system

Abstract

The system according to this embodiment aims to deeply analyze the characteristics and challenges of sparsely populated areas and propose optimal revitalization strategies. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a strategy proposal unit, and a matching unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The strategy proposal unit proposes an optimal revitalization strategy based on the analysis results obtained by the analysis unit. The matching unit matches idle assets in a region with personnel and companies that can utilize them.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the characteristics and problems of depopulated areas have not been deeply analyzed, and an optimal activation strategy has not been sufficiently proposed, leaving room for improvement.

[0005] The system according to the embodiment aims to deeply analyze the characteristics and problems of depopulated areas and propose an optimal activation strategy.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a strategy proposal unit, and a matching unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The strategy proposal unit proposes an optimal revitalization strategy based on the analysis results obtained by the analysis unit. The matching unit matches idle assets in a region with individuals and companies that can utilize them. [Effects of the Invention]

[0007] The system according to this embodiment can deeply analyze the characteristics and challenges of sparsely populated areas and propose optimal revitalization strategies. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The AI-based depopulated area revitalization assistant system according to an embodiment of the present invention is an AI-based platform that supports local governments and communities struggling with depopulation. This system deeply analyzes the characteristics and challenges of a region and proposes the optimal revitalization strategy. Furthermore, it connects local residents, government, and businesses, and promotes collaboration to realize sustainable community development. For example, the AI-based depopulated area revitalization assistant system analyzes big data such as population dynamics, industrial structure, and natural resources, and the AI ​​proposes the most suitable revitalization strategy for the region. For example, the AI ​​matches idle assets in the region (vacant houses, abandoned farmland, etc.) with people and companies that can utilize them. For example, it plans tourism strategies that effectively promote the region's attractions and disseminates information tailored to the target audience. For example, it matches prospective migrants with the needs of the region and supports smooth migration. For example, the AI ​​proposes the creation of new industries utilizing local resources and supports the formulation of business plans. Through this, the AI-based depopulated area revitalization assistant system aims for the sustainable development of depopulated areas and the realization of prosperous local communities. As a result, the AI-based depopulated area revitalization assistant system can deeply analyze the characteristics and challenges of a region and propose the optimal revitalization strategy.

[0029] The AI-powered revitalization assistant system for sparsely populated areas according to this embodiment comprises a data collection unit, an analysis unit, a strategy proposal unit, and a matching unit. The data collection unit collects data. The data collection unit can collect big data such as regional demographics, industrial structure, and natural resources. The data collection unit can also collect sensor data and social media data. The data collection unit can also collect survey data of local residents. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can analyze the data using AI to clarify the characteristics and challenges of the region. The analysis unit can cluster the data using machine learning algorithms and classify the characteristics of the region. The analysis unit can also analyze the voices of local residents using natural language processing technology. The strategy proposal unit proposes an optimal revitalization strategy based on the analysis results obtained by the analysis unit. The strategy proposal unit can formulate a revitalization strategy tailored to the characteristics of the region. The strategy proposal unit can propose solutions to regional challenges. The strategy proposal unit can propose the creation of new industries utilizing local resources. The matching unit matches underutilized local assets with individuals and companies that can utilize them. For example, the matching unit can match vacant houses with people who wish to relocate. For example, the matching unit can match abandoned farmland with agricultural workers. For example, the matching unit can also match local companies with external personnel. In this way, the AI-powered depopulated area revitalization assistant system according to the embodiment can support regional revitalization through data collection, analysis, strategy proposals, and matching.

[0030] The data collection unit collects data. For example, the data collection unit can collect big data such as regional demographics, industrial structure, and natural resources. Specifically, as demographic data, it collects statistics on the age distribution of the population, birth rates, death rates, and migration / exit. As industrial structure data, it collects information on major industries in the region, employment status, number of companies, and sales by industry. As natural resource data, it collects information on land use, forest resources, water resources, and mineral resources in the region. This data can be obtained from publicly available databases of government agencies and local authorities, statistical data, and satellite imagery using remote sensing technology. The data collection unit can also collect sensor data and social media data. As sensor data, it collects meteorological data such as temperature, humidity, atmospheric pressure, wind speed, and precipitation from environmental sensors installed in the region, and vehicle traffic volume and traffic congestion information from traffic sensors. As social media data, it collects SNS posts, blog articles, and comments on online forums from local residents to understand local concerns and problems. The data collection unit can also collect survey data from local residents. The survey data will collect information on residents' satisfaction with their lives, their perception of local issues, their desired services and facilities, and their willingness to participate in local events. This will allow the data collection department to gather a wide range of information from diverse data sources and gain a multifaceted understanding of the current situation in the region. Furthermore, the data collection department will centrally manage this data and be able to link it with other systems and departments as needed. For example, the collected data will be stored on a cloud server and made accessible to the analysis department and the strategy proposal department. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions will be possible. As a result, the data collection department can collect data efficiently and effectively and improve the overall performance of the system.

