System

Abstract

An object of the system according to the embodiment is to consistently provide support for poor countries and support the construction of continuous social and economic activities.SOLUTION: A system includes a collection part, an analysis part, a planning part, a monitoring part, and a procurement part. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The formulation unit formulates a support plan based on the analysis result obtained by the analysis unit. The monitoring section monitors the progress of the support plan formulated by the formulating section in real time. The procurement unit procures funds.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] With conventional technology, it is difficult to consistently collect data, develop aid plans, monitor progress, and raise funds when providing aid to poor countries, and there is room for improvement.

[0005] The system of the embodiment aims to provide consistent support to poor countries and support the creation of continuous social and economic activities. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a formulation unit, a monitoring unit, and a procurement unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The formulation unit formulates a support plan based on the analysis results obtained by the analysis unit. The monitoring unit monitors the progress of the support plan formulated by the formulation unit in real time. The procurement unit raises funds. [Effects of the Invention]

[0007] The system according to the embodiment can provide consistent support to poor countries and support the creation of continuous social and economic activities. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A platform according to an embodiment of the present invention provides comprehensive solutions to poor countries. This system aims to solve fundamental issues without using its own capital and build sustainable, prosperous societies and economies. The platform conducts a detailed analysis of each country's current situation and identifies key challenges. It then collaborates with experts and companies in various fields to provide optimal solutions to the identified challenges. Examples of support include support in areas such as education, healthcare, infrastructure, agriculture, financial services, and entrepreneurship support. The platform utilizes AI to analyze data from each country and develop optimal support plans. AI collects and analyzes each country's economic, social, environmental, health, and educational data to propose the most effective support methods. For example, in the education sector, AI analyzes the current state of education and recommends necessary educational materials and programs. Furthermore, the platform monitors the progress of support in real time and makes adjustments as needed. This ensures effective support and sustainable growth. For example, in the agriculture sector, AI monitors crop growth and recommends optimal cultivation methods. The platform utilizes crowdfunding and fundraising from international organizations to support each country's growth without using its own capital. This enables sustainable assistance, resolves the fundamental challenges facing poor countries, and helps build prosperous societies and economic activities. The platform is able to address these challenges and support sustainable growth. For example, by conducting a detailed analysis of each country's current situation and providing optimal solutions, it can provide assistance in areas such as education, healthcare, infrastructure, agriculture, financial services, and entrepreneurship support. Effective assistance is achieved by utilizing AI to analyze each country's data and develop optimal assistance plans. Furthermore, the progress of assistance is monitored in real time and adjustments made as needed, ensuring sustainable growth. The platform is able to address the fundamental challenges facing poor countries and help build prosperous societies and economic activities.

[0029] The support platform according to the embodiment includes a collection unit, an analysis unit, a formulation unit, a monitoring unit, and a procurement unit. The collection unit collects data. The data includes, but is not limited to, numerical data, text data, and image data. The collection unit collects, for example, economic data, social data, environmental data, health data, and education data from each country. The analysis unit analyzes the data collected by the collection unit. The analysis is performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to these examples. The analysis unit proposes, for example, an optimal support method based on the collected data. The formulation unit formulates a support plan based on the analysis results obtained by the analysis unit. The support plan includes, for example, an educational support plan, a medical support plan, and the like, but is not limited to these examples. The formulation unit formulates, for example, a specific support plan based on the analysis results. The monitoring unit monitors the progress of the support plan formulated by the formulation unit in real time. The real-time monitoring is performed, for example, on a second-by-second or minute-by-minute basis, but is not limited to these examples. The monitoring unit monitors, for example, the progress of the support plan and makes adjustments as necessary. The procurement department raises funds. Examples of fundraising include, but are not limited to, crowdfunding and bank loans. For example, the procurement department raises funds through crowdfunding and from international organizations. This allows the support platform according to the embodiment to consistently perform processes from data collection and analysis, to formulating support plans, progress monitoring, and fundraising.

[0030] The support platform includes a support unit that provides financial services or business startup support. The support unit provides financial services or business startup support. Financial services include, but are not limited to, loans, investments, and insurance. For example, the support unit provides loans to support economic activities in poor countries. The support unit can also make investments and provide business startup support. The support unit can also provide insurance services to reduce economic risks. Business startup support includes, but is not limited to, support for creating business plans and fundraising. For example, the support unit supports entrepreneurs by supporting them in creating business plans. The support unit can also support entrepreneurs in fundraising and promote their growth. This makes it possible to support economic activities in poor countries through financial services and business startup support.

[0031] The assistance platform includes a collection unit that collects health data or educational data. The collection unit collects the health data or educational data. Health data includes, but is not limited to, vital signs, medical history, etc. For example, the collection unit collects vital signs and monitors health status. The collection unit can also collect medical history and provide medical assistance. Education data includes, but is not limited to, learning history, performance data, etc. For example, the collection unit collects learning history and provides educational assistance. The collection unit can also collect performance data and improve educational programs. This enables more comprehensive assistance by collecting health data and educational data.

[0032] The assistance platform includes a monitoring unit that monitors the progress of assistance using sensors or IoT technology. The monitoring unit monitors the progress of assistance using sensors or IoT technology. Examples of sensors include, but are not limited to, temperature sensors and pressure sensors. The monitoring unit collects environmental data using, for example, a temperature sensor and monitors the progress of assistance. The monitoring unit can also monitor the state of infrastructure using a pressure sensor. Examples of IoT technology include, but are not limited to, network protocols, device types, and the like. The monitoring unit collects data in real time using, for example, IoT devices and monitors the progress of assistance. The monitoring unit can also communicate between devices using network protocols and integrate the data. This makes it possible to more accurately monitor the progress of assistance by utilizing sensors and IoT technology.

[0033] The support platform includes a procurement department that raises funds through crowdfunding or from international organizations. The procurement department raises funds through crowdfunding or from international organizations. Crowdfunding includes, but is not limited to, platforms such as Kickstarter and Makuake. The procurement department raises funds through crowdfunding, for example, to carry out support activities. The procurement department can also raise funds from international organizations. International organizations include, but are not limited to, the International Monetary Fund (IMF) and the World Bank. The procurement department can carry out support activities with loans from the IMF, for example. The procurement department can also implement projects with funding from the World Bank. This allows support to be provided without using equity capital by raising funds through crowdfunding or from international organizations.

[0034] The collection unit may collect economic data, social data, environmental data, health data, or education data for each country. The collection unit may, for example, collect economic data for each country. The economic data may include, but is not limited to, GDP and unemployment rate. For example, the collection unit may collect GDP data for each country to understand the economic situation. The collection unit may also collect unemployment rate data to analyze the labor market situation. The collection unit may, for example, collect social data for each country. The social data may include, but is not limited to, demographics and crime rate. For example, the collection unit may collect demographic data for each country to analyze the social structure. The collection unit may also collect crime rate data to understand the public safety situation. The collection unit may, for example, collect environmental data for each country. The environmental data may include, but is not limited to, temperature and precipitation. For example, the collection unit may collect temperature data for each country to analyze the impact of climate change. The collection unit may also collect precipitation data to understand the status of water resources. This will allow us to collect diverse data from each country, allowing us to formulate more accurate support plans.

[0035] The analysis unit can analyze the collected data and propose an appropriate support method. The analysis unit, for example, performs statistical analysis on the collected data. Examples of statistical analysis include, but are not limited to, regression analysis and analysis of variance. The analysis unit can clarify the relationship between data using, for example, regression analysis. The analysis unit can also evaluate the variance of data using analysis of variance. The analysis unit, for example, analyzes the collected data using a machine learning algorithm. Examples of machine learning algorithms include, but are not limited to, clustering and classification. The analysis unit can group data using, for example, clustering. The analysis unit can also categorize data using a classification algorithm. In this way, the collected data can be analyzed to propose an optimal support method.

[0036] The formulation unit can formulate a support plan based on the analysis results. The formulation unit, for example, formulates an educational support plan based on the analysis results. The educational support plan includes, for example, the provision of teaching materials and the implementation of an educational program, but is not limited to these examples. The formulation unit, for example, includes the provision of teaching materials in the support plan. The formulation unit can also include the implementation of an educational program in the support plan. The formulation unit, for example, formulates a medical support plan based on the analysis results. The medical support plan includes, for example, the provision of medicines and the construction of medical facilities, but is not limited to these examples. The formulation unit, for example, includes the provision of medicines in the support plan. The formulation unit can also include the construction of medical facilities in the support plan. In this way, by formulating a support plan based on the analysis results, effective support is possible.

[0037] The monitoring unit monitors the progress of the support plan in real time and can make adjustments as necessary. The monitoring unit, for example, monitors the progress of the support plan in real time. Real-time monitoring includes, for example, second by second or minute by minute, but is not limited to these examples. The monitoring unit monitors the progress of the support plan in second by second, for example. The monitoring unit can also monitor the progress of the support plan in minute by minute. The monitoring unit, for example, monitors the progress of the support plan and makes adjustments as necessary. Adjustments include, for example, changes to the support content and the support schedule, but are not limited to these examples. The monitoring unit, for example, changes the support content. The monitoring unit can also change the support schedule. In this way, by monitoring the progress of the support plan in real time and making adjustments as necessary, the effectiveness of the support can be maximized.

[0038] The collection unit can analyze past data collection histories for each country and select an optimal collection method. The collection unit, for example, analyzes past data collection histories for each country. The past data collection history includes, for example, collection date and time, collection method, etc., but is not limited to these examples. The collection unit, for example, analyzes collection date and time and selects an optimal collection method. The collection unit can also analyze the collection method and identify areas for improvement in the collection method. The collection unit, for example, customizes the collection method based on the past data collection histories for each country. Customization includes, for example, changing the collection means and adjusting the collection frequency, but is not limited to these examples. The collection unit, for example, changes the collection means and selects an optimal collection method. The collection unit can also adjust the collection frequency to perform efficient data collection. In this way, by analyzing past data collection histories, an optimal collection method can be selected and efficient data collection is possible.

