newborn should have, and what hobbies the newborn should have. The diagnostic unit can generate optimal educational and career plans using the generation AI. The generation AI receives data provided by the analysis unit as input and generates a plan that is optimal for the characteristics and environment of the subject. For example, the generation AI can propose an optimal educational curriculum taking into account the genetic characteristics and environmental factors of the subject. In addition, the generation AI can propose an occupation that will provide high satisfaction in the future based on the subject's interests and aptitudes. Furthermore, the generation AI can suggest hobbies that will help relieve stress and self-realization based on the subject's hobbies and interests. The diagnostic unit provides specific advice to the subject based on these suggestions. For example, it can specifically indicate what kind of educational institution the subject should attend, what kind of vocational training he or she should receive, and what hobby activities he or she should participate in. This allows the diagnostic unit to show the subject a specific path to feeling the most happy.

[0033] The providing unit provides the diagnosis results obtained by the diagnosing unit. Specifically, the diagnosis results can be provided in a report format. The report details the characteristics and environmental factors of the subject, the analysis results, the proposed plan, etc., and is provided in a format that is easy for the subject to understand. The providing unit can also provide the diagnosis results through an application. The application is designed so that the subject can check the diagnosis results through a smartphone or tablet, and has an interactive function. For example, the subject can check the proposed plan through the application and provide feedback as necessary. Furthermore, the providing unit can periodically update the diagnosis results and provide optimal advice according to changes in the subject's situation and environment. This allows the providing unit to ensure that the subject can always receive advice based on the latest information. Furthermore, the providing unit also has a function for sharing the diagnosis results. For example, the subject can share the diagnosis results with family and friends and exchange opinions. This allows the providing unit to provide specific advice for the subject to feel the happiest, and to support important decision-making in the subject's life.

[0034] The collection unit can collect DNA information or environmental information. For example, in order to collect DNA information, the collection unit collects a blood sample or a saliva sample and performs genetic analysis. In addition, in order to collect environmental information, the collection unit can also collect data such as temperature, humidity, and concentration of pollutants using a sensor. For example, the collection unit collects a blood sample and analyzes DNA information using a genetic analysis device. In addition, the collection unit can collect a saliva sample and obtain genetic marker information. Furthermore, the collection unit can also collect temperature and humidity data in real time using a sensor. This makes it possible to perform more detailed analysis by collecting DNA information and environmental information. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input environmental data acquired from a sensor to the generation AI and cause the generation AI to execute analysis of the data.

[0035] The analysis unit may include a genetic analysis unit that analyzes genetic characteristics. The genetic analysis unit may analyze, for example, gene mutations and gene expression patterns. The genetic analysis unit may use, for example, next-generation sequencing technology to analyze gene mutations. The genetic analysis unit may also use microarray technology to analyze gene expression patterns. For example, the genetic analysis unit may perform detailed analysis of gene mutations using next-generation sequencing technology. The genetic analysis unit may also analyze gene expression patterns using microarray technology. Furthermore, the genetic analysis unit may also analyze the association between gene mutations and disease risk. This allows for more accurate diagnosis by analyzing genetic characteristics. Some or all of the above-mentioned processing in the genetic analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the genetic analysis unit may input next-generation sequencing data to the generation AI and cause the generation AI to execute analysis of gene mutations.

[0036] The analysis unit may include an environmental analysis unit that analyzes environmental information. The environmental analysis unit may analyze, for example, climate data and environmental pollution data. The environmental analysis unit may use, for example, a weather forecast model to analyze the climate data. The environmental analysis unit may also use data mining technology to analyze the environmental pollution data. For example, the environmental analysis unit may use a weather forecast model to predict temperature and precipitation. The environmental analysis unit may also use data mining technology to analyze the environmental pollution data. Furthermore, the environmental analysis unit may also analyze the association between climate data and health risks. This allows for more detailed diagnosis by analyzing the environmental information. A part or all of the above-mentioned processing in the environmental analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the environmental analysis unit may input weather data to the generation AI and cause the generation AI to execute a weather forecast.

[0037] The diagnosis unit may include a suggestion unit that suggests education or a career. The suggestion unit suggests, for example, an optimal education plan or career plan. The suggestion unit considers, for example, an education history or a career aptitude diagnosis to suggest an education plan. The suggestion unit may also consider a career aptitude diagnosis or job information to suggest a career plan. For example, the suggestion unit suggests an optimal online course or vocational school based on the education history. The suggestion unit may also suggest an optimal career based on a career aptitude diagnosis. Furthermore, the suggestion unit may provide specific job information based on the job information. This allows for more specific advice by suggesting education or a career. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit may input data on the education history or career aptitude diagnosis to the generation AI and cause the generation AI to execute a proposal for an optimal education plan or career plan.

[0038] The diagnosis unit may include a hobby diagnosis unit that diagnoses a hobby. The hobby diagnosis unit, for example, diagnoses an optimal hobby. The hobby diagnosis unit, for example, considers the type of hobby and the diagnosis method. The hobby diagnosis unit, for example, diagnoses a hobby such as sports, music, or art. The hobby diagnosis unit may also use a questionnaire or an interview as a hobby diagnosis method. For example, the hobby diagnosis unit may suggest an optimal sports or music hobby based on a questionnaire. The hobby diagnosis unit may also suggest an optimal art hobby based on an interview. Furthermore, the hobby diagnosis unit may also suggest a specific hobby activity based on the hobby diagnosis result. This allows for more individualized advice by diagnosing the hobby. A part or all of the above-mentioned processing in the hobby diagnosis unit may be performed, for example, using AI, or may be performed without using AI. For example, the hobby diagnosis unit may input questionnaire data to the generation AI and cause the generation AI to execute a suggestion of an optimal hobby.

[0039] The providing unit can provide the diagnostic result in a report format. The providing unit provides the diagnostic result in, for example, a PDF format or a web page format. The providing unit uses report generation software to provide the diagnostic result in, for example, a PDF format. The providing unit can also use a web application to provide the diagnostic result in a web page format. For example, the providing unit uses report generation software to generate the diagnostic result in a PDF format and provide it to the user. The providing unit can also display the diagnostic result in a web page format using a web application. Furthermore, the providing unit can also transmit the diagnostic result by email. As a result, by providing the diagnostic result in a report format, the user can easily understand it. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the diagnostic result to the generation AI and cause the generation AI to execute generation in a report format.

[0040] The providing unit can provide the diagnostic result through an application. The providing unit provides the diagnostic result through, for example, a mobile app or a web app. The providing unit develops a mobile application to provide the diagnostic result using, for example, a mobile app. The providing unit can also develop a web application to provide the diagnostic result using a web app. For example, the providing unit uses a mobile application to display the diagnostic result on a smartphone or tablet. The providing unit can also use a web application to display the diagnostic result on a personal computer or tablet. Furthermore, the providing unit can store the diagnostic result in cloud storage so that the user can access it at any time. This makes it easier for the user to access the diagnostic result by providing it through an application. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the diagnostic result to the generating AI and cause the generating AI to execute the provision through the application.

[0041] The collection unit can analyze the parent's past health history and select an information collection method. The collection unit, for example, collects medical history and health examination results to analyze the parent's past health history. The collection unit, for example, acquires data from a medical institution to collect medical history. The collection unit can also acquire data from a health examination institution to collect health examination results. For example, the collection unit acquires medical history data of the parent from a medical institution and preferentially collects information with a high genetic risk. The collection unit can also acquire the parent's health examination results from a health examination institution and collect specific environmental information with a focus. Furthermore, the collection unit can select the type of DNA information to be collected with reference to the parent's health history. This makes it possible to collect information more effectively by selecting an information collection method based on the parent's health history. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the parent's health examination results to the generation AI and cause the generation AI to select the information collection method.

[0042] The collection unit can perform filtering based on the parent's lifestyle and dietary habits when collecting information. The collection unit, for example, filters information based on the parent's lifestyle and dietary habits. The collection unit, for example, collects information on nutritional status based on the parent's dietary habits. The collection unit can also collect information on stress levels based on the parent's lifestyle habits. For example, the collection unit collects information on meal frequency and nutritional balance based on the parent's dietary habits. The collection unit can also collect information on exercise habits and sleep patterns based on the parent's lifestyle habits. Furthermore, the collection unit can also collect information on physical activity based on the parent's exercise habits. As a result, by filtering information based on the parent's lifestyle and dietary habits, more relevant information can be collected. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the parent's dietary habits data to the generation AI and cause the generation AI to execute filtering of information.

[0043] The collection unit can collect information with high relevance based on the geographical location information of the parent at the time of collecting information. The collection unit collects information, for example, taking into consideration the geographical location information of the parent. The collection unit collects, for example, environmental information of the area where the parent lives with high relevance. The collection unit can also collect area-specific health risk information based on the geographical location of the parent. For example, the collection unit collects environmental information such as temperature, humidity, and pollutant concentration of the area where the parent lives with high relevance. The collection unit can also collect area-specific health risk information based on the geographical location of the parent. Furthermore, the collection unit can collect area-specific educational environment information based on the geographical location of the parent. In this way, by taking into consideration the geographical location information of the parent, it is possible to collect area-specific information with high relevance. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location data of the parent to the generation AI and cause the generation AI to execute collection of information with high relevance.

