System

Abstract

An object of a system according to an embodiment is to construct sustainable social and economic activities without using capital equity.SOLUTION: A system includes a data analysis part, a fund raising part, and a growth strategy proposing part. The data analysis unit analyzes data in each field. The financing department provides a financing method that does not utilize net worth equity. The Growth Strategy Suggester suggests strategies for building continuous social and economic activities.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have faced the challenge of making it difficult to build sustainable social and economic activities in poor countries without utilizing their own capital.

[0005] The system according to the embodiment aims to build sustainable social and economic activities without using capital. [Means for solving the problem]

[0006] The system according to the embodiment includes a data analysis unit, a fundraising unit, and a growth strategy proposal unit. The data analysis unit analyzes data in each field. The fundraising unit provides a fundraising method that does not use equity capital. The growth strategy proposal unit proposes a strategy for building sustainable social and economic activities. [Effects of the Invention]

[0007] The system according to the embodiment can build sustainable social and economic activities without using capital. [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) The Profect platform, according to an embodiment of the present invention, is a system that provides comprehensive solutions to poor countries, solves their fundamental problems without using their own capital, and builds continuously prosperous social and economic activities, thereby enabling poor countries to solve their fundamental problems and achieve sustainable growth.

[0029] The Profect platform according to the embodiment includes a data analysis unit, a fundraising unit, and a growth strategy proposal unit. The data analysis unit analyzes data in various fields. For example, the data analysis unit may analyze data in the education field and propose optimal educational curricula. The data analysis unit may also analyze medical data and propose optimal treatment methods. The data analysis unit may also analyze economic data and propose sustainable growth strategies. The fundraising unit provides fundraising methods that do not require the use of equity capital. For example, the fundraising unit may propose strategies for optimizing crowdfunding. The fundraising unit may also propose strategies for optimizing fundraising from international aid organizations. The fundraising unit may also create presentations that maximize the appeal of projects and effectively appeal to donors. The growth strategy proposal unit proposes strategies for building sustainable social and economic activities. For example, the growth strategy proposal unit may propose optimal cultivation methods to improve agricultural efficiency. The growth strategy proposal unit may also propose optimal plans for infrastructure development. The growth strategy proposal unit may also propose growth strategies customized to the characteristics and needs of each country. As a result, the Profect platform according to the embodiment can solve the fundamental problems facing poor countries and achieve sustainable growth.

[0030] The data analysis unit can optimize educational curricula and provide online educational programs. For example, in the data analysis unit, a generative AI analyzes data in the education field and uses an emotion estimation function to propose curricula that elicit positive emotions to increase students' motivation to learn. For example, it selects topics and learning methods that are likely to interest students. In the medical field, the data analysis unit analyzes patients' emotional data and optimizes treatment methods and medical service delivery methods. For example, it proposes treatment explanations and support that give patients a sense of security. In the agricultural field, the data analysis unit analyzes farmers' emotional data and proposes cultivation methods that elicit positive emotions to improve agricultural productivity. For example, it provides cultivation schedules and techniques that motivate farmers. This makes it possible to provide optimal curricula in the education field and improve the quality of education through online educational programs.

[0031] The data analysis unit can analyze medical data and propose optimal treatment methods. For example, the generative AI in the data analysis unit analyzes the educational system and cultural background of each region and proposes the optimal educational curriculum for that region. For example, it can provide teaching materials that reflect the traditions and values ​​of the region. In the medical field, the generative AI analyzes the medical practices and cultural background of each region and proposes the optimal treatment method for the patient. For example, it can provide a treatment plan that combines traditional and modern medicine in the region. In the agricultural field, the generative AI analyzes the agricultural practices and climatic conditions of each region and proposes the optimal cultivation method for that region. For example, it can provide a cultivation plan that combines traditional farming methods of the region with the latest technology. This makes it possible to propose optimal treatment methods in the medical field and improve the quality of medical services.

[0032] The fundraising department can propose strategies to optimize fundraising through crowdfunding or from international aid organizations. For example, when the generative AI creates a crowdfunding presentation, the fundraising department analyzes donors' emotional data and incorporates stories that resonate with them. For example, it introduces moving anecdotes and success stories. When the fundraising department creates application documents for international aid organizations, the generative AI uses its emotion estimation function to include content that appeals to donors' emotions. For example, it emphasizes the need for and impact of aid. When the generative AI creates presentations for companies, the fundraising department analyzes emotional data and emphasizes the company's social responsibility and elements that resonate with them. For example, it suggests content that matches the company's brand image. This enables effective fundraising without using equity capital.

[0033] The Growth Strategy Proposal Department can analyze economic or social data and propose sustainable growth strategies. For example, the Growth Strategy Proposal Department uses generative AI to collect emotional data from local residents and propose social activities to elicit positive emotions. For example, it could plan events or community activities that are easy for residents to participate in. In addition, the Growth Strategy Proposal Department uses generative AI to analyze residents' emotional data in economic activities and propose business models that elicit positive responses. For example, it could recommend businesses that utilize local specialties. The Growth Strategy Proposal Department also uses generative AI to make policy recommendations to elicit positive emotions based on residents' emotional data. For example, it could propose social welfare programs to increase residents' happiness. This can propose sustainable growth strategies and continuously improve social and economic activities in poor countries.

[0034] The Growth Strategy Proposal Department can propose customized growth strategies tailored to the characteristics and needs of each country. For example, the Growth Strategy Proposal Department uses generative AI to collect emotional data from citizens and propose growth strategies that elicit positive emotions. For example, it proposes policies and programs that are easy for citizens to support. In addition, for economic growth, the Growth Strategy Proposal Department uses generative AI to analyze emotional data from citizens and proposes industrial development plans that elicit positive responses. For example, it prioritizes the development of industries that interest citizens. In addition, the Growth Strategy Proposal Department uses generative AI to propose social welfare programs that elicit positive emotions based on emotional data from citizens. For example, it proposes policies to increase citizens' happiness. This makes it possible to provide growth strategies tailored to the characteristics and needs of each country and support growth on a country-by-country basis.