[0039] The collection unit may perform filtering based on the current situation and areas of interest of each country when collecting data. For example, the collection unit may consider the current situation of each country when collecting data. Examples of current situations include, but are not limited to, economic situations and social situations. For example, the collection unit may filter the data to be collected based on the economic situation of each country. The collection unit may also filter the data to be collected based on the social situation of each country. For example, the collection unit may consider the areas of interest of each country when collecting data. Examples of areas of interest include, but are not limited to, education and healthcare. For example, the collection unit may filter the data to be collected based on the education field of each country. The collection unit may also filter the data to be collected based on the healthcare field of each country. In this way, by filtering data based on the current situation and areas of interest of each country, highly relevant data can be collected.

[0040] The collection unit can select the optimal collection means according to the input method of each country when collecting data. For example, the collection unit takes into account the input method of each country when collecting data. Input methods include, but are not limited to, voice input, text input, and image input. For example, the collection unit selects the optimal collection means according to the voice input method of each country. The collection unit can also select the optimal collection means according to the text input method of each country. For example, the collection unit selects the optimal collection means according to the image input method of each country. This enables efficient data collection by selecting the optimal collection means according to the input method of each country.

[0041] The collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of each country when collecting data. For example, the collection unit considers the geographical location information of each country when collecting data. Geographical location information includes, but is not limited to, GPS data, address information, etc. For example, the collection unit prioritizes collecting highly relevant data based on the GPS data of each country. The collection unit can also determine the priority of data to be collected based on the address information of each country. For example, the collection unit filters the data to be collected by referring to the geographical location information of each country. In this way, highly relevant data can be prioritized by taking into account the geographical location information of each country.

[0042] The collection unit may analyze social media activity of each country during data collection and collect related data. For example, the collection unit may analyze social media activity of each country during data collection. Social media activity may include, but is not limited to, posted content and engagement rate. For example, the collection unit may analyze posted content of each country and collect related data. The collection unit may also analyze engagement rate and determine the priority of data to be collected. For example, the collection unit may filter the data to be collected based on social media activity of each country. This allows for efficient collection of related data by analyzing social media activity of each country.

[0043] The collection unit can customize the collection method by reflecting past feedback from each country when collecting data. For example, the collection unit reflects past feedback from each country when collecting data. Past feedback includes, but is not limited to, user opinions and improvements, for example. The collection unit customizes the collection method based on, for example, user opinions. The collection unit can also change the collection means by reflecting improvements. For example, the collection unit determines the priority of data to be collected based on past feedback from each country. In this way, by reflecting past feedback from each country, the collection method can be optimized and efficient data collection can be achieved.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. The analysis unit, for example, takes into account the importance of the data during analysis. The importance of the data includes, for example, impact and urgency, but is not limited to these examples. The analysis unit, for example, performs a detailed analysis on data with a high impact. The analysis unit can also perform a quick analysis on data with a high urgency. The analysis unit, for example, performs a simplified analysis on data with a low importance. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the data.

[0045] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit takes into account the category of data during analysis. Data categories include, but are not limited to, health data, education data, etc. For example, the analysis unit applies a health analysis algorithm to health data. The analysis unit can also apply an education analysis algorithm to education data. Analysis algorithms include, but are not limited to, regression analysis, clustering, etc. For example, the analysis unit can clarify relationships between data using regression analysis. The analysis unit can also group data using clustering. This improves the accuracy of the analysis by applying an appropriate analysis algorithm depending on the category of data.

[0046] The analysis unit can improve the accuracy of the analysis by referring to past analysis results for each country during analysis. The analysis unit, for example, refers to past analysis results for each country during analysis. Past analysis results include, for example, past success cases and failure cases, but are not limited to these examples. The analysis unit, for example, improves the accuracy of the analysis based on past success cases. The analysis unit can also improve the analysis algorithm by referring to past failure cases. The analysis unit, for example, analyzes past analysis results for each country and improves the accuracy of the analysis. In this way, the accuracy of the analysis can be improved by referring to past analysis results.

[0047] The analysis unit can determine the priority of analysis based on the time of data submission during analysis. The analysis unit, for example, takes into account the time of data submission during analysis. The time of data submission includes, for example, but is not limited to, the submission deadline and the frequency of submission. For example, the analysis unit prioritizes analysis of data with an earlier submission deadline. The analysis unit can also prioritize analysis of data with a higher frequency of submission. The analysis unit determines the priority of analysis based on, for example, the time of data submission. This enables efficient analysis by determining the priority of analysis based on the time of data submission.

[0048] The analysis unit can adjust the order of analysis based on the relevance of data during analysis. The analysis unit, for example, takes into account the relevance of data during analysis. Data relevance includes, for example, correlation and causal relationship, but is not limited to such examples. The analysis unit, for example, prioritizes analysis of highly correlated data. The analysis unit can also prioritize analysis of data with a strong causal relationship. The analysis unit, for example, adjusts the order of analysis based on the relevance of data. This enables efficient analysis by adjusting the order of analysis based on the relevance of data.

[0049] The analysis unit can adjust the use of technical terms in the analysis according to the expertise level of each country during the analysis. The analysis unit, for example, takes into account the expertise level of each country during the analysis. Expertise levels include, but are not limited to, beginner, intermediate, and expert levels. For example, the analysis unit provides analysis results that use a lot of technical terms to countries with a high level of expertise. The analysis unit can also provide analysis results that are explained in simple language to countries with a low level of expertise. The analysis unit, for example, adjusts the use of technical terms in the analysis according to the expertise level of each country. In this way, by adjusting the use of technical terms in the analysis according to the expertise level of each country, it is possible to provide analysis results that are easy to understand.

[0050] The formulation unit can adjust the level of detail of the support plan based on the importance of the data when formulating the support plan. For example, the formulation unit takes into account the importance of the data when formulating the support plan. The importance of the data includes, for example, impact and urgency, but is not limited to these examples. For example, the formulation unit formulates a detailed support plan based on data with a high impact. The formulation unit can also formulate a quick support plan based on data with a high urgency. For example, the formulation unit formulates a simplified support plan based on data with a low importance. This makes it possible to formulate an efficient support plan by adjusting the level of detail of the plan according to the importance of the data.

[0051] The formulation unit can apply different formulation algorithms depending on the data category when formulating a support plan. For example, the formulation unit takes into account the data category when formulating a support plan. Data categories include, but are not limited to, health data, education data, etc. For example, the formulation unit formulates a health support plan based on health data. The formulation unit can also formulate an education support plan based on education data. Formulation algorithms include, but are not limited to, regression analysis, clustering, etc. For example, the formulation unit formulates a support plan using regression analysis. The formulation unit can also formulate a support plan using clustering. This improves the accuracy of the support plan by applying an appropriate formulation algorithm depending on the data category.

[0052] When formulating a support plan, the formulation unit can improve the accuracy of the plan by referring to past plan results for each country. For example, the formulation unit refers to past plan results for each country when formulating a support plan. Past plan results include, but are not limited to, past success cases and failure cases. For example, the formulation unit improves the accuracy of the support plan based on past success cases. The formulation unit can also improve the formulation algorithm by referring to past failure cases. For example, the formulation unit analyzes past plan results for each country to improve the accuracy of the support plan. In this way, the accuracy of the support plan can be improved by referring to past plan results.

[0053] When formulating a support plan, the formulation unit can determine the priority of the plan based on the time of data submission. For example, the formulation unit takes into account the time of data submission when formulating a support plan. The time of data submission includes, for example, a submission deadline, a submission frequency, etc., but is not limited to these examples. For example, the formulation unit preferentially reflects data with an earlier submission deadline in the support plan. The formulation unit can also preferentially reflect data with a higher submission frequency in the support plan. For example, the formulation unit determines the priority of the support plan based on the time of data submission. Thus, by determining the priority of plans based on the time of data submission, efficient support plans can be formulated.

[0054] The formulation unit can adjust the order of the support plans based on the relevance of the data when formulating the support plans. The formulation unit, for example, takes into account the relevance of the data when formulating the support plans. Data relevance includes, for example, correlation and causal relationship, but is not limited to these examples. For example, the formulation unit preferentially reflects highly correlated data in the support plan. The formulation unit can also preferentially reflect data with strong causal relationships in the support plan. For example, the formulation unit adjusts the order of the support plans based on the relevance of the data. As a result, by adjusting the order of the plans based on the relevance of the data, it becomes possible to formulate efficient support plans.

[0055] When formulating a support plan, the formulation unit can adjust the use of technical terminology in the plan according to the expertise level of each country. For example, the formulation unit takes into account the expertise level of each country when formulating the support plan. Expertise levels include, but are not limited to, beginner, intermediate, and expert, for example. For example, the formulation unit provides a support plan that uses a lot of technical terminology to countries with a high level of expertise. The formulation unit can also provide a support plan that is explained in simple language to countries with a low level of expertise. For example, the formulation unit adjusts the use of technical terminology in the support plan according to the expertise level of each country. In this way, by adjusting the use of technical terminology in the plan according to the expertise level of each country, it is possible to provide a support plan that is easy to understand.

[0056] The monitoring unit can optimize the current monitoring method by referring to past monitoring data during monitoring. The monitoring unit, for example, refers to past monitoring data during monitoring. Past monitoring data includes, for example, past success cases and failure cases, but is not limited to these examples. The monitoring unit, for example, optimizes the current monitoring method based on past success cases. The monitoring unit can also identify areas for improvement in the monitoring method by referring to past failure cases. The monitoring unit, for example, analyzes past monitoring data and optimizes the current monitoring method. In this way, by referring to past monitoring data, the current monitoring method can be optimized and efficient monitoring becomes possible.

