system

Abstract

The system according to the embodiment aims to predict future trends and evaluate the prediction accuracy and social impact. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and an evaluation unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes a strategy based on the analysis results obtained by the analysis unit. The evaluation unit evaluates the prediction accuracy and social impact of the strategy proposed by the proposal unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately evaluate the accuracy of predictions and the social impact of proposing strategies when predicting future trends, and there is room for improvement.

[0005] The system according to the embodiment aims to predict future trends and evaluate the prediction accuracy and social impact. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, and an evaluation unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes a strategy based on the analysis results obtained by the analysis unit. The evaluation unit evaluates the prediction accuracy and social impact of the strategy proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can predict future trends and evaluate the prediction accuracy and social impact. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A future prediction system according to an embodiment of the present invention uses AI to predict future trends in fields such as health, environment, technology, and education, and to gain insights from the data and propose strategies. The future prediction system collects data related to each field, analyzes it using AI, and predicts future trends. Furthermore, it proposes strategies in each field based on the predicted trends. This system is used in competitions to evaluate prediction accuracy and social impact. For example, in the health field, the future prediction system collects hospital medical records and health checkup data and uses AI to analyze them to predict future disease incidence and health risks. In the environment field, the future prediction system collects weather data and environmental monitoring data and uses AI to analyze them to predict future climate change and environmental risks. In the technology field, the future prediction system collects patent data and technical papers and uses AI to analyze them to predict future technological trends and innovations. In the education field, the future prediction system collects learning outcome data and educational program data and uses AI to analyze them to predict future educational trends and educational effects. This allows the future prediction system to play an important role in competitions to evaluate prediction accuracy and social impact in each field. This allows the future prediction system to predict future trends in each field, gain insights from the data, and propose strategies. For example, in the health field, it can propose preventive measures and treatments based on future disease incidence and health risks. In the environment field, it can propose environmental protection measures and risk management measures based on future climate change and environmental risks. In the technology field, it can propose research and development strategies and technology introduction strategies based on future technological trends and innovations. In the education field, it can propose improvements to educational programs and educational policies based on future educational trends and educational effects. This allows future prediction systems to play an important role in competitions to evaluate prediction accuracy and social impact in each field.

[0029] A future prediction system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and an evaluation unit. The collection unit collects data related to each field. For example, in the health field, the collection unit can collect hospital medical records and health checkup data. In the environment field, the collection unit can collect weather data and environmental monitoring data. In the technology field, the collection unit can collect patent data and technical papers. In the education field, the collection unit can collect learning outcome data and educational program data. For example, the collection unit can acquire hospital medical records from an electronic medical record system. The collection unit can acquire weather data from a database of the Japan Meteorological Agency. The collection unit can acquire patent data from a database of the Japan Patent Office. The collection unit can acquire learning outcome data from a database of an educational institution. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze data in the health field to predict future disease incidence rates and health risks. The analysis unit can analyze data in the environment field to predict future climate change and environmental risks. The analysis unit can also analyze data in the technology field to predict future technological trends and technological innovations. The analysis unit can also analyze data in the education field to predict future educational trends and educational effects. For example, the analysis unit can use a machine learning algorithm to analyze data in the health field to predict future disease incidence rates. The analysis unit can use statistical analysis to analyze data in the environment field to predict future climate change. The analysis unit can also use natural language processing technology to analyze data in the technology field to predict future technological trends. The analysis unit can also use data mining technology to analyze data in the education field to predict future educational trends. The proposal unit proposes strategies based on the analysis results obtained by the analysis unit. For example, the proposal unit can propose preventive measures and treatments based on predicted disease incidence rates and health risks in the health field. The proposal unit can also propose environmental protection measures and risk management measures based on predicted climate change and environmental risks in the environment field.The proposal unit may also propose research and development strategies and technology introduction strategies based on predicted technological trends and technological innovations in the technology field. The proposal unit may also propose improvements to educational programs and educational policies based on predicted educational trends and educational effects in the education field. For example, in the health field, the proposal unit may recommend vaccinations and lifestyle improvements based on predicted disease incidence rates. In the environmental field, the proposal unit may propose the introduction of renewable energy and strengthening of environmental regulations based on predicted climate change. The proposal unit may also propose research and development of new technologies and technology transfer based on predicted technological trends in the technology field. In the education field, the proposal unit may propose curriculum revisions and improvements to teaching methods based on predicted educational trends. The evaluation unit evaluates the prediction accuracy and social impact of the strategies proposed by the proposal unit. For example, the evaluation unit may evaluate the degree of agreement between actual data and the prediction results as a method for evaluating prediction accuracy. The evaluation unit may also evaluate the feasibility and effectiveness of the proposed strategies as a criterion for evaluating social impact. For example, the evaluation unit may quantify and evaluate the degree of agreement between actual data and the prediction results. The evaluation unit can also quantify and evaluate the feasibility of the proposed strategy. The evaluation unit can also quantify and evaluate the effectiveness of the proposed strategy. This allows the future prediction system according to the embodiment to collect data, analyze it, propose a strategy, and evaluate it in a continuous process.

[0030] The collection unit can collect hospital medical records or health checkup data as data in the health field. Examples of data in the health field include, but are not limited to, hospital medical records and health checkup data. For example, the collection unit can acquire hospital medical records from an electronic medical record system. Furthermore, the collection unit can acquire health checkup data from a database at a health checkup center. For example, the collection unit can acquire hospital medical records from the electronic medical record system via an API. Furthermore, the collection unit can acquire health checkup data in CSV format from the database at the health checkup center. Furthermore, when collecting data in the health field, the collection unit can evaluate the reliability of the data and prioritize collecting highly reliable data. For example, the collection unit can evaluate the reliability of hospital medical records and prioritize collecting highly reliable hospital data. This enables data collection in the health field.

[0031] The collection unit can collect meteorological data or environmental monitoring data as data in the environmental field. Examples of data in the environmental field include, but are not limited to, meteorological data and environmental monitoring data. For example, the collection unit can acquire meteorological data from a database of the Japan Meteorological Agency. The collection unit can also acquire environmental monitoring data from a database of the Ministry of the Environment. For example, the collection unit can acquire meteorological data from the database of the Japan Meteorological Agency via an API. The collection unit can also acquire environmental monitoring data in CSV format from the database of the Ministry of the Environment. When collecting data in the environmental field, the collection unit can evaluate the reliability of the data and prioritize collecting highly reliable data. For example, the collection unit can evaluate the reliability of meteorological data and prioritize collecting highly reliable data. This enables data collection in the environmental field.

[0032] The collection unit may collect patent data or technical papers as data in the technical field. Data in the technical field includes, but is not limited to, patent data and technical papers. For example, the collection unit may acquire patent data from a patent office database. The collection unit may also acquire technical papers from an academic database. For example, the collection unit may acquire patent data from the patent office database via an API. The collection unit may also acquire technical papers in PDF format from the academic database. When collecting data in the technical field, the collection unit may evaluate the reliability of the data and prioritize collection of highly reliable data. For example, the collection unit may evaluate the reliability of patent data and prioritize collection of highly reliable data. This enables data collection in the technical field.

[0033] The collection unit can collect learning outcome data or educational program data as data in the education field. Data in the education field includes, but is not limited to, learning outcome data and educational program data. For example, the collection unit can acquire learning outcome data from an educational institution's database. The collection unit can also acquire educational program data from the educational institution's database. For example, the collection unit can acquire learning outcome data from the educational institution's database via an API. The collection unit can also acquire educational program data from the educational institution's database in CSV format. When collecting data in the education field, the collection unit can evaluate the reliability of the data and prioritize collecting highly reliable data. For example, the collection unit can evaluate the reliability of learning outcome data and prioritize collecting highly reliable data. This makes it possible to collect data in the education field.

[0034] The analysis unit can analyze data in the health field and predict future disease incidence or health risks. The analysis unit can, for example, analyze data in the health field and predict future disease incidence or health risks. For example, the analysis unit can analyze data in the health field using a machine learning algorithm and predict future disease incidence. The analysis unit can also analyze data in the health field using statistical analysis and predict future health risks. The analysis unit can also analyze data in the health field and predict future disease incidence using data mining technology. For example, the analysis unit can analyze health checkup data and predict future disease incidence using a machine learning algorithm. The analysis unit can also analyze hospital medical records using statistical analysis and predict future health risks. The analysis unit can also analyze health checkup data and predict future disease incidence using data mining technology. This makes it possible to make future predictions in the health field.

[0035] The analysis unit can analyze data in the environmental field and predict future climate change or environmental risks. The analysis unit can, for example, analyze data in the environmental field and predict future climate change or environmental risks. For example, the analysis unit can analyze data in the environmental field using statistical analysis and predict future climate change. The analysis unit can also analyze data in the environmental field using simulation technology and predict future environmental risks. The analysis unit can also analyze data in the environmental field and predict future climate change using data mining technology. For example, the analysis unit can analyze meteorological data using statistical analysis and predict future climate change. The analysis unit can also analyze environmental monitoring data using simulation technology and predict future environmental risks. The analysis unit can also analyze meteorological data using data mining technology and predict future climate change. This makes it possible to make future predictions in the environmental field.

[0036] The analysis unit can analyze data in a technology field and predict future technology trends or technological innovations. The analysis unit can, for example, analyze data in a technology field and predict future technology trends or technological innovations. For example, the analysis unit can analyze data in a technology field and predict future technology trends using natural language processing technology. The analysis unit can also analyze data in a technology field and predict future technological innovations using patent analysis technology. The analysis unit can also analyze data in a technology field and predict future technology trends using data mining technology. For example, the analysis unit can analyze technical papers using natural language processing technology and predict future technology trends. The analysis unit can also analyze patent data using patent analysis technology and predict future technological innovations. The analysis unit can also analyze technical papers using data mining technology and predict future technology trends. This makes it possible to predict the future in a technology field.

[0037] The analysis unit can analyze data in the field of education and predict future educational trends or educational effects. The analysis unit can, for example, analyze data in the field of education and predict future educational trends or educational effects. For example, the analysis unit can analyze data in the field of education using data mining technology and predict future educational trends. The analysis unit can also analyze data in the field of education using educational data analysis technology and predict future educational effects. The analysis unit can also analyze data in the field of education using machine learning algorithms and predict future educational trends. For example, the analysis unit can analyze learning outcome data using data mining technology and predict future educational trends. The analysis unit can also analyze educational program data using educational data analysis technology and predict future educational effects. The analysis unit can also analyze learning outcome data using machine learning algorithms and predict future educational trends. This makes it possible to make future predictions in the field of education.