[0035] The data analysis unit can analyze the cultural background or social customs of each region and provide customized solutions based on that. For example, in the data analysis unit, the generative AI analyzes the educational system and cultural background of each region and proposes the most appropriate educational curriculum for that region. For example, it provides teaching materials that reflect the traditions and values ​​of the region. In the medical field, the generative AI analyzes the medical customs and cultural background of each region and proposes the most appropriate treatment method for the patient. For example, it provides a treatment plan that combines traditional medicine and modern medicine in the region. In the agricultural field, the generative AI analyzes the agricultural customs and climatic conditions of each region and proposes the most appropriate cultivation method for that region. For example, it provides a cultivation plan that combines traditional farming methods of the region with the latest technology. This makes it possible to propose more effective solutions by providing customized solutions that take into account the cultural background and social customs of each region.

[0036] The data analysis unit can monitor local conditions in real time and propose immediate countermeasures according to the situation. For example, the data analysis unit's generating AI can monitor the situation in an educational setting in real time and propose immediate countermeasures to teachers and students. For example, it can provide support based on problems during class and the student's level of understanding. In the medical field, the data analysis unit's generating AI can monitor a patient's condition in real time and propose immediate countermeasures to medical staff. For example, it can adjust emergency responses and treatment plans. In the agricultural field, the data analysis unit's generating AI can monitor the condition of farmland in real time and propose immediate countermeasures to farmers. For example, it can provide countermeasures in response to changes in weather or the outbreak of pests and diseases. This allows for real-time monitoring of local conditions and proposal of immediate countermeasures, enabling rapid and appropriate responses.

[0037] The Data Analysis Department can provide locally rooted solutions by combining local traditional knowledge or technology. For example, in the field of education, the Generative AI would propose a curriculum that incorporates local traditional educational methods and knowledge. For example, it would incorporate lessons that teach local history and culture. In the field of medicine, the Generative AI would propose a treatment plan that combines local traditional medicine with modern medicine. For example, it would provide a treatment method that incorporates herbal therapy and acupuncture. In the field of agriculture, the Generative AI would propose cultivation methods that incorporate local traditional farming methods and knowledge. For example, it would recommend cultivating traditional crops that are suited to the local climate and soil. In this way, by utilizing local traditional knowledge and technology, it is possible to provide locally rooted solutions and achieve sustainable growth that makes use of local characteristics.

[0038] The data analysis department can collaborate with experts from different fields to provide comprehensive solutions through a multidisciplinary approach. For example, the generative AI in the data analysis department can collaborate with experts in the fields of education and medicine to propose health education programs. For example, it can incorporate health education and nutritional guidance in schools. In the medical field, the generative AI in the data analysis department can collaborate with experts in the field of agriculture to recommend the cultivation of nutritious crops. For example, it can provide an agricultural plan to improve local diets. In the infrastructure field, the generative AI can collaborate with experts in the fields of transportation and communications to propose efficient infrastructure development plans. For example, it can provide an urban plan that integrates transportation and communications networks. This allows collaboration between experts in different fields to provide comprehensive and multifaceted solutions, resulting in more effective solutions.

[0039] The fundraising department can analyze past crowdfunding data and propose fundraising strategies with a high probability of success. For example, the generation AI in the fundraising department analyzes data from past crowdfunding projects and proposes strategies with a high probability of success. For example, it extracts commonalities and trends among successful projects. The generation AI in the fundraising department also analyzes data to optimize crowdfunding campaign periods and reward settings. For example, it proposes the optimal campaign period and type of reward. The generation AI in the fundraising department also analyzes the characteristics of the target audience based on past crowdfunding data and proposes effective marketing strategies. For example, it proposes advertising aimed at specific age groups or interest groups. In this way, by analyzing past data and proposing fundraising strategies with a high probability of success, effective fundraising is possible.

[0040] The fundraising department can analyze the funding conditions of international aid organizations and automatically generate optimal application documents. For example, the generation AI in the fundraising department analyzes the funding conditions of international aid organizations and optimizes the format and content of application documents. For example, it automatically prepares the necessary information and format. The generation AI in the fundraising department also analyzes successful application documents from the past and automatically generates optimal application documents based on that knowledge. For example, it creates application documents that incorporate common points from successful cases. The generation AI in the fundraising department also monitors the funding conditions of international aid organizations in real time and automatically generates application documents based on the latest information. For example, it provides application documents that respond to changes in conditions or new requirements. In this way, effective fundraising is possible by analyzing the funding conditions of international aid organizations and automatically generating optimal application documents.

[0041] The fundraising department can propose a combination of different fundraising methods. For example, the generation AI will propose a fundraising strategy that combines social impact bonds and crowdfunding. For example, a project that emphasizes social impact is funded through crowdfunding. The fundraising department can also propose a strategy that combines microfinance and fundraising from international aid agencies. For example, obtaining a small loan while applying for large-scale aid. The fundraising department can also propose a strategy that diversifies risk by combining different fundraising methods. For example, raising funds from multiple sources simultaneously. This makes it possible to diversify risk and raise funds effectively by combining different fundraising methods.

[0042] The fundraising department can analyze the characteristics of funders in each region and propose fundraising strategies specialized for that region. For example, the generation AI in the fundraising department analyzes the characteristics of funders in each region and proposes fundraising strategies specialized for that region. For example, it creates a presentation that suits the local culture and values. The fundraising department also analyzes the economic situation and funder interests in each region and proposes the optimal fundraising method. For example, it proposes projects that meet the needs of the region. The fundraising department also analyzes the past investment history of funders in each region and proposes fundraising strategies with a high probability of success. For example, it incorporates commonalities between projects that have been successful in the past. This enables effective fundraising by analyzing the characteristics of funders in each region and proposing fundraising strategies specialized for that region.

[0043] The Growth Strategy Proposal Department can analyze economic data for each region and propose optimal industry development plans. For example, the Growth Strategy Proposal Department's generation AI analyzes economic data for each region and proposes the optimal industry development plan for that region. For example, it recommends industries that make use of the region's resources and characteristics. The Growth Strategy Proposal Department's generation AI also proposes industry development plans to achieve sustainable growth based on the region's economic data. For example, it recommends the development of environmentally friendly industries. The Growth Strategy Proposal Department's generation AI also analyzes economic data for each region and proposes industry development plans to create jobs and revitalize the economy. For example, it provides an entrepreneurship support program for local young people. In this way, by analyzing economic data for each region and proposing the optimal industry development plan, it is possible to promote regional economic growth.