[0057] The monitoring unit can apply different monitoring methods to each data category during monitoring. For example, the monitoring unit takes into account the data category during monitoring. Data categories include, but are not limited to, health data, educational data, etc. For example, the monitoring unit applies a health monitoring method to health data. The monitoring unit can also apply an educational monitoring method to educational data. For example, monitoring methods include, but are not limited to, real-time monitoring, regular monitoring, etc. The monitoring unit can collect and monitor data using, for example, real-time monitoring. The monitoring unit can also collect and monitor data using regular monitoring. This improves the accuracy of monitoring by applying an appropriate monitoring method to each data category.

[0058] The monitoring unit can perform monitoring while taking into account attribute information of each country. For example, the monitoring unit can perform monitoring while taking into account attribute information of each country. Attribute information includes, but is not limited to, demographics and economic indicators. For example, the monitoring unit can perform monitoring based on demographic data of each country. The monitoring unit can also perform monitoring based on economic indicators of each country. For example, the monitoring unit can determine monitoring priorities with reference to attribute information of each country. This allows for more accurate monitoring by taking into account attribute information of each country.

[0059] The monitoring unit can analyze monitoring changes based on the data submission timing during monitoring. The monitoring unit, for example, takes into account the data submission timing during monitoring. The data submission timing includes, for example, but is not limited to, a submission deadline and a submission frequency. The monitoring unit, for example, prioritizes monitoring data with an early submission deadline. The monitoring unit can also prioritize monitoring data with a high submission frequency. The monitoring unit, for example, analyzes monitoring changes based on the data submission timing. The analysis of monitoring changes includes, for example, but is not limited to, a change detection method and a change evaluation criterion. The monitoring unit, for example, prioritizes analyzing changes in data with an early submission deadline. The monitoring unit can also prioritize analyzing changes in data with a high submission frequency. This enables efficient monitoring by analyzing monitoring changes based on the data submission timing.

[0060] The monitoring unit can analyze the monitoring by referring to related market data during monitoring. The monitoring unit, for example, refers to related market data during monitoring. The related market data includes, for example, market trend data, competitive analysis data, etc., but is not limited to these examples. The monitoring unit analyzes the monitoring based on, for example, the market trend data. The monitoring unit can also improve the monitoring method based on the competitive analysis data. The monitoring unit, for example, analyzes the related market data to improve the accuracy of the monitoring. In this way, the accuracy of the monitoring can be improved by referring to the related market data.

[0061] The monitoring unit can analyze the monitoring taking into account the technological maturity during monitoring. The monitoring unit, for example, takes into account the technological maturity during monitoring. The technological maturity includes, for example, the prevalence of technology and the evolutionary stage of technology, but is not limited to these examples. For example, the monitoring unit performs detailed monitoring when the prevalence of technology is high. Furthermore, the monitoring unit can also perform simplified monitoring when the evolutionary stage of technology is low. The monitoring unit, for example, adjusts the level of detail of monitoring according to the technological maturity. This enables appropriate monitoring by analyzing the monitoring according to the technological maturity.

[0062] When raising funds, the procurement department can refer to past procurement data to select the optimal procurement method. For example, when raising funds, the procurement department refers to past procurement data. Past procurement data includes, for example, past success cases and failure cases, but is not limited to such examples. For example, the procurement department selects the optimal procurement method based on past success cases. The procurement department can also identify areas for improvement in the procurement method by referring to past failure cases. For example, the procurement department analyzes past procurement data to select the optimal procurement method. In this way, by referring to past procurement data, the optimal procurement method can be selected, enabling efficient fund raising.

[0063] The procurement department can customize procurement methods based on the current economic conditions of each country when raising funds. For example, the procurement department takes into account the current economic conditions of each country when raising funds. Current economic conditions include, but are not limited to, GDP, unemployment rate, etc. For example, the procurement department selects the optimal procurement method based on GDP data of each country. The procurement department can also customize procurement methods based on unemployment rate data of each country. For example, the procurement department identifies areas for improvement in procurement methods by referring to the economic conditions of each country. This enables efficient fundraising by customizing procurement methods based on the economic conditions of each country.

[0064] The procurement department can improve the procurement method by reflecting feedback from each country when raising funds. The procurement department, for example, reflects feedback from each country when raising funds. Feedback includes, for example, user opinions and improvements, but is not limited to these examples. The procurement department improves the procurement method, for example, based on user opinions. The procurement department can also change the procurement method by reflecting improvements. The procurement department, for example, determines the priority of procurement methods based on feedback from each country. In this way, by reflecting feedback from each country, the procurement method can be optimized and efficient fundraising can be achieved.

[0065] The procurement department can select the optimal procurement method by taking into account the geographical location information of each country when raising funds. For example, the procurement department considers the geographical location information of each country when raising funds. Geographical location information includes, but is not limited to, GPS data, address information, etc. For example, the procurement department selects the optimal procurement method based on the GPS data of each country. The procurement department can also customize the procurement method based on the address information of each country. For example, the procurement department identifies areas for improvement in the procurement method by referring to the geographical location information of each country. In this way, by taking into account the geographical location information of each country, the optimal procurement method can be selected, enabling efficient fundraising.

[0066] The procurement department can analyze social media activity in each country and propose procurement methods when raising funds. For example, the procurement department analyzes social media activity in each country when raising funds. Social media activity includes, but is not limited to, the content of posts and engagement rates. For example, the procurement department analyzes the content of posts in each country and proposes the optimal procurement method. The procurement department can also analyze the engagement rate and determine the priority of procurement methods. For example, the procurement department customizes procurement methods based on the social media activity in each country. In this way, by analyzing the social media activity in each country, the optimal procurement method can be proposed, enabling efficient fundraising.

[0067] The procurement department can customize the procurement method by reflecting past feedback from each country when raising funds. The procurement department, for example, reflects past feedback from each country when raising funds. Past feedback includes, for example, user opinions and areas for improvement, but is not limited to these examples. The procurement department customizes the procurement method based on, for example, user opinions. The procurement department can also change the procurement method by reflecting areas for improvement. The procurement department, for example, determines the priority of procurement methods based on past feedback from each country. In this way, by reflecting past feedback from each country, the procurement method can be optimized and efficient fundraising can be achieved.

[0068] The support department can analyze the past support history of each country when providing support and select the optimal support method. The support department, for example, analyzes the past support history of each country when providing support. The past support history includes, for example, past success cases and failure cases, but is not limited to these examples. The support department, for example, selects the optimal support method based on past success cases. The support department can also identify areas for improvement in the support method by referring to past failure cases. The support department, for example, analyzes the past support history and selects the optimal support method. In this way, by analyzing the past support history of each country, the optimal support method can be selected and efficient support can be provided.

[0069] The support department can customize support measures based on the current situation of each country when providing support. For example, the support department takes into account the current situation of each country when providing support. Current situations include, but are not limited to, economic situations, social situations, etc. For example, the support department selects the optimal support measures based on the economic situation of each country. The support department can also customize support measures based on the social situation of each country. For example, the support department identifies areas for improvement in the support measures by referring to the current situation of each country. This enables efficient support by customizing support measures based on the current situation of each country.

[0070] The support unit can improve the support method by reflecting feedback from each country when providing support. The support unit, for example, reflects feedback from each country when providing support. Feedback includes, for example, user opinions and improvements, but is not limited to these examples. The support unit improves the support method, for example, based on user opinions. The support unit can also change the support means by reflecting improvements. For example, the support unit determines the priority of support means based on feedback from each country. In this way, by reflecting feedback from each country, the support method can be optimized and efficient support can be provided.

[0071] The support unit can select the optimal support method by taking into consideration the geographical location information of each country when providing support. For example, the support unit considers the geographical location information of each country when providing support. Geographical location information includes, but is not limited to, GPS data, address information, etc. For example, the support unit selects the optimal support method based on the GPS data of each country. The support unit can also customize the support method based on the address information of each country. For example, the support unit identifies areas for improvement in the support method by referring to the geographical location information of each country. This allows the optimal support method to be selected by taking into consideration the geographical location information of each country, enabling efficient support.

[0072] The support department can analyze the social media activity of each country and propose support measures when providing support. For example, the support department analyzes the social media activity of each country when providing support. Social media activity includes, but is not limited to, for example, the content of posts and engagement rates. For example, the support department analyzes the content of posts of each country and proposes the optimal support measures. The support department can also analyze the engagement rates and determine the priorities of support measures. For example, the support department customizes support measures based on the social media activity of each country. In this way, by analyzing the social media activity of each country, optimal support measures can be proposed, enabling efficient support.

[0073] The support unit can customize the support method by reflecting past feedback from each country when providing support. The support unit, for example, reflects past feedback from each country when providing support. Past feedback includes, for example, user opinions, areas for improvement, etc., but is not limited to these examples. The support unit customizes the support method based on, for example, user opinions. The support unit can also change the support means by reflecting areas for improvement. The support unit, for example, determines the priority of support means based on past feedback from each country. In this way, by reflecting past feedback from each country, the support method can be optimized and efficient support can be provided.

[0074] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0075] The support platform can also be equipped with a communications department, which provides functions to facilitate communication with residents and stakeholders in the recipient country. For example, the communications department may have a translation function for local languages, allowing information to be conveyed across language barriers. The communications department may also provide video conferencing and chat functions, enabling real-time exchange of opinions and feedback. Furthermore, the communications department may provide communication guidelines that take local culture and customs into consideration, avoiding misunderstandings and friction. This allows the support platform to build trust with local residents and stakeholders and provide effective support.