[0031] The Analysis Department analyzes data collected by the Data Collection Department. For example, the Analysis Department uses AI to analyze data and identify regional characteristics and challenges. Specifically, it can use machine learning algorithms to cluster data and classify regional characteristics. For instance, based on regional demographic data, it can cluster the population distribution by age group to identify areas with aging populations or areas with a large young population. It can also analyze the distribution and growth potential of major industries based on industrial structure data to identify the region's economic strengths and weaknesses. Furthermore, it can analyze natural resource data to evaluate the utilization status and potential use of regional resources. The Analysis Department can also use natural language processing technology to analyze the voices of local residents. Specifically, it can perform text mining on social media data and survey data to extract the concerns, complaints, and requests of local residents. This provides basic data for understanding regional challenges and needs and formulating appropriate countermeasures. In addition, the Analysis Department can use historical data and statistical information to predict long-term trends and risks. For example, based on historical demographic data, it can predict future population trends and formulate countermeasures that take into account future challenges in the region. Furthermore, by using anomaly detection algorithms, it is possible to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The Strategy Proposal Department proposes optimal revitalization strategies based on the analysis results obtained by the Analysis Department. For example, the Strategy Proposal Department formulates revitalization strategies tailored to the characteristics of each region. Specifically, in regions with an aging population, it proposes the enhancement of welfare services and medical facilities for the elderly, and in regions with a large young population, it proposes the expansion of educational facilities and employment opportunities. Furthermore, based on the analysis results of the industrial structure, it can propose the creation of new industries that leverage the region's strengths or the strengthening of existing industries. For example, in regions where agriculture is the main industry, it promotes the branding of agricultural products and the development of sixth-sector industrialization, and in regions with abundant tourism resources, it proposes the promotion of the tourism industry and the development of infrastructure. The Strategy Proposal Department can also propose solutions to regional challenges. Specifically, in regions with poor transportation access, it proposes the development of public transportation and the introduction of community buses, and in regions with a shortage of medical facilities, it proposes the introduction of telemedicine systems. It is also important to propose measures that reflect the voices of local residents, and based on survey data and social media data, it can propose the development of services and facilities that meet the needs of local residents. In addition, the Strategy Proposal Department can also propose the creation of new industries that utilize local resources. For example, we propose the introduction of renewable energy utilizing local natural resources and the development of tourism resources that leverage local traditional culture. This allows the Strategic Proposal Department to propose specific revitalization strategies tailored to the characteristics and challenges of each region, thereby supporting the sustainable development of the area.

[0033] The Matching Department matches underutilized local assets with individuals and companies that can utilize them. Specifically, it matches vacant houses with people who wish to relocate. For example, it creates a database of vacant house information in the area and introduces properties that meet the needs of those who wish to relocate. It also provides those who wish to relocate with information on local living conditions and support systems to support a smooth relocation. The Matching Department can also match abandoned farmland with agricultural workers. Specifically, it collects information on abandoned farmland and provides it to agricultural workers and agricultural corporations. Furthermore, it provides agricultural workers with agricultural technology and management know-how, as well as support for fundraising, to promote the effective use of abandoned farmland. The Matching Department can also match local companies with external personnel. Specifically, it collects job information from local companies and provides it to external personnel. Furthermore, it provides external personnel with information on local corporate culture and living environment to support their settlement in the area. In this way, the Matching Department can effectively match underutilized local assets with individuals and companies that can utilize them, thereby supporting regional revitalization. In addition, the Matching Department can provide feedback on the matching results to improve the matching process. For example, the matching department can analyze successful and unsuccessful matching cases to improve the accuracy of its matching algorithms. Furthermore, it can provide follow-up support after matching to help the matched individuals and companies settle into the region. In this way, the matching department can promote the effective utilization of underutilized regional assets and the retention of talent, thereby supporting the sustainable development of the region.

[0034] The Promotion Department conducts tourism promotion. For example, the Promotion Department develops strategies to effectively promote local tourism resources. For example, the Promotion Department can disseminate information tailored to specific target audiences. For example, the Promotion Department can use social media to promote the region's attractions. For example, the Promotion Department can plan tourism events to showcase local tourism resources. For example, the Promotion Department can create tourism brochures and websites to provide tourism information. In this way, the Promotion Department can effectively communicate the region's attractions through tourism promotion.

[0035] The Relocation Support Department matches prospective migrants with the needs of the region. For example, the Relocation Support Department collects profiles of prospective migrants and proposes relocation destinations that meet the needs of the region. For example, the Relocation Support Department can match prospective migrants with local businesses. For example, the Relocation Support Department can match prospective migrants with local housing. For example, the Relocation Support Department can match prospective migrants with local communities. For example, the Relocation Support Department can provide prospective migrants with information about local living. In this way, the Relocation Support Department can support a smooth relocation by matching prospective migrants with the needs of the region.

[0036] The Industrial Creation Department proposes the creation of new industries utilizing local resources. For example, the Industrial Creation Department can propose new business models utilizing local specialty products. For example, the Industrial Creation Department can propose ecotourism utilizing local natural resources. For example, the Industrial Creation Department can propose the development of new products utilizing local traditional crafts. For example, the Industrial Creation Department can propose the development of processed foods utilizing local agricultural products. For example, the Industrial Creation Department can propose tourism businesses utilizing local tourism resources. In this way, the Industrial Creation Department can revitalize the local economy by proposing the creation of new industries utilizing local resources.