[0044] The Growth Strategy Proposal Department can monitor economic data in real time and propose immediate countermeasures according to the situation. For example, the Growth Strategy Proposal Department's Generative AI monitors regional economic data in real time and proposes immediate countermeasures according to the economic situation. For example, it provides emergency measures in the event of an economic crisis. The Growth Strategy Proposal Department's Generative AI also monitors market data in real time and proposes immediate countermeasures to ensure business opportunities are not missed. For example, it recommends products and services that meet new market needs. The Growth Strategy Proposal Department's Generative AI also monitors employment data in real time and proposes immediate countermeasures to rising unemployment rates. For example, it provides job creation programs and vocational training. This enables rapid and appropriate responses by monitoring economic data in real time and proposing immediate countermeasures.

[0045] The Growth Strategy Proposal Department can analyze success stories from different regions and propose customized strategies for applying them to other regions. For example, the generation AI analyzes success stories from different regions and proposes customized strategies for applying them to other regions based on that knowledge. For example, introducing successful agricultural technology to other regions. The Growth Strategy Proposal Department can also analyze economic data from different regions and propose strategies for applying them to other regions based on success stories. For example, expanding a successful tourism industry model to other regions. The Growth Strategy Proposal Department can also analyze social data from different regions and propose strategies for applying them to other regions based on success stories. For example, introducing successful community activities to other regions. In this way, by analyzing success stories from different regions and proposing customized strategies for applying them to other regions, it is possible to provide growth strategies that take advantage of the characteristics of each region.

[0046] The Growth Strategy Proposal Department can collaborate with experts from different fields to build social / economic activities using a multidisciplinary approach. For example, the Growth Strategy Proposal Department's generative AI can connect experts in the fields of education and medicine to propose health education programs. For example, it can incorporate health education and nutritional guidance in schools. The Growth Strategy Proposal Department's generative AI can also connect experts in the fields of agriculture and economics to propose sustainable agricultural economic models. For example, it can provide economic support plans to improve agricultural productivity. The Growth Strategy Proposal Department's generative AI can also connect experts in the fields of infrastructure and social welfare to propose plans that integrate regional infrastructure development and social welfare. For example, it can link transportation infrastructure and welfare facilities. By collaborating with experts in different fields, it is possible to build comprehensive and multifaceted social / economic activities and achieve sustainable regional growth.

[0047] The Growth Strategy Proposal Department can analyze each country's policy data and make optimal policy recommendations. For example, the Generative AI in the Growth Strategy Proposal Department analyzes each country's policy data and makes optimal policy recommendations for that country. For example, it can propose tax reforms to promote economic growth. The Growth Strategy Proposal Department also uses the Generative AI to make policy recommendations to achieve sustainable growth based on each country's social data. For example, it can propose policies that balance environmental protection and economic growth. The Growth Strategy Proposal Department also uses the Generative AI to analyze each country's economic data and make policy recommendations to create jobs and revitalize the economy. For example, it can propose education reforms to promote youth employment. In this way, by analyzing each country's policy data and making optimal policy recommendations, it is possible to promote growth throughout the country.

[0048] The Growth Strategy Proposal Department can monitor the economic situation of each country in real time and propose immediate countermeasures according to the situation. For example, the Growth Strategy Proposal Department's Generative AI monitors economic data of each country in real time and proposes immediate countermeasures according to the economic situation. For example, it provides emergency measures in the event of an economic crisis. The Growth Strategy Proposal Department's Generative AI also monitors market data in real time and proposes immediate countermeasures to ensure business opportunities are not missed. For example, it recommends products and services that meet new market needs. The Growth Strategy Proposal Department's Generative AI also monitors employment data in real time and proposes immediate countermeasures to rising unemployment rates. For example, it provides job creation programs and vocational training. This enables rapid and appropriate responses by monitoring the economic situation of each country in real time and proposing immediate countermeasures.

[0049] The Growth Strategy Proposal Department can analyze success stories from different countries and propose customized strategies for applying them to other countries. For example, the generative AI analyzes success stories from different countries and proposes customized strategies for applying them to other countries based on that knowledge. For example, introducing successful agricultural technology to other countries. The Growth Strategy Proposal Department also analyzes economic data from different countries and proposes strategies for applying them to other countries based on success stories. For example, expanding a successful tourism industry model to other countries. The Growth Strategy Proposal Department also analyzes social data from different countries and proposes strategies for applying them to other countries based on success stories. For example, introducing successful community activities to other countries. In this way, by analyzing success stories from different countries and proposing customized strategies for applying them to other countries, it is possible to provide growth strategies that take advantage of the characteristics of each country.

[0050] The Growth Strategy Proposal Department can collaborate with experts from different fields to provide growth strategies using a multidisciplinary approach. For example, the Growth Strategy Proposal Department's generative AI can connect experts in the fields of education and medicine to propose health education programs. For example, it can incorporate health education and nutritional guidance in schools. The Growth Strategy Proposal Department's generative AI can also connect experts in the fields of agriculture and economics to propose sustainable agricultural economic models. For example, it can provide economic support plans to improve agricultural productivity. The Growth Strategy Proposal Department's generative AI can also connect experts in the fields of infrastructure and social welfare to propose plans that integrate regional infrastructure development and social welfare. For example, it can link transportation infrastructure and welfare facilities. By collaborating with experts in different fields, the Growth Strategy Proposal Department can provide comprehensive and multifaceted growth strategies and achieve sustainable growth for the entire country.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The Profect platform can also have a community engagement department. This department can directly gather information about local needs and issues through dialogue with local residents and propose solutions based on that information. For example, it could hold workshops in which local residents participate to share local issues and areas for improvement. The community engagement department can also collaborate with local leaders and organizations to carry out joint projects. For example, it could work with local schools and hospitals to implement measures to improve education and medical care. The community engagement department can also make policy recommendations that reflect the opinions of local residents. For example, it could make proposals regarding local infrastructure development and environmental protection. This makes it possible to provide solutions that reflect the voices of local residents and support sustainable growth in the region.

[0053] The Profect platform can also have a technical support department. This department can provide programs to help improve local technological capabilities. For example, it can implement technical training programs for local youth to improve their IT and engineering skills. The technical support department can also provide technical consulting to local companies and startups. For example, it can support the introduction of new technologies and the improvement of existing technologies. The technical support department can also host events and contests to promote local technological innovation. For example, it can provide a forum for local engineers and companies to present new ideas through hackathons and technology exhibitions. This can improve local technological capabilities and achieve sustainable growth.

[0054] The Profect platform may further include an Environmental Protection Department. The Environmental Protection Department may provide programs to support local environmental protection activities. For example, it may implement environmental education programs for local residents and businesses to raise environmental awareness. The Environmental Protection Department may also support local environmental protection projects. For example, it may implement tree planting activities and recycling programs. The Environmental Protection Department may also collect local environmental data and make policy recommendations for environmental protection. For example, it may propose measures to improve local air and water quality. This may support local environmental protection activities and achieve sustainable growth.