[0044] The collection unit can analyze the parent's social media activity and collect related information when collecting information. For example, the collection unit collects the content of posts and the number of followers to analyze the parent's social media activity. For example, the collection unit collects information on lifestyle habits from the parent's social media posts. The collection unit can also collect information on hobbies and interests from the parent's social media activity. For example, the collection unit analyzes the parent's social media posts and collects information on eating habits and exercise habits. The collection unit can also collect information on hobbies and interests from the parent's social media activity. Furthermore, the collection unit can also collect information on social connections from the parent's social media network. This makes it possible to collect more relevant information by analyzing the parent's social media activity. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the parent's social media data to the generation AI and cause the generation AI to collect related information.

[0045] The analysis unit can adjust the level of detail of the analysis based on the importance of the genetic characteristic during the analysis. The analysis unit, for example, considers the influence of genetic mutations and disease risk in order to evaluate the importance of the genetic characteristic. The analysis unit, for example, uses next-generation sequencing technology in order to evaluate the influence of genetic mutations. The analysis unit can also use genetic marker information in order to evaluate disease risk. For example, the analysis unit uses next-generation sequencing technology to perform detailed analysis of genetic mutations and evaluate the influence. The analysis unit can also evaluate disease risk using genetic marker information. Furthermore, the analysis unit adjusts the level of detail of the analysis based on the importance of the genetic characteristic. For example, a detailed analysis is performed for characteristics with high genetic risk, and a brief analysis is performed for characteristics with low risk. As a result, by adjusting the level of detail of the analysis based on the importance of the genetic characteristic, more important information can be analyzed in detail. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input next-generation sequencing data to the generation AI and cause the generation AI to execute the analysis of genetic mutations.

[0046] The analysis unit can apply different analysis algorithms depending on the category of environmental information during analysis. The analysis unit selects an analysis algorithm, for example, taking into consideration the category of environmental information. For example, when the environmental information is related to health, the analysis unit applies a health analysis algorithm. Also, when the environmental information is related to education, the analysis unit can apply an education analysis algorithm. For example, the analysis unit uses a health analysis algorithm to analyze data such as temperature, humidity, and pollutant concentration. Also, the analysis unit can analyze data related to the educational environment using an education analysis algorithm. Furthermore, when the environmental information is related to social connections, the analysis unit can apply a social analysis algorithm. This enables more appropriate analysis by applying different analysis algorithms depending on the category of environmental information. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input environmental data to the generation AI and cause the generation AI to perform an analysis according to the category.

[0047] The analysis unit can determine the priority of the analysis based on the collection time of the information at the time of analysis. The analysis unit determines the priority of the analysis, for example, taking into account the collection time of the information. The analysis unit, for example, preferentially analyzes the most recently collected information. The analysis unit can also analyze old information as necessary. For example, the analysis unit preferentially analyzes the most recently collected information and performs diagnosis based on the latest information. The analysis unit can also analyze old information as necessary and use it as reference information. Furthermore, the analysis unit can adjust the analysis schedule based on the collection time of the information. As a result, by determining the priority of the analysis based on the collection time of the information, it is possible to preferentially analyze more recent information. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the collection time data of the information to the generation AI and cause the generation AI to execute the determination of the analysis priority.

[0048] The analysis unit can adjust the order of analysis based on the relevance of information during analysis. The analysis unit, for example, considers co-occurrence frequency and correlation to evaluate the relevance of information. The analysis unit, for example, uses text mining technology to evaluate the co-occurrence frequency. The analysis unit can also use statistical analysis technology to evaluate the correlation. For example, the analysis unit uses text mining technology to evaluate the co-occurrence frequency of information and preferentially analyzes information with high relevance. The analysis unit can also use statistical analysis technology to evaluate the correlation of information and preferentially analyze information with high relevance. Furthermore, the analysis unit can adjust the schedule of analysis based on the relevance of information. As a result, by adjusting the order of analysis based on the relevance of information, information with higher relevance can be preferentially analyzed. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the relevance data of information to the generation AI and cause the generation AI to adjust the order of analysis.

[0049] The diagnostic unit can improve the accuracy of the diagnosis based on the interrelationship between education and occupation at the time of diagnosis. The diagnostic unit, for example, performs a diagnosis taking into account the interrelationship between education and occupation. The diagnostic unit, for example, takes into account an occupational aptitude diagnosis and an education history in order to analyze the interrelationship between education and occupation. The diagnostic unit can also adjust the education plan taking into account the skills required for the occupation. For example, the diagnostic unit proposes an optimal education plan based on the occupational aptitude diagnosis. The diagnostic unit can also adjust the education plan taking into account the skills required for the occupation based on the education history. Furthermore, the diagnostic unit improves the accuracy of the diagnosis based on the interrelationship between education and occupation. As a result, by taking into account the interrelationship between education and occupation, a more accurate diagnosis is possible. A part or all of the above-mentioned processing in the diagnostic unit may be performed using, for example, AI, or may be performed without using AI. For example, the diagnostic unit can input occupational aptitude diagnostic data to the generation AI and cause the generation AI to execute a proposal for an education plan.

[0050] The diagnostic unit can perform diagnosis based on the attribute information of the parent at the time of diagnosis. The diagnostic unit performs diagnosis, for example, taking into consideration the attribute information of the parent. The diagnostic unit proposes an optimal education plan, for example, taking into consideration the educational background of the parent. The diagnostic unit can also propose an optimal career plan, taking into consideration the occupation of the parent. For example, the diagnostic unit proposes an optimal education plan based on the educational background of the parent. The diagnostic unit can also propose an optimal career plan based on the occupation of the parent. Furthermore, the diagnostic unit can also propose an optimal hobby plan, taking into consideration the hobby of the parent. This makes it possible to perform a more individualized diagnosis by taking into consideration the attribute information of the parent. Part or all of the above-mentioned processing in the diagnostic unit may be performed, for example, using AI, or may be performed without using AI. For example, the diagnostic unit can input the attribute information data of the parent to the generation AI and cause the generation AI to execute the diagnosis.

[0051] The diagnosis unit can perform diagnosis based on the geographical distribution of information at the time of diagnosis. The diagnosis unit performs diagnosis, for example, taking into account the geographical distribution of information. The diagnosis unit, for example, preferentially uses geographically close information for diagnosis. In addition, the diagnosis unit can also use geographically distant information for diagnosis as necessary. For example, the diagnosis unit preferentially uses geographically close information for diagnosis and performs a region-specific diagnosis. In addition, the diagnosis unit can use geographically distant information for diagnosis as necessary and use it as reference information. Furthermore, the diagnosis unit can adjust the schedule of diagnosis based on the geographical distribution of information. As a result, region-specific diagnosis is possible by taking into account the geographical distribution of information. Part or all of the above-mentioned processing in the diagnosis unit may be performed, for example, using AI, or may be performed without using AI. For example, the diagnosis unit can input geographical distribution data of information to the generation AI and cause the generation AI to execute the execution of diagnosis.

[0052] The diagnostic unit can improve the accuracy of diagnosis based on related literature at the time of diagnosis. The diagnostic unit, for example, performs diagnosis by referring to related literature. The diagnostic unit, for example, improves the accuracy of diagnosis by referring to related literature. The diagnostic unit can also complement the diagnosis result based on information obtained from the related literature. For example, the diagnostic unit improves the accuracy of diagnosis by referring to related literature. The diagnostic unit can also complement the diagnosis result based on information obtained from the related literature. Furthermore, the diagnostic unit can adjust the diagnostic criteria based on the related literature. As a result, the accuracy of diagnosis is improved by referring to the related literature. A part or all of the above-mentioned processing in the diagnostic unit may be performed using, for example, AI, or may be performed without using AI. For example, the diagnostic unit can input related literature data to the generation AI and cause the generation AI to execute the improvement of the accuracy of diagnosis.

[0053] The providing unit can select the optimal providing method by referring to the parent's past feedback at the time of providing. The providing unit, for example, collects questionnaire results and review comments in order to refer to the parent's past feedback. The providing unit, for example, selects a detailed report format from the parent's past feedback. The providing unit can also select a concise report format from the parent's past feedback. For example, the providing unit selects a detailed report format based on the questionnaire results. The providing unit can also select a concise report format based on the review comments. Furthermore, the providing unit can adjust the information providing method based on the parent's past feedback. This makes it possible to provide information more effectively by referring to the parent's past feedback. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the parent's feedback data to the generating AI and cause the generating AI to select the optimal providing method.