[0037] The Collaboration Promotion Department promotes collaboration among residents, government, and businesses. For example, the Collaboration Promotion Department can hold workshops aimed at solving local issues. For example, the Collaboration Promotion Department can plan joint local projects and promote cooperation among residents, government, and businesses. For example, the Collaboration Promotion Department can provide a local information sharing platform to promote collaboration. For example, the Collaboration Promotion Department can share local success stories and allow people to experience the effects of collaboration. For example, the Collaboration Promotion Department can promote interaction among residents, government, and businesses through local events. In this way, the Collaboration Promotion Department can realize sustainable community development by promoting collaboration among residents, government, and businesses.

[0038] The Success Stories Database Department creates a database of success stories and shares the knowledge gained. For example, the Success Stories Database Department collects regional success stories and registers them in its database. For example, the Success Stories Database Department can provide detailed information on success stories and promote their use in other regions. For example, the Success Stories Database Department can analyze success stories and identify the factors contributing to their success. For example, the Success Stories Database Department can publish the database of success stories online for wider sharing. For example, the Success Stories Database Department can use the database of success stories to formulate regional revitalization strategies. In this way, the Success Stories Database Department can create a database of success stories and share the knowledge gained, which can be used as a reference for regional revitalization.

[0039] The natural language processing unit performs natural language processing. For example, the natural language processing unit can collect the voices of local residents and analyze them as text data. For example, the natural language processing unit can perform morphological analysis to divide the text data into individual words. For example, the natural language processing unit can perform grammatical analysis to analyze the sentence structure of the text data. For example, the natural language processing unit can perform semantic analysis to understand the meaning of the text data. For example, the natural language processing unit can perform sentiment analysis to estimate the emotions of local residents from the text data. In this way, the natural language processing unit can analyze local issues and the voices of residents by performing natural language processing.

[0040] The image recognition unit performs image recognition. For example, the image recognition unit collects and analyzes images of local resources and tourist spots. For example, the image recognition unit can detect specific objects in an image using object detection technology. For example, the image recognition unit can identify people in an image using face recognition technology. For example, the image recognition unit can understand scenes in an image using scene analysis technology. For example, the image recognition unit can extract image features and classify the content of the image. As a result, the image recognition unit can perform image recognition to evaluate the value of local resources and discover tourist spots.

[0041] The Predictive Model Unit uses predictive models. For example, the Predictive Model Unit constructs a model to predict regional population dynamics. For example, the Predictive Model Unit can predict population growth and decline using regression analysis. For example, the Predictive Model Unit can also predict population fluctuation patterns using time series analysis. For example, the Predictive Model Unit can improve the accuracy of population dynamics predictions using machine learning models. For example, the Predictive Model Unit can construct a model to predict economic effects. For example, the Predictive Model Unit can predict economic effects using regional economic activity data. For example, the Predictive Model Unit can predict the number of tourists using regional tourism data. In this way, the Predictive Model Unit can predict population dynamics and economic effects by using predictive models.

[0042] The Recommendation Department makes recommendations. For example, the Recommendation Department can suggest the most suitable relocation destination to someone who wants to move. For example, the Recommendation Department can analyze the profile of someone who wants to move and suggest a relocation destination that meets the needs of the region. For example, the Recommendation Department can suggest the most suitable personnel to a company. For example, the Recommendation Department can suggest companies that can utilize local resources. For example, the Recommendation Department can suggest the most suitable tourist spots to tourists in the region. In this way, the Recommendation Department can make matching suggestions to relocators and companies by making recommendations.

[0043] The text generation unit generates text. For example, the text generation unit can generate tourism promotional text. For example, the text generation unit can generate text introducing local tourism resources. For example, the text generation unit can also generate text introducing local specialty products. For example, the text generation unit can generate text introducing local event information. For example, the text generation unit can generate text introducing local history and culture. For example, the text generation unit can generate text introducing local relocation support information. In this way, the text generation unit can create tourism promotional text and local introduction content by generating text.

[0044] The data collection unit can analyze the region's past data collection history and select the optimal collection method. For example, the unit can identify the method that yielded the highest response rate from past data collection history and reuse that method. For example, the unit can analyze past data collection history to confirm that the quality of data collected during a particular season or event was high, and then collect data again at that time. For example, the unit can select an effective collection method for a specific age group or occupation based on past data collection history. In this way, the optimal collection method can be selected by analyzing past data collection history.

[0045] The data collection unit can filter data based on regional characteristics and challenges. For example, based on regional characteristics, the unit can prioritize collecting agriculture-related data in areas where agriculture is prevalent. For example, based on regional challenges, the unit can focus on collecting data on people wishing to relocate to areas experiencing serious population decline. For example, depending on regional characteristics and challenges, the unit can collect tourist behavior data in tourist areas to aid in the development of tourism strategies. In this way, more useful data can be collected by filtering data based on regional characteristics and challenges.

[0046] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of the region during data collection. For example, the data collection unit can prioritize the collection of traffic volume data in a specific area based on geographical location information. For example, the data collection unit can prioritize the collection of occupancy rate data for accommodations around tourist destinations by considering geographical location information. For example, the data collection unit can also prioritize the collection of crop production data in agricultural areas based on geographical location information. In this way, by collecting data while considering the geographical location information of the region, it is possible to prioritize the collection of more relevant data.

[0047] The data collection unit can analyze local social media activity and collect relevant data during data collection. For example, the data collection unit can analyze the response to local events on social media and collect data related to those events. For example, the data collection unit can analyze the opinions and requests of local residents on social media and collect data related to local issues. For example, the data collection unit can analyze the ratings of tourist destinations on social media and collect data useful for tourism strategies. In this way, relevant data can be collected by analyzing local social media activity.