[0038] The suggestion unit can suggest preventive measures or treatments based on the predicted incidence of disease or health risks in the health field. For example, the suggestion unit can suggest preventive measures or treatments based on the predicted incidence of disease or health risks in the health field. For example, the suggestion unit can suggest vaccination recommendations or lifestyle improvements based on the predicted incidence of disease. The suggestion unit can also suggest health education or risk management measures based on the predicted health risks. The suggestion unit can also suggest allocation of medical resources or selection of treatment methods based on the predicted incidence of disease. For example, the suggestion unit can suggest influenza vaccination as a recommended vaccination. The suggestion unit can suggest establishing exercise habits or improving diet as a lifestyle improvement. The suggestion unit can also suggest providing information and raising awareness about health risks as health education. This makes it possible to suggest preventive measures and treatments in the health field.

[0039] The proposal unit can propose environmental protection measures or risk management measures based on predicted climate change or environmental risks in the environmental field. For example, the proposal unit can propose environmental protection measures or risk management measures based on predicted climate change or environmental risks in the environmental field. For example, the proposal unit can propose the introduction of renewable energy or the strengthening of environmental regulations based on predicted climate change. The proposal unit can also propose risk assessments and risk management plans based on predicted environmental risks. The proposal unit can also propose environmental protection activities and environmental education based on predicted climate change. For example, the proposal unit can propose the introduction of solar power generation or wind power generation as the introduction of renewable energy. The proposal unit can also propose the strengthening of exhaust gas regulations or waste management as the strengthening of environmental regulations. The proposal unit can also propose the evaluation of environmental risks and the formulation of risk mitigation measures as risk assessments. This makes it possible to propose protection measures and risk management measures in the environmental field.

[0040] The proposal department can propose a research and development strategy or a technology introduction strategy based on predicted technological trends or technological innovations in the technology field. For example, the proposal department can propose a research and development strategy or a technology introduction strategy based on predicted technological trends or technological innovations in the technology field. For example, the proposal department can propose research and development of new technologies or technology transfer based on predicted technological trends. The proposal department can also propose a technology introduction plan or technology evaluation based on predicted technological innovations. The proposal department can also propose the formulation of a technology roadmap or a review of a technology strategy based on predicted technological trends. For example, the proposal department can propose research and development of AI technology or blockchain technology as research and development of new technologies. The proposal department can also propose collaboration with universities and research institutes as technology transfer. The proposal department can also propose the evaluation of new technologies and the formulation of an introduction process as a technology introduction plan. This makes it possible to propose research and development strategies and technology introduction strategies in the technology field.

[0041] The proposal unit can propose improvement measures for educational programs or educational policies based on predicted educational trends or educational effects in the field of education. For example, the proposal unit can propose improvement measures for educational programs or educational policies based on predicted educational trends or educational effects in the field of education. For example, the proposal unit can propose a curriculum review or an improvement to teaching methods based on predicted educational trends. The proposal unit can also propose evaluations and improvement measures for educational programs based on predicted educational effects. The proposal unit can also propose the formulation of educational policies and the allocation of educational budgets based on predicted educational trends. For example, the proposal unit can propose the introduction of digital education or remote learning as a curriculum review. The proposal unit can also propose the introduction of active learning or project-based learning as an improvement to teaching methods. The proposal unit can also propose measuring learning outcomes and providing feedback as an evaluation of educational programs. This makes it possible to propose program improvement measures and educational policies in the field of education.

[0042] The evaluation unit can evaluate the degree of agreement between actual data or the prediction result as a method for evaluating prediction accuracy. The evaluation unit can evaluate the degree of agreement between actual data and the prediction result, for example, as a method for evaluating prediction accuracy. For example, the evaluation unit can quantify and evaluate the degree of agreement between the actual data and the prediction result. The evaluation unit can also graph the degree of agreement between the actual data and the prediction result for visual evaluation. The evaluation unit can also evaluate prediction accuracy based on the degree of agreement between the actual data and the prediction result. For example, the evaluation unit can evaluate the degree of agreement between the actual data and the prediction result using a correlation coefficient or an error rate. The evaluation unit can also graph the degree of agreement between the actual data and the prediction result for visual evaluation. The evaluation unit can also evaluate prediction accuracy based on the degree of agreement between the actual data and the prediction result. This makes it possible to evaluate prediction accuracy.

[0043] The evaluation unit can evaluate the feasibility or effectiveness of the proposed strategy as an evaluation criterion for social impact. The evaluation unit can evaluate, for example, the feasibility or effectiveness of the proposed strategy as an evaluation criterion for social impact. For example, the evaluation unit can quantify and evaluate the feasibility of the proposed strategy. The evaluation unit can also quantify and evaluate the effectiveness of the proposed strategy. The evaluation unit can also evaluate the social impact based on the feasibility and effectiveness of the proposed strategy. For example, the evaluation unit can evaluate the feasibility of the proposed strategy in terms of technical feasibility and economic feasibility. The evaluation unit can also evaluate the effectiveness of the proposed strategy in terms of outcome indicators and impact assessment. The evaluation unit can also evaluate the social impact based on the feasibility and effectiveness of the proposed strategy. This makes it possible to evaluate the social impact.

[0044] The collection unit can evaluate the reliability of the data when collecting data in each field and prioritize collecting reliable data. For example, the collection unit can evaluate the reliability of the data when collecting data in each field and prioritize collecting reliable data. For example, in the health field, the collection unit can prioritize collecting reliable hospital medical records. Furthermore, in the environment field, the collection unit can prioritize collecting reliable weather data. Furthermore, in the technical field, the collection unit can prioritize collecting reliable patent data. For example, the collection unit can evaluate the reliability of hospital medical records and prioritize collecting reliable hospital data. Furthermore, the collection unit can evaluate the reliability of weather data and prioritize collecting reliable data. Furthermore, the collection unit can evaluate the reliability of patent data and prioritize collecting reliable data. This enables the collection of highly reliable data.

[0045] The collection unit can select an appropriate collection means depending on the type of data when collecting data. For example, the collection unit can select the optimal collection means depending on the type of data (text, image, audio, etc.) when collecting data. For example, in the case of text data, the collection unit can collect it directly from a database via an API. In the case of image data, the collection unit can collect it using image recognition technology. In the case of audio data, the collection unit can collect it using voice recognition technology. For example, the collection unit can acquire text data from a database via an API. In addition, the collection unit can collect image data using image recognition technology. In addition, the collection unit can collect audio data using voice recognition technology. This enables optimal collection depending on the type of data.

[0046] The collection unit can appropriately adjust the collection method by referring to past data collection history when collecting data. For example, the collection unit can optimize the collection method by referring to past data collection history when collecting data. For example, the collection unit can analyze past data collection history and select the most efficient collection method. Furthermore, the collection unit can prioritize collecting data that takes a long time to collect from the past data collection history. Furthermore, the collection unit can optimize the timing of collection based on the past data collection history. For example, the collection unit can analyze past data collection history and select the most efficient collection method. Furthermore, the collection unit can prioritize collecting data that takes a long time to collect from the past data collection history. Furthermore, the collection unit can optimize the timing of collection based on the past data collection history. This enables optimal data collection based on past history.

[0047] The collection unit can prioritize collecting relevant data based on geographical data distribution when collecting data. For example, the collection unit can prioritize collecting highly relevant data by taking geographical data distribution into consideration when collecting data. For example, in the health field, the collection unit can prioritize collecting medical records from a specific region. Furthermore, in the environmental field, the collection unit can prioritize collecting weather data from a specific region. Furthermore, in the technical field, the collection unit can prioritize collecting patent data from a specific region. For example, the collection unit can prioritize collecting medical records from a specific region. Furthermore, the collection unit can prioritize collecting weather data from a specific region. Furthermore, the collection unit can prioritize collecting patent data from a specific region. This makes it possible to collect data based on geographical data distribution.

[0048] The collection unit can analyze data from social media during data collection and collect relevant data. For example, during data collection, the collection unit can analyze data from social media and collect relevant data. For example, in the health field, the collection unit can collect health-related posts on social media. Furthermore, in the environmental field, the collection unit can collect environment-related posts on social media. Furthermore, in the technical field, the collection unit can collect technology-related posts on social media. For example, the collection unit can collect health-related posts on social media. Furthermore, the collection unit can collect environment-related posts on social media. Furthermore, the collection unit can collect technology-related posts on social media. This makes it possible to collect relevant data from social media.

[0049] The collection unit can adjust the collection method by reflecting the user's past feedback when collecting data. For example, the collection unit can customize the collection method by reflecting the user's past feedback when collecting data. For example, the collection unit can adjust the type of data to be collected based on the user's past feedback. Furthermore, the collection unit can adjust the timing of collection based on the user's past feedback. Furthermore, the collection unit can customize the collection means based on the user's past feedback. For example, the collection unit can adjust the type of data to be collected based on the user's past feedback. Furthermore, the collection unit can adjust the timing of collection based on the user's past feedback. Furthermore, the collection unit can customize the collection means based on the user's past feedback. This makes it possible to customize the collection method based on the user's feedback.

[0050] The analysis unit can set the level of detail of the analysis based on the importance of the data during analysis. The analysis unit can, for example, adjust the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit can perform a detailed analysis on important data. Furthermore, the analysis unit can perform a basic analysis on general data. Furthermore, the analysis unit can perform a simplified analysis on unnecessary data. For example, the analysis unit can evaluate the importance of data and perform a detailed analysis on important data. Furthermore, the analysis unit can evaluate the importance of data and perform a basic analysis on general data. Furthermore, the analysis unit can evaluate the importance of data and perform a simplified analysis on unnecessary data. This makes it possible to perform analysis according to the importance of data.