[0054] The providing unit can select the optimal providing method in consideration of the parent's device information at the time of providing. The providing unit provides information in consideration of, for example, the parent's device information. For example, when the parent is using a smartphone, the providing unit provides a display method that matches the screen size. In addition, when the parent is using a tablet, the providing unit can also provide a display method optimized for a large screen. For example, when the parent is using a smartphone, the providing unit provides a display method that matches the screen size. In addition, when the parent is using a tablet, the providing unit can also provide a display method optimized for a large screen. In addition, when the parent is using a personal computer, the providing unit can also provide information in a detailed report format. This makes it possible to provide more appropriate information by considering the parent's device information. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the parent's device information data to the generating AI and cause the generating AI to select the optimal providing method.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The happiness diagnosis system may further include a health monitoring unit that monitors the newborn's health in real time. The health monitoring unit continuously monitors the newborn's vital signs, such as body temperature, heart rate, and respiratory rate, and can issue an alert if an abnormality is detected. For example, if the body temperature rises suddenly, a notification is sent to the parents, urging them to contact a medical institution. In addition, if an abnormality in the heart rate or respiratory rate is detected, it can determine whether emergency response is necessary and provide appropriate instructions. Furthermore, the health monitoring unit can analyze the collected data and grasp trends in the newborn's health, thereby supporting preventive health management. This makes it possible to reduce health risks by monitoring the newborn's health in real time and detecting and responding to abnormalities early.

[0057] The happiness diagnosis system may further include a growth record unit that automatically creates a growth record of the newborn. The growth record unit may periodically collect growth data such as the newborn's height, weight, and head circumference, and create a growth curve. For example, data from regular health checkups may be automatically imported, and the progress of growth may be displayed in a graph format. The growth record unit may also import data manually entered by the parent, and the growth record may be centrally managed. Furthermore, the growth record unit may analyze the growth data and detect growth abnormalities at an early stage. For example, if the growth curve falls outside the standard range, a notification may be sent to the parent, encouraging them to consult a medical institution. This allows the growth of the newborn to be continuously monitored, and abnormalities to be detected and dealt with at an early stage, thereby supporting healthy growth.

[0058] The happiness diagnostic system may further include a sleep analysis unit that analyzes the sleep pattern of the newborn. The sleep analysis unit may monitor the newborn's sleep time, sleep quality, number of nighttime awakenings, and the like, and provide the analysis results. For example, the sleep analysis unit may analyze the sleep pattern of the newborn using data collected from a baby monitor or a wearable device. The sleep analysis unit may also provide suggestions to parents on how to improve the sleep environment based on the analysis results. For example, advice on adjusting the temperature, humidity, and lighting in the room may be provided. Furthermore, the sleep analysis unit may accumulate long-term sleep data and grasp changes in sleep patterns that occur with growth. This allows the newborn's sleep pattern to be analyzed and an appropriate sleep environment to be provided, thereby supporting healthy growth.

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

[0060] Step 1: The collection unit collects information such as the newborn's DNA information, the parents' DNA information, background, birth and upbringing environment, educational background, occupation, hobbies, etc. For example, to collect DNA information, blood samples and saliva samples are taken and genetic analysis is performed. In addition, questionnaires and interviews can be conducted to collect information on background and birth and upbringing environment. Furthermore, data from schools and workplaces can be obtained to collect information on educational background and occupation. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, a genetic analysis algorithm is used to analyze genetic characteristics. Data mining techniques and machine learning algorithms can also be used to analyze background and environmental information. Furthermore, statistical analysis and predictive models can be used to analyze educational background and occupational information. Step 3: The diagnostic unit performs a diagnosis based on the analysis results obtained by the analysis unit. For example, it suggests what kind of education the newborn should receive, what kind of occupation they should have, and what hobbies they should have. The diagnostic unit can use generative AI to generate optimal educational and career plans. Step 4: The providing unit provides the diagnosis result obtained by the diagnosing unit. For example, the diagnosis result can be provided in a report format. Also, the diagnosis result can be provided through an application.

[0061] (Example 2) The happiness diagnosis system according to the embodiment of the present invention is a system that reads information such as the DNA information of the newborn, the DNA information of the parents, the background, the environment in which the newborn was born and raised, the academic background, the occupation, and the hobbies into a generation AI, and diagnoses what kind of education the newborn will receive, how they will grow up, which school they will go to, what kind of member of society they will become, and what kind of old age they will have in order to live the happiest life. This system aims to reduce the suicide rate and improve happiness. First, the DNA information of the newborn and the DNA information of the parents are collected. Next, information such as the background, the environment in which the newborn was born and raised, the academic background, the occupation, and the hobbies are collected. These pieces of information are read into the generation AI. The generation AI analyzes these pieces of information and diagnoses what kind of education the newborn will receive, how they will grow up, which school they will go to, what kind of member of society they will become, and what kind of old age they will have in order to feel the happiest. For example, the generation AI analyzes the genetic characteristics from the DNA information of the newborn and considers the genetic influence from the DNA information of the parents. In addition, it determines what kind of environment is best for the newborn from the information on the background and the environment in which the newborn was born and raised. Furthermore, it suggests what kind of education and occupation are best for the newborn from the information on the academic background and occupation. From the hobby information, it is possible to diagnose what hobbies a newborn should have in order to improve their happiness. This system can provide specific advice to help the newborn live the life they feel happiest in. For example, advice can be given on which school to go to, what career to pursue, and what hobbies to take up. This is expected to lead to a reduction in suicide rates and an increase in happiness. This allows the happiness diagnosis system to provide specific advice to help the newborn feel the happiest in life.

[0062] The happiness diagnosis system according to the embodiment includes a collection unit, an analysis unit, a diagnosis unit, and a provision unit. The collection unit collects information such as DNA information of a newborn, DNA information of a parent, background, environment where the person was born and raised, academic background, occupation, and hobbies. For example, the collection unit collects blood samples and saliva samples and performs genetic analysis in order to collect DNA information. The collection unit can also conduct questionnaires and interviews in order to collect information on background and environment where the person was born and raised. Furthermore, the collection unit can also acquire data from schools and workplaces in order to collect information on educational background and occupation. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit uses a genetic analysis algorithm in order to analyze genetic characteristics. Furthermore, the analysis unit can also use data mining technology and machine learning algorithms in order to analyze background and environment information. Furthermore, the analysis unit can use statistical analysis and prediction models in order to analyze information on educational background and occupation. The diagnosis unit makes a diagnosis based on the analysis results obtained by the analysis unit. The diagnosis unit, for example, suggests what kind of education the newborn should receive, what kind of occupation the newborn should have, and what hobbies the newborn should have. The diagnosis unit can generate an optimal education plan or occupation plan using a generation AI. The provision unit provides the diagnosis result obtained by the diagnosis unit. The provision unit can provide the diagnosis result in a report format, for example. The provision unit can also provide the diagnosis result through an application. In this way, the happiness diagnosis system according to the embodiment can provide specific advice for the newborn to feel the happiest in his or her life. A part or all of the above-mentioned processing in the diagnosis unit may be performed, for example, using AI, or may be performed without using AI. For example, the diagnosis unit can perform a diagnosis using an AI model that inputs the analysis result obtained by the analysis unit and outputs a diagnosis result.

[0063] The collection department collects information such as the newborn's DNA information, parent's DNA information, upbringing, birth and upbringing environment, educational background, occupation, and hobbies. Specifically, to collect DNA information, blood samples and saliva samples are taken, and these samples are put into a genetic analyzer to obtain detailed genetic information. The genetic analyzer can analyze millions of DNA fragments at high speed and with high accuracy using next-generation sequencing technology. In addition, the collection department conducts questionnaires and interviews to collect information on upbringing and birth and upbringing environment. The questionnaires are provided in online forms or paper media, and the interviews are conducted face-to-face or through video calls. This allows detailed background information of each subject to be collected. In addition, the collection department obtains data from schools and workplaces to collect educational background and occupational information. From schools, official documents such as report cards and graduation certificates can be obtained, and from workplaces, official documents such as resumes and evaluation reports can be obtained. This information is collected in digital format and stored in a central database. Information on hobbies can be collected from questionnaires filled out by the subjects themselves and from social media activity histories. For example, to analyze what hobbies the subject has and what activities they participate in, the content of their social media posts and the history of events they have participated in are analyzed. This allows the collection unit to comprehensively collect a wide range of information and get a complete picture of the subject.

[0064] The analysis unit analyzes the information collected by the collection unit. Specifically, a genetic analysis algorithm is used to analyze genetic characteristics. The genetic analysis algorithm analyzes DNA sequence data and identifies specific gene mutations and polymorphisms. This makes it possible to clarify the genetic risks and characteristics of the subject. In addition, the analysis unit uses data mining technology and machine learning algorithms to analyze background and environmental information. Data mining technology is used to extract useful patterns and relationships from large amounts of data. For example, the background and environmental information of the subject can be analyzed to evaluate the impact of specific environmental factors on happiness. The machine learning algorithm is used to build a model based on the collected data and predict the subject's future happiness. Furthermore, the analysis unit uses statistical analysis and predictive models to analyze educational background and occupational information. Statistical analysis is used to analyze educational background and occupational data and evaluate the impact of specific educational background and occupation on happiness. The predictive model is used to predict the future career path and job satisfaction based on the educational background and occupational information of the subject. This makes it possible for the analysis unit to analyze the collected information from multiple angles and clarify factors that affect the subject's happiness.