[0048] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on high-importance data, and a simplified analysis on low-importance data. The analysis unit can also prioritize analyses based on the importance of the data. This allows for efficient data analysis by adjusting the level of detail based on the importance of the data.

[0049] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, it can apply a population forecasting algorithm to demographic data. For example, it can apply an industry analysis algorithm to industrial structure data. For example, it can apply a resource assessment algorithm to natural resource data. By applying different analysis algorithms depending on the data category, more accurate analysis becomes possible.

[0050] The Strategy Proposal Department can adjust the level of detail in its proposals based on the importance of the regional issues. For example, it can provide detailed strategic proposals for high-priority issues and simplified proposals for low-priority issues. The department can also prioritize strategic proposals based on the importance of the issues. This allows for more efficient strategic proposals by adjusting the level of detail based on the importance of the regional issues.

[0051] The Strategy Proposal Department can apply different proposal algorithms depending on the characteristics of the region when proposing strategies. For example, in a region where agriculture is prevalent, the Strategy Proposal Department can apply an agriculture-related strategy proposal algorithm. For example, in a tourist area, the Strategy Proposal Department can apply a tourism-related strategy proposal algorithm. For example, in an industrial area, the Strategy Proposal Department can apply an industrial strategy proposal algorithm. By applying different proposal algorithms according to the characteristics of the region, it becomes possible to make more accurate strategy proposals.

[0052] The Strategy Proposal Department can prioritize proposals based on the timing of regional data collection when proposing strategies. For example, the Strategy Proposal Department will prioritize strategic proposals based on the latest data. For example, the Strategy Proposal Department can make strategic proposals based on the latest data while referring to past data. For example, the Strategy Proposal Department can also prioritize strategic proposals according to the timing of data collection. This makes it possible to make strategic proposals based on the latest information by prioritizing proposals based on the timing of regional data collection.

[0053] The Strategy Proposal Department can adjust the order of proposals based on relevant regional data when proposing strategies. For example, the Department can prioritize strategic proposals based on highly relevant data. For example, the Department can postpone strategic proposals based on less relevant data. The Department can also adjust the order of strategic proposals according to the relevance of the data. This allows for more effective strategic proposals by adjusting the order of proposals based on relevant regional data.

[0054] The matching unit can improve the accuracy of matching by considering the interrelationships between local resources and personnel during the matching process. For example, the matching unit can analyze the skill sets of local resources and personnel to perform optimal matching. For example, the matching unit can improve the accuracy of matching by referring to the past cooperation history of local resources and personnel. For example, the matching unit can also consider the interrelationships between local resources and personnel to perform matching that can build long-term cooperative relationships. In this way, considering the interrelationships between local resources and personnel enables more accurate matching.

[0055] The matching unit can perform matching while considering regional characteristics. For example, based on regional characteristics, the matching unit can match agricultural personnel and resources in areas where agriculture is thriving. For example, considering regional characteristics, the matching unit can match tourism-related personnel and resources in tourist areas. For example, depending on regional characteristics, the matching unit can match industrial personnel and resources in industrial areas. This allows for more appropriate matching by considering regional characteristics.

[0056] The matching unit can perform matching while considering the geographical distribution of the region. For example, the matching unit can prioritize matching resources and personnel that are geographically close. For example, the matching unit can consider geographical distribution and perform matching in areas with good transportation access. For example, the matching unit can also prioritize matching within a specific area based on geographical distribution. This makes it possible to perform more appropriate matching by considering the geographical distribution of the region.

[0057] The matching unit can improve the accuracy of matching by referring to relevant local literature during the matching process. For example, the matching unit can refer to relevant local literature and perform matching based on past success stories. For example, the matching unit can analyze relevant local literature and select the optimal matching method. For example, the matching unit can improve the accuracy of matching by referring to relevant local literature. As a result, more accurate matching becomes possible by referring to relevant local literature.

[0058] The promotion department can adjust the level of detail in promotions based on the importance of the region's attractions. For example, the promotion department can conduct detailed promotions for highly important regional attractions, and simplified promotions for less important ones. The promotion department can also prioritize promotions based on the importance of the region's attractions. This allows for more efficient promotions by adjusting the level of detail based on the importance of the region's attractions.

[0059] The Promotion Department can apply different promotional methods depending on the characteristics of the region. For example, in areas where agriculture is thriving, the Promotion Department can apply agriculture-related promotional methods. In tourist areas, for example, the Promotion Department can apply tourism-related promotional methods. In industrial areas, for example, the Promotion Department can apply industry-related promotional methods. By applying different promotional methods according to the characteristics of the region, more effective promotion becomes possible.

[0060] The promotion department can prioritize promotions based on the timing of regional data collection. For example, the promotion department can prioritize promotions based on the latest data. For example, the promotion department can conduct promotions based on the latest data while referring to past data. For example, the promotion department can also prioritize promotions according to the timing of data collection. This makes it possible to conduct promotions based on the latest information by prioritizing promotions based on the timing of regional data collection.

[0061] The promotions department can adjust the order of promotions based on relevant local data. For example, the promotions department can prioritize promotions based on highly relevant data. For example, the promotions department can postpone promotions based on less relevant data. The promotions department can also adjust the order of promotions according to the relevance of the data. This allows for more effective promotions by adjusting the order of promotions based on relevant local data.