[0051] The analysis unit can apply an appropriate analysis algorithm depending on the category of data during analysis. The analysis unit can, for example, apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit can apply a medical-specific analysis algorithm to health data. Furthermore, the analysis unit can apply an environment-specific analysis algorithm to environmental data. Furthermore, the analysis unit can apply a technology-specific analysis algorithm to technical data. For example, the analysis unit can apply a medical-specific analysis algorithm to health data. Furthermore, the analysis unit can apply an environment-specific analysis algorithm to environmental data. Furthermore, the analysis unit can apply a technology-specific analysis algorithm to technical data. This enables optimal analysis depending on the category of data.

[0052] The analysis unit can improve the accuracy of the analysis by referring to past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to past analysis results during analysis. For example, the analysis unit can improve the analysis algorithm based on past analysis results. Furthermore, the analysis unit can adjust the analysis parameters by referring to past analysis results. Furthermore, the analysis unit can optimize the analysis procedure based on past analysis results. For example, the analysis unit can improve the analysis algorithm based on past analysis results. Furthermore, the analysis unit can adjust the analysis parameters by referring to past analysis results. Furthermore, the analysis unit can optimize the analysis procedure based on past analysis results. This makes it possible to improve the accuracy based on past analysis results.

[0053] The analysis unit can set an analysis priority based on the time of data collection during analysis. The analysis unit can, for example, determine an analysis priority based on the time of data collection during analysis. For example, the analysis unit can prioritize analysis of the latest data. The analysis unit can also determine an analysis priority by referring to past data. The analysis unit can also adjust the order of analysis based on the time of data collection. For example, the analysis unit can prioritize analysis of the latest data. The analysis unit can also determine an analysis priority by referring to past data. The analysis unit can also adjust the order of analysis based on the time of data collection. This makes it possible to prioritize analysis based on the time of data collection.

[0054] The analysis unit can set the order of analysis based on the relevance of the data during analysis. The analysis unit can, for example, adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also optimize the order of analysis based on the relevance of the data. For example, the analysis unit can prioritize analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also optimize the order of analysis based on the relevance of the data. This makes it possible to adjust the order of analysis based on the relevance of the data.

[0055] The analysis unit can set the use of technical terms in the analysis results according to the user's level of expertise during analysis. The analysis unit can, for example, adjust the use of technical terms in the analysis results according to the user's level of expertise during analysis. For example, the analysis unit can provide analysis results that use a lot of technical terms to a user with high expertise. The analysis unit can also provide analysis results that are explained in simple terms to a user with low expertise. The analysis unit can also adjust the way the analysis results are expressed according to the user's level of expertise. For example, the analysis unit can provide analysis results that use a lot of technical terms to a user with high expertise. The analysis unit can also provide analysis results that are explained in simple terms to a user with low expertise. The analysis unit can also adjust the way the analysis results are expressed according to the user's level of expertise. This makes it possible to provide analysis results that are according to the user's level of expertise.

[0056] The suggestion unit can set the level of detail of the suggestion based on the importance of the predicted trend when making a suggestion. The suggestion unit can, for example, adjust the level of detail of the suggestion based on the importance of the predicted trend when making a suggestion. For example, the suggestion unit can make a detailed suggestion for an important trend. Furthermore, the suggestion unit can make a basic suggestion for a general trend. Furthermore, the suggestion unit can make a simplified suggestion for an unnecessary trend. For example, the suggestion unit can evaluate the importance of the predicted trend and make a detailed suggestion for an important trend. Furthermore, the suggestion unit can evaluate the importance of the predicted trend and make a basic suggestion for a general trend. Furthermore, the suggestion unit can evaluate the importance of the predicted trend and make a simplified suggestion for an unnecessary trend. This enables suggestions according to the importance of trends.

[0057] The suggestion unit can apply an appropriate suggestion algorithm depending on the trend category when making a suggestion. For example, the suggestion unit can apply different suggestion algorithms depending on the trend category when making a suggestion. For example, the suggestion unit can apply a medical-specific suggestion algorithm to a health trend. Furthermore, the suggestion unit can apply an environmental-specific suggestion algorithm to an environmental trend. Furthermore, the suggestion unit can apply a technology-specific suggestion algorithm to a technology trend. For example, the suggestion unit can apply a medical-specific suggestion algorithm to a health trend. Furthermore, the suggestion unit can apply an environmental-specific suggestion algorithm to an environmental trend. Furthermore, the suggestion unit can apply a technology-specific suggestion algorithm to a technology trend. This enables optimal suggestions depending on the trend category.

[0058] The proposal unit can improve the accuracy of the proposal by referring to past proposal results when making a proposal. For example, the proposal unit can improve the accuracy of the proposal by referring to past proposal results when making a proposal. For example, the proposal unit can improve the proposal algorithm based on past proposal results. Furthermore, the proposal unit can adjust proposal parameters by referring to past proposal results. Furthermore, the proposal unit can optimize the proposal procedure based on past proposal results. For example, the proposal unit can improve the proposal algorithm based on past proposal results. Furthermore, the proposal unit can adjust proposal parameters by referring to past proposal results. Furthermore, the proposal unit can optimize the proposal procedure based on past proposal results. This makes it possible to improve accuracy based on past proposal results.

[0059] The suggestion unit can set the priority of the proposals based on the time of trend occurrence when making a proposal. The suggestion unit can, for example, determine the priority of the proposals based on the time of trend occurrence when making a proposal. For example, the suggestion unit can give priority to making a proposal for the most recent trend. Furthermore, the suggestion unit can make a proposal for a long-term trend at a later date. Furthermore, the suggestion unit can optimize the order of the proposals based on the time of trend occurrence. For example, the suggestion unit can give priority to making a proposal for the most recent trend. Furthermore, the suggestion unit can make a proposal for a long-term trend at a later date. Furthermore, the suggestion unit can optimize the order of the proposals based on the time of trend occurrence. This makes it possible to prioritize the proposals based on the time of trend occurrence.

[0060] The suggestion unit can set the order of suggestions based on the relevance of trends when making suggestions. The suggestion unit can, for example, adjust the order of suggestions based on the relevance of trends when making suggestions. For example, the suggestion unit can prioritize suggestions for highly relevant trends. Furthermore, the suggestion unit can postpone suggestions for less relevant trends. Furthermore, the suggestion unit can optimize the order of suggestions based on the relevance of trends. For example, the suggestion unit can prioritize suggestions for highly relevant trends. Furthermore, the suggestion unit can postpone suggestions for less relevant trends. Furthermore, the suggestion unit can optimize the order of suggestions based on the relevance of trends. This makes it possible to adjust the order of suggestions based on the relevance of trends.

[0061] The suggestion unit can set the use of technical terms in the proposal according to the user's level of expertise when making the proposal. For example, the suggestion unit can adjust the use of technical terms in the proposal according to the user's level of expertise when making the proposal. For example, the suggestion unit can provide a proposal that uses a lot of technical terms to a user with high expertise. Furthermore, the suggestion unit can provide a proposal that is explained in simple terms to a user with low expertise. Furthermore, the suggestion unit can adjust the way the proposal is expressed according to the user's level of expertise. For example, the suggestion unit can provide a proposal that uses a lot of technical terms to a user with high expertise. Furthermore, the suggestion unit can provide a proposal that is explained in simple terms to a user with low expertise. Furthermore, the suggestion unit can adjust the way the proposal is expressed according to the user's level of expertise. This makes it possible to provide a proposal that is according to the user's level of expertise.

[0062] The evaluation unit can evaluate the degree of agreement between actual data or the prediction result during evaluation as a method for evaluating prediction accuracy. For example, the evaluation unit can evaluate the degree of agreement between actual data and the prediction result during evaluation as a method for evaluating prediction accuracy. For example, the evaluation unit can quantify and evaluate the degree of agreement between the actual data and the prediction result. The evaluation unit can also graph the degree of agreement between the actual data and the prediction result for visual evaluation. The evaluation unit can also evaluate prediction accuracy based on the degree of agreement between the actual data and the prediction result. For example, the evaluation unit can evaluate the degree of agreement between the actual data and the prediction result using a correlation coefficient or an error rate. The evaluation unit can also graph the degree of agreement between the actual data and the prediction result for visual evaluation. The evaluation unit can also evaluate prediction accuracy based on the degree of agreement between the actual data and the prediction result. This makes it possible to evaluate prediction accuracy.

[0063] The evaluation unit can evaluate the feasibility or effectiveness of the proposed strategy as an evaluation criterion for social impact during the evaluation. The evaluation unit can, for example, evaluate the feasibility or effectiveness of the proposed strategy as an evaluation criterion for social impact during the evaluation. For example, the evaluation unit can quantify and evaluate the feasibility of the proposed strategy. The evaluation unit can also quantify and evaluate the effectiveness of the proposed strategy. The evaluation unit can also evaluate the social impact based on the feasibility and effectiveness of the proposed strategy. For example, the evaluation unit can evaluate the feasibility of the proposed strategy in terms of technical feasibility and economic feasibility. The evaluation unit can also evaluate the effectiveness of the proposed strategy in terms of outcome indicators and impact assessments. The evaluation unit can also evaluate the social impact based on the feasibility and effectiveness of the proposed strategy. This makes it possible to evaluate the social impact.

[0064] The evaluation unit can appropriately adjust the evaluation criteria by referring to past evaluation results during evaluation. The evaluation unit can, for example, optimize the evaluation criteria by referring to past evaluation results during evaluation. For example, the evaluation unit can improve the evaluation criteria based on past evaluation results. Furthermore, the evaluation unit can adjust evaluation parameters by referring to past evaluation results. Furthermore, the evaluation unit can optimize the evaluation procedure based on past evaluation results. For example, the evaluation unit can improve the evaluation criteria based on past evaluation results. Furthermore, the evaluation unit can adjust evaluation parameters by referring to past evaluation results. Furthermore, the evaluation unit can optimize the evaluation procedure based on past evaluation results. This makes it possible to optimize the evaluation criteria based on past evaluation results.

[0065] The evaluation unit can perform the evaluation based on the geographical distribution of the evaluation target during the evaluation. For example, the evaluation unit can perform the evaluation taking into account the geographical distribution of the evaluation target during the evaluation. For example, in the health field, the evaluation unit can perform the evaluation based on medical records of a specific region. Furthermore, in the environmental field, the evaluation unit can perform the evaluation based on weather data of a specific region. Furthermore, in the technical field, the evaluation unit can perform the evaluation based on patent data of a specific region. For example, the evaluation unit can perform the evaluation based on medical records of a specific region. Furthermore, the evaluation unit can perform the evaluation based on weather data of a specific region. Furthermore, the evaluation unit can perform the evaluation based on patent data of a specific region. This makes it possible to perform the evaluation based on geographical distribution.