[0065] The diagnostic unit performs a diagnosis based on the analysis results obtained by the analysis unit. Specifically, it suggests what kind of education the newborn should receive, what kind of occupation the newborn should have, and what hobbies the newborn should have. The diagnostic unit can generate optimal educational and career plans using the generation AI. The generation AI receives data provided by the analysis unit as input and generates a plan that is optimal for the characteristics and environment of the subject. For example, the generation AI can propose an optimal educational curriculum taking into account the genetic characteristics and environmental factors of the subject. In addition, the generation AI can propose an occupation that will provide high satisfaction in the future based on the subject's interests and aptitudes. Furthermore, the generation AI can suggest hobbies that will help relieve stress and self-realization based on the subject's hobbies and interests. The diagnostic unit provides specific advice to the subject based on these suggestions. For example, it can specifically indicate what kind of educational institution the subject should attend, what kind of vocational training he or she should receive, and what hobby activities he or she should participate in. This allows the diagnostic unit to show the subject a specific path to feeling the most happy.

[0066] The providing unit provides the diagnosis results obtained by the diagnosing unit. Specifically, the diagnosis results can be provided in a report format. The report details the characteristics and environmental factors of the subject, the analysis results, the proposed plan, etc., and is provided in a format that is easy for the subject to understand. The providing unit can also provide the diagnosis results through an application. The application is designed so that the subject can check the diagnosis results through a smartphone or tablet, and has an interactive function. For example, the subject can check the proposed plan through the application and provide feedback as necessary. Furthermore, the providing unit can periodically update the diagnosis results and provide optimal advice according to changes in the subject's situation and environment. This allows the providing unit to ensure that the subject can always receive advice based on the latest information. Furthermore, the providing unit also has a function for sharing the diagnosis results. For example, the subject can share the diagnosis results with family and friends and exchange opinions. This allows the providing unit to provide specific advice for the subject to feel the happiest, and to support important decision-making in the subject's life.

[0067] The collection unit can collect DNA information or environmental information. For example, in order to collect DNA information, the collection unit collects a blood sample or a saliva sample and performs genetic analysis. In addition, in order to collect environmental information, the collection unit can also collect data such as temperature, humidity, and concentration of pollutants using a sensor. For example, the collection unit collects a blood sample and analyzes DNA information using a genetic analysis device. In addition, the collection unit can collect a saliva sample and obtain genetic marker information. Furthermore, the collection unit can also collect temperature and humidity data in real time using a sensor. This makes it possible to perform more detailed analysis by collecting DNA information and environmental information. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input environmental data acquired from a sensor to the generation AI and cause the generation AI to execute analysis of the data.

[0068] The analysis unit may include a genetic analysis unit that analyzes genetic characteristics. The genetic analysis unit may analyze, for example, gene mutations and gene expression patterns. The genetic analysis unit may use, for example, next-generation sequencing technology to analyze gene mutations. The genetic analysis unit may also use microarray technology to analyze gene expression patterns. For example, the genetic analysis unit may perform detailed analysis of gene mutations using next-generation sequencing technology. The genetic analysis unit may also analyze gene expression patterns using microarray technology. Furthermore, the genetic analysis unit may also analyze the association between gene mutations and disease risk. This allows for more accurate diagnosis by analyzing genetic characteristics. Some or all of the above-mentioned processing in the genetic analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the genetic analysis unit may input next-generation sequencing data to the generation AI and cause the generation AI to execute analysis of gene mutations.

[0069] The analysis unit may include an environmental analysis unit that analyzes environmental information. The environmental analysis unit may analyze, for example, climate data and environmental pollution data. The environmental analysis unit may use, for example, a weather forecast model to analyze the climate data. The environmental analysis unit may also use data mining technology to analyze the environmental pollution data. For example, the environmental analysis unit may use a weather forecast model to predict temperature and precipitation. The environmental analysis unit may also use data mining technology to analyze the environmental pollution data. Furthermore, the environmental analysis unit may also analyze the association between climate data and health risks. This allows for more detailed diagnosis by analyzing the environmental information. A part or all of the above-mentioned processing in the environmental analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the environmental analysis unit may input weather data to the generation AI and cause the generation AI to execute a weather forecast.

[0070] The diagnosis unit may include a suggestion unit that suggests education or a career. The suggestion unit suggests, for example, an optimal education plan or career plan. The suggestion unit considers, for example, an education history or a career aptitude diagnosis to suggest an education plan. The suggestion unit may also consider a career aptitude diagnosis or job information to suggest a career plan. For example, the suggestion unit suggests an optimal online course or vocational school based on the education history. The suggestion unit may also suggest an optimal career based on a career aptitude diagnosis. Furthermore, the suggestion unit may provide specific job information based on the job information. This allows for more specific advice by suggesting education or a career. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit may input data on the education history or career aptitude diagnosis to the generation AI and cause the generation AI to execute a proposal for an optimal education plan or career plan.

[0071] The diagnosis unit may include a hobby diagnosis unit that diagnoses a hobby. The hobby diagnosis unit, for example, diagnoses an optimal hobby. The hobby diagnosis unit, for example, considers the type of hobby and the diagnosis method. The hobby diagnosis unit, for example, diagnoses a hobby such as sports, music, or art. The hobby diagnosis unit may also use a questionnaire or an interview as a hobby diagnosis method. For example, the hobby diagnosis unit may suggest an optimal sports or music hobby based on a questionnaire. The hobby diagnosis unit may also suggest an optimal art hobby based on an interview. Furthermore, the hobby diagnosis unit may also suggest a specific hobby activity based on the hobby diagnosis result. This allows for more individualized advice by diagnosing the hobby. A part or all of the above-mentioned processing in the hobby diagnosis unit may be performed, for example, using AI, or may be performed without using AI. For example, the hobby diagnosis unit may input questionnaire data to the generation AI and cause the generation AI to execute a suggestion of an optimal hobby.

[0072] The providing unit can provide the diagnostic result in a report format. The providing unit provides the diagnostic result in, for example, a PDF format or a web page format. The providing unit uses report generation software to provide the diagnostic result in, for example, a PDF format. The providing unit can also use a web application to provide the diagnostic result in a web page format. For example, the providing unit uses report generation software to generate the diagnostic result in a PDF format and provide it to the user. The providing unit can also display the diagnostic result in a web page format using a web application. Furthermore, the providing unit can also transmit the diagnostic result by email. As a result, by providing the diagnostic result in a report format, the user can easily understand it. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the diagnostic result to the generation AI and cause the generation AI to execute generation in a report format.

[0073] The providing unit can provide the diagnostic result through an application. The providing unit provides the diagnostic result through, for example, a mobile app or a web app. The providing unit develops a mobile application to provide the diagnostic result using, for example, a mobile app. The providing unit can also develop a web application to provide the diagnostic result using a web app. For example, the providing unit uses a mobile application to display the diagnostic result on a smartphone or tablet. The providing unit can also use a web application to display the diagnostic result on a personal computer or tablet. Furthermore, the providing unit can store the diagnostic result in cloud storage so that the user can access it at any time. This makes it easier for the user to access the diagnostic result by providing it through an application. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the diagnostic result to the generating AI and cause the generating AI to execute the provision through the application.

[0074] The collection unit can estimate the emotion of the newborn and adjust the timing of information collection based on the estimated emotion of the newborn. The collection unit uses, for example, facial expression analysis and voice analysis to estimate the emotion of the newborn. For example, to estimate the emotion of the newborn using facial expression analysis, the collection unit photographs the facial expression of the newborn using a camera and uses an emotion estimation algorithm. In addition, the collection unit can also record the voice of the newborn using a microphone and use a voice analysis algorithm to estimate the emotion of the newborn using voice analysis. For example, the collection unit photographs the facial expression of the newborn using a camera and judges whether the newborn is relaxed using an emotion estimation algorithm. In addition, the collection unit can also record the voice of the newborn using a microphone and judge whether the newborn is crying using a voice analysis algorithm. Furthermore, the collection unit adjusts the timing of information collection based on the emotion of the newborn. For example, DNA information is collected when the newborn is relaxed, and environmental information is collected when the newborn is sleeping. In addition, information collection can be paused when the newborn is crying and resumed after the newborn has calmed down. This allows more appropriate information collection by adjusting the timing of information collection according to the emotion of the newborn. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without using AI. For example, the collection unit can input image data of a newborn captured by a camera to the generation AI and cause the generation AI to estimate the newborn's emotions.