[0062] The relocation support department can select the most suitable support method when providing relocation assistance by referring to the relocation history of prospective migrants. For example, the relocation support department can propose the most suitable support method based on the prospective migrants' past relocation history. For example, the relocation support department can analyze the prospective migrants' past relocation history to help them select a relocation destination. For example, the relocation support department can also determine the priority of relocation support by referring to the prospective migrants' past relocation history. In this way, the most suitable support method can be selected by referring to the prospective migrants' past relocation history.

[0063] The relocation support department can select the most suitable support method when providing relocation assistance, taking into account the geographical location information of the prospective migrant. For example, the relocation support department can propose the most suitable relocation destination based on the prospective migrant's geographical location information. For example, the relocation support department can use the prospective migrant's geographical location information to help select a relocation destination. For example, the relocation support department can also determine the priority of relocation support by referring to the prospective migrant's geographical location information. In this way, the most suitable support method can be selected by taking into account the prospective migrant's geographical location information.

[0064] The Industrial Creation Department can select the optimal creation method when creating an industry by referring to the region's past industrial creation history. For example, the Industrial Creation Department can propose the optimal creation method based on the region's past industrial creation history. For example, the Industrial Creation Department can analyze the region's past industrial creation history and use that information to select industries for creation. For example, the Industrial Creation Department can also determine the priority of industries for creation by referring to the region's past industrial creation history. In this way, the optimal creation method can be selected by referring to the region's past industrial creation history.

[0065] The Industrial Creation Department can select the optimal creation method when creating industries, taking into account the geographical location information of the region. For example, the Industrial Creation Department can propose the optimal industrial creation method based on geographical location information. For example, the Industrial Creation Department can use geographical location information to help select industries for creation. For example, the Industrial Creation Department can also determine the priority of industries for creation by referring to geographical location information. In this way, the optimal creation method can be selected by taking into account the geographical location information of the region.

[0066] The Collaboration Promotion Department can select the optimal promotion method by referring to the region's past collaboration history when promoting collaboration. For example, the Collaboration Promotion Department can propose the optimal promotion method based on the region's past collaboration history. For example, the Collaboration Promotion Department can analyze the region's past collaboration history and use this to help select collaboration promotion methods. For example, the Collaboration Promotion Department can determine the priority of collaboration promotion by referring to the region's past collaboration history. In this way, the optimal promotion method can be selected by referring to the region's past collaboration history.

[0067] The Collaboration Promotion Department can select the optimal promotion method when promoting collaboration, taking into account the geographical location information of the region. For example, the Collaboration Promotion Department can propose the optimal collaboration promotion method based on geographical location information. For example, the Collaboration Promotion Department can use geographical location information to help select collaboration promotion methods. For example, the Collaboration Promotion Department can also determine the priority of collaboration promotion by referring to geographical location information. In this way, the optimal promotion method can be selected by taking into account the geographical location information of the region.

[0068] The Success Stories Database Department can select the optimal construction method by referring to past success stories when building a database. For example, the Success Stories Database Department can propose the optimal database construction method based on past success stories. For example, the Success Stories Database Department can analyze past success stories and use that information to select a database construction method. For example, the Success Stories Database Department can also determine the priority of database construction by referring to past success stories. This allows for the selection of the optimal database construction method by referring to past success stories.

[0069] The Success Stories Database Department can select the optimal construction method when building a database, taking into account the geographical location information of success stories. For example, the Success Stories Database Department can propose the optimal database construction method based on geographical location information. For example, the Success Stories Database Department can use geographical location information to help select database construction methods. For example, the Success Stories Database Department can also determine the priority of database construction by referring to geographical location information. This allows for the selection of the optimal database construction method by considering the geographical location information of success stories.

[0070] The natural language processing unit (NLP) can select the optimal processing method based on regional characteristics during natural language processing. For example, based on regional characteristics, the NLP can apply agriculture-related natural language processing methods to areas where agriculture is prevalent. For example, it can apply tourism-related natural language processing methods to tourist destinations. For example, it can apply industry-related natural language processing methods to industrial areas. By selecting the optimal processing method based on regional characteristics, more accurate natural language processing becomes possible.

[0071] The natural language processing unit can select the optimal processing method by considering the geographical location information of the region during natural language processing. For example, the natural language processing unit can propose the optimal natural language processing method based on geographical location information. For example, the natural language processing unit can consider geographical location information and use it to help select natural language processing methods. For example, the natural language processing unit can also determine the priority of natural language processing by referring to geographical location information. In this way, the optimal processing method can be selected by considering the geographical location information of the region.

[0072] The image recognition unit can select the optimal recognition method based on the characteristics of the region during image recognition. For example, based on the characteristics of the region, the image recognition unit can apply an agriculture-related image recognition method in areas where agriculture is prevalent. For example, the image recognition unit can apply a tourism-related image recognition method to a tourist destination. For example, the image recognition unit can apply an industrial-related image recognition method to an industrial area. By selecting the optimal recognition method based on the characteristics of the region, more accurate image recognition becomes possible.