[0066] The evaluation unit can improve the accuracy of the evaluation by referring to related literature or data during the evaluation. The evaluation unit can improve the accuracy of the evaluation by referring to related literature or data during the evaluation, for example. For example, in the health field, the evaluation unit can make the evaluation by referring to related medical literature. Furthermore, in the environmental field, the evaluation unit can make the evaluation by referring to related environmental data. Furthermore, in the technical field, the evaluation unit can make the evaluation by referring to related technical papers. For example, in the health field, the evaluation unit can make the evaluation by referring to related medical literature. Furthermore, in the environmental field, the evaluation unit can make the evaluation by referring to related environmental data. Furthermore, in the technical field, the evaluation unit can make the evaluation by referring to related technical papers. This makes it possible to improve the accuracy of the evaluation based on related literature and data.

[0067] The evaluation unit can perform the evaluation based on the market value of the evaluation target at the time of evaluation. For example, the evaluation unit can perform the evaluation taking into account the market value of the evaluation target at the time of evaluation. For example, in the health field, the evaluation unit can perform the evaluation based on the market value of a proposed treatment. Furthermore, in the environmental field, the evaluation unit can perform the evaluation based on the market value of a proposed environmental protection measure. Furthermore, in the technical field, the evaluation unit can perform the evaluation based on the market value of a proposed technology. For example, in the health field, the evaluation unit can perform the evaluation based on the market value of a proposed treatment. Furthermore, in the environmental field, the evaluation unit can perform the evaluation based on the market value of a proposed environmental protection measure. Furthermore, in the technical field, the evaluation unit can perform the evaluation based on the market value of a proposed technology. This makes it possible to perform evaluation based on market value.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The future prediction system can further include a reliability evaluation unit that evaluates the reliability of data. The reliability evaluation unit can evaluate the reliability of collected data and prioritize analysis of highly reliable data. For example, the reliability evaluation unit can evaluate the reliability of hospital medical records and prioritize analysis of highly reliable hospital data. It can also evaluate the reliability of weather data and prioritize analysis of highly reliable data. It can also evaluate the reliability of patent data and prioritize analysis of highly reliable data. This enables analysis based on highly reliable data.

[0070] The future prediction system can further include a priority setting unit that sets analysis priorities based on the time of data collection. The priority setting unit can prioritize analysis of the most recent data. It can also determine analysis priorities by referring to past data. Furthermore, it can adjust the order of analysis based on the time of data collection. This enables efficient analysis based on the time of data collection.

[0071] The future prediction system can further include an accuracy improvement unit that improves the accuracy of the analysis by referring to past analysis results. The accuracy improvement unit can improve the analysis algorithm based on past analysis results. It can also adjust the analysis parameters by referring to past analysis results. Furthermore, it can optimize the analysis procedure based on past analysis results. This makes it possible to improve accuracy based on past analysis results.

[0072] The future prediction system can further include a market value assessment unit that performs assessment based on the market value of the assessment target. In the health field, the market value assessment unit can perform assessment based on the market value of a proposed treatment. In the environmental field, the market value assessment unit can perform assessment based on the market value of a proposed environmental protection measure. Furthermore, in the technology field, the market value assessment unit can perform assessment based on the market value of a proposed technology. This makes it possible to perform assessment based on market value.

[0073] The future prediction system can further include a literature reference unit that refers to related literature and data to improve the accuracy of the evaluation. In the health field, the literature reference unit can make an evaluation by referring to related medical literature. In the environmental field, the literature reference unit can make an evaluation by referring to related environmental data. Furthermore, in the technical field, the literature reference unit can make an evaluation by referring to related technical papers. This makes it possible to improve the accuracy of the evaluation based on related literature and data.

[0074] The processing flow of the first embodiment will be briefly explained below.

[0075] Step 1: The collection department collects data related to each field. For example, in the health field, hospital medical records and health checkup data are collected; in the environment field, weather data and environmental monitoring data are collected; in the technology field, patent data and technical papers are collected; and in the education field, learning outcome data and educational program data are collected. This data is obtained from electronic medical record systems, databases of the Japan Meteorological Agency, databases of the Japan Patent Office, databases of educational institutions, etc. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it analyzes data in the health field to predict future disease incidence and health risks, analyzes data in the environment field to predict future climate change and environmental risks, analyzes data in the technology field to predict future technological trends and innovations, and analyzes data in the education field to predict future educational trends and educational effects. Machine learning algorithms, statistical analysis, natural language processing technology, and data mining technology are used for the analysis. Step 3: The proposal section proposes strategies based on the analysis results obtained by the analysis section. For example, in the health field, it proposes preventive measures and treatments based on predicted disease incidence and health risks, and in the environment field, it proposes environmental protection measures and risk management measures based on predicted climate change and environmental risks. In the technology field, it proposes research and development strategies and technology introduction strategies based on predicted technological trends and technological innovations, and in the education field, it proposes improvement measures for educational programs and educational policies based on predicted educational trends and educational effects. Step 4: The evaluation unit evaluates the prediction accuracy and social impact of the strategy proposed by the proposal unit. For example, the degree of agreement between actual data and the prediction results is evaluated as a method for evaluating prediction accuracy, and the feasibility and effectiveness of the proposed strategy are evaluated as criteria for evaluating social impact. This makes it possible to quantify and evaluate the feasibility and effectiveness of the proposed strategy.

[0076] (Example 2) A future prediction system according to an embodiment of the present invention uses AI to predict future trends in fields such as health, environment, technology, and education, and to gain insights from the data and propose strategies. The future prediction system collects data related to each field, analyzes it using AI, and predicts future trends. Furthermore, it proposes strategies in each field based on the predicted trends. This system is used in competitions to evaluate prediction accuracy and social impact. For example, in the health field, the future prediction system collects hospital medical records and health checkup data and uses AI to analyze them to predict future disease incidence and health risks. In the environment field, the future prediction system collects weather data and environmental monitoring data and uses AI to analyze them to predict future climate change and environmental risks. In the technology field, the future prediction system collects patent data and technical papers and uses AI to analyze them to predict future technological trends and innovations. In the education field, the future prediction system collects learning outcome data and educational program data and uses AI to analyze them to predict future educational trends and educational effects. This allows the future prediction system to play an important role in competitions to evaluate prediction accuracy and social impact in each field. This allows the future prediction system to predict future trends in each field, gain insights from the data, and propose strategies. For example, in the health field, it can propose preventive measures and treatments based on future disease incidence and health risks. In the environment field, it can propose environmental protection measures and risk management measures based on future climate change and environmental risks. In the technology field, it can propose research and development strategies and technology introduction strategies based on future technological trends and innovations. In the education field, it can propose improvements to educational programs and educational policies based on future educational trends and educational effects. This allows future prediction systems to play an important role in competitions to evaluate prediction accuracy and social impact in each field.

[0077] A future prediction system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and an evaluation unit. The collection unit collects data related to each field. For example, in the health field, the collection unit can collect hospital medical records and health checkup data. In the environment field, the collection unit can collect weather data and environmental monitoring data. In the technology field, the collection unit can collect patent data and technical papers. In the education field, the collection unit can collect learning outcome data and educational program data. For example, the collection unit can acquire hospital medical records from an electronic medical record system. The collection unit can acquire weather data from a database of the Japan Meteorological Agency. The collection unit can acquire patent data from a database of the Japan Patent Office. The collection unit can acquire learning outcome data from a database of an educational institution. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze data in the health field to predict future disease incidence rates and health risks. The analysis unit can analyze data in the environment field to predict future climate change and environmental risks. The analysis unit can also analyze data in the technology field to predict future technological trends and technological innovations. The analysis unit can also analyze data in the education field to predict future educational trends and educational effects. For example, the analysis unit can use a machine learning algorithm to analyze data in the health field to predict future disease incidence rates. The analysis unit can use statistical analysis to analyze data in the environment field to predict future climate change. The analysis unit can also use natural language processing technology to analyze data in the technology field to predict future technological trends. The analysis unit can also use data mining technology to analyze data in the education field to predict future educational trends. The proposal unit proposes strategies based on the analysis results obtained by the analysis unit. For example, the proposal unit can propose preventive measures and treatments based on predicted disease incidence rates and health risks in the health field. The proposal unit can also propose environmental protection measures and risk management measures based on predicted climate change and environmental risks in the environment field.The proposal unit may also propose research and development strategies and technology introduction strategies based on predicted technological trends and technological innovations in the technology field. The proposal unit may also propose improvements to educational programs and educational policies based on predicted educational trends and educational effects in the education field. For example, in the health field, the proposal unit may recommend vaccinations and lifestyle improvements based on predicted disease incidence rates. In the environmental field, the proposal unit may propose the introduction of renewable energy and strengthening of environmental regulations based on predicted climate change. The proposal unit may also propose research and development of new technologies and technology transfer based on predicted technological trends in the technology field. In the education field, the proposal unit may propose curriculum revisions and improvements to teaching methods based on predicted educational trends. The evaluation unit evaluates the prediction accuracy and social impact of the strategies proposed by the proposal unit. For example, the evaluation unit may evaluate the degree of agreement between actual data and the prediction results as a method for evaluating prediction accuracy. The evaluation unit may also evaluate the feasibility and effectiveness of the proposed strategies as a criterion for evaluating social impact. For example, the evaluation unit may quantify and evaluate the degree of agreement between actual data and the prediction results. The evaluation unit can also quantify and evaluate the feasibility of the proposed strategy. The evaluation unit can also quantify and evaluate the effectiveness of the proposed strategy. This allows the future prediction system according to the embodiment to collect data, analyze it, propose a strategy, and evaluate it in a continuous process.

[0078] The collection unit can collect hospital medical records or health checkup data as data in the health field. Examples of data in the health field include, but are not limited to, hospital medical records and health checkup data. For example, the collection unit can acquire hospital medical records from an electronic medical record system. Furthermore, the collection unit can acquire health checkup data from a database at a health checkup center. For example, the collection unit can acquire hospital medical records from the electronic medical record system via an API. Furthermore, the collection unit can acquire health checkup data in CSV format from the database at the health checkup center. Furthermore, when collecting data in the health field, the collection unit can evaluate the reliability of the data and prioritize collecting highly reliable data. For example, the collection unit can evaluate the reliability of hospital medical records and prioritize collecting highly reliable hospital data. This enables data collection in the health field.