[0075] The collection unit can analyze the parent's past health history and select an information collection method. The collection unit, for example, collects medical history and health examination results to analyze the parent's past health history. The collection unit, for example, acquires data from a medical institution to collect medical history. The collection unit can also acquire data from a health examination institution to collect health examination results. For example, the collection unit acquires medical history data of the parent from a medical institution and preferentially collects information with a high genetic risk. The collection unit can also acquire the parent's health examination results from a health examination institution and collect specific environmental information with a focus. Furthermore, the collection unit can select the type of DNA information to be collected with reference to the parent's health history. This makes it possible to collect information more effectively by selecting an information collection method based on the parent's health history. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the parent's health examination results to the generation AI and cause the generation AI to select the information collection method.

[0076] The collection unit can perform filtering based on the parent's lifestyle and dietary habits when collecting information. The collection unit, for example, filters information based on the parent's lifestyle and dietary habits. The collection unit, for example, collects information on nutritional status based on the parent's dietary habits. The collection unit can also collect information on stress levels based on the parent's lifestyle habits. For example, the collection unit collects information on meal frequency and nutritional balance based on the parent's dietary habits. The collection unit can also collect information on exercise habits and sleep patterns based on the parent's lifestyle habits. Furthermore, the collection unit can also collect information on physical activity based on the parent's exercise habits. As a result, by filtering information based on the parent's lifestyle and dietary habits, more relevant information can be collected. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the parent's dietary habits data to the generation AI and cause the generation AI to execute filtering of information.

[0077] The collection unit can estimate the emotion of the newborn and determine the priority of the information to be collected based on the estimated emotion of the newborn. For example, the collection unit uses facial expression analysis and voice analysis to estimate the emotion of the newborn. For example, the collection unit uses a camera to capture the facial expression of the newborn and uses an emotion estimation algorithm to estimate the emotion of the newborn using facial expression analysis. In addition, the collection unit can also record the voice of the newborn using a microphone and use a voice analysis algorithm to estimate the emotion of the newborn using voice analysis. For example, the collection unit can capture the facial expression of the newborn using a camera and determine whether the newborn is relaxed using an emotion estimation algorithm. In addition, the collection unit can also record the voice of the newborn using a microphone and determine whether the newborn is crying using a voice analysis algorithm. Furthermore, the collection unit determines the priority of the information to be collected based on the emotion of the newborn. For example, important DNA information is preferentially collected when the newborn is relaxed, and environmental information is preferentially collected when the newborn is excited. In addition, detailed background information can be collected when the newborn is sleeping. In this way, by determining the priority of information according to the emotion of the newborn, more important information can be preferentially collected. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without using AI. For example, the collection unit can input image data of a newborn captured by a camera to the generation AI and cause the generation AI to estimate the newborn's emotions.

[0078] The collection unit can collect information with high relevance based on the geographical location information of the parent at the time of collecting information. The collection unit collects information, for example, taking into consideration the geographical location information of the parent. The collection unit collects, for example, environmental information of the area where the parent lives with high relevance. The collection unit can also collect area-specific health risk information based on the geographical location of the parent. For example, the collection unit collects environmental information such as temperature, humidity, and pollutant concentration of the area where the parent lives with high relevance. The collection unit can also collect area-specific health risk information based on the geographical location of the parent. Furthermore, the collection unit can collect area-specific educational environment information based on the geographical location of the parent. In this way, by taking into consideration the geographical location information of the parent, it is possible to collect area-specific information with high relevance. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location data of the parent to the generation AI and cause the generation AI to execute collection of information with high relevance.

[0079] The collection unit can analyze the parent's social media activity and collect related information when collecting information. For example, the collection unit collects the content of posts and the number of followers to analyze the parent's social media activity. For example, the collection unit collects information on lifestyle habits from the parent's social media posts. The collection unit can also collect information on hobbies and interests from the parent's social media activity. For example, the collection unit analyzes the parent's social media posts and collects information on eating habits and exercise habits. The collection unit can also collect information on hobbies and interests from the parent's social media activity. Furthermore, the collection unit can also collect information on social connections from the parent's social media network. This makes it possible to collect more relevant information by analyzing the parent's social media activity. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the parent's social media data to the generation AI and cause the generation AI to collect related information.

[0080] The analysis unit can estimate the emotion of the newborn and adjust the expression method of the analysis based on the estimated emotion of the newborn. The analysis unit uses, for example, facial expression analysis and voice analysis to estimate the emotion of the newborn. For example, to estimate the emotion of the newborn using facial expression analysis, the analysis unit photographs the facial expression of the newborn using a camera and uses an emotion estimation algorithm. In addition, the analysis unit can also record the voice of the newborn using a microphone and use a voice analysis algorithm to estimate the emotion of the newborn using voice analysis. For example, the analysis unit photographs the facial expression of the newborn using a camera and judges whether the newborn is relaxed using an emotion estimation algorithm. In addition, the analysis unit can also record the voice of the newborn using a microphone and judge whether the newborn is crying using a voice analysis algorithm. Furthermore, the analysis unit adjusts the expression method of the analysis based on the emotion of the newborn. For example, if the newborn is relaxed, a detailed analysis result is provided, and if the newborn is excited, a concise analysis result is provided. In addition, if the newborn is sleeping, the analysis result can be saved so that it can be checked later. In this way, by adjusting the expression method of the analysis according to the emotion of the newborn, a more appropriate analysis result can be provided. The estimation of emotions is realized, for example, by using an emotion estimation function using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using an AI or may be performed without using an AI. For example, the analysis unit can input image data of a newborn baby taken by a camera to the generation AI and cause the generation AI to estimate the newborn baby's emotions.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the genetic characteristic during the analysis. The analysis unit, for example, considers the influence of genetic mutations and disease risk in order to evaluate the importance of the genetic characteristic. The analysis unit, for example, uses next-generation sequencing technology in order to evaluate the influence of genetic mutations. The analysis unit can also use genetic marker information in order to evaluate disease risk. For example, the analysis unit uses next-generation sequencing technology to perform detailed analysis of genetic mutations and evaluate the influence. The analysis unit can also evaluate disease risk using genetic marker information. Furthermore, the analysis unit adjusts the level of detail of the analysis based on the importance of the genetic characteristic. For example, a detailed analysis is performed for characteristics with high genetic risk, and a brief analysis is performed for characteristics with low risk. As a result, by adjusting the level of detail of the analysis based on the importance of the genetic characteristic, more important information can be analyzed in detail. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input next-generation sequencing data to the generation AI and cause the generation AI to execute the analysis of genetic mutations.

[0082] The analysis unit can apply different analysis algorithms depending on the category of environmental information during analysis. The analysis unit selects an analysis algorithm, for example, taking into consideration the category of environmental information. For example, when the environmental information is related to health, the analysis unit applies a health analysis algorithm. Also, when the environmental information is related to education, the analysis unit can apply an education analysis algorithm. For example, the analysis unit uses a health analysis algorithm to analyze data such as temperature, humidity, and pollutant concentration. Also, the analysis unit can analyze data related to the educational environment using an education analysis algorithm. Furthermore, when the environmental information is related to social connections, the analysis unit can apply a social analysis algorithm. This enables more appropriate analysis by applying different analysis algorithms depending on the category of environmental information. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input environmental data to the generation AI and cause the generation AI to perform an analysis according to the category.

[0083] The analysis unit can estimate the emotion of the newborn and adjust the length of the analysis based on the estimated emotion of the newborn. The analysis unit uses, for example, facial expression analysis and voice analysis to estimate the emotion of the newborn. For example, to estimate the emotion of the newborn using facial expression analysis, the analysis unit uses a camera to capture the facial expression of the newborn and uses an emotion estimation algorithm. In addition, the analysis unit can also record the voice of the newborn using a microphone and use a voice analysis algorithm to estimate the emotion of the newborn using voice analysis. For example, the analysis unit can capture the facial expression of the newborn using a camera and determine whether the newborn is relaxed using an emotion estimation algorithm. In addition, the analysis unit can also record the voice of the newborn using a microphone and determine whether the newborn is crying using a voice analysis algorithm. In addition, the analysis unit adjusts the length of the analysis based on the emotion of the newborn. For example, if the newborn is relaxed, a detailed analysis is performed, and if the newborn is excited, a brief analysis is performed. In addition, if the newborn is sleeping, the analysis can be saved so that it can be checked later. This allows for more appropriate analysis by adjusting the length of the analysis according to the emotion of the newborn. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit may input image data of a newborn baby taken by a camera to the generation AI, and cause the generation AI to estimate the newborn baby's emotions.