[0076] The support platform can further include a motivation section. The motivation section provides functions to increase the motivation of residents and related parties in the support country. For example, the motivation section can provide success stories and encouraging messages to motivate residents. The motivation section can also provide tools for goal setting and progress management, supporting residents in working toward their goals while checking their own progress. Furthermore, the motivation section can introduce reward systems and incentive programs to evaluate residents' efforts and encourage further efforts. In this way, the support platform can increase residents' motivation and support sustainable growth.

[0077] The support platform may further include a feedback unit. The feedback unit provides a function for collecting feedback from residents and related parties in the support recipient country and using the feedback to improve the support plan. For example, the feedback unit may collect residents' opinions and requests through questionnaires and surveys. The feedback unit may also provide an online platform where residents can freely post their opinions and receive feedback in real time. The feedback unit may also analyze the collected feedback and identify areas for improvement in the support plan. This enables the support platform to provide flexible support that meets the needs of residents and achieve effective support.

[0078] The support platform can also have a training department. The training department provides functions for providing necessary skills and knowledge to residents and stakeholders in the supported countries. For example, the training department can provide educational and technical training to residents through online courses and workshops. The training department can also provide customized training programs tailored to local needs and support residents in dealing with actual challenges. Furthermore, the training department can evaluate the effectiveness of the training and improve the programs as needed. This enables the support platform to improve residents' skills and knowledge and support sustainable growth.

[0079] The support platform can also have a networking section, which provides functions that enable residents and stakeholders in the recipient countries to connect and cooperate with each other. For example, the networking section can provide online communities and forums where residents can share information and exchange opinions. The networking section can also host events and workshops, providing opportunities for residents to interact directly. Furthermore, the networking section can provide a platform for residents and stakeholders to work together on projects and promote collaboration. In this way, the support platform can strengthen collaboration between residents and stakeholders and achieve effective support.

[0080] The processing flow of the first embodiment will be briefly explained below.

[0081] Step 1: The collection department collects data. The data includes numerical data, text data, image data, etc. The collection department collects economic data, social data, environmental data, health data, and education data from each country. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using statistical analysis and machine learning algorithms. The analysis unit proposes the optimal support method based on the collected data. Step 3: The formulation department formulates a support plan based on the analysis results obtained by the analysis department. Support plans include educational support plans, medical support plans, etc. The formulation department formulates a specific support plan based on the analysis results. Step 4: The Monitoring Department monitors the progress of the support plan formulated by the Formulating Department in real time. Real-time monitoring is done in seconds or minutes. The Monitoring Department monitors the progress of the support plan and makes adjustments as necessary. Step 5: The Procurement Department raises funds. Funding can be through crowdfunding or bank loans. The Procurement Department raises funds through crowdfunding or from international organizations.

[0082] (Example 2) A platform according to an embodiment of the present invention provides comprehensive solutions to poor countries. This system aims to solve fundamental issues without using its own capital and build sustainable, prosperous societies and economies. The platform conducts a detailed analysis of each country's current situation and identifies key challenges. It then collaborates with experts and companies in various fields to provide optimal solutions to the identified challenges. Examples of support include support in areas such as education, healthcare, infrastructure, agriculture, financial services, and entrepreneurship support. The platform utilizes AI to analyze data from each country and develop optimal support plans. AI collects and analyzes each country's economic, social, environmental, health, and educational data to propose the most effective support methods. For example, in the education sector, AI analyzes the current state of education and recommends necessary educational materials and programs. Furthermore, the platform monitors the progress of support in real time and makes adjustments as needed. This ensures effective support and sustainable growth. For example, in the agriculture sector, AI monitors crop growth and recommends optimal cultivation methods. The platform utilizes crowdfunding and fundraising from international organizations to support each country's growth without using its own capital. This enables sustainable assistance, resolves the fundamental challenges facing poor countries, and helps build prosperous societies and economic activities. The platform is able to address these challenges and support sustainable growth. For example, by conducting a detailed analysis of each country's current situation and providing optimal solutions, it can provide assistance in areas such as education, healthcare, infrastructure, agriculture, financial services, and entrepreneurship support. Effective assistance is achieved by utilizing AI to analyze each country's data and develop optimal assistance plans. Furthermore, the progress of assistance is monitored in real time and adjustments made as needed, ensuring sustainable growth. The platform is able to address the fundamental challenges facing poor countries and help build prosperous societies and economic activities.

[0083] The support platform according to the embodiment includes a collection unit, an analysis unit, a formulation unit, a monitoring unit, and a procurement unit. The collection unit collects data. The data includes, but is not limited to, numerical data, text data, and image data. The collection unit collects, for example, economic data, social data, environmental data, health data, and education data from each country. The analysis unit analyzes the data collected by the collection unit. The analysis is performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to these examples. The analysis unit proposes, for example, an optimal support method based on the collected data. The formulation unit formulates a support plan based on the analysis results obtained by the analysis unit. The support plan includes, for example, an educational support plan, a medical support plan, and the like, but is not limited to these examples. The formulation unit formulates, for example, a specific support plan based on the analysis results. The monitoring unit monitors the progress of the support plan formulated by the formulation unit in real time. The real-time monitoring is performed, for example, on a second-by-second or minute-by-minute basis, but is not limited to these examples. The monitoring unit monitors, for example, the progress of the support plan and makes adjustments as necessary. The procurement department raises funds. Examples of fundraising include, but are not limited to, crowdfunding and bank loans. For example, the procurement department raises funds through crowdfunding and from international organizations. This allows the support platform according to the embodiment to consistently perform processes from data collection and analysis, to formulating support plans, progress monitoring, and fundraising.

[0084] The support platform includes a support unit that provides financial services or business startup support. The support unit provides financial services or business startup support. Financial services include, but are not limited to, loans, investments, and insurance. For example, the support unit provides loans to support economic activities in poor countries. The support unit can also make investments and provide business startup support. The support unit can also provide insurance services to reduce economic risks. Business startup support includes, but is not limited to, support for creating business plans and fundraising. For example, the support unit supports entrepreneurs by supporting them in creating business plans. The support unit can also support entrepreneurs in fundraising and promote their growth. This makes it possible to support economic activities in poor countries through financial services and business startup support.

[0085] The assistance platform includes a collection unit that collects health data or educational data. The collection unit collects the health data or educational data. Health data includes, but is not limited to, vital signs, medical history, etc. For example, the collection unit collects vital signs and monitors health status. The collection unit can also collect medical history and provide medical assistance. Education data includes, but is not limited to, learning history, performance data, etc. For example, the collection unit collects learning history and provides educational assistance. The collection unit can also collect performance data and improve educational programs. This enables more comprehensive assistance by collecting health data and educational data.

[0086] The assistance platform includes a monitoring unit that monitors the progress of assistance using sensors or IoT technology. The monitoring unit monitors the progress of assistance using sensors or IoT technology. Examples of sensors include, but are not limited to, temperature sensors and pressure sensors. The monitoring unit collects environmental data using, for example, a temperature sensor and monitors the progress of assistance. The monitoring unit can also monitor the state of infrastructure using a pressure sensor. Examples of IoT technology include, but are not limited to, network protocols, device types, and the like. The monitoring unit collects data in real time using, for example, IoT devices and monitors the progress of assistance. The monitoring unit can also communicate between devices using network protocols and integrate the data. This makes it possible to more accurately monitor the progress of assistance by utilizing sensors and IoT technology.

[0087] The support platform includes a procurement department that raises funds through crowdfunding or from international organizations. The procurement department raises funds through crowdfunding or from international organizations. Crowdfunding includes, but is not limited to, platforms such as Kickstarter and Makuake. The procurement department raises funds through crowdfunding, for example, to carry out support activities. The procurement department can also raise funds from international organizations. International organizations include, but are not limited to, the International Monetary Fund (IMF) and the World Bank. The procurement department can carry out support activities with loans from the IMF, for example. The procurement department can also implement projects with funding from the World Bank. This allows support to be provided without using equity capital by raising funds through crowdfunding or from international organizations.

[0088] The collection unit may collect economic data, social data, environmental data, health data, or education data for each country. The collection unit may, for example, collect economic data for each country. The economic data may include, but is not limited to, GDP and unemployment rate. For example, the collection unit may collect GDP data for each country to understand the economic situation. The collection unit may also collect unemployment rate data to analyze the labor market situation. The collection unit may, for example, collect social data for each country. The social data may include, but is not limited to, demographics and crime rate. For example, the collection unit may collect demographic data for each country to analyze the social structure. The collection unit may also collect crime rate data to understand the public safety situation. The collection unit may, for example, collect environmental data for each country. The environmental data may include, but is not limited to, temperature and precipitation. For example, the collection unit may collect temperature data for each country to analyze the impact of climate change. The collection unit may also collect precipitation data to understand the status of water resources. This will allow us to collect diverse data from each country, allowing us to formulate more accurate support plans.

[0089] The analysis unit can analyze the collected data and propose an appropriate support method. The analysis unit, for example, performs statistical analysis on the collected data. Examples of statistical analysis include, but are not limited to, regression analysis and analysis of variance. The analysis unit can clarify the relationship between data using, for example, regression analysis. The analysis unit can also evaluate the variance of data using analysis of variance. The analysis unit, for example, analyzes the collected data using a machine learning algorithm. Examples of machine learning algorithms include, but are not limited to, clustering and classification. The analysis unit can group data using, for example, clustering. The analysis unit can also categorize data using a classification algorithm. In this way, the collected data can be analyzed to propose an optimal support method.

[0090] The formulation unit can formulate a support plan based on the analysis results. The formulation unit, for example, formulates an educational support plan based on the analysis results. The educational support plan includes, for example, the provision of teaching materials and the implementation of an educational program, but is not limited to these examples. The formulation unit, for example, includes the provision of teaching materials in the support plan. The formulation unit can also include the implementation of an educational program in the support plan. The formulation unit, for example, formulates a medical support plan based on the analysis results. The medical support plan includes, for example, the provision of medicines and the construction of medical facilities, but is not limited to these examples. The formulation unit, for example, includes the provision of medicines in the support plan. The formulation unit can also include the construction of medical facilities in the support plan. In this way, by formulating a support plan based on the analysis results, effective support is possible.