[0079] The collection unit can collect meteorological data or environmental monitoring data as data in the environmental field. Examples of data in the environmental field include, but are not limited to, meteorological data and environmental monitoring data. For example, the collection unit can acquire meteorological data from a database of the Japan Meteorological Agency. The collection unit can also acquire environmental monitoring data from a database of the Ministry of the Environment. For example, the collection unit can acquire meteorological data from the database of the Japan Meteorological Agency via an API. The collection unit can also acquire environmental monitoring data in CSV format from the database of the Ministry of the Environment. When collecting data in the environmental field, the collection unit can evaluate the reliability of the data and prioritize collecting highly reliable data. For example, the collection unit can evaluate the reliability of meteorological data and prioritize collecting highly reliable data. This enables data collection in the environmental field.

[0080] The collection unit may collect patent data or technical papers as data in the technical field. Data in the technical field includes, but is not limited to, patent data and technical papers. For example, the collection unit may acquire patent data from a patent office database. The collection unit may also acquire technical papers from an academic database. For example, the collection unit may acquire patent data from the patent office database via an API. The collection unit may also acquire technical papers in PDF format from the academic database. When collecting data in the technical field, the collection unit may evaluate the reliability of the data and prioritize collection of highly reliable data. For example, the collection unit may evaluate the reliability of patent data and prioritize collection of highly reliable data. This enables data collection in the technical field.

[0081] The collection unit can collect learning outcome data or educational program data as data in the education field. Data in the education field includes, but is not limited to, learning outcome data and educational program data. For example, the collection unit can acquire learning outcome data from an educational institution's database. The collection unit can also acquire educational program data from the educational institution's database. For example, the collection unit can acquire learning outcome data from the educational institution's database via an API. The collection unit can also acquire educational program data from the educational institution's database in CSV format. When collecting data in the education field, the collection unit can evaluate the reliability of the data and prioritize collecting highly reliable data. For example, the collection unit can evaluate the reliability of learning outcome data and prioritize collecting highly reliable data. This makes it possible to collect data in the education field.

[0082] The analysis unit can analyze data in the health field and predict future disease incidence or health risks. The analysis unit can, for example, analyze data in the health field and predict future disease incidence or health risks. For example, the analysis unit can analyze data in the health field using a machine learning algorithm and predict future disease incidence. The analysis unit can also analyze data in the health field using statistical analysis and predict future health risks. The analysis unit can also analyze data in the health field and predict future disease incidence using data mining technology. For example, the analysis unit can analyze health checkup data and predict future disease incidence using a machine learning algorithm. The analysis unit can also analyze hospital medical records using statistical analysis and predict future health risks. The analysis unit can also analyze health checkup data and predict future disease incidence using data mining technology. This makes it possible to make future predictions in the health field.

[0083] The analysis unit can analyze data in the environmental field and predict future climate change or environmental risks. The analysis unit can, for example, analyze data in the environmental field and predict future climate change or environmental risks. For example, the analysis unit can analyze data in the environmental field using statistical analysis and predict future climate change. The analysis unit can also analyze data in the environmental field using simulation technology and predict future environmental risks. The analysis unit can also analyze data in the environmental field and predict future climate change using data mining technology. For example, the analysis unit can analyze meteorological data using statistical analysis and predict future climate change. The analysis unit can also analyze environmental monitoring data using simulation technology and predict future environmental risks. The analysis unit can also analyze meteorological data using data mining technology and predict future climate change. This makes it possible to make future predictions in the environmental field.

[0084] The analysis unit can analyze data in a technology field and predict future technology trends or technological innovations. The analysis unit can, for example, analyze data in a technology field and predict future technology trends or technological innovations. For example, the analysis unit can analyze data in a technology field and predict future technology trends using natural language processing technology. The analysis unit can also analyze data in a technology field and predict future technological innovations using patent analysis technology. The analysis unit can also analyze data in a technology field and predict future technology trends using data mining technology. For example, the analysis unit can analyze technical papers using natural language processing technology and predict future technology trends. The analysis unit can also analyze patent data using patent analysis technology and predict future technological innovations. The analysis unit can also analyze technical papers using data mining technology and predict future technology trends. This makes it possible to predict the future in a technology field.

[0085] The analysis unit can analyze data in the field of education and predict future educational trends or educational effects. The analysis unit can, for example, analyze data in the field of education and predict future educational trends or educational effects. For example, the analysis unit can analyze data in the field of education using data mining technology and predict future educational trends. The analysis unit can also analyze data in the field of education using educational data analysis technology and predict future educational effects. The analysis unit can also analyze data in the field of education using machine learning algorithms and predict future educational trends. For example, the analysis unit can analyze learning outcome data using data mining technology and predict future educational trends. The analysis unit can also analyze educational program data using educational data analysis technology and predict future educational effects. The analysis unit can also analyze learning outcome data using machine learning algorithms and predict future educational trends. This makes it possible to make future predictions in the field of education.

[0086] The suggestion unit can suggest preventive measures or treatments based on the predicted incidence of disease or health risks in the health field. For example, the suggestion unit can suggest preventive measures or treatments based on the predicted incidence of disease or health risks in the health field. For example, the suggestion unit can suggest vaccination recommendations or lifestyle improvements based on the predicted incidence of disease. The suggestion unit can also suggest health education or risk management measures based on the predicted health risks. The suggestion unit can also suggest allocation of medical resources or selection of treatment methods based on the predicted incidence of disease. For example, the suggestion unit can suggest influenza vaccination as a recommended vaccination. The suggestion unit can suggest establishing exercise habits or improving diet as a lifestyle improvement. The suggestion unit can also suggest providing information and raising awareness about health risks as health education. This makes it possible to suggest preventive measures and treatments in the health field.

[0087] The proposal unit can propose environmental protection measures or risk management measures based on predicted climate change or environmental risks in the environmental field. For example, the proposal unit can propose environmental protection measures or risk management measures based on predicted climate change or environmental risks in the environmental field. For example, the proposal unit can propose the introduction of renewable energy or the strengthening of environmental regulations based on predicted climate change. The proposal unit can also propose risk assessments and risk management plans based on predicted environmental risks. The proposal unit can also propose environmental protection activities and environmental education based on predicted climate change. For example, the proposal unit can propose the introduction of solar power generation or wind power generation as the introduction of renewable energy. The proposal unit can also propose the strengthening of exhaust gas regulations or waste management as the strengthening of environmental regulations. The proposal unit can also propose the evaluation of environmental risks and the formulation of risk mitigation measures as risk assessments. This makes it possible to propose protection measures and risk management measures in the environmental field.

[0088] The proposal department can propose a research and development strategy or a technology introduction strategy based on predicted technological trends or technological innovations in the technology field. For example, the proposal department can propose a research and development strategy or a technology introduction strategy based on predicted technological trends or technological innovations in the technology field. For example, the proposal department can propose research and development of new technologies or technology transfer based on predicted technological trends. The proposal department can also propose a technology introduction plan or technology evaluation based on predicted technological innovations. The proposal department can also propose the formulation of a technology roadmap or a review of a technology strategy based on predicted technological trends. For example, the proposal department can propose research and development of AI technology or blockchain technology as research and development of new technologies. The proposal department can also propose collaboration with universities and research institutes as technology transfer. The proposal department can also propose the evaluation of new technologies and the formulation of an introduction process as a technology introduction plan. This makes it possible to propose research and development strategies and technology introduction strategies in the technology field.

[0089] The proposal unit can propose improvement measures for educational programs or educational policies based on predicted educational trends or educational effects in the field of education. For example, the proposal unit can propose improvement measures for educational programs or educational policies based on predicted educational trends or educational effects in the field of education. For example, the proposal unit can propose a curriculum review or an improvement to teaching methods based on predicted educational trends. The proposal unit can also propose evaluations and improvement measures for educational programs based on predicted educational effects. The proposal unit can also propose the formulation of educational policies and the allocation of educational budgets based on predicted educational trends. For example, the proposal unit can propose the introduction of digital education or remote learning as a curriculum review. The proposal unit can also propose the introduction of active learning or project-based learning as an improvement to teaching methods. The proposal unit can also propose measuring learning outcomes and providing feedback as an evaluation of educational programs. This makes it possible to propose program improvement measures and educational policies in the field of education.

[0090] The evaluation unit can evaluate the degree of agreement between actual data or the prediction result as a method for evaluating prediction accuracy. The evaluation unit can evaluate the degree of agreement between actual data and the prediction result, for example, as a method for evaluating prediction accuracy. For example, the evaluation unit can quantify and evaluate the degree of agreement between the actual data and the prediction result. The evaluation unit can also graph the degree of agreement between the actual data and the prediction result for visual evaluation. The evaluation unit can also evaluate prediction accuracy based on the degree of agreement between the actual data and the prediction result. For example, the evaluation unit can evaluate the degree of agreement between the actual data and the prediction result using a correlation coefficient or an error rate. The evaluation unit can also graph the degree of agreement between the actual data and the prediction result for visual evaluation. The evaluation unit can also evaluate prediction accuracy based on the degree of agreement between the actual data and the prediction result. This makes it possible to evaluate prediction accuracy.

[0091] The evaluation unit can evaluate the feasibility or effectiveness of the proposed strategy as an evaluation criterion for social impact. The evaluation unit can evaluate, for example, the feasibility or effectiveness of the proposed strategy as an evaluation criterion for social impact. For example, the evaluation unit can quantify and evaluate the feasibility of the proposed strategy. The evaluation unit can also quantify and evaluate the effectiveness of the proposed strategy. The evaluation unit can also evaluate the social impact based on the feasibility and effectiveness of the proposed strategy. For example, the evaluation unit can evaluate the feasibility of the proposed strategy in terms of technical feasibility and economic feasibility. The evaluation unit can also evaluate the effectiveness of the proposed strategy in terms of outcome indicators and impact assessment. The evaluation unit can also evaluate the social impact based on the feasibility and effectiveness of the proposed strategy. This makes it possible to evaluate the social impact.