[0055] The Profect platform can also have a Cultural Promotion Department. The Cultural Promotion Department can provide programs to support local cultural activities. For example, it can hold events to introduce local traditional culture and arts and promote local culture. The Cultural Promotion Department can also provide support to local artists and cultural organizations. For example, it can support the planning and management of art projects and cultural events. The Cultural Promotion Department can also propose tourism plans that utilize local cultural resources. For example, it can propose tourist routes that utilize local historical buildings and natural landscapes. This can support local cultural activities and achieve sustainable growth.

[0056] The Profect platform can further include a health promotion department. The health promotion department can provide programs to improve the health status of the local community. For example, it can implement health education programs for local residents to raise health awareness. The health promotion department can also collaborate with local medical institutions to implement health checkup and vaccination campaigns. For example, it can provide regular health checkups and influenza vaccinations. The health promotion department can also plan and manage local sports events and fitness programs. For example, it can hold marathons and yoga classes. This can improve the health status of the local community and achieve sustainable growth.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The Data Analysis Department analyzes data in various fields. For example, analyzing data in the education field can suggest optimal educational curricula, analyzing medical data can suggest optimal treatment methods, and analyzing economic data can suggest sustainable growth strategies. Step 2: The Fundraising Department provides fundraising methods that do not require the use of equity capital. For example, we propose strategies to optimize crowdfunding and strategies to optimize fundraising from international aid organizations. We can also create presentations that maximize the appeal of your project and effectively appeal to funders. Step 3: The Growth Strategy Proposal Department proposes strategies to build sustainable social and economic activities. For example, it proposes optimal cultivation methods to improve agricultural efficiency and optimal plans for infrastructure development. It can also propose customized growth strategies tailored to the characteristics and needs of each country.

[0059] (Example 2) The Profect platform, according to an embodiment of the present invention, is a system that provides comprehensive solutions to poor countries, solves their fundamental problems without using their own capital, and builds continuously prosperous social and economic activities, thereby enabling poor countries to solve their fundamental problems and achieve sustainable growth.

[0060] The Profect platform according to the embodiment includes a data analysis unit, a fundraising unit, and a growth strategy proposal unit. The data analysis unit analyzes data in various fields. For example, the data analysis unit may analyze data in the education field and propose optimal educational curricula. The data analysis unit may also analyze medical data and propose optimal treatment methods. The data analysis unit may also analyze economic data and propose sustainable growth strategies. The fundraising unit provides fundraising methods that do not require the use of equity capital. For example, the fundraising unit may propose strategies for optimizing crowdfunding. The fundraising unit may also propose strategies for optimizing fundraising from international aid organizations. The fundraising unit may also create presentations that maximize the appeal of projects and effectively appeal to donors. The growth strategy proposal unit proposes strategies for building sustainable social and economic activities. For example, the growth strategy proposal unit may propose optimal cultivation methods to improve agricultural efficiency. The growth strategy proposal unit may also propose optimal plans for infrastructure development. The growth strategy proposal unit may also propose growth strategies customized to the characteristics and needs of each country. As a result, the Profect platform according to the embodiment can solve the fundamental problems facing poor countries and achieve sustainable growth.

[0061] The data analysis unit can optimize educational curricula and provide online educational programs. For example, in the data analysis unit, a generative AI analyzes data in the education field and uses an emotion estimation function to propose curricula that elicit positive emotions to increase students' motivation to learn. For example, it selects topics and learning methods that are likely to interest students. In the medical field, the data analysis unit analyzes patients' emotional data and optimizes treatment methods and medical service delivery methods. For example, it proposes treatment explanations and support that give patients a sense of security. In the agricultural field, the data analysis unit analyzes farmers' emotional data and proposes cultivation methods that elicit positive emotions to improve agricultural productivity. For example, it provides cultivation schedules and techniques that motivate farmers. This makes it possible to provide optimal curricula in the education field and improve the quality of education through online educational programs.

[0062] The data analysis unit can analyze medical data and propose optimal treatment methods. For example, the generative AI in the data analysis unit analyzes the educational system and cultural background of each region and proposes the optimal educational curriculum for that region. For example, it can provide teaching materials that reflect the traditions and values ​​of the region. In the medical field, the generative AI analyzes the medical practices and cultural background of each region and proposes the optimal treatment method for the patient. For example, it can provide a treatment plan that combines traditional and modern medicine in the region. In the agricultural field, the generative AI analyzes the agricultural practices and climatic conditions of each region and proposes the optimal cultivation method for that region. For example, it can provide a cultivation plan that combines traditional farming methods of the region with the latest technology. This makes it possible to propose optimal treatment methods in the medical field and improve the quality of medical services.

[0063] The fundraising department can propose strategies to optimize fundraising through crowdfunding or from international aid organizations. For example, when the generative AI creates a crowdfunding presentation, the fundraising department analyzes donors' emotional data and incorporates stories that resonate with them. For example, it introduces moving anecdotes and success stories. When the fundraising department creates application documents for international aid organizations, the generative AI uses its emotion estimation function to include content that appeals to donors' emotions. For example, it emphasizes the need for and impact of aid. When the generative AI creates presentations for companies, the fundraising department analyzes emotional data and emphasizes the company's social responsibility and elements that resonate with them. For example, it suggests content that matches the company's brand image. This enables effective fundraising without using equity capital.

[0064] The Growth Strategy Proposal Department can analyze economic or social data and propose sustainable growth strategies. For example, the Growth Strategy Proposal Department uses generative AI to collect emotional data from local residents and propose social activities to elicit positive emotions. For example, it could plan events or community activities that are easy for residents to participate in. In addition, the Growth Strategy Proposal Department uses generative AI to analyze residents' emotional data in economic activities and propose business models that elicit positive responses. For example, it could recommend businesses that utilize local specialties. The Growth Strategy Proposal Department also uses generative AI to make policy recommendations to elicit positive emotions based on residents' emotional data. For example, it could propose social welfare programs to increase residents' happiness. This can propose sustainable growth strategies and continuously improve social and economic activities in poor countries.