[0091] The monitoring unit monitors the progress of the support plan in real time and can make adjustments as necessary. The monitoring unit, for example, monitors the progress of the support plan in real time. Real-time monitoring includes, for example, second by second or minute by minute, but is not limited to these examples. The monitoring unit monitors the progress of the support plan in second by second, for example. The monitoring unit can also monitor the progress of the support plan in minute by minute. The monitoring unit, for example, monitors the progress of the support plan and makes adjustments as necessary. Adjustments include, for example, changes to the support content and the support schedule, but are not limited to these examples. The monitoring unit, for example, changes the support content. The monitoring unit can also change the support schedule. In this way, by monitoring the progress of the support plan in real time and making adjustments as necessary, the effectiveness of the support can be maximized.

[0092] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user's emotions. The collection unit, for example, estimates the user's emotions. Estimation of the user's emotions includes, but is not limited to, facial expression recognition and voice analysis. The collection unit, for example, estimates the user's emotions using facial expression recognition. The collection unit can also estimate the user's emotions using voice analysis. The collection unit, for example, adjusts the timing of data collection based on the estimated user's emotions. Adjustment of the timing of data collection includes, but is not limited to, time of day, the user's activity status, etc. For example, if the user is feeling stressed, the collection unit reduces the frequency of data collection to reduce the user's burden. Furthermore, if the user is relaxed, the collection unit can increase the frequency of data collection to collect more detailed data. In this way, adjusting the timing of data collection according to the user's emotions reduces the user's burden and enables efficient data collection.

[0093] The collection unit can analyze past data collection histories for each country and select an optimal collection method. The collection unit, for example, analyzes past data collection histories for each country. The past data collection history includes, for example, collection date and time, collection method, etc., but is not limited to these examples. The collection unit, for example, analyzes collection date and time and selects an optimal collection method. The collection unit can also analyze the collection method and identify areas for improvement in the collection method. The collection unit, for example, customizes the collection method based on the past data collection histories for each country. Customization includes, for example, changing the collection means and adjusting the collection frequency, but is not limited to these examples. The collection unit, for example, changes the collection means and selects an optimal collection method. The collection unit can also adjust the collection frequency to perform efficient data collection. In this way, by analyzing past data collection histories, an optimal collection method can be selected and efficient data collection is possible.

[0094] The collection unit may perform filtering based on the current situation and areas of interest of each country when collecting data. For example, the collection unit may consider the current situation of each country when collecting data. Examples of current situations include, but are not limited to, economic situations and social situations. For example, the collection unit may filter the data to be collected based on the economic situation of each country. The collection unit may also filter the data to be collected based on the social situation of each country. For example, the collection unit may consider the areas of interest of each country when collecting data. Examples of areas of interest include, but are not limited to, education and healthcare. For example, the collection unit may filter the data to be collected based on the education field of each country. The collection unit may also filter the data to be collected based on the healthcare field of each country. In this way, by filtering data based on the current situation and areas of interest of each country, highly relevant data can be collected.

[0095] The collection unit can select the optimal collection means according to the input method of each country when collecting data. For example, the collection unit takes into account the input method of each country when collecting data. Input methods include, but are not limited to, voice input, text input, and image input. For example, the collection unit selects the optimal collection means according to the voice input method of each country. The collection unit can also select the optimal collection means according to the text input method of each country. For example, the collection unit selects the optimal collection means according to the image input method of each country. This enables efficient data collection by selecting the optimal collection means according to the input method of each country.

[0096] The collection unit can estimate the user's emotion and determine the priority of data to be collected based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion. Estimation of the user's emotion includes, but is not limited to, facial expression recognition and voice analysis. The collection unit, for example, estimates the user's emotion using facial expression recognition. The collection unit can also estimate the user's emotion using voice analysis. The collection unit, for example, determines the priority of data to be collected based on the estimated user's emotion. Determination of the priority of data includes, but is not limited to, importance and urgency. For example, when the user is feeling stressed, the collection unit prioritizes collecting data with high importance. Furthermore, when the user is relaxed, the collection unit can also prioritize collecting detailed data. In this way, by determining the priority of data to be collected according to the user's emotion, important data can be collected with priority.

[0097] The collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of each country when collecting data. For example, the collection unit considers the geographical location information of each country when collecting data. Geographical location information includes, but is not limited to, GPS data, address information, etc. For example, the collection unit prioritizes collecting highly relevant data based on the GPS data of each country. The collection unit can also determine the priority of data to be collected based on the address information of each country. For example, the collection unit filters the data to be collected by referring to the geographical location information of each country. In this way, highly relevant data can be prioritized by taking into account the geographical location information of each country.

[0098] The collection unit may analyze social media activity of each country during data collection and collect related data. For example, the collection unit may analyze social media activity of each country during data collection. Social media activity may include, but is not limited to, posted content and engagement rate. For example, the collection unit may analyze posted content of each country and collect related data. The collection unit may also analyze engagement rate and determine the priority of data to be collected. For example, the collection unit may filter the data to be collected based on social media activity of each country. This allows for efficient collection of related data by analyzing social media activity of each country.

[0099] The collection unit can customize the collection method by reflecting past feedback from each country when collecting data. For example, the collection unit reflects past feedback from each country when collecting data. Past feedback includes, but is not limited to, user opinions and improvements, for example. The collection unit customizes the collection method based on, for example, user opinions. The collection unit can also change the collection means by reflecting improvements. For example, the collection unit determines the priority of data to be collected based on past feedback from each country. In this way, by reflecting past feedback from each country, the collection method can be optimized and efficient data collection can be achieved.

[0100] The analysis unit can estimate the user's emotion and adjust the presentation method of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion. Estimation of the user's emotion includes, but is not limited to, facial expression recognition and voice analysis. The analysis unit, for example, estimates the user's emotion using facial expression recognition. The analysis unit can also estimate the user's emotion using voice analysis. The analysis unit, for example, adjusts the presentation method of the analysis based on the estimated user's emotion. Adjustment of the presentation method of the analysis includes, but is not limited to, graphical display, text display, etc. For example, when the user is nervous, the analysis unit provides a simple and highly visible analysis result. Furthermore, when the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, when the user is in a hurry, the analysis unit can provide a summary of the analysis result. In this way, by adjusting the presentation method of the analysis according to the user's emotion, it is possible to provide an analysis result that is easy for the user to understand.

[0101] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. The analysis unit, for example, takes into account the importance of the data during analysis. The importance of the data includes, for example, impact and urgency, but is not limited to these examples. The analysis unit, for example, performs a detailed analysis on data with a high impact. The analysis unit can also perform a quick analysis on data with a high urgency. The analysis unit, for example, performs a simplified analysis on data with a low importance. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the data.

[0102] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit takes into account the category of data during analysis. Data categories include, but are not limited to, health data, education data, etc. For example, the analysis unit applies a health analysis algorithm to health data. The analysis unit can also apply an education analysis algorithm to education data. Analysis algorithms include, but are not limited to, regression analysis, clustering, etc. For example, the analysis unit can clarify relationships between data using regression analysis. The analysis unit can also group data using clustering. This improves the accuracy of the analysis by applying an appropriate analysis algorithm depending on the category of data.

[0103] The analysis unit can improve the accuracy of the analysis by referring to past analysis results for each country during analysis. The analysis unit, for example, refers to past analysis results for each country during analysis. Past analysis results include, for example, past success cases and failure cases, but are not limited to these examples. The analysis unit, for example, improves the accuracy of the analysis based on past success cases. The analysis unit can also improve the analysis algorithm by referring to past failure cases. The analysis unit, for example, analyzes past analysis results for each country and improves the accuracy of the analysis. In this way, the accuracy of the analysis can be improved by referring to past analysis results.

[0104] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion. Estimation of the user's emotion includes, but is not limited to, facial expression recognition and voice analysis. The analysis unit, for example, estimates the user's emotion using facial expression recognition. The analysis unit can also estimate the user's emotion using voice analysis. The analysis unit, for example, adjusts the length of the analysis based on the estimated user's emotion. Adjustments to the length of the analysis include, but are not limited to, analysis time and the number of analysis items. For example, the analysis unit can provide a short and concise analysis result when the user is in a hurry. The analysis unit can also provide a detailed analysis result when the user is relaxed. Furthermore, the analysis unit can provide a visually stimulating analysis result when the user is excited. In this way, by adjusting the length of the analysis according to the user's emotion, it is possible to provide an optimal analysis result for the user.

[0105] The analysis unit can determine the priority of analysis based on the time of data submission during analysis. The analysis unit, for example, takes into account the time of data submission during analysis. The time of data submission includes, for example, but is not limited to, the submission deadline and the frequency of submission. For example, the analysis unit prioritizes analysis of data with an earlier submission deadline. The analysis unit can also prioritize analysis of data with a higher frequency of submission. The analysis unit determines the priority of analysis based on, for example, the time of data submission. This enables efficient analysis by determining the priority of analysis based on the time of data submission.

[0106] The analysis unit can adjust the order of analysis based on the relevance of data during analysis. The analysis unit, for example, takes into account the relevance of data during analysis. Data relevance includes, for example, correlation and causal relationship, but is not limited to such examples. The analysis unit, for example, prioritizes analysis of highly correlated data. The analysis unit can also prioritize analysis of data with a strong causal relationship. The analysis unit, for example, adjusts the order of analysis based on the relevance of data. This enables efficient analysis by adjusting the order of analysis based on the relevance of data.