[0092] The collection unit can estimate a user's emotions and determine the timing of data collection based on the estimated user emotions. The collection unit can, for example, estimate a user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can temporarily delay data collection and resume collection when the user is relaxed. Furthermore, if the user is concentrating, the collection unit can quickly collect data to utilize the user's concentration. Furthermore, if the user is tired, the collection unit can minimize data collection to reduce the user's burden. For example, the collection unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the collection unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the collection unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This enables data collection according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using AI, or may be performed without using AI.

[0093] The collection unit can evaluate the reliability of the data when collecting data in each field and prioritize collecting reliable data. For example, the collection unit can evaluate the reliability of the data when collecting data in each field and prioritize collecting reliable data. For example, in the health field, the collection unit can prioritize collecting reliable hospital medical records. Furthermore, in the environment field, the collection unit can prioritize collecting reliable weather data. Furthermore, in the technical field, the collection unit can prioritize collecting reliable patent data. For example, the collection unit can evaluate the reliability of hospital medical records and prioritize collecting reliable hospital data. Furthermore, the collection unit can evaluate the reliability of weather data and prioritize collecting reliable data. Furthermore, the collection unit can evaluate the reliability of patent data and prioritize collecting reliable data. This enables the collection of highly reliable data.

[0094] The collection unit can select an appropriate collection means depending on the type of data when collecting data. For example, the collection unit can select the optimal collection means depending on the type of data (text, image, audio, etc.) when collecting data. For example, in the case of text data, the collection unit can collect it directly from a database via an API. In the case of image data, the collection unit can collect it using image recognition technology. In the case of audio data, the collection unit can collect it using voice recognition technology. For example, the collection unit can acquire text data from a database via an API. In addition, the collection unit can collect image data using image recognition technology. In addition, the collection unit can collect audio data using voice recognition technology. This enables optimal collection depending on the type of data.

[0095] The collection unit can appropriately adjust the collection method by referring to past data collection history when collecting data. For example, the collection unit can optimize the collection method by referring to past data collection history when collecting data. For example, the collection unit can analyze past data collection history and select the most efficient collection method. Furthermore, the collection unit can prioritize collecting data that takes a long time to collect from the past data collection history. Furthermore, the collection unit can optimize the timing of collection based on the past data collection history. For example, the collection unit can analyze past data collection history and select the most efficient collection method. Furthermore, the collection unit can prioritize collecting data that takes a long time to collect from the past data collection history. Furthermore, the collection unit can optimize the timing of collection based on the past data collection history. This enables optimal data collection based on past history.

[0096] The collection unit can estimate the user's emotions and prioritize the data to be collected based on the estimated user emotions. For example, the collection unit can estimate the user's emotions and prioritize the data to be collected based on the estimated user emotions. For example, if the user is excited, the collection unit can prioritize collecting important data. Furthermore, if the user is relaxed, the collection unit can collect detailed data. Furthermore, if the user is tired, the collection unit can prioritize collecting simple data. For example, the collection unit can capture the user's facial expressions with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the collection unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the collection unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This enables prioritization of data according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI.

[0097] The collection unit can prioritize collecting relevant data based on geographical data distribution when collecting data. For example, the collection unit can prioritize collecting highly relevant data by taking geographical data distribution into consideration when collecting data. For example, in the health field, the collection unit can prioritize collecting medical records from a specific region. Furthermore, in the environmental field, the collection unit can prioritize collecting weather data from a specific region. Furthermore, in the technical field, the collection unit can prioritize collecting patent data from a specific region. For example, the collection unit can prioritize collecting medical records from a specific region. Furthermore, the collection unit can prioritize collecting weather data from a specific region. Furthermore, the collection unit can prioritize collecting patent data from a specific region. This makes it possible to collect data based on geographical data distribution.

[0098] The collection unit can analyze data from social media during data collection and collect relevant data. For example, during data collection, the collection unit can analyze data from social media and collect relevant data. For example, in the health field, the collection unit can collect health-related posts on social media. Furthermore, in the environmental field, the collection unit can collect environment-related posts on social media. Furthermore, in the technical field, the collection unit can collect technology-related posts on social media. For example, the collection unit can collect health-related posts on social media. Furthermore, the collection unit can collect environment-related posts on social media. Furthermore, the collection unit can collect technology-related posts on social media. This makes it possible to collect relevant data from social media.

[0099] The collection unit can adjust the collection method by reflecting the user's past feedback when collecting data. For example, the collection unit can customize the collection method by reflecting the user's past feedback when collecting data. For example, the collection unit can adjust the type of data to be collected based on the user's past feedback. Furthermore, the collection unit can adjust the timing of collection based on the user's past feedback. Furthermore, the collection unit can customize the collection means based on the user's past feedback. For example, the collection unit can adjust the type of data to be collected based on the user's past feedback. Furthermore, the collection unit can adjust the timing of collection based on the user's past feedback. Furthermore, the collection unit can customize the collection means based on the user's past feedback. This makes it possible to customize the collection method based on the user's feedback.

[0100] The analysis unit can estimate the user's emotions and set the presentation method of the analysis results based on the estimated user emotions. The analysis unit can, for example, estimate the user's emotions and adjust the presentation method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide simple, highly visible analysis results. If the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can provide analysis results that focus on the main points. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The analysis unit can record the user's voice and estimate the emotions using voice analysis technology. The analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. This enables the presentation of analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without using AI.

[0101] The analysis unit can set the level of detail of the analysis based on the importance of the data during analysis. The analysis unit can, for example, adjust the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit can perform a detailed analysis on important data. Furthermore, the analysis unit can perform a basic analysis on general data. Furthermore, the analysis unit can perform a simplified analysis on unnecessary data. For example, the analysis unit can evaluate the importance of data and perform a detailed analysis on important data. Furthermore, the analysis unit can evaluate the importance of data and perform a basic analysis on general data. Furthermore, the analysis unit can evaluate the importance of data and perform a simplified analysis on unnecessary data. This makes it possible to perform analysis according to the importance of data.

[0102] The analysis unit can apply an appropriate analysis algorithm depending on the category of data during analysis. The analysis unit can, for example, apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit can apply a medical-specific analysis algorithm to health data. Furthermore, the analysis unit can apply an environment-specific analysis algorithm to environmental data. Furthermore, the analysis unit can apply a technology-specific analysis algorithm to technical data. For example, the analysis unit can apply a medical-specific analysis algorithm to health data. Furthermore, the analysis unit can apply an environment-specific analysis algorithm to environmental data. Furthermore, the analysis unit can apply a technology-specific analysis algorithm to technical data. This enables optimal analysis depending on the category of data.

[0103] The analysis unit can improve the accuracy of the analysis by referring to past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to past analysis results during analysis. For example, the analysis unit can improve the analysis algorithm based on past analysis results. Furthermore, the analysis unit can adjust the analysis parameters by referring to past analysis results. Furthermore, the analysis unit can optimize the analysis procedure based on past analysis results. For example, the analysis unit can improve the analysis algorithm based on past analysis results. Furthermore, the analysis unit can adjust the analysis parameters by referring to past analysis results. Furthermore, the analysis unit can optimize the analysis procedure based on past analysis results. This makes it possible to improve the accuracy based on past analysis results.

[0104] The analysis unit can estimate the user's emotion and set the length of the analysis result based on the estimated user emotion. The analysis unit can, for example, estimate the user's emotion and adjust the length of the analysis result based on the estimated user emotion. For example, the analysis unit can provide a short and concise analysis result if the user is in a hurry. The analysis unit can provide a detailed analysis result if the user is relaxed. The analysis unit can provide a visually stimulating analysis result if the user is excited. For example, the analysis unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate the emotion using voice analysis technology. The analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This makes it possible to adjust the length of the analysis result according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without using AI.

[0105] The analysis unit can set an analysis priority based on the time of data collection during analysis. The analysis unit can, for example, determine an analysis priority based on the time of data collection during analysis. For example, the analysis unit can prioritize analysis of the latest data. The analysis unit can also determine an analysis priority by referring to past data. The analysis unit can also adjust the order of analysis based on the time of data collection. For example, the analysis unit can prioritize analysis of the latest data. The analysis unit can also determine an analysis priority by referring to past data. The analysis unit can also adjust the order of analysis based on the time of data collection. This makes it possible to prioritize analysis based on the time of data collection.

[0106] The analysis unit can set the order of analysis based on the relevance of the data during analysis. The analysis unit can, for example, adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also optimize the order of analysis based on the relevance of the data. For example, the analysis unit can prioritize analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also optimize the order of analysis based on the relevance of the data. This makes it possible to adjust the order of analysis based on the relevance of the data.

[0107] The analysis unit can set the use of technical terms in the analysis results according to the user's level of expertise during analysis. The analysis unit can, for example, adjust the use of technical terms in the analysis results according to the user's level of expertise during analysis. For example, the analysis unit can provide analysis results that use a lot of technical terms to a user with high expertise. The analysis unit can also provide analysis results that are explained in simple terms to a user with low expertise. The analysis unit can also adjust the way the analysis results are expressed according to the user's level of expertise. For example, the analysis unit can provide analysis results that use a lot of technical terms to a user with high expertise. The analysis unit can also provide analysis results that are explained in simple terms to a user with low expertise. The analysis unit can also adjust the way the analysis results are expressed according to the user's level of expertise. This makes it possible to provide analysis results that are according to the user's level of expertise.

[0108] The suggestion unit can estimate the user's emotions and set a method for expressing suggestions based on the estimated user emotions. For example, the suggestion unit can estimate the user's emotions and adjust the method for expressing suggestions based on the estimated user emotions. For example, if the user is nervous, the suggestion unit can provide simple, highly visible suggestions. Furthermore, if the user is relaxed, the suggestion unit can provide detailed suggestions. Furthermore, if the user is in a hurry, the suggestion unit can provide suggestions that focus on the main points. For example, the suggestion unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the suggestion unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the suggestion unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This enables suggestions to be expressed according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the proposal unit may be performed using AI, or may be performed without using AI.