[0065] The Growth Strategy Proposal Department can propose customized growth strategies tailored to the characteristics and needs of each country. For example, the Growth Strategy Proposal Department uses generative AI to collect emotional data from citizens and propose growth strategies that elicit positive emotions. For example, it proposes policies and programs that are easy for citizens to support. In addition, for economic growth, the Growth Strategy Proposal Department uses generative AI to analyze emotional data from citizens and proposes industrial development plans that elicit positive responses. For example, it prioritizes the development of industries that interest citizens. In addition, the Growth Strategy Proposal Department uses generative AI to propose social welfare programs that elicit positive emotions based on emotional data from citizens. For example, it proposes policies to increase citizens' happiness. This makes it possible to provide growth strategies tailored to the characteristics and needs of each country and support growth on a country-by-country basis.

[0066] The data analysis unit can use the emotion estimation function to propose solutions that elicit the most positive responses based on the user's emotions. For example, the data analysis unit uses the generative AI to analyze data in the education field and use the emotion estimation function to propose curricula that elicit positive emotions to increase students' motivation to learn. For example, it selects topics and learning methods that are likely to interest students. In the medical field, the data analysis unit uses the generative AI to analyze patients' emotional data and optimize treatment methods and medical service delivery methods. For example, it proposes treatment explanations and support that give patients a sense of security. In the agricultural field, the data analysis unit uses the generative AI to analyze farmers' emotional data and propose cultivation methods that elicit positive emotions to improve agricultural productivity. For example, it provides cultivation schedules and techniques that motivate farmers. This allows the system to provide solutions that take user emotions into account and propose more effective solutions.

[0067] The data analysis unit can analyze the cultural background or social customs of each region and provide customized solutions based on that. For example, in the data analysis unit, the generative AI analyzes the educational system and cultural background of each region and proposes the most appropriate educational curriculum for that region. For example, it provides teaching materials that reflect the traditions and values ​​of the region. In the medical field, the generative AI analyzes the medical customs and cultural background of each region and proposes the most appropriate treatment method for the patient. For example, it provides a treatment plan that combines traditional medicine and modern medicine in the region. In the agricultural field, the generative AI analyzes the agricultural customs and climatic conditions of each region and proposes the most appropriate cultivation method for that region. For example, it provides a cultivation plan that combines traditional farming methods of the region with the latest technology. This makes it possible to propose more effective solutions by providing customized solutions that take into account the cultural background and social customs of each region.

[0068] The data analysis unit can monitor local conditions in real time and propose immediate countermeasures according to the situation. For example, the data analysis unit's generating AI can monitor the situation in an educational setting in real time and propose immediate countermeasures to teachers and students. For example, it can provide support based on problems during class and the student's level of understanding. In the medical field, the data analysis unit's generating AI can monitor a patient's condition in real time and propose immediate countermeasures to medical staff. For example, it can adjust emergency responses and treatment plans. In the agricultural field, the data analysis unit's generating AI can monitor the condition of farmland in real time and propose immediate countermeasures to farmers. For example, it can provide countermeasures in response to changes in weather or the outbreak of pests and diseases. This allows for real-time monitoring of local conditions and proposal of immediate countermeasures, enabling rapid and appropriate responses.

[0069] The Data Analysis Department can provide locally rooted solutions by combining local traditional knowledge or technology. For example, in the field of education, the Generative AI would propose a curriculum that incorporates local traditional educational methods and knowledge. For example, it would incorporate lessons that teach local history and culture. In the field of medicine, the Generative AI would propose a treatment plan that combines local traditional medicine with modern medicine. For example, it would provide a treatment method that incorporates herbal therapy and acupuncture. In the field of agriculture, the Generative AI would propose cultivation methods that incorporate local traditional farming methods and knowledge. For example, it would recommend cultivating traditional crops that are suited to the local climate and soil. In this way, by utilizing local traditional knowledge and technology, it is possible to provide locally rooted solutions and achieve sustainable growth that makes use of local characteristics.

[0070] The data analysis department can collaborate with experts from different fields to provide comprehensive solutions through a multidisciplinary approach. For example, the generative AI in the data analysis department can collaborate with experts in the fields of education and medicine to propose health education programs. For example, it can incorporate health education and nutritional guidance in schools. In the medical field, the generative AI in the data analysis department can collaborate with experts in the field of agriculture to recommend the cultivation of nutritious crops. For example, it can provide an agricultural plan to improve local diets. In the infrastructure field, the generative AI can collaborate with experts in the fields of transportation and communications to propose efficient infrastructure development plans. For example, it can provide an urban plan that integrates transportation and communications networks. This allows collaboration between experts in different fields to provide comprehensive and multifaceted solutions, resulting in more effective solutions.

[0071] The data analysis unit uses the emotion estimation function to collect feedback based on user emotions and use it to improve solutions. For example, in the education field, the generative AI collects student emotion data to help improve the curriculum. For example, it selects topics and learning methods that are likely to interest students. In the medical field, the generative AI collects patient emotion data to help improve treatment methods and medical services. For example, it provides treatment explanations and support that reassure patients. In the agricultural field, the generative AI collects farmers' emotion data to help improve cultivation methods and agricultural techniques. For example, it provides cultivation schedules and techniques that motivate farmers. In this way, by collecting feedback based on user emotions and using it to improve solutions, more effective solutions can be provided.

[0072] The fundraising department can use the emotion estimation function to analyze donors' emotions and create the most relatable presentation. For example, when the generation AI creates a crowdfunding presentation, the fundraising department analyzes donors' emotional data and incorporates relatable stories. For example, it introduces moving anecdotes and success stories. When the fundraising department creates application documents for international aid agencies, the generation AI uses the emotion estimation function to include content that appeals to donors' emotions. For example, it emphasizes the need for and impact of aid. When the fundraising department creates presentations for companies, the generation AI analyzes emotional data and emphasizes the company's social responsibility and relatable elements. For example, it suggests content that matches the company's brand image. This enables effective fundraising by analyzing donors' emotions and creating relatable presentations.

[0073] The fundraising department can analyze past crowdfunding data and propose fundraising strategies with a high probability of success. For example, the generation AI in the fundraising department analyzes data from past crowdfunding projects and proposes strategies with a high probability of success. For example, it extracts commonalities and trends among successful projects. The generation AI in the fundraising department also analyzes data to optimize crowdfunding campaign periods and reward settings. For example, it proposes the optimal campaign period and type of reward. The generation AI in the fundraising department also analyzes the characteristics of the target audience based on past crowdfunding data and proposes effective marketing strategies. For example, it proposes advertising aimed at specific age groups or interest groups. In this way, by analyzing past data and proposing fundraising strategies with a high probability of success, effective fundraising is possible.