[0107] The analysis unit can adjust the use of technical terms in the analysis according to the expertise level of each country during the analysis. The analysis unit, for example, takes into account the expertise level of each country during the analysis. Expertise levels include, but are not limited to, beginner, intermediate, and expert levels. For example, the analysis unit provides analysis results that use a lot of technical terms to countries with a high level of expertise. The analysis unit can also provide analysis results that are explained in simple language to countries with a low level of expertise. The analysis unit, for example, adjusts the use of technical terms in the analysis according to the expertise level of each country. In this way, by adjusting the use of technical terms in the analysis according to the expertise level of each country, it is possible to provide analysis results that are easy to understand.

[0108] The formulation unit can estimate the user's emotions and adjust the way the support plan is presented based on the estimated user's emotions. The formulation unit, for example, estimates the user's emotions. Estimation of the user's emotions includes, but is not limited to, facial expression recognition and voice analysis. The formulation unit, for example, estimates the user's emotions using facial expression recognition. The formulation unit can also estimate the user's emotions using voice analysis. The formulation unit, for example, adjusts the way the support plan is presented based on the estimated user's emotions. Adjustment of the way the support plan is presented includes, but is not limited to, text display and graphic display. For example, if the user is nervous, the formulation unit can provide a simple, highly visible support plan. Furthermore, if the user is relaxed, the formulation unit can provide a detailed support plan. Furthermore, if the user is in a hurry, the formulation unit can provide a support plan that focuses on the main points. In this way, by adjusting the way the support plan is presented based on the user's emotions, a support plan that is easy for the user to understand can be provided.

[0109] The formulation unit can adjust the level of detail of the support plan based on the importance of the data when formulating the support plan. For example, the formulation unit takes into account the importance of the data when formulating the support plan. The importance of the data includes, for example, impact and urgency, but is not limited to these examples. For example, the formulation unit formulates a detailed support plan based on data with a high impact. The formulation unit can also formulate a quick support plan based on data with a high urgency. For example, the formulation unit formulates a simplified support plan based on data with a low importance. This makes it possible to formulate an efficient support plan by adjusting the level of detail of the plan according to the importance of the data.

[0110] The formulation unit can apply different formulation algorithms depending on the data category when formulating a support plan. For example, the formulation unit takes into account the data category when formulating a support plan. Data categories include, but are not limited to, health data, education data, etc. For example, the formulation unit formulates a health support plan based on health data. The formulation unit can also formulate an education support plan based on education data. Formulation algorithms include, but are not limited to, regression analysis, clustering, etc. For example, the formulation unit formulates a support plan using regression analysis. The formulation unit can also formulate a support plan using clustering. This improves the accuracy of the support plan by applying an appropriate formulation algorithm depending on the data category.

[0111] When formulating a support plan, the formulation unit can improve the accuracy of the plan by referring to past plan results for each country. For example, the formulation unit refers to past plan results for each country when formulating a support plan. Past plan results include, but are not limited to, past success cases and failure cases. For example, the formulation unit improves the accuracy of the support plan based on past success cases. The formulation unit can also improve the formulation algorithm by referring to past failure cases. For example, the formulation unit analyzes past plan results for each country to improve the accuracy of the support plan. In this way, the accuracy of the support plan can be improved by referring to past plan results.

[0112] The formulation unit can estimate the user's emotions and adjust the length of the support plan based on the estimated user's emotions. The formulation unit, for example, estimates the user's emotions. Estimation of the user's emotions includes, but is not limited to, facial expression recognition and voice analysis. The formulation unit, for example, estimates the user's emotions using facial expression recognition. The formulation unit can also estimate the user's emotions using voice analysis. The formulation unit, for example, adjusts the length of the support plan based on the estimated user's emotions. Adjustment of the length of the support plan includes, but is not limited to, the plan duration and the number of plan items. For example, if the user is in a hurry, the formulation unit can provide a short and to-the-point support plan. Furthermore, if the user is relaxed, the formulation unit can provide a detailed support plan. Furthermore, if the user is excited, the formulation unit can provide a support plan with visually stimulating effects. In this way, by adjusting the length of the support plan according to the user's emotions, it is possible to provide an optimal support plan for the user.

[0113] When formulating a support plan, the formulation unit can determine the priority of the plan based on the time of data submission. For example, the formulation unit takes into account the time of data submission when formulating a support plan. The time of data submission includes, for example, a submission deadline, a submission frequency, etc., but is not limited to these examples. For example, the formulation unit preferentially reflects data with an earlier submission deadline in the support plan. The formulation unit can also preferentially reflect data with a higher submission frequency in the support plan. For example, the formulation unit determines the priority of the support plan based on the time of data submission. Thus, by determining the priority of plans based on the time of data submission, efficient support plans can be formulated.

[0114] The formulation unit can adjust the order of the support plans based on the relevance of the data when formulating the support plans. The formulation unit, for example, takes into account the relevance of the data when formulating the support plans. Data relevance includes, for example, correlation and causal relationship, but is not limited to these examples. For example, the formulation unit preferentially reflects highly correlated data in the support plan. The formulation unit can also preferentially reflect data with strong causal relationships in the support plan. For example, the formulation unit adjusts the order of the support plans based on the relevance of the data. As a result, by adjusting the order of the plans based on the relevance of the data, it becomes possible to formulate efficient support plans.

[0115] When formulating a support plan, the formulation unit can adjust the use of technical terminology in the plan according to the expertise level of each country. For example, the formulation unit takes into account the expertise level of each country when formulating the support plan. Expertise levels include, but are not limited to, beginner, intermediate, and expert, for example. For example, the formulation unit provides a support plan that uses a lot of technical terminology to countries with a high level of expertise. The formulation unit can also provide a support plan that is explained in simple language to countries with a low level of expertise. For example, the formulation unit adjusts the use of technical terminology in the support plan according to the expertise level of each country. In this way, by adjusting the use of technical terminology in the plan according to the expertise level of each country, it is possible to provide a support plan that is easy to understand.

[0116] The monitoring unit can estimate the user's emotions and adjust the monitoring display method based on the estimated user's emotions. The monitoring unit, for example, estimates the user's emotions. Estimation of the user's emotions includes, but is not limited to, facial expression recognition and voice analysis. The monitoring unit, for example, estimates the user's emotions using facial expression recognition. The monitoring unit can also estimate the user's emotions using voice analysis. The monitoring unit, for example, adjusts the monitoring display method based on the estimated user's emotions. Adjustment of the monitoring display method includes, but is not limited to, graphical display and text display. For example, when the user is nervous, the monitoring unit provides a simple, highly visible display method. Furthermore, when the user is relaxed, the monitoring unit can provide a display method including detailed information. Furthermore, when the user is in a hurry, the monitoring unit can provide a display method that focuses on the main points. In this way, by adjusting the monitoring display method according to the user's emotions, it is possible to provide monitoring information that is easy for the user to understand.

[0117] The monitoring unit can optimize the current monitoring method by referring to past monitoring data during monitoring. The monitoring unit, for example, refers to past monitoring data during monitoring. Past monitoring data includes, for example, past success cases and failure cases, but is not limited to these examples. The monitoring unit, for example, optimizes the current monitoring method based on past success cases. The monitoring unit can also identify areas for improvement in the monitoring method by referring to past failure cases. The monitoring unit, for example, analyzes past monitoring data and optimizes the current monitoring method. In this way, by referring to past monitoring data, the current monitoring method can be optimized and efficient monitoring becomes possible.

[0118] The monitoring unit can apply different monitoring methods to each data category during monitoring. For example, the monitoring unit takes into account the data category during monitoring. Data categories include, but are not limited to, health data, educational data, etc. For example, the monitoring unit applies a health monitoring method to health data. The monitoring unit can also apply an educational monitoring method to educational data. For example, monitoring methods include, but are not limited to, real-time monitoring, regular monitoring, etc. The monitoring unit can collect and monitor data using, for example, real-time monitoring. The monitoring unit can also collect and monitor data using regular monitoring. This improves the accuracy of monitoring by applying an appropriate monitoring method to each data category.

[0119] The monitoring unit can perform monitoring while taking into account attribute information of each country. For example, the monitoring unit can perform monitoring while taking into account attribute information of each country. Attribute information includes, but is not limited to, demographics and economic indicators. For example, the monitoring unit can perform monitoring based on demographic data of each country. The monitoring unit can also perform monitoring based on economic indicators of each country. For example, the monitoring unit can determine monitoring priorities with reference to attribute information of each country. This allows for more accurate monitoring by taking into account attribute information of each country.

[0120] The monitoring unit can estimate the user's emotions and adjust the importance of monitoring based on the estimated user's emotions. The monitoring unit, for example, estimates the user's emotions. Estimation of the user's emotions includes, but is not limited to, facial expression recognition and voice analysis. The monitoring unit, for example, estimates the user's emotions using facial expression recognition. The monitoring unit can also estimate the user's emotions using voice analysis. The monitoring unit, for example, adjusts the importance of monitoring based on the estimated user's emotions. Adjustment of the importance of monitoring includes, but is not limited to, importance evaluation criteria and a priority determination method. For example, if the user is nervous, the monitoring unit can prioritize displaying monitoring items with high importance. Furthermore, if the user is relaxed, the monitoring unit can display detailed monitoring items. Furthermore, if the user is in a hurry, the monitoring unit can display monitoring items that focus on the main points. In this way, by adjusting the importance of monitoring according to the user's emotions, important information can be provided preferentially.