[0084] The analysis unit can determine the priority of the analysis based on the collection time of the information at the time of analysis. The analysis unit determines the priority of the analysis, for example, taking into account the collection time of the information. The analysis unit, for example, preferentially analyzes the most recently collected information. The analysis unit can also analyze old information as necessary. For example, the analysis unit preferentially analyzes the most recently collected information and performs diagnosis based on the latest information. The analysis unit can also analyze old information as necessary and use it as reference information. Furthermore, the analysis unit can adjust the analysis schedule based on the collection time of the information. As a result, by determining the priority of the analysis based on the collection time of the information, it is possible to preferentially analyze more recent information. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the collection time data of the information to the generation AI and cause the generation AI to execute the determination of the analysis priority.

[0085] The analysis unit can adjust the order of analysis based on the relevance of information during analysis. The analysis unit, for example, considers co-occurrence frequency and correlation to evaluate the relevance of information. The analysis unit, for example, uses text mining technology to evaluate the co-occurrence frequency. The analysis unit can also use statistical analysis technology to evaluate the correlation. For example, the analysis unit uses text mining technology to evaluate the co-occurrence frequency of information and preferentially analyzes information with high relevance. The analysis unit can also use statistical analysis technology to evaluate the correlation of information and preferentially analyze information with high relevance. Furthermore, the analysis unit can adjust the schedule of analysis based on the relevance of information. As a result, by adjusting the order of analysis based on the relevance of information, information with higher relevance can be preferentially analyzed. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the relevance data of information to the generation AI and cause the generation AI to adjust the order of analysis.

[0086] The diagnosis unit can estimate the emotion of the newborn and adjust the criteria for diagnosis based on the estimated emotion of the newborn. The diagnosis unit uses, for example, facial expression analysis and voice analysis to estimate the emotion of the newborn. For example, the diagnosis unit uses a camera to capture the facial expression of the newborn and uses an emotion estimation algorithm to estimate the emotion of the newborn using facial expression analysis. In addition, the diagnosis unit can also record the voice of the newborn using a microphone and use a voice analysis algorithm to estimate the emotion of the newborn using voice analysis. For example, the diagnosis unit can capture the facial expression of the newborn using a camera and determine whether the newborn is relaxed using an emotion estimation algorithm. In addition, the diagnosis unit can also record the voice of the newborn using a microphone and determine whether the newborn is crying using a voice analysis algorithm. In addition, the diagnosis unit adjusts the criteria for diagnosis based on the emotion of the newborn. For example, if the newborn is relaxed, detailed diagnostic criteria are applied, and if the newborn is excited, simple diagnostic criteria are applied. In addition, if the newborn is sleeping, the diagnostic criteria can be saved so that they can be checked later. This allows for a more appropriate diagnosis by adjusting the criteria for diagnosis according to the emotion of the newborn. The estimation of emotions is realized, for example, by using an emotion estimation function using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the diagnosis unit may be performed, for example, using an AI or may be performed without using an AI. For example, the diagnosis unit can input image data of a newborn baby taken by a camera to the generation AI and cause the generation AI to estimate the newborn baby's emotions.

[0087] The diagnostic unit can improve the accuracy of the diagnosis based on the interrelationship between education and occupation at the time of diagnosis. The diagnostic unit, for example, performs a diagnosis taking into account the interrelationship between education and occupation. The diagnostic unit, for example, takes into account an occupational aptitude diagnosis and an education history in order to analyze the interrelationship between education and occupation. The diagnostic unit can also adjust the education plan taking into account the skills required for the occupation. For example, the diagnostic unit proposes an optimal education plan based on the occupational aptitude diagnosis. The diagnostic unit can also adjust the education plan taking into account the skills required for the occupation based on the education history. Furthermore, the diagnostic unit improves the accuracy of the diagnosis based on the interrelationship between education and occupation. As a result, by taking into account the interrelationship between education and occupation, a more accurate diagnosis is possible. A part or all of the above-mentioned processing in the diagnostic unit may be performed using, for example, AI, or may be performed without using AI. For example, the diagnostic unit can input occupational aptitude diagnostic data to the generation AI and cause the generation AI to execute a proposal for an education plan.

[0088] The diagnostic unit can perform diagnosis based on the attribute information of the parent at the time of diagnosis. The diagnostic unit performs diagnosis, for example, taking into consideration the attribute information of the parent. The diagnostic unit proposes an optimal education plan, for example, taking into consideration the educational background of the parent. The diagnostic unit can also propose an optimal career plan, taking into consideration the occupation of the parent. For example, the diagnostic unit proposes an optimal education plan based on the educational background of the parent. The diagnostic unit can also propose an optimal career plan based on the occupation of the parent. Furthermore, the diagnostic unit can also propose an optimal hobby plan, taking into consideration the hobby of the parent. This makes it possible to perform a more individualized diagnosis by taking into consideration the attribute information of the parent. Part or all of the above-mentioned processing in the diagnostic unit may be performed, for example, using AI, or may be performed without using AI. For example, the diagnostic unit can input the attribute information data of the parent to the generation AI and cause the generation AI to execute the diagnosis.

[0089] The diagnostic unit can estimate the emotion of the newborn and adjust the display order of the diagnostic results based on the estimated emotion of the newborn. The diagnostic unit uses, for example, facial expression analysis and voice analysis to estimate the emotion of the newborn. For example, the diagnostic unit uses a camera to capture the facial expression of the newborn and uses an emotion estimation algorithm to estimate the emotion of the newborn using facial expression analysis. In addition, the diagnostic unit can also record the voice of the newborn using a microphone and use a voice analysis algorithm to estimate the emotion of the newborn using voice analysis. For example, the diagnostic unit can capture the facial expression of the newborn using a camera and determine whether the newborn is relaxed using an emotion estimation algorithm. In addition, the diagnostic unit can also record the voice of the newborn using a microphone and determine whether the newborn is crying using a voice analysis algorithm. Furthermore, the diagnostic unit adjusts the display order of the diagnostic results based on the emotion of the newborn. For example, if the newborn is relaxed, a detailed diagnostic result is displayed first, and if the newborn is excited, a concise diagnostic result is displayed first. In addition, if the newborn is sleeping, the diagnostic result can be saved so that it can be checked later. This makes it possible to provide more appropriate information by adjusting the display order of the diagnostic results according to the emotion of the newborn. The estimation of emotions is realized, for example, by using an emotion estimation function using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the diagnosis unit may be performed, for example, using an AI or may be performed without using an AI. For example, the diagnosis unit can input image data of a newborn baby taken by a camera to the generation AI and cause the generation AI to estimate the newborn baby's emotions.

[0090] The diagnosis unit can perform diagnosis based on the geographical distribution of information at the time of diagnosis. The diagnosis unit performs diagnosis, for example, taking into account the geographical distribution of information. The diagnosis unit, for example, preferentially uses geographically close information for diagnosis. In addition, the diagnosis unit can also use geographically distant information for diagnosis as necessary. For example, the diagnosis unit preferentially uses geographically close information for diagnosis and performs a region-specific diagnosis. In addition, the diagnosis unit can use geographically distant information for diagnosis as necessary and use it as reference information. Furthermore, the diagnosis unit can adjust the schedule of diagnosis based on the geographical distribution of information. As a result, region-specific diagnosis is possible by taking into account the geographical distribution of information. Part or all of the above-mentioned processing in the diagnosis unit may be performed, for example, using AI, or may be performed without using AI. For example, the diagnosis unit can input geographical distribution data of information to the generation AI and cause the generation AI to execute the execution of diagnosis.

[0091] The diagnostic unit can improve the accuracy of diagnosis based on related literature at the time of diagnosis. The diagnostic unit, for example, performs diagnosis by referring to related literature. The diagnostic unit, for example, improves the accuracy of diagnosis by referring to related literature. The diagnostic unit can also complement the diagnosis result based on information obtained from the related literature. For example, the diagnostic unit improves the accuracy of diagnosis by referring to related literature. The diagnostic unit can also complement the diagnosis result based on information obtained from the related literature. Furthermore, the diagnostic unit can adjust the diagnostic criteria based on the related literature. As a result, the accuracy of diagnosis is improved by referring to the related literature. A part or all of the above-mentioned processing in the diagnostic unit may be performed using, for example, AI, or may be performed without using AI. For example, the diagnostic unit can input related literature data to the generation AI and cause the generation AI to execute the improvement of the accuracy of diagnosis.

[0092] The providing unit can estimate the emotion of the newborn and adjust the format of the information to be provided based on the estimated emotion of the newborn. The providing unit uses, for example, facial expression analysis and voice analysis to estimate the emotion of the newborn. For example, to estimate the emotion of the newborn using facial expression analysis, the providing unit uses a camera to capture the facial expression of the newborn and uses an emotion estimation algorithm. In addition, the providing unit can also record the voice of the newborn using a microphone and use a voice analysis algorithm to estimate the emotion of the newborn using voice analysis. For example, the providing unit can capture the facial expression of the newborn using a camera and determine whether the newborn is relaxed using an emotion estimation algorithm. In addition, the providing unit can also record the voice of the newborn using a microphone and determine whether the newborn is crying using a voice analysis algorithm. Furthermore, the providing unit adjusts the format of the information to be provided based on the emotion of the newborn. For example, if the newborn is relaxed, information is provided in a detailed report format, and if the newborn is excited, information is provided in a concise report format. In addition, if the newborn is sleeping, the information can be saved so that it can be checked later. As a result, by adjusting the format of information according to the emotion of the newborn, more appropriate information can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit may input image data of a newborn baby taken by a camera to the generation AI, and cause the generation AI to estimate the newborn baby's emotions.