[0074] The fundraising department can analyze the funding conditions of international aid organizations and automatically generate optimal application documents. For example, the generation AI in the fundraising department analyzes the funding conditions of international aid organizations and optimizes the format and content of application documents. For example, it automatically prepares the necessary information and format. The generation AI in the fundraising department also analyzes successful application documents from the past and automatically generates optimal application documents based on that knowledge. For example, it creates application documents that incorporate common points from successful cases. The generation AI in the fundraising department also monitors the funding conditions of international aid organizations in real time and automatically generates application documents based on the latest information. For example, it provides application documents that respond to changes in conditions or new requirements. In this way, effective fundraising is possible by analyzing the funding conditions of international aid organizations and automatically generating optimal application documents.

[0075] The fundraising department can propose a combination of different fundraising methods. For example, the generation AI will propose a fundraising strategy that combines social impact bonds and crowdfunding. For example, a project that emphasizes social impact is funded through crowdfunding. The fundraising department can also propose a strategy that combines microfinance and fundraising from international aid agencies. For example, obtaining a small loan while applying for large-scale aid. The fundraising department can also propose a strategy that diversifies risk by combining different fundraising methods. For example, raising funds from multiple sources simultaneously. This makes it possible to diversify risk and raise funds effectively by combining different fundraising methods.

[0076] The fundraising department can analyze the characteristics of funders in each region and propose fundraising strategies specialized for that region. For example, the generation AI in the fundraising department analyzes the characteristics of funders in each region and proposes fundraising strategies specialized for that region. For example, it creates a presentation that suits the local culture and values. The fundraising department also analyzes the economic situation and funder interests in each region and proposes the optimal fundraising method. For example, it proposes projects that meet the needs of the region. The fundraising department also analyzes the past investment history of funders in each region and proposes fundraising strategies with a high probability of success. For example, it incorporates commonalities between projects that have been successful in the past. This enables effective fundraising by analyzing the characteristics of funders in each region and proposing fundraising strategies specialized for that region.

[0077] The fundraising department can use the emotion estimation function to collect feedback based on donors' emotions and use it to improve fundraising strategies. For example, the generation AI collects emotional data from donors during a crowdfunding campaign and uses that data to improve the campaign. For example, it corrects parts with low emotion scores. The fundraising department can also use the emotion estimation function to collect feedback on application documents for international aid organizations and use the results to improve the application documents. For example, it can strengthen emotionally appealing content. The fundraising department can also use the emotion estimation function to collect feedback on presentations for companies and use that data to improve the presentations. For example, it can add elements that evoke empathy. In this way, collecting feedback based on donors' emotions and using it to improve fundraising strategies enables effective fundraising.

[0078] The Growth Strategy Proposal Department can use the emotion estimation function to analyze residents' emotions and propose social / economic activities that will elicit the most positive responses. For example, the Growth Strategy Proposal Department uses the generative AI to collect emotional data from local residents and propose social activities that will elicit positive emotions. For example, it could plan events or community activities that are easy for residents to participate in. In addition, in economic activities, the generative AI analyzes residents' emotional data and proposes business models that will elicit positive responses. For example, it could recommend businesses that utilize local specialties. Furthermore, the Growth Strategy Proposal Department uses the generative AI to make policy recommendations to elicit positive emotions based on residents' emotional data. For example, it could propose social welfare programs to increase residents' happiness. This makes it possible to revitalize the region by analyzing residents' emotions and proposing social / economic activities that will elicit positive responses.

[0079] The Growth Strategy Proposal Department can analyze economic data for each region and propose optimal industry development plans. For example, the Growth Strategy Proposal Department's generation AI analyzes economic data for each region and proposes the optimal industry development plan for that region. For example, it recommends industries that make use of the region's resources and characteristics. The Growth Strategy Proposal Department's generation AI also proposes industry development plans to achieve sustainable growth based on the region's economic data. For example, it recommends the development of environmentally friendly industries. The Growth Strategy Proposal Department's generation AI also analyzes economic data for each region and proposes industry development plans to create jobs and revitalize the economy. For example, it provides an entrepreneurship support program for local young people. In this way, by analyzing economic data for each region and proposing the optimal industry development plan, it is possible to promote regional economic growth.

[0080] The Growth Strategy Proposal Department can monitor economic data in real time and propose immediate countermeasures according to the situation. For example, the Growth Strategy Proposal Department's Generative AI monitors regional economic data in real time and proposes immediate countermeasures according to the economic situation. For example, it provides emergency measures in the event of an economic crisis. The Growth Strategy Proposal Department's Generative AI also monitors market data in real time and proposes immediate countermeasures to ensure business opportunities are not missed. For example, it recommends products and services that meet new market needs. The Growth Strategy Proposal Department's Generative AI also monitors employment data in real time and proposes immediate countermeasures to rising unemployment rates. For example, it provides job creation programs and vocational training. This enables rapid and appropriate responses by monitoring economic data in real time and proposing immediate countermeasures.

[0081] The Growth Strategy Proposal Department can analyze success stories from different regions and propose customized strategies for applying them to other regions. For example, the generation AI analyzes success stories from different regions and proposes customized strategies for applying them to other regions based on that knowledge. For example, introducing successful agricultural technology to other regions. The Growth Strategy Proposal Department can also analyze economic data from different regions and propose strategies for applying them to other regions based on success stories. For example, expanding a successful tourism industry model to other regions. The Growth Strategy Proposal Department can also analyze social data from different regions and propose strategies for applying them to other regions based on success stories. For example, introducing successful community activities to other regions. In this way, by analyzing success stories from different regions and proposing customized strategies for applying them to other regions, it is possible to provide growth strategies that take advantage of the characteristics of each region.

[0082] The Growth Strategy Proposal Department can collaborate with experts from different fields to build social / economic activities using a multidisciplinary approach. For example, the Growth Strategy Proposal Department's generative AI can connect experts in the fields of education and medicine to propose health education programs. For example, it can incorporate health education and nutritional guidance in schools. The Growth Strategy Proposal Department's generative AI can also connect experts in the fields of agriculture and economics to propose sustainable agricultural economic models. For example, it can provide economic support plans to improve agricultural productivity. The Growth Strategy Proposal Department's generative AI can also connect experts in the fields of infrastructure and social welfare to propose plans that integrate regional infrastructure development and social welfare. For example, it can link transportation infrastructure and welfare facilities. By collaborating with experts in different fields, it is possible to build comprehensive and multifaceted social / economic activities and achieve sustainable regional growth.