[0121] The monitoring unit can analyze monitoring changes based on the data submission timing during monitoring. The monitoring unit, for example, takes into account the data submission timing during monitoring. The data submission timing includes, for example, but is not limited to, a submission deadline and a submission frequency. The monitoring unit, for example, prioritizes monitoring data with an early submission deadline. The monitoring unit can also prioritize monitoring data with a high submission frequency. The monitoring unit, for example, analyzes monitoring changes based on the data submission timing. The analysis of monitoring changes includes, for example, but is not limited to, a change detection method and a change evaluation criterion. The monitoring unit, for example, prioritizes analyzing changes in data with an early submission deadline. The monitoring unit can also prioritize analyzing changes in data with a high submission frequency. This enables efficient monitoring by analyzing monitoring changes based on the data submission timing.

[0122] The monitoring unit can analyze the monitoring by referring to related market data during monitoring. The monitoring unit, for example, refers to related market data during monitoring. The related market data includes, for example, market trend data, competitive analysis data, etc., but is not limited to these examples. The monitoring unit analyzes the monitoring based on, for example, the market trend data. The monitoring unit can also improve the monitoring method based on the competitive analysis data. The monitoring unit, for example, analyzes the related market data to improve the accuracy of the monitoring. In this way, the accuracy of the monitoring can be improved by referring to the related market data.

[0123] The monitoring unit can analyze the monitoring taking into account the technological maturity during monitoring. The monitoring unit, for example, takes into account the technological maturity during monitoring. The technological maturity includes, for example, the prevalence of technology and the evolutionary stage of technology, but is not limited to these examples. For example, the monitoring unit performs detailed monitoring when the prevalence of technology is high. Furthermore, the monitoring unit can also perform simplified monitoring when the evolutionary stage of technology is low. The monitoring unit, for example, adjusts the level of detail of monitoring according to the technological maturity. This enables appropriate monitoring by analyzing the monitoring according to the technological maturity.

[0124] The fundraising department can estimate the user's emotions and adjust the fundraising method based on the estimated user's emotions. The fundraising department, for example, estimates the user's emotions. Estimating the user's emotions includes, but is not limited to, facial expression recognition, voice analysis, etc. The fundraising department can estimate the user's emotions using, for example, facial expression recognition. The fundraising department can also estimate the user's emotions using voice analysis. The fundraising department can adjust the fundraising method based on the estimated user's emotions. Adjusting the fundraising method can include, but is not limited to, crowdfunding, bank loans, etc. For example, if the user is nervous, the fundraising department can provide a simple and highly visible fundraising method. Furthermore, if the user is relaxed, the fundraising department can provide a detailed fundraising method. Furthermore, if the user is in a hurry, the fundraising department can provide a fundraising method that focuses on the main points. In this way, by adjusting the fundraising method according to the user's emotions, the optimal fundraising method can be provided for the user.

[0125] When raising funds, the procurement department can refer to past procurement data to select the optimal procurement method. For example, when raising funds, the procurement department refers to past procurement data. Past procurement data includes, for example, past success cases and failure cases, but is not limited to such examples. For example, the procurement department selects the optimal procurement method based on past success cases. The procurement department can also identify areas for improvement in the procurement method by referring to past failure cases. For example, the procurement department analyzes past procurement data to select the optimal procurement method. In this way, by referring to past procurement data, the optimal procurement method can be selected, enabling efficient fund raising.

[0126] The procurement department can customize procurement methods based on the current economic conditions of each country when raising funds. For example, the procurement department takes into account the current economic conditions of each country when raising funds. Current economic conditions include, but are not limited to, GDP, unemployment rate, etc. For example, the procurement department selects the optimal procurement method based on GDP data of each country. The procurement department can also customize procurement methods based on unemployment rate data of each country. For example, the procurement department identifies areas for improvement in procurement methods by referring to the economic conditions of each country. This enables efficient fundraising by customizing procurement methods based on the economic conditions of each country.

[0127] The procurement department can improve the procurement method by reflecting feedback from each country when raising funds. The procurement department, for example, reflects feedback from each country when raising funds. Feedback includes, for example, user opinions and improvements, but is not limited to these examples. The procurement department improves the procurement method, for example, based on user opinions. The procurement department can also change the procurement method by reflecting improvements. The procurement department, for example, determines the priority of procurement methods based on feedback from each country. In this way, by reflecting feedback from each country, the procurement method can be optimized and efficient fundraising can be achieved.

[0128] The procurement department can estimate the user's emotions and determine the priority of fundraising based on the estimated user's emotions. The procurement department, for example, estimates the user's emotions. Estimation of the user's emotions includes, but is not limited to, facial expression recognition and voice analysis. The procurement department, for example, estimates the user's emotions using facial expression recognition. The procurement department can also estimate the user's emotions using voice analysis. The procurement department, for example, determines the priority of fundraising based on the estimated user's emotions. Determination of the priority of fundraising includes, but is not limited to, importance and urgency. For example, if the user is nervous, the procurement department can prioritize fundraising that is more important. Furthermore, if the user is relaxed, the procurement department can perform detailed fundraising. Furthermore, if the user is in a hurry, the procurement department can perform fundraising that focuses on the main points. In this way, by determining the priority of fundraising based on the user's emotions, important fundraising can be prioritized.

[0129] The procurement department can select the optimal procurement method by taking into account the geographical location information of each country when raising funds. For example, the procurement department considers the geographical location information of each country when raising funds. Geographical location information includes, but is not limited to, GPS data, address information, etc. For example, the procurement department selects the optimal procurement method based on the GPS data of each country. The procurement department can also customize the procurement method based on the address information of each country. For example, the procurement department identifies areas for improvement in the procurement method by referring to the geographical location information of each country. In this way, by taking into account the geographical location information of each country, the optimal procurement method can be selected, enabling efficient fundraising.

[0130] The procurement department can analyze social media activity in each country and propose procurement methods when raising funds. For example, the procurement department analyzes social media activity in each country when raising funds. Social media activity includes, but is not limited to, the content of posts and engagement rates. For example, the procurement department analyzes the content of posts in each country and proposes the optimal procurement method. The procurement department can also analyze the engagement rate and determine the priority of procurement methods. For example, the procurement department customizes procurement methods based on the social media activity in each country. In this way, by analyzing the social media activity in each country, the optimal procurement method can be proposed, enabling efficient fundraising.

[0131] The procurement department can customize the procurement method by reflecting past feedback from each country when raising funds. The procurement department, for example, reflects past feedback from each country when raising funds. Past feedback includes, for example, user opinions and areas for improvement, but is not limited to these examples. The procurement department customizes the procurement method based on, for example, user opinions. The procurement department can also change the procurement method by reflecting areas for improvement. The procurement department, for example, determines the priority of procurement methods based on past feedback from each country. In this way, by reflecting past feedback from each country, the procurement method can be optimized and efficient fundraising can be achieved.

[0132] The support unit can estimate the user's emotion and adjust the support method based on the estimated user's emotion. The support unit, for example, estimates the user's emotion. Estimation of the user's emotion includes, but is not limited to, facial expression recognition and voice analysis. The support unit, for example, estimates the user's emotion using facial expression recognition. The support unit can also estimate the user's emotion using voice analysis. The support unit, for example, adjusts the support method based on the estimated user's emotion. Adjustment of the support method includes, but is not limited to, educational support, medical support, and the like. For example, when the user is nervous, the support unit provides a simple and highly visible support method. Furthermore, when the user is relaxed, the support unit can provide a detailed support method. Furthermore, when the user is in a hurry, the support unit can provide a support method that focuses on the main points. In this way, by adjusting the support method according to the user's emotion, it is possible to provide the optimal support method for the user.

[0133] The support department can analyze the past support history of each country when providing support and select the optimal support method. The support department, for example, analyzes the past support history of each country when providing support. The past support history includes, for example, past success cases and failure cases, but is not limited to these examples. The support department, for example, selects the optimal support method based on past success cases. The support department can also identify areas for improvement in the support method by referring to past failure cases. The support department, for example, analyzes the past support history and selects the optimal support method. In this way, by analyzing the past support history of each country, the optimal support method can be selected and efficient support can be provided.

[0134] The support department can customize support measures based on the current situation of each country when providing support. For example, the support department takes into account the current situation of each country when providing support. Current situations include, but are not limited to, economic situations, social situations, etc. For example, the support department selects the optimal support measures based on the economic situation of each country. The support department can also customize support measures based on the social situation of each country. For example, the support department identifies areas for improvement in the support measures by referring to the current situation of each country. This enables efficient support by customizing support measures based on the current situation of each country.

[0135] The support unit can improve the support method by reflecting feedback from each country when providing support. The support unit, for example, reflects feedback from each country when providing support. Feedback includes, for example, user opinions and improvements, but is not limited to these examples. The support unit improves the support method, for example, based on user opinions. The support unit can also change the support means by reflecting improvements. For example, the support unit determines the priority of support means based on feedback from each country. In this way, by reflecting feedback from each country, the support method can be optimized and efficient support can be provided.

[0136] The support unit can estimate the user's emotion and determine the priority of support based on the estimated user's emotion. The support unit, for example, estimates the user's emotion. Estimation of the user's emotion includes, but is not limited to, facial expression recognition and voice analysis. The support unit, for example, estimates the user's emotion using facial expression recognition. The support unit can also estimate the user's emotion using voice analysis. The support unit, for example, determines the priority of support based on the estimated user's emotion. Determination of the priority of support includes, but is not limited to, importance and urgency. For example, if the user is nervous, the support unit can prioritize support with high importance. Furthermore, if the user is relaxed, the support unit can provide detailed support. Furthermore, if the user is in a hurry, the support unit can provide support that focuses on the main points. In this way, by determining the priority of support according to the user's emotion, important support can be prioritized.

[0137] The support unit can select the optimal support method by taking into consideration the geographical location information of each country when providing support. For example, the support unit considers the geographical location information of each country when providing support. Geographical location information includes, but is not limited to, GPS data, address information, etc. For example, the support unit selects the optimal support method based on the GPS data of each country. The support unit can also customize the support method based on the address information of each country. For example, the support unit identifies areas for improvement in the support method by referring to the geographical location information of each country. This allows the optimal support method to be selected by taking into consideration the geographical location information of each country, enabling efficient support.