[0073] The image recognition unit can select the optimal recognition method by considering the geographical location information of the region during image recognition. For example, the image recognition unit can propose the optimal image recognition method based on geographical location information. For example, the image recognition unit can consider geographical location information and use it to help select image recognition methods. For example, the image recognition unit can also determine the priority of image recognition by referring to geographical location information. In this way, the optimal recognition method can be selected by considering the geographical location information of the region.

[0074] The prediction model unit can select the optimal model based on regional characteristics when building a prediction model. For example, based on regional characteristics, the prediction model unit can apply an agriculture-related prediction model to areas where agriculture is prevalent. For example, the prediction model unit can apply a tourism-related prediction model to tourist destinations. For example, the prediction model unit can apply an industry-related prediction model to industrial areas. By selecting the optimal model based on regional characteristics, more accurate predictions become possible.

[0075] The prediction model unit can select the optimal model by considering the geographical location information of the region when constructing a prediction model. For example, the prediction model unit proposes the optimal prediction model based on geographical location information. For example, the prediction model unit can consider geographical location information and use it to help select a prediction model. For example, the prediction model unit can also determine the priority of prediction models by referring to geographical location information. In this way, the optimal model can be selected by considering the geographical location information of the region.

[0076] The recommendation unit can select the most suitable recommendation method based on regional characteristics during the recommendation process. For example, based on regional characteristics, the recommendation unit can apply agriculture-related recommendation methods to areas where agriculture is prevalent. For example, the recommendation unit can apply tourism-related recommendation methods to tourist destinations. For example, the recommendation unit can apply industry-related recommendation methods to industrial areas. By selecting the most suitable recommendation method based on regional characteristics, more accurate recommendations become possible.

[0077] The recommendation unit can select the optimal suggestion method by considering the geographical location information of the region during the recommendation process. For example, the recommendation unit can propose the optimal recommendation method based on geographical location information. For example, the recommendation unit can consider geographical location information and use it to help select recommendations. For example, the recommendation unit can also determine the priority of recommendations by referring to geographical location information. In this way, the optimal suggestion method can be selected by considering the geographical location information of the region.

[0078] The text generation unit can select the optimal generation method based on regional characteristics during text generation. For example, based on regional characteristics, the text generation unit can apply agriculture-related text generation methods in areas where agriculture is prevalent. For example, the text generation unit can apply tourism-related text generation methods in tourist areas. For example, the text generation unit can apply industry-related text generation methods in industrial areas. By selecting the optimal generation method based on regional characteristics, more accurate text generation becomes possible.

[0079] The text generation unit can select the optimal generation method by considering the geographical location information of the region during text generation. For example, the text generation unit can propose the optimal text generation method based on geographical location information. For example, the text generation unit can consider geographical location information and use it to select the text generation method. For example, the text generation unit can also determine the priority of text generation by referring to geographical location information. This allows for the selection of the optimal generation method by considering the geographical location information of the region.

[0080] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0081] The data collection unit can analyze the region's past data collection history and select the optimal collection method. For example, it can identify the method that yielded the highest response rate from past data collection history and reuse that method. It can also analyze past data collection history to confirm that the quality of data collected during specific seasons or events was high, and then collect data again at those times. Furthermore, based on past data collection history, it can select collection methods that are effective for specific age groups or occupational groups. In this way, the optimal collection method can be selected by analyzing past data collection history.

[0082] The analysis department can adjust the level of detail of the analysis based on the importance of the data. For example, it can perform a detailed analysis on high-importance data, and a simplified analysis on low-importance data. Furthermore, it can determine the priority of the analysis based on the importance of the data. This allows for efficient data analysis by adjusting the level of detail based on the importance of the data.

[0083] The Strategy Proposal Department can adjust the level of detail in its proposals based on the importance of the regional issues. For example, it can provide detailed strategic proposals for high-priority issues and simplified proposals for low-priority issues. Furthermore, it can prioritize strategic proposals according to the importance of the issues. This allows for more efficient strategic proposals by adjusting the level of detail based on the importance of the regional issues.

[0084] The matching function can improve the accuracy of matching by considering the interrelationships between local resources and personnel. For example, it can analyze the skill sets of local resources and personnel to make the optimal match. It can also improve matching accuracy by referring to the past cooperation history of local resources and personnel. Furthermore, it can make matches that allow for the establishment of long-term cooperative relationships by considering the interrelationships between local resources and personnel. In this way, by considering the interrelationships between local resources and personnel, more accurate matching becomes possible.

[0085] The promotion department can adjust the level of detail in promotions based on the importance of each region's attractions. For example, they can conduct detailed promotions for highly important regional attractions, and simplified promotions for less important ones. Furthermore, they can determine the priority of promotions based on the importance of each region's attractions. This allows for more efficient promotions by adjusting the level of detail based on the importance of each region's attractions.

[0086] The following briefly describes the processing flow for example form 1.