[0083] The Growth Strategy Proposal Department uses the emotion estimation function to collect feedback based on residents' emotions and use it to improve social and economic activities. For example, the Growth Strategy Proposal Department uses the generation AI to collect emotional data from local residents and use that data to improve social activities. For example, it plans events and community activities that are easy for residents to participate in. In addition, in economic activities, the generation AI analyzes residents' emotional data and proposes business models that elicit positive responses. For example, it recommends businesses that utilize local specialties. In addition, the Growth Strategy Proposal Department uses the generation AI to make policy recommendations to elicit positive emotions based on residents' emotional data. For example, it proposes social welfare programs to increase residents' happiness. In this way, feedback based on residents' emotions can be collected and used to improve social and economic activities, thereby achieving sustainable growth in the region.

[0084] The Growth Strategy Proposal Department can use the emotion estimation function to analyze public emotions and propose growth strategies that will elicit the most positive responses. For example, the Growth Strategy Proposal Department uses a generation AI to collect public emotional data and propose growth strategies that will elicit positive emotions. For example, it proposes policies and programs that are easy for the public to support. In addition, in terms of economic growth, the Growth Strategy Proposal Department uses a generation AI to analyze public emotional data and propose industry development plans that will elicit positive responses. For example, it will prioritize the development of industries that interest the public. In addition, the Growth Strategy Proposal Department uses a generation AI to propose social welfare programs that will elicit positive emotions based on public emotional data. For example, it will propose policies to increase public happiness. In this way, by analyzing public emotions and proposing growth strategies that will elicit positive responses, it is possible to promote the growth of the entire country.

[0085] The Growth Strategy Proposal Department can analyze each country's policy data and make optimal policy recommendations. For example, the Generative AI in the Growth Strategy Proposal Department analyzes each country's policy data and makes optimal policy recommendations for that country. For example, it can propose tax reforms to promote economic growth. The Growth Strategy Proposal Department also uses the Generative AI to make policy recommendations to achieve sustainable growth based on each country's social data. For example, it can propose policies that balance environmental protection and economic growth. The Growth Strategy Proposal Department also uses the Generative AI to analyze each country's economic data and make policy recommendations to create jobs and revitalize the economy. For example, it can propose education reforms to promote youth employment. In this way, by analyzing each country's policy data and making optimal policy recommendations, it is possible to promote growth throughout the country.

[0086] The Growth Strategy Proposal Department can monitor the economic situation of each country in real time and propose immediate countermeasures according to the situation. For example, the Growth Strategy Proposal Department's Generative AI monitors economic data of each country in real time and proposes immediate countermeasures according to the economic situation. For example, it provides emergency measures in the event of an economic crisis. The Growth Strategy Proposal Department's Generative AI also monitors market data in real time and proposes immediate countermeasures to ensure business opportunities are not missed. For example, it recommends products and services that meet new market needs. The Growth Strategy Proposal Department's Generative AI also monitors employment data in real time and proposes immediate countermeasures to rising unemployment rates. For example, it provides job creation programs and vocational training. This enables rapid and appropriate responses by monitoring the economic situation of each country in real time and proposing immediate countermeasures.

[0087] The Growth Strategy Proposal Department can analyze success stories from different countries and propose customized strategies for applying them to other countries. For example, the generative AI analyzes success stories from different countries and proposes customized strategies for applying them to other countries based on that knowledge. For example, introducing successful agricultural technology to other countries. The Growth Strategy Proposal Department also analyzes economic data from different countries and proposes strategies for applying them to other countries based on success stories. For example, expanding a successful tourism industry model to other countries. The Growth Strategy Proposal Department also analyzes social data from different countries and proposes strategies for applying them to other countries based on success stories. For example, introducing successful community activities to other countries. In this way, by analyzing success stories from different countries and proposing customized strategies for applying them to other countries, it is possible to provide growth strategies that take advantage of the characteristics of each country.

[0088] The Growth Strategy Proposal Department can collaborate with experts from different fields to provide growth strategies using a multidisciplinary approach. For example, the Growth Strategy Proposal Department's generative AI can connect experts in the fields of education and medicine to propose health education programs. For example, it can incorporate health education and nutritional guidance in schools. The Growth Strategy Proposal Department's generative AI can also connect experts in the fields of agriculture and economics to propose sustainable agricultural economic models. For example, it can provide economic support plans to improve agricultural productivity. The Growth Strategy Proposal Department's generative AI can also connect experts in the fields of infrastructure and social welfare to propose plans that integrate regional infrastructure development and social welfare. For example, it can link transportation infrastructure and welfare facilities. By collaborating with experts in different fields, the Growth Strategy Proposal Department can provide comprehensive and multifaceted growth strategies and achieve sustainable growth for the entire country.

[0089] The Growth Strategy Proposal Department can use the emotion estimation function to collect feedback based on public emotions and use it to improve the growth strategy. For example, the Growth Strategy Proposal Department uses generation AI to collect public emotional data and improve the growth strategy based on that data. For example, it proposes policies and programs that the public will find easy to support. In addition, for economic growth, the Growth Strategy Proposal Department uses generation AI to analyze public emotional data and propose industry development plans that elicit positive responses. For example, it prioritizes the development of industries that interest the public. In addition, the Growth Strategy Proposal Department uses generation AI to propose social welfare programs that elicit positive emotions based on public emotional data. For example, it proposes policies to increase public happiness. In this way, by collecting feedback based on public emotions and using it to improve the growth strategy, it is possible to achieve sustainable growth for the entire country.

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

[0091] The Profect platform can also have a community engagement department. This department can directly gather information about local needs and issues through dialogue with local residents and propose solutions based on that information. For example, it could hold workshops in which local residents participate to share local issues and areas for improvement. The community engagement department can also collaborate with local leaders and organizations to carry out joint projects. For example, it could work with local schools and hospitals to implement measures to improve education and medical care. The community engagement department can also make policy recommendations that reflect the opinions of local residents. For example, it could make proposals regarding local infrastructure development and environmental protection. This makes it possible to provide solutions that reflect the voices of local residents and support sustainable growth in the region.