[0109] The suggestion unit can set the level of detail of the suggestion based on the importance of the predicted trend when making a suggestion. The suggestion unit can, for example, adjust the level of detail of the suggestion based on the importance of the predicted trend when making a suggestion. For example, the suggestion unit can make a detailed suggestion for an important trend. Furthermore, the suggestion unit can make a basic suggestion for a general trend. Furthermore, the suggestion unit can make a simplified suggestion for an unnecessary trend. For example, the suggestion unit can evaluate the importance of the predicted trend and make a detailed suggestion for an important trend. Furthermore, the suggestion unit can evaluate the importance of the predicted trend and make a basic suggestion for a general trend. Furthermore, the suggestion unit can evaluate the importance of the predicted trend and make a simplified suggestion for an unnecessary trend. This enables suggestions according to the importance of trends.

[0110] The suggestion unit can apply an appropriate suggestion algorithm depending on the trend category when making a suggestion. For example, the suggestion unit can apply different suggestion algorithms depending on the trend category when making a suggestion. For example, the suggestion unit can apply a medical-specific suggestion algorithm to a health trend. Furthermore, the suggestion unit can apply an environmental-specific suggestion algorithm to an environmental trend. Furthermore, the suggestion unit can apply a technology-specific suggestion algorithm to a technology trend. For example, the suggestion unit can apply a medical-specific suggestion algorithm to a health trend. Furthermore, the suggestion unit can apply an environmental-specific suggestion algorithm to an environmental trend. Furthermore, the suggestion unit can apply a technology-specific suggestion algorithm to a technology trend. This enables optimal suggestions depending on the trend category.

[0111] The proposal unit can improve the accuracy of the proposal by referring to past proposal results when making a proposal. For example, the proposal unit can improve the accuracy of the proposal by referring to past proposal results when making a proposal. For example, the proposal unit can improve the proposal algorithm based on past proposal results. Furthermore, the proposal unit can adjust proposal parameters by referring to past proposal results. Furthermore, the proposal unit can optimize the proposal procedure based on past proposal results. For example, the proposal unit can improve the proposal algorithm based on past proposal results. Furthermore, the proposal unit can adjust proposal parameters by referring to past proposal results. Furthermore, the proposal unit can optimize the proposal procedure based on past proposal results. This makes it possible to improve accuracy based on past proposal results.

[0112] The suggestion unit can estimate the user's emotion and set the length of the suggestion based on the estimated user's emotion. For example, the suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. For example, the suggestion unit can provide short and to-the-point suggestions when the user is in a hurry. The suggestion unit can provide detailed suggestions when the user is relaxed. The suggestion unit can provide visually stimulating suggestions when the user is excited. For example, the suggestion unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The suggestion unit can record the user's voice and estimate the emotion using voice analysis technology. The suggestion unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This enables the length of the suggestion to be adjusted according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the proposing unit may be performed using AI, for example, or may be performed without using AI.

[0113] The suggestion unit can set the priority of the proposals based on the time of trend occurrence when making a proposal. The suggestion unit can, for example, determine the priority of the proposals based on the time of trend occurrence when making a proposal. For example, the suggestion unit can give priority to making a proposal for the most recent trend. Furthermore, the suggestion unit can make a proposal for a long-term trend at a later date. Furthermore, the suggestion unit can optimize the order of the proposals based on the time of trend occurrence. For example, the suggestion unit can give priority to making a proposal for the most recent trend. Furthermore, the suggestion unit can make a proposal for a long-term trend at a later date. Furthermore, the suggestion unit can optimize the order of the proposals based on the time of trend occurrence. This makes it possible to prioritize the proposals based on the time of trend occurrence.

[0114] The suggestion unit can set the order of suggestions based on the relevance of trends when making suggestions. The suggestion unit can, for example, adjust the order of suggestions based on the relevance of trends when making suggestions. For example, the suggestion unit can prioritize suggestions for highly relevant trends. Furthermore, the suggestion unit can postpone suggestions for less relevant trends. Furthermore, the suggestion unit can optimize the order of suggestions based on the relevance of trends. For example, the suggestion unit can prioritize suggestions for highly relevant trends. Furthermore, the suggestion unit can postpone suggestions for less relevant trends. Furthermore, the suggestion unit can optimize the order of suggestions based on the relevance of trends. This makes it possible to adjust the order of suggestions based on the relevance of trends.

[0115] The suggestion unit can set the use of technical terms in the proposal according to the user's level of expertise when making the proposal. For example, the suggestion unit can adjust the use of technical terms in the proposal according to the user's level of expertise when making the proposal. For example, the suggestion unit can provide a proposal that uses a lot of technical terms to a user with high expertise. Furthermore, the suggestion unit can provide a proposal that is explained in simple terms to a user with low expertise. Furthermore, the suggestion unit can adjust the way the proposal is expressed according to the user's level of expertise. For example, the suggestion unit can provide a proposal that uses a lot of technical terms to a user with high expertise. Furthermore, the suggestion unit can provide a proposal that is explained in simple terms to a user with low expertise. Furthermore, the suggestion unit can adjust the way the proposal is expressed according to the user's level of expertise. This makes it possible to provide a proposal that is according to the user's level of expertise.

[0116] The evaluation unit can estimate the user's emotions and set evaluation criteria based on the estimated user emotions. For example, the evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated user emotions. For example, if the user is nervous, the evaluation unit can provide simple, highly visible evaluation criteria. Furthermore, if the user is relaxed, the evaluation unit can provide detailed evaluation criteria. Furthermore, if the user is in a hurry, the evaluation unit can provide evaluation criteria that focus on the key points. For example, the evaluation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the evaluation unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the evaluation unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This enables the evaluation criteria to be adjusted according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the evaluation unit may be performed using AI, or may be performed without using AI.

[0117] The evaluation unit can evaluate the degree of agreement between actual data or the prediction result during evaluation as a method for evaluating prediction accuracy. For example, the evaluation unit can evaluate the degree of agreement between actual data and the prediction result during evaluation as a method for evaluating prediction accuracy. For example, the evaluation unit can quantify and evaluate the degree of agreement between the actual data and the prediction result. The evaluation unit can also graph the degree of agreement between the actual data and the prediction result for visual evaluation. The evaluation unit can also evaluate prediction accuracy based on the degree of agreement between the actual data and the prediction result. For example, the evaluation unit can evaluate the degree of agreement between the actual data and the prediction result using a correlation coefficient or an error rate. The evaluation unit can also graph the degree of agreement between the actual data and the prediction result for visual evaluation. The evaluation unit can also evaluate prediction accuracy based on the degree of agreement between the actual data and the prediction result. This makes it possible to evaluate prediction accuracy.

[0118] The evaluation unit can evaluate the feasibility or effectiveness of the proposed strategy as an evaluation criterion for social impact during the evaluation. The evaluation unit can, for example, evaluate the feasibility or effectiveness of the proposed strategy as an evaluation criterion for social impact during the evaluation. For example, the evaluation unit can quantify and evaluate the feasibility of the proposed strategy. The evaluation unit can also quantify and evaluate the effectiveness of the proposed strategy. The evaluation unit can also evaluate the social impact based on the feasibility and effectiveness of the proposed strategy. For example, the evaluation unit can evaluate the feasibility of the proposed strategy in terms of technical feasibility and economic feasibility. The evaluation unit can also evaluate the effectiveness of the proposed strategy in terms of outcome indicators and impact assessments. The evaluation unit can also evaluate the social impact based on the feasibility and effectiveness of the proposed strategy. This makes it possible to evaluate the social impact.

[0119] The evaluation unit can appropriately adjust the evaluation criteria by referring to past evaluation results during evaluation. The evaluation unit can, for example, optimize the evaluation criteria by referring to past evaluation results during evaluation. For example, the evaluation unit can improve the evaluation criteria based on past evaluation results. Furthermore, the evaluation unit can adjust evaluation parameters by referring to past evaluation results. Furthermore, the evaluation unit can optimize the evaluation procedure based on past evaluation results. For example, the evaluation unit can improve the evaluation criteria based on past evaluation results. Furthermore, the evaluation unit can adjust evaluation parameters by referring to past evaluation results. Furthermore, the evaluation unit can optimize the evaluation procedure based on past evaluation results. This makes it possible to optimize the evaluation criteria based on past evaluation results.

[0120] The evaluation unit can estimate the user's emotions and prioritize the evaluations based on the estimated user emotions. For example, the evaluation unit can estimate the user's emotions and prioritize the evaluations based on the estimated user emotions. For example, if the user is excited, the evaluation unit can prioritize evaluation of important evaluation items. Furthermore, if the user is relaxed, the evaluation unit can perform detailed evaluations. Furthermore, if the user is tired, the evaluation unit can prioritize simple evaluations. For example, the evaluation unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. Furthermore, the evaluation unit can record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the evaluation unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. This enables prioritization of evaluations according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI.

[0121] The evaluation unit can perform the evaluation based on the geographical distribution of the evaluation target during the evaluation. For example, the evaluation unit can perform the evaluation taking into account the geographical distribution of the evaluation target during the evaluation. For example, in the health field, the evaluation unit can perform the evaluation based on medical records of a specific region. Furthermore, in the environmental field, the evaluation unit can perform the evaluation based on weather data of a specific region. Furthermore, in the technical field, the evaluation unit can perform the evaluation based on patent data of a specific region. For example, the evaluation unit can perform the evaluation based on medical records of a specific region. Furthermore, the evaluation unit can perform the evaluation based on weather data of a specific region. Furthermore, the evaluation unit can perform the evaluation based on patent data of a specific region. This makes it possible to perform the evaluation based on geographical distribution.

[0122] The evaluation unit can improve the accuracy of the evaluation by referring to related literature or data during the evaluation. The evaluation unit can improve the accuracy of the evaluation by referring to related literature or data during the evaluation, for example. For example, in the health field, the evaluation unit can make the evaluation by referring to related medical literature. Furthermore, in the environmental field, the evaluation unit can make the evaluation by referring to related environmental data. Furthermore, in the technical field, the evaluation unit can make the evaluation by referring to related technical papers. For example, in the health field, the evaluation unit can make the evaluation by referring to related medical literature. Furthermore, in the environmental field, the evaluation unit can make the evaluation by referring to related environmental data. Furthermore, in the technical field, the evaluation unit can make the evaluation by referring to related technical papers. This makes it possible to improve the accuracy of the evaluation based on related literature and data.