[0087] Step 1: The data collection unit collects data. The data collection unit can collect big data such as regional demographics, industrial structure, and natural resources. The data collection unit can also collect sensor data and social media data. The data collection unit can also collect survey data from local residents. Step 2: The analysis department analyzes the data collected by the collection department. The analysis department may, for example, use AI to analyze the data and identify regional characteristics and challenges. The analysis department may, for example, use machine learning algorithms to cluster the data and classify regional characteristics. The analysis department may also, for example, use natural language processing technology to analyze the voices of local residents. Step 3: The Strategy Proposal Department proposes the optimal revitalization strategy based on the analysis results obtained by the Analysis Department. For example, the Strategy Proposal Department can formulate a revitalization strategy tailored to the characteristics of the region. For example, the Strategy Proposal Department can propose solutions to regional challenges. For example, the Strategy Proposal Department can propose the creation of new industries utilizing regional resources. Step 4: The matching department matches local underutilized assets with individuals and companies that can utilize them. For example, the matching department can match vacant houses with people who wish to relocate. For example, the matching department can match abandoned farmland with agricultural workers. For example, the matching department can also match local companies with external talent.

[0088] (Example of form 2) The AI-based depopulated area revitalization assistant system according to an embodiment of the present invention is an AI-based platform that supports local governments and communities struggling with depopulation. This system deeply analyzes the characteristics and challenges of a region and proposes the optimal revitalization strategy. Furthermore, it connects local residents, government, and businesses, and promotes collaboration to realize sustainable community development. For example, the AI-based depopulated area revitalization assistant system analyzes big data such as population dynamics, industrial structure, and natural resources, and the AI ​​proposes the most suitable revitalization strategy for the region. For example, the AI ​​matches idle assets in the region (vacant houses, abandoned farmland, etc.) with people and companies that can utilize them. For example, it plans tourism strategies that effectively promote the region's attractions and disseminates information tailored to the target audience. For example, it matches prospective migrants with the needs of the region and supports smooth migration. For example, the AI ​​proposes the creation of new industries utilizing local resources and supports the formulation of business plans. Through this, the AI-based depopulated area revitalization assistant system aims for the sustainable development of depopulated areas and the realization of prosperous local communities. As a result, the AI-based depopulated area revitalization assistant system can deeply analyze the characteristics and challenges of a region and propose the optimal revitalization strategy.

[0089] The AI-powered revitalization assistant system for sparsely populated areas according to this embodiment comprises a data collection unit, an analysis unit, a strategy proposal unit, and a matching unit. The data collection unit collects data. The data collection unit can collect big data such as regional demographics, industrial structure, and natural resources. The data collection unit can also collect sensor data and social media data. The data collection unit can also collect survey data of local residents. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can analyze the data using AI to clarify the characteristics and challenges of the region. The analysis unit can cluster the data using machine learning algorithms and classify the characteristics of the region. The analysis unit can also analyze the voices of local residents using natural language processing technology. The strategy proposal unit proposes an optimal revitalization strategy based on the analysis results obtained by the analysis unit. The strategy proposal unit can formulate a revitalization strategy tailored to the characteristics of the region. The strategy proposal unit can propose solutions to regional challenges. The strategy proposal unit can propose the creation of new industries utilizing local resources. The matching unit matches underutilized local assets with individuals and companies that can utilize them. For example, the matching unit can match vacant houses with people who wish to relocate. For example, the matching unit can match abandoned farmland with agricultural workers. For example, the matching unit can also match local companies with external personnel. In this way, the AI-powered depopulated area revitalization assistant system according to the embodiment can support regional revitalization through data collection, analysis, strategy proposals, and matching.

[0090] The data collection unit collects data. For example, the data collection unit can collect big data such as regional demographics, industrial structure, and natural resources. Specifically, as demographic data, it collects statistics on the age distribution of the population, birth rates, death rates, and migration / exit. As industrial structure data, it collects information on major industries in the region, employment status, number of companies, and sales by industry. As natural resource data, it collects information on land use, forest resources, water resources, and mineral resources in the region. This data can be obtained from publicly available databases of government agencies and local authorities, statistical data, and satellite imagery using remote sensing technology. The data collection unit can also collect sensor data and social media data. As sensor data, it collects meteorological data such as temperature, humidity, atmospheric pressure, wind speed, and precipitation from environmental sensors installed in the region, and vehicle traffic volume and traffic congestion information from traffic sensors. As social media data, it collects SNS posts, blog articles, and comments on online forums from local residents to understand local concerns and problems. The data collection unit can also collect survey data from local residents. The survey data will collect information on residents' satisfaction with their lives, their perception of local issues, their desired services and facilities, and their willingness to participate in local events. This will allow the data collection department to gather a wide range of information from diverse data sources and gain a multifaceted understanding of the current situation in the region. Furthermore, the data collection department will centrally manage this data and be able to link it with other systems and departments as needed. For example, the collected data will be stored on a cloud server and made accessible to the analysis department and the strategy proposal department. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions will be possible. As a result, the data collection department can collect data efficiently and effectively and improve the overall performance of the system.