[0092] The Profect platform can also have a technical support department. This department can provide programs to help improve local technological capabilities. For example, it can implement technical training programs for local youth to improve their IT and engineering skills. The technical support department can also provide technical consulting to local companies and startups. For example, it can support the introduction of new technologies and the improvement of existing technologies. The technical support department can also host events and contests to promote local technological innovation. For example, it can provide a forum for local engineers and companies to present new ideas through hackathons and technology exhibitions. This can improve local technological capabilities and achieve sustainable growth.

[0093] The Profect platform may further include an Environmental Protection Department. The Environmental Protection Department may provide programs to support local environmental protection activities. For example, it may implement environmental education programs for local residents and businesses to raise environmental awareness. The Environmental Protection Department may also support local environmental protection projects. For example, it may implement tree planting activities and recycling programs. The Environmental Protection Department may also collect local environmental data and make policy recommendations for environmental protection. For example, it may propose measures to improve local air and water quality. This may support local environmental protection activities and achieve sustainable growth.

[0094] The Profect platform can also have a Cultural Promotion Department. The Cultural Promotion Department can provide programs to support local cultural activities. For example, it can hold events to introduce local traditional culture and arts and promote local culture. The Cultural Promotion Department can also provide support to local artists and cultural organizations. For example, it can support the planning and management of art projects and cultural events. The Cultural Promotion Department can also propose tourism plans that utilize local cultural resources. For example, it can propose tourist routes that utilize local historical buildings and natural landscapes. This can support local cultural activities and achieve sustainable growth.

[0095] The Profect platform can further include a health promotion department. The health promotion department can provide programs to improve the health status of the local community. For example, it can implement health education programs for local residents to raise health awareness. The health promotion department can also collaborate with local medical institutions to implement health checkup and vaccination campaigns. For example, it can provide regular health checkups and influenza vaccinations. The health promotion department can also plan and manage local sports events and fitness programs. For example, it can hold marathons and yoga classes. This can improve the health status of the local community and achieve sustainable growth.

[0096] The Profect platform can also use its emotion estimation function to collect feedback based on user emotions and use it to improve solutions. For example, in the education field, collecting student emotion data can be used to improve curricula, for example, by selecting topics and learning methods that are likely to interest students. In the medical field, collecting patient emotion data can be used to improve treatment methods and medical services, for example, by providing treatment explanations and support that reassure patients. In the agricultural field, collecting farmers' emotion data can be used to improve cultivation methods and agricultural techniques, for example, by providing cultivation schedules and techniques that motivate farmers. In this way, collecting user emotion feedback can be used to improve solutions and provide more effective solutions.

[0097] The Profect platform also uses its emotion estimation function to analyze donors' emotions and create presentations that resonate most with them. For example, when creating a crowdfunding presentation, it analyzes donors' emotional data and incorporates stories that resonate with them, such as introducing moving anecdotes and success stories. When creating application documents for international aid organizations, it uses the emotion estimation function to include content that will appeal to donors' emotions, such as emphasizing the need for aid and its impact. When creating presentations for companies, it analyzes emotional data and emphasizes the company's social responsibility and relatable elements, such as suggesting content that matches the company's brand image. This allows for effective fundraising by analyzing donors' emotions and creating presentations that resonate with them.

[0098] The Profect platform can also use its emotion estimation function to analyze residents' emotions and suggest social / economic activities that will elicit the most positive responses. For example, it can collect emotional data from local residents and suggest social activities that will elicit positive emotions. For example, it can plan events and community activities that are easy for residents to participate in. It can also analyze residents' emotional data in economic activities and suggest business models that will elicit positive responses. For example, it can recommend businesses that utilize local specialties. It can also make policy recommendations based on residents' emotional data to elicit positive emotions. For example, it can propose social welfare programs to increase residents' happiness. This makes it possible to revitalize local areas by analyzing residents' emotions and suggesting social / economic activities that will elicit positive responses.

[0099] The Profect platform can also use its emotion estimation function to analyze public sentiment and propose growth strategies that will elicit the most positive responses. For example, it can collect public sentiment data and propose growth strategies that will elicit positive sentiment. For example, it can propose policies and programs that the public will find easy to support. In addition, when it comes to economic growth, it can analyze public sentiment data and propose industrial development plans that will elicit positive responses. For example, it can prioritize the development of industries that interest the public. It can also propose social welfare programs that elicit positive sentiment based on public sentiment data. For example, it can propose policies to increase public happiness. In this way, by analyzing public sentiment and proposing growth strategies that elicit positive responses, it is possible to promote the growth of the entire country.

[0100] The Profect platform can also use its emotion estimation function to collect feedback based on public sentiment and use it to improve growth strategies. For example, it can collect public sentiment data and use that data to improve growth strategies. For example, it can propose policies and programs that the public will find easy to support. In addition, in terms of economic growth, it can analyze public sentiment data and propose industry development plans that will elicit positive responses. For example, it can prioritize the development of industries that interest the public. It can also propose social welfare programs that elicit positive emotions based on public sentiment. For example, it can propose policies to increase public happiness. In this way, by collecting feedback based on public sentiment and using it to improve growth strategies, it can achieve sustainable growth for the entire country.

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

[0102] Step 1: The Data Analysis Department analyzes data in various fields. For example, analyzing data in the education field can suggest optimal educational curricula, analyzing medical data can suggest optimal treatment methods, and analyzing economic data can suggest sustainable growth strategies. Step 2: The Fundraising Department provides fundraising methods that do not require the use of equity capital. For example, we propose strategies to optimize crowdfunding and strategies to optimize fundraising from international aid organizations. We can also create presentations that maximize the appeal of your project and effectively appeal to funders. Step 3: The Growth Strategy Proposal Department proposes strategies to build sustainable social and economic activities. For example, it proposes optimal cultivation methods to improve agricultural efficiency and optimal plans for infrastructure development. It can also propose customized growth strategies tailored to the characteristics and needs of each country.

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

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

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

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] In the robot 414, 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 robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A data analysis department that analyzes data from each field, A fundraising department that provides fundraising methods that do not use equity capital; A Growth Strategy Proposal Department that proposes strategies for building sustainable social and economic activities. A system characterized by:

2. The data analysis unit Optimize educational curriculum and provide online educational programs 2. The system of claim 1.

3. The data analysis unit Analyzing medical data and proposing optimal treatment methods 2. The system of claim 1.

4. The fundraising department Propose strategies to optimize fundraising from crowdfunding or international donors.

2. The system of claim 1.

5. The growth strategy proposal department Analyze economic or social data and propose sustainable growth strategies 2. The system of claim 1.

Images