[0093] The providing unit can select the optimal providing method by referring to the parent's past feedback at the time of providing. The providing unit, for example, collects questionnaire results and review comments in order to refer to the parent's past feedback. The providing unit, for example, selects a detailed report format from the parent's past feedback. The providing unit can also select a concise report format from the parent's past feedback. For example, the providing unit selects a detailed report format based on the questionnaire results. The providing unit can also select a concise report format based on the review comments. Furthermore, the providing unit can adjust the information providing method based on the parent's past feedback. This makes it possible to provide information more effectively by referring to the parent's past feedback. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the parent's feedback data to the generating AI and cause the generating AI to select the optimal providing method.

[0094] The providing unit can estimate the emotion of the newborn and determine the priority of information to be provided based on the estimated emotion of the newborn. The providing unit uses, for example, facial expression analysis and voice analysis to estimate the emotion of the newborn. For example, to estimate the emotion of the newborn using facial expression analysis, the providing unit photographs the facial expression of the newborn using a camera and uses an emotion estimation algorithm. In addition, the providing unit can also record the voice of the newborn using a microphone and use a voice analysis algorithm to estimate the emotion of the newborn using voice analysis. For example, the providing unit photographs the facial expression of the newborn using a camera and determines whether the newborn is relaxed using an emotion estimation algorithm. In addition, the providing unit can also record the voice of the newborn using a microphone and determine whether the newborn is crying using a voice analysis algorithm. Furthermore, the providing unit determines the priority of information to be provided based on the emotion of the newborn. For example, if the newborn is relaxed, detailed information is provided preferentially, and if the newborn is excited, brief information is provided preferentially. In addition, if the newborn is sleeping, the information can be saved so that it can be checked later. In this way, by determining the priority of information according to the emotion of the newborn, more important information can be provided preferentially. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input image data of a newborn baby taken by a camera to the generation AI, and cause the generation AI to estimate the newborn baby's emotions.

[0095] The providing unit can select the optimal providing method in consideration of the parent's device information at the time of providing. The providing unit provides information in consideration of, for example, the parent's device information. For example, when the parent is using a smartphone, the providing unit provides a display method that matches the screen size. In addition, when the parent is using a tablet, the providing unit can also provide a display method optimized for a large screen. For example, when the parent is using a smartphone, the providing unit provides a display method that matches the screen size. In addition, when the parent is using a tablet, the providing unit can also provide a display method optimized for a large screen. In addition, when the parent is using a personal computer, the providing unit can also provide information in a detailed report format. This makes it possible to provide more appropriate information by considering the parent's device information. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the parent's device information data to the generating AI and cause the generating AI to select the optimal providing method.

[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 happiness diagnosis system may further include a health monitoring unit that monitors the newborn's health condition in real time. The health monitoring unit continuously monitors the newborn's vital signs, such as body temperature, heart rate, and respiratory rate, and can issue an alert if an abnormality is detected. For example, if the body temperature rises suddenly, a notification is sent to the parents, urging them to contact a medical institution. In addition, if an abnormality in the heart rate or respiratory rate is detected, it can determine whether emergency response is necessary and provide appropriate instructions. Furthermore, the health monitoring unit can analyze the collected data and grasp trends in the newborn's health condition to support preventive health management. This makes it possible to reduce health risks by monitoring the newborn's health condition in real time and detecting and responding to abnormalities early.

[0098] The happiness diagnosis system may further include a growth record unit that automatically creates a growth record of the newborn. The growth record unit may periodically collect growth data such as the newborn's height, weight, and head circumference, and create a growth curve. For example, data from regular health checkups may be automatically imported, and the progress of growth may be displayed in a graph format. The growth record unit may also import data manually entered by the parent, and the growth record may be centrally managed. Furthermore, the growth record unit may analyze the growth data and detect growth abnormalities at an early stage. For example, if the growth curve falls outside the standard range, a notification may be sent to the parent, encouraging them to consult a medical institution. This allows the growth of the newborn to be continuously monitored, and abnormalities to be detected and dealt with at an early stage, thereby supporting healthy growth.

[0099] The happiness diagnostic system may further include a sleep analysis unit that analyzes the sleep pattern of the newborn. The sleep analysis unit may monitor the newborn's sleep time, sleep quality, number of nighttime awakenings, and the like, and provide the analysis results. For example, the sleep analysis unit may analyze the sleep pattern of the newborn using data collected from a baby monitor or a wearable device. The sleep analysis unit may also provide suggestions to parents on how to improve the sleep environment based on the analysis results. For example, advice on adjusting the temperature, humidity, and lighting in the room may be provided. Furthermore, the sleep analysis unit may accumulate long-term sleep data and grasp changes in sleep patterns that occur with growth. This allows the newborn's sleep pattern to be analyzed and an appropriate sleep environment to be provided, thereby supporting healthy growth.

[0100] The happiness diagnosis system may further include an emotion advice unit that estimates the emotion of the newborn and provides childcare advice based on the estimated emotion. The emotion advice unit analyzes the facial expression and tone of voice of the newborn to estimate the emotion. For example, if the newborn is crying, the crying pattern can be analyzed to estimate the cause, such as hunger, sleepiness, or poor health. In addition, the emotion advice unit provides specific childcare advice to the parent based on the estimated emotion. For example, if the newborn is crying because of hunger, the timing of breastfeeding can be suggested. Furthermore, the emotion advice unit can provide more accurate advice by accumulating long-term emotion data and grasping the pattern of emotion changes. In this way, the emotion advice unit can analyze the emotion of the newborn and provide appropriate childcare advice, thereby reducing the parent's childcare burden and deepening the bond between parent and child.

[0101] The happiness diagnosis system may further include a music selection unit that estimates the newborn's emotions and selects music based on the estimated emotions. The music selection unit analyzes the newborn's facial expressions and tone of voice to estimate the emotions. For example, if the newborn is relaxed, calm music can be selected and played. Also, if the newborn is excited, music for calming the newborn can be selected. Furthermore, the music selection unit can provide more appropriate music by accumulating long-term emotion data and learning the newborn's favorite music. For example, if the newborn has a tendency to relax to a particular piece of music, that piece of music can be preferentially played. In this way, by providing music according to the newborn's emotions, a relaxing effect and emotional stability can be achieved.

[0102] The happiness diagnosis system may further include a lighting adjustment unit that estimates the newborn's emotions and adjusts the lighting based on the estimated emotions. The lighting adjustment unit analyzes the newborn's facial expressions and tone of voice to estimate the emotions. For example, if the newborn is relaxed, soft lighting can be provided to create a comfortable environment. Also, if the newborn is excited, the lighting can be dimmed to calm the newborn. Furthermore, the lighting adjustment unit can accumulate long-term emotional data and optimize the lighting settings based on the newborn's emotional patterns. For example, if the newborn tends to relax with a specific lighting setting, that setting can be used preferentially. As a result, a comfortable environment can be created and emotional stability can be achieved by providing lighting according to the newborn's emotions.

[0103] The happiness diagnosis system may further include a toy selection unit that estimates the newborn's emotions and selects a toy based on the estimated emotions. The toy selection unit analyzes the newborn's facial expressions and tone of voice to estimate the emotions. For example, if the newborn is excited, a toy that calms the newborn can be selected and provided. Also, if the newborn is relaxed, a toy that attracts the newborn's interest can be selected. Furthermore, the toy selection unit can provide a more appropriate toy by accumulating long-term emotion data and learning the newborn's favorite toys. For example, if the newborn has a tendency to be interested in a specific toy, that toy can be provided preferentially. In this way, by providing a toy according to the newborn's emotions, it is possible to stabilize the emotions and arouse the interest of the newborn.

[0104] The happiness diagnosis system may further include a childcare support unit that estimates the emotions of the newborn and provides childcare support based on the estimated emotions. The childcare support unit analyzes the facial expressions and tone of voice of the newborn to estimate the emotions. For example, if the newborn is crying, the crying pattern can be analyzed to estimate the cause, such as hunger, sleepiness, or poor health. In addition, the childcare support unit provides specific childcare support to the parent based on the estimated emotions. For example, if the newborn is crying because of hunger, the timing of breastfeeding can be suggested. Furthermore, the childcare support unit can provide more accurate support by accumulating long-term emotion data and grasping the pattern of changes in emotions. In this way, the newborn's emotions can be analyzed and appropriate childcare support can be provided, thereby reducing the burden on the parent and deepening the bond between the parent and child.