[0138] The support department can analyze the social media activity of each country and propose support measures when providing support. For example, the support department analyzes the social media activity of each country when providing support. Social media activity includes, but is not limited to, for example, the content of posts and engagement rates. For example, the support department analyzes the content of posts of each country and proposes the optimal support measures. The support department can also analyze the engagement rates and determine the priorities of support measures. For example, the support department customizes support measures based on the social media activity of each country. In this way, by analyzing the social media activity of each country, optimal support measures can be proposed, enabling efficient support.

[0139] The support unit can customize the support method by reflecting past feedback from each country when providing support. The support unit, for example, reflects past feedback from each country when providing support. Past feedback includes, for example, user opinions, areas for improvement, etc., but is not limited to these examples. The support unit customizes the support method based on, for example, user opinions. The support unit can also change the support means by reflecting areas for improvement. The support unit, for example, determines the priority of support means based on past feedback from each country. In this way, by reflecting past feedback from each country, the support method can be optimized and efficient support can be provided. === Hard Collateral 1-1 === For example, each of the multiple elements including the collection unit, analysis unit, formulation unit, monitoring unit, and procurement unit is realized by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects data using the camera 42 and microphone 38B of the smart device 14 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The formulation unit is realized by the specific processing unit 290 of the data processing device 12 and formulates a support plan based on the analysis results. The monitoring unit monitors the progress of the support using sensors and IoT technology of the smart device 14 and transmits the progress to the data processing device 12 via the control unit 46A. The procurement unit is realized by the specific processing unit 290 of the data processing device 12 and raises funds through crowdfunding or from international organizations. === Hard Collateral 1-2 === For example, each of the multiple elements including the collection unit, analysis unit, formulation unit, monitoring unit, and procurement unit is realized by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects data using the camera 42 and microphone 238 of the smart glasses 214 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The formulation unit is realized by the specific processing unit 290 of the data processing device 12 and formulates a support plan based on the analysis results. The monitoring unit monitors the progress of the support using sensors and IoT technology of the smart glasses 214 and transmits the progress to the data processing device 12 via the control unit 46A. The procurement unit is realized by the specific processing unit 290 of the data processing device 12 and raises funds through crowdfunding or from international organizations. === Hard Collateral 1-3 === For example, each of multiple elements including a collection unit, an analysis unit, a formulation unit, a monitoring unit, and a procurement unit is realized by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects data using the camera 42 and the microphone 238 of the headset type terminal 314 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The formulation unit is realized by the specific processing unit 290 of the data processing device 12 and formulates a support plan based on the analysis results. The monitoring unit monitors the progress of the support using sensors and IoT technology of the headset type terminal 314 and transmits the progress to the data processing device 12 via the control unit 46A. The procurement unit is realized by the specific processing unit 290 of the data processing device 12 and raises funds through crowdfunding or from international organizations. === Hard Collateral 1-4 === For example, each of multiple elements including a collection unit, an analysis unit, a formulation unit, a monitoring unit, and a procurement unit is realized by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects data using the camera 42 and microphone 238 of the robot 414 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The formulation unit is realized by the specific processing unit 290 of the data processing device 12 and formulates a support plan based on the analysis results. The monitoring unit monitors the progress of the support using sensors of the robot 414 and IoT technology and transmits the progress to the data processing device 12 via the control unit 46A. The procurement unit is realized by the specific processing unit 290 of the data processing device 12 and raises funds through crowdfunding or from international organizations.

[0140] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0141] The support platform can also be equipped with a communications department, which provides functions to facilitate communication with residents and stakeholders in the recipient country. For example, the communications department may have a translation function for local languages, allowing information to be conveyed across language barriers. The communications department may also provide video conferencing and chat functions, enabling real-time exchange of opinions and feedback. Furthermore, the communications department may provide communication guidelines that take local culture and customs into consideration, avoiding misunderstandings and friction. This allows the support platform to build trust with local residents and stakeholders and provide effective support.

[0142] The support platform can further include a motivation section. The motivation section provides functions to increase the motivation of residents and related parties in the support country. For example, the motivation section can provide success stories and encouraging messages to motivate residents. The motivation section can also provide tools for goal setting and progress management, supporting residents in working toward their goals while checking their own progress. Furthermore, the motivation section can introduce reward systems and incentive programs to evaluate residents' efforts and encourage further efforts. In this way, the support platform can increase residents' motivation and support sustainable growth.

[0143] The support platform may further include a feedback unit. The feedback unit provides a function for collecting feedback from residents and related parties in the support recipient country and using the feedback to improve the support plan. For example, the feedback unit may collect residents' opinions and requests through questionnaires and surveys. The feedback unit may also provide an online platform where residents can freely post their opinions and receive feedback in real time. The feedback unit may also analyze the collected feedback and identify areas for improvement in the support plan. This enables the support platform to provide flexible support that meets the needs of residents and achieve effective support.

[0144] The support platform can also have a training department. The training department provides functions for providing necessary skills and knowledge to residents and stakeholders in the supported countries. For example, the training department can provide educational and technical training to residents through online courses and workshops. The training department can also provide customized training programs tailored to local needs and support residents in dealing with actual challenges. Furthermore, the training department can evaluate the effectiveness of the training and improve the programs as needed. This enables the support platform to improve residents' skills and knowledge and support sustainable growth.

[0145] The support platform can also have a networking section, which provides functions that enable residents and stakeholders in the recipient countries to connect and cooperate with each other. For example, the networking section can provide online communities and forums where residents can share information and exchange opinions. The networking section can also host events and workshops, providing opportunities for residents to interact directly. Furthermore, the networking section can provide a platform for residents and stakeholders to work together on projects and promote collaboration. In this way, the support platform can strengthen collaboration between residents and stakeholders and achieve effective support.

[0146] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the frequency of data collection can be reduced to reduce the burden on the user. Also, if the user is relaxed, the frequency of data collection can be increased to collect more detailed data. In this way, adjusting the timing of data collection according to the user's emotions reduces the burden on the user and enables efficient data collection.

[0147] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, if the user is nervous, it can provide simple, highly visible analysis results. If the user is relaxed, it can also provide detailed analysis results. Furthermore, if the user is in a hurry, it can also provide analysis results that focus on the main points. In this way, by adjusting the way the analysis is presented according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand.

[0148] The formulation unit can estimate the user's emotions and adjust the way the support plan is expressed based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible support plan can be provided. If the user is relaxed, a detailed support plan can be provided. Furthermore, if the user is in a hurry, a support plan that focuses on the main points can be provided. In this way, by adjusting the way the support plan is expressed according to the user's emotions, a support plan that is easy for the user to understand can be provided.

[0149] The monitoring unit can estimate the user's emotions and adjust the monitoring display method based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, by adjusting the monitoring display method according to the user's emotions, it is possible to provide monitoring information that is easy for the user to understand.

[0150] The procurement department can estimate the user's emotions and adjust the fundraising method based on the estimated user's emotions. For example, if the user is nervous, a simple and highly visible fundraising method can be provided. If the user is relaxed, a detailed fundraising method can be provided. Furthermore, if the user is in a hurry, a fundraising method that focuses on the main points can be provided. In this way, by adjusting the fundraising method according to the user's emotions, the optimal fundraising method can be provided for the user.

[0151] The processing flow of the second embodiment will be briefly explained below.

[0152] Step 1: The collection department collects data. The data includes numerical data, text data, image data, etc. The collection department collects economic data, social data, environmental data, health data, and education data from each country. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using statistical analysis and machine learning algorithms. The analysis unit proposes the optimal support method based on the collected data. Step 3: The formulation department formulates a support plan based on the analysis results obtained by the analysis department. Support plans include educational support plans, medical support plans, etc. The formulation department formulates a specific support plan based on the analysis results. Step 4: The Monitoring Department monitors the progress of the support plan formulated by the Formulating Department in real time. Real-time monitoring is done in seconds or minutes. The Monitoring Department monitors the progress of the support plan and makes adjustments as necessary. Step 5: The Procurement Department raises funds. Funding can be through crowdfunding or bank loans. The Procurement Department raises funds through crowdfunding or from international organizations.

[0153] 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.

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

[0155] 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.

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

[0157] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0158] 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.

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

[0160] The 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.

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

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

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

[0164] Fig. 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.

[0165] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0167] In the 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.

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

[0169] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0171] The data processing system 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.

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

[0173] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0174] 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.

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

[0176] The 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.

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

[0178] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

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

[0180] 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.

[0181] 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.

[0182] 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.

[0183] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0189] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0190] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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).

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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).

[0210] 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.

[0211] 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."

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] [Explanation of symbols]

[0225] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects data; an analysis unit that analyzes the data collected by the collection unit; a formulation unit that formulates a support plan based on the analysis results obtained by the analysis unit; a monitoring unit that monitors in real time the progress of the support plan formulated by the formulation unit; A procurement department that raises funds. A system characterized by:

2. Equipped with a support department that provides financial services or entrepreneurial support The system of claim 1 .

3. A collection unit for collecting health data or education data is provided. The system of claim 1 .

4. Equipped with a monitoring unit that uses sensors or IoT technology to monitor the progress of support The system of claim 1 .

5. Have a procurement department that handles fundraising through crowdfunding or international organizations The system of claim 1 .

6. The collecting unit Collecting economic, social, environmental, health or education data for each country The system of claim 1 .

7. The analysis unit Analyze the collected data and propose appropriate support methods The system of claim 1 .

8. The formulation unit Formulate a support plan based on the analysis results The system of claim 1 .

9. The monitoring unit Monitor progress of support plans in real time and make adjustments as needed The system of claim 1 .

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