[0123] The evaluation unit can perform the evaluation based on the market value of the evaluation target at the time of evaluation. For example, the evaluation unit can perform the evaluation taking into account the market value of the evaluation target at the time of evaluation. For example, in the health field, the evaluation unit can perform the evaluation based on the market value of a proposed treatment. Furthermore, in the environmental field, the evaluation unit can perform the evaluation based on the market value of a proposed environmental protection measure. Furthermore, in the technical field, the evaluation unit can perform the evaluation based on the market value of a proposed technology. For example, in the health field, the evaluation unit can perform the evaluation based on the market value of a proposed treatment. Furthermore, in the environmental field, the evaluation unit can perform the evaluation based on the market value of a proposed environmental protection measure. Furthermore, in the technical field, the evaluation unit can perform the evaluation based on the market value of a proposed technology. This makes it possible to perform evaluation based on market value. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, and evaluation unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects data using the camera 42 and microphone 38B of the smart device 14, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit, realized by the specific processing unit 290 of the data processing device 12, analyzes the collected data and predicts future trends. The proposal unit, realized by the specific processing unit 290 of the data processing device 12, proposes a strategy based on the analysis results. The evaluation unit, realized by the specific processing unit 290 of the data processing device 12, evaluates the prediction accuracy and social impact of the proposed strategy. The collection unit may be realized, for example, by the control unit 46A of the smart device 14, and the analysis unit, proposal unit, and evaluation unit may be realized, for example, by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, and evaluation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects data using the camera 42 and microphone 238 of the smart glasses 214, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit, realized by the specific processing unit 290 of the data processing device 12, analyzes the collected data and predicts future trends. The proposal unit, realized by the specific processing unit 290 of the data processing device 12, proposes a strategy based on the analysis results. The evaluation unit, realized by the specific processing unit 290 of the data processing device 12, evaluates the prediction accuracy and social impact of the proposed strategy. The collection unit may be realized, for example, by the control unit 46A of the smart glasses 214, and the analysis unit, proposal unit, and evaluation unit may be realized, for example, by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, and evaluation unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects data using the camera 42 and microphone 238 of the headset-type terminal 314, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to predict future trends. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes a strategy based on the analysis results. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and evaluates the prediction accuracy and social impact of the proposed strategy. The collection unit may be realized, for example, by the control unit 46A of the headset-type terminal 314, and the analysis unit, proposal unit, and evaluation unit may be realized, for example, by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, and evaluation unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects data using the camera 42 and microphone 238 of the robot 414, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to predict future trends. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes a strategy based on the analysis results. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and evaluates the prediction accuracy and social impact of the proposed strategy. The collection unit may be realized, for example, by the control unit 46A of the robot 414, and the analysis unit, proposal unit, and evaluation unit may be realized, for example, by the specific processing unit 290 of the data processing device 12.

[0124] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0125] The future prediction system may further include an emotion adjustment unit that estimates the user's emotions and adjusts the data collection method based on the estimated emotions. For example, if the user is feeling stressed, the emotion adjustment unit may temporarily delay data collection and resume collection when the user is relaxed. Also, if the user is concentrating, data collection may be performed quickly to make the most of the user's concentration. Furthermore, if the user is tired, data collection may be minimized to reduce the user's burden. This enables flexible data collection according to the user's emotions.

[0126] The future prediction system can further include a reliability evaluation unit that evaluates the reliability of data. The reliability evaluation unit can evaluate the reliability of collected data and prioritize analysis of highly reliable data. For example, the reliability evaluation unit can evaluate the reliability of hospital medical records and prioritize analysis of highly reliable hospital data. It can also evaluate the reliability of weather data and prioritize analysis of highly reliable data. It can also evaluate the reliability of patent data and prioritize analysis of highly reliable data. This enables analysis based on highly reliable data.

[0127] The future prediction system can further include an emotion analysis unit that estimates the user's emotions and adjusts the presentation of the analysis results based on the estimated emotions. For example, if the user is nervous, the emotion analysis unit can provide simple, highly visible analysis results. If the user is relaxed, the emotion analysis unit can provide detailed analysis results. If the user is in a hurry, the emotion analysis unit can provide analysis results that focus on the main points. This makes it possible to provide analysis results that correspond to the user's emotions.

[0128] The future prediction system can further include a priority setting unit that sets analysis priorities based on the time of data collection. The priority setting unit can prioritize analysis of the most recent data. It can also determine analysis priorities by referring to past data. Furthermore, it can adjust the order of analysis based on the time of data collection. This enables efficient analysis based on the time of data collection.

[0129] The future prediction system may further include an emotion suggestion unit that estimates the user's emotion and adjusts the way suggestions are presented based on the estimated emotion. For example, if the user is nervous, the emotion suggestion unit may provide simple, highly visible suggestions. If the user is relaxed, the emotion suggestion unit may provide detailed suggestions. If the user is in a hurry, the emotion suggestion unit may provide suggestions that focus on the main points. This makes it possible to provide suggestions that correspond to the user's emotion.

[0130] The future prediction system can further include an accuracy improvement unit that improves the accuracy of the analysis by referring to past analysis results. The accuracy improvement unit can improve the analysis algorithm based on past analysis results. It can also adjust the analysis parameters by referring to past analysis results. Furthermore, it can optimize the analysis procedure based on past analysis results. This makes it possible to improve accuracy based on past analysis results.

[0131] The future prediction system can further include an emotion evaluation unit that estimates the user's emotion and adjusts the evaluation criteria based on the estimated emotion. For example, the emotion evaluation unit can provide simple, highly visible evaluation criteria when the user is nervous. It can also provide detailed evaluation criteria when the user is relaxed. It can also provide evaluation criteria that focus on the main points when the user is in a hurry. This makes it possible to provide evaluation criteria that correspond to the user's emotion.

[0132] The future prediction system can further include a market value assessment unit that performs assessment based on the market value of the assessment target. In the health field, the market value assessment unit can perform assessment based on the market value of a proposed treatment. In the environmental field, the market value assessment unit can perform assessment based on the market value of a proposed environmental protection measure. Furthermore, in the technology field, the market value assessment unit can perform assessment based on the market value of a proposed technology. This makes it possible to perform assessment based on market value.

[0133] The future prediction system may further include an emotion prioritization unit that estimates the user's emotion and prioritizes the evaluations based on the estimated emotion. For example, if the user is excited, the emotion prioritization unit may prioritize evaluation of important evaluation items. If the user is relaxed, the emotion prioritization unit may prioritize detailed evaluations. If the user is tired, the emotion prioritization unit may prioritize simple evaluations. This makes it possible to prioritize evaluations according to the user's emotion.

[0134] The future prediction system can further include a literature reference unit that refers to related literature and data to improve the accuracy of the evaluation. In the health field, the literature reference unit can make an evaluation by referring to related medical literature. In the environmental field, the literature reference unit can make an evaluation by referring to related environmental data. Furthermore, in the technical field, the literature reference unit can make an evaluation by referring to related technical papers. This makes it possible to improve the accuracy of the evaluation based on related literature and data.

[0135] The processing flow of the second embodiment will be briefly explained below.

[0136] Step 1: The collection department collects data related to each field. For example, in the health field, hospital medical records and health checkup data are collected; in the environment field, weather data and environmental monitoring data are collected; in the technology field, patent data and technical papers are collected; and in the education field, learning outcome data and educational program data are collected. This data is obtained from electronic medical record systems, databases of the Japan Meteorological Agency, databases of the Japan Patent Office, databases of educational institutions, etc. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it analyzes data in the health field to predict future disease incidence and health risks, analyzes data in the environment field to predict future climate change and environmental risks, analyzes data in the technology field to predict future technological trends and innovations, and analyzes data in the education field to predict future educational trends and educational effects. Machine learning algorithms, statistical analysis, natural language processing technology, and data mining technology are used for the analysis. Step 3: The proposal section proposes strategies based on the analysis results obtained by the analysis section. For example, in the health field, it proposes preventive measures and treatments based on predicted disease incidence and health risks, and in the environment field, it proposes environmental protection measures and risk management measures based on predicted climate change and environmental risks. In the technology field, it proposes research and development strategies and technology introduction strategies based on predicted technological trends and technological innovations, and in the education field, it proposes improvement measures for educational programs and educational policies based on predicted educational trends and educational effects. Step 4: The evaluation unit evaluates the prediction accuracy and social impact of the strategy proposed by the proposal unit. For example, the degree of agreement between actual data and the prediction results is evaluated as a method for evaluating prediction accuracy, and the feasibility and effectiveness of the proposed strategy are evaluated as criteria for evaluating social impact. This makes it possible to quantify and evaluate the feasibility and effectiveness of the proposed strategy.

[0137] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0142] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0143] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0144] 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.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0148] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0149] 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.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0153] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0155] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0158] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0159] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0160] 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.

[0161] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0163] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0164] 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.

[0165] 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.

[0166] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0167] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0168] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0169] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0174] 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.

[0175] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0176] 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.

[0177] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0178] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0179] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0180] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0181] 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.

[0182] 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.

[0183] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0184] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0185] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0186] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0187] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0188] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0189] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0190] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0191] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0192] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0193] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0194] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0195] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0196] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0197] 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.

[0198] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0199] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0200] 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.

[0201] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0202] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0203] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0204] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0205] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0206] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0207] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0208] [Explanation of symbols]

[0209] 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 data; an analysis unit that analyzes the data collected by the collection unit; a proposal unit that proposes a strategy based on the analysis result obtained by the analysis unit; an evaluation unit that evaluates the prediction accuracy and social impact of the strategy proposed by the proposal unit; Equipped with A system characterized by:

2. The collecting unit Collecting hospital medical records or health checkup data as health data 2. The system of claim 1.

3. The collecting unit Collecting meteorological data or environmental monitoring data as environmental data 2. The system of claim 1.

4. The collecting unit Collect patent data or technical papers as data in the technical field 2. The system of claim 1.

5. The collecting unit Collecting learning outcomes data or educational program data in the education field 2. The system of claim 1.

6. The analysis unit Analyzing health data to predict future disease incidence or health risks 2. The system of claim 1.

7. The analysis unit Analyzing environmental data to predict future climate change or environmental risks 2. The system of claim 1.

8. The analysis unit Analyzing data in the technology field and predicting future technological trends or innovations 2. The system of claim 1.

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