[0105] The happiness diagnosis system may further include an emotion advice unit that estimates the emotion of the newborn and provides childcare advice based on the estimated emotion. The emotion advice unit analyzes the facial expression and tone of voice of the newborn to estimate the emotion. For example, if the newborn is crying, the crying pattern can be analyzed to estimate the cause, such as hunger, sleepiness, or poor health. In addition, the emotion advice unit provides specific childcare advice to the parent based on the estimated emotion. For example, if the newborn is crying because of hunger, the timing of breastfeeding can be suggested. Furthermore, the emotion advice unit can provide more accurate advice by accumulating long-term emotion data and grasping the pattern of emotion changes. In this way, the emotion advice unit can analyze the emotion of the newborn and provide appropriate childcare advice, thereby reducing the parent's childcare burden and deepening the bond between parent and child.

[0106] The happiness diagnosis system may further include a music selection unit that estimates the newborn's emotions and selects music based on the estimated emotions. The music selection unit analyzes the newborn's facial expressions and tone of voice to estimate the emotions. For example, if the newborn is relaxed, calm music can be selected and played. Also, if the newborn is excited, music for calming the newborn can be selected. Furthermore, the music selection unit can provide more appropriate music by accumulating long-term emotion data and learning the newborn's favorite music. For example, if the newborn has a tendency to relax to a particular piece of music, that piece of music can be preferentially played. In this way, by providing music according to the newborn's emotions, a relaxing effect and emotional stability can be achieved.

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

[0108] Step 1: The collection unit collects information such as the newborn's DNA information, the parents' DNA information, background, birth and upbringing environment, educational background, occupation, hobbies, etc. For example, to collect DNA information, blood samples and saliva samples are taken and genetic analysis is performed. In addition, questionnaires and interviews can be conducted to collect information on background and birth and upbringing environment. Furthermore, data from schools and workplaces can be obtained to collect information on educational background and occupation. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, a genetic analysis algorithm is used to analyze genetic characteristics. Data mining techniques and machine learning algorithms can also be used to analyze background and environmental information. Furthermore, statistical analysis and predictive models can be used to analyze educational background and occupational information. Step 3: The diagnostic unit performs a diagnosis based on the analysis results obtained by the analysis unit. For example, it suggests what kind of education the newborn should receive, what kind of occupation they should have, and what hobbies they should have. The diagnostic unit can use generative AI to generate optimal educational and career plans. Step 4: The providing unit provides the diagnosis result obtained by the diagnosing unit. For example, the diagnosis result can be provided in a report format. Also, the diagnosis result can be provided through an application.

[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 collection unit, analysis unit, diagnosis unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects the facial expressions and voice of the newborn using the camera 42 and microphone 38B of the smart device 14, and estimates the emotions by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information. The diagnosis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs diagnosis based on the analysis result. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and provides the diagnosis result to the user. The correspondence between each unit and the device or 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 collection unit, analysis unit, diagnosis unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects the facial expressions and voice of the newborn using the camera 42 and microphone 238 of the smart glasses 214, and estimates the emotion by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information. The diagnosis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs a diagnosis based on the analysis result. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides the diagnosis result to the user. The correspondence between each unit and the device or 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 collection unit, analysis unit, diagnosis unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects the facial expressions and voice of the newborn using the camera 42 and microphone 238 of the headset type terminal 314, and estimates the emotion by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information. The diagnosis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs a diagnosis based on the analysis result. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides the diagnosis result to the user. The correspondence between each unit and the device or 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 collection unit, analysis unit, diagnosis unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects the facial expressions and voice of the newborn using the camera 42 and microphone 238 of the robot 414, and estimates the emotions by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information. The diagnosis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs diagnosis based on the analysis result. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides the diagnosis result to the user. The correspondence between each unit and the device or 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 three 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 on emotion map 400 you go, 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 has 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 collection unit that collects information; an analysis unit that analyzes the information collected by the collection unit; a diagnosis unit that performs diagnosis based on the analysis result obtained by the analysis unit; a providing unit that provides a diagnosis result obtained by the diagnosing unit; Equipped A system characterized by: (Appendix 2) The collecting unit includes: Collect DNA or environmental information 2. The system of claim 1, (Appendix 3) The analysis unit is Equipped with a genetic analysis department that analyzes genetic characteristics 2. The system of claim 1, (Appendix 4) The analysis unit is Equipped with an environmental analysis unit that analyzes environmental information 2. The system of claim 1, (Appendix 5) The diagnosis unit includes: Have a suggestion department that offers educational or career suggestions 2. The system of claim 1, (Appendix 6) The diagnosis unit includes: Equipped with a hobby diagnostic section to diagnose hobbies 2. The system of claim 1, (Appendix 7) The providing unit is Provide diagnostic results in report format 2. The system of claim 1, (Appendix 8) The providing unit is Provide diagnostic results through the application 2. The system of claim 1, (Appendix 9) The collecting unit includes: Estimating the newborn's emotions and adjusting the timing of information collection based on the estimated newborn's emotions 2. The system of claim 1, (Appendix 10) The collecting unit includes: Analyze the parents' past health history and select the method of collecting information 2. The system of claim 1, (Appendix 11) The collecting unit includes: When collecting information, filtering is done based on the parents’ lifestyle and dietary habits. 2. The system of claim 1, (Appendix 12) The collecting unit includes: Estimate the emotions of a newborn and prioritize information to be collected based on the estimated emotions of the newborn. 2. The system of claim 1, (Appendix 13) The collecting unit includes: When collecting information, prioritize collection of relevant information based on parent's geographic location 2. The system of claim 1, (Appendix 14) The collecting unit includes: When collecting information, analyze the social media activity of parents and collect relevant information. 2. The system of claim 1, (Appendix 15) The analysis unit is Estimate the newborn's emotions and adjust the analysis expression method based on the estimated newborn's emotions. 2. The system of claim 1, (Appendix 16) The analysis unit is During analysis, adjust the level of detail of the analysis based on the importance of genetic traits 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, different analysis algorithms are applied depending on the category of environmental information 2. The system of claim 1, (Appendix 18) The analysis unit is Estimate the newborn's emotions and adjust the length of the analysis based on the estimated newborn's emotions. 2. The system of claim 1, (Appendix 19) The analysis unit is During analysis, prioritize analysis based on when the information was collected 2. The system of claim 1, (Appendix 20) The analysis unit is During analysis, adjust the order of analysis based on the relevance of information 2. The system of claim 1, (Appendix 21) The diagnosis unit includes: Estimate the newborn's emotions and adjust diagnostic criteria based on the estimated emotions of the newborn 2. The system of claim 1, (Appendix 22) The diagnosis unit includes: At the time of diagnosis, improve diagnostic accuracy based on correlations of education and occupation 2. The system of claim 1, (Appendix 23) The diagnosis unit includes: At the time of diagnosis, diagnosis is made based on parent attribute information 2. The system of claim 1, (Appendix 24) The diagnosis unit includes: Estimating the newborn's emotions and adjusting the display order of diagnosis results based on the estimated newborn's emotions 2. The system of claim 1, (Appendix 25) The diagnosis unit includes: At the time of diagnosis, the diagnosis is based on the geographic distribution of information 2. The system of claim 1, (Appendix 26) The diagnosis unit includes: At the time of diagnosis, improve diagnostic accuracy based on relevant literature 2. The system of claim 1, (Appendix 27) The providing unit is Estimating the newborn's emotions and adjusting the format of information provided based on the estimated newborn's emotions 2. The system of claim 1, (Appendix 28) The providing unit is At the time of delivery, the optimal delivery method is selected based on past parent feedback 2. The system of claim 1, (Appendix 29) The providing unit is Estimate the newborn's emotions and determine the priority of information to be provided based on the estimated newborn's emotions 2. The system of claim 1, (Appendix 30) The providing unit is When providing the service, the optimal method of providing the service is selected taking into account the parent's device 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 collection unit that collects information; an analysis unit that analyzes the information collected by the collection unit; a diagnosis unit that performs diagnosis based on the analysis result obtained by the analysis unit; a providing unit that provides a diagnosis result obtained by the diagnosing unit; Equipped A system characterized by:

2. The collecting unit includes: Collect DNA or environmental information 2. The system of claim 1 .

3. The analysis unit is Equipped with a genetic analysis department that analyzes genetic characteristics 2. The system of claim 1 .

4. The analysis unit is Equipped with an environmental analysis unit that analyzes environmental information 2. The system of claim 1 .

5. The diagnosis unit includes: Have a suggestion department that offers educational or career suggestions 2. The system of claim 1 .

6. The diagnosis unit includes: Equipped with a hobby diagnostic section to diagnose hobbies 2. The system of claim 1 .

7. The providing unit is Provide the diagnostic results in report format 2. The system of claim 1 .

8. The providing unit is The diagnostic results are provided through an application.

2. The system of claim 1 .

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