[0091] The Analysis Department analyzes data collected by the Data Collection Department. For example, the Analysis Department uses AI to analyze data and identify regional characteristics and challenges. Specifically, it can use machine learning algorithms to cluster data and classify regional characteristics. For instance, based on regional demographic data, it can cluster the population distribution by age group to identify areas with aging populations or areas with a large young population. It can also analyze the distribution and growth potential of major industries based on industrial structure data to identify the region's economic strengths and weaknesses. Furthermore, it can analyze natural resource data to evaluate the utilization status and potential use of regional resources. The Analysis Department can also use natural language processing technology to analyze the voices of local residents. Specifically, it can perform text mining on social media data and survey data to extract the concerns, complaints, and requests of local residents. This provides basic data for understanding regional challenges and needs and formulating appropriate countermeasures. In addition, the Analysis Department can use historical data and statistical information to predict long-term trends and risks. For example, based on historical demographic data, it can predict future population trends and formulate countermeasures that take into account future challenges in the region. Furthermore, by using anomaly detection algorithms, it is possible to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0092] The Strategy Proposal Department proposes optimal revitalization strategies based on the analysis results obtained by the Analysis Department. For example, the Strategy Proposal Department formulates revitalization strategies tailored to the characteristics of each region. Specifically, in regions with an aging population, it proposes the enhancement of welfare services and medical facilities for the elderly, and in regions with a large young population, it proposes the expansion of educational facilities and employment opportunities. Furthermore, based on the analysis results of the industrial structure, it can propose the creation of new industries that leverage the region's strengths or the strengthening of existing industries. For example, in regions where agriculture is the main industry, it promotes the branding of agricultural products and the development of sixth-sector industrialization, and in regions with abundant tourism resources, it proposes the promotion of the tourism industry and the development of infrastructure. The Strategy Proposal Department can also propose solutions to regional challenges. Specifically, in regions with poor transportation access, it proposes the development of public transportation and the introduction of community buses, and in regions with a shortage of medical facilities, it proposes the introduction of telemedicine systems. It is also important to propose measures that reflect the voices of local residents, and based on survey data and social media data, it can propose the development of services and facilities that meet the needs of local residents. In addition, the Strategy Proposal Department can also propose the creation of new industries that utilize local resources. For example, we propose the introduction of renewable energy utilizing local natural resources and the development of tourism resources that leverage local traditional culture. This allows the Strategic Proposal Department to propose specific revitalization strategies tailored to the characteristics and challenges of each region, thereby supporting the sustainable development of the area.

[0093] The Matching Department matches underutilized local assets with individuals and companies that can utilize them. Specifically, it matches vacant houses with people who wish to relocate. For example, it creates a database of vacant house information in the area and introduces properties that meet the needs of those who wish to relocate. It also provides those who wish to relocate with information on local living conditions and support systems to support a smooth relocation. The Matching Department can also match abandoned farmland with agricultural workers. Specifically, it collects information on abandoned farmland and provides it to agricultural workers and agricultural corporations. Furthermore, it provides agricultural workers with agricultural technology and management know-how, as well as support for fundraising, to promote the effective use of abandoned farmland. The Matching Department can also match local companies with external personnel. Specifically, it collects job information from local companies and provides it to external personnel. Furthermore, it provides external personnel with information on local corporate culture and living environment to support their settlement in the area. In this way, the Matching Department can effectively match underutilized local assets with individuals and companies that can utilize them, thereby supporting regional revitalization. In addition, the Matching Department can provide feedback on the matching results to improve the matching process. For example, the matching department can analyze successful and unsuccessful matching cases to improve the accuracy of its matching algorithms. Furthermore, it can provide follow-up support after matching to help the matched individuals and companies settle into the region. In this way, the matching department can promote the effective utilization of underutilized regional assets and the retention of talent, thereby supporting the sustainable development of the region.

[0094] The Promotion Department conducts tourism promotion. For example, the Promotion Department develops strategies to effectively promote local tourism resources. For example, the Promotion Department can disseminate information tailored to specific target audiences. For example, the Promotion Department can use social media to promote the region's attractions. For example, the Promotion Department can plan tourism events to showcase local tourism resources. For example, the Promotion Department can create tourism brochures and websites to provide tourism information. In this way, the Promotion Department can effectively communicate the region's attractions through tourism promotion.

[0095] The Relocation Support Department matches prospective migrants with the needs of the region. For example, the Relocation Support Department collects profiles of prospective migrants and proposes relocation destinations that meet the needs of the region. For example, the Relocation Support Department can match prospective migrants with local businesses. For example, the Relocation Support Department can match prospective migrants with local housing. For example, the Relocation Support Department can match prospective migrants with local communities. For example, the Relocation Support Department can provide prospective migrants with information about local living. In this way, the Relocation Support Department can support a smooth relocation by matching prospective migrants with the needs of the region.

[0096] The Industrial Creation Department proposes the creation of new industries utilizing local resources. For example, the Industrial Creation Department can propose new business models utilizing local specialty products. For example, the Industrial Creation Department can propose ecotourism utilizing local natural resources. For example, the Industrial Creation Department can propose the development of new products utilizing local traditional crafts. For example, the Industrial Creation Department can propose the development of processed foods utilizing local agricultural products. For example, the Industrial Creation Department can propose tourism businesses utilizing local tourism resources. In this way, the Industrial Creation Department can revitalize the local economy by proposing the creation of new industries utilizing local resources.

[0097] The Collaboration Promotion Department promotes collaboration among residents, government, and businesses. For example, the Collaboration Promotion Department can hold workshops aimed at solving local issues. For example, the Collaboration Promotion Department can plan joint local projects and promote cooperation among residents, government, and businesses. For example, the Collaboration Promotion Department can provide a local information sharing platform to promote collaboration. For example, the Collaboration Promotion Department can share local success stories and allow people to experience the effects of collaboration. For example, the Collaboration Promotion Department can promote interaction among residents, government, and businesses through local events. In this way, the Collaboration Promotion Department can realize sustainable community development by promoting collaboration among residents, government, and businesses.

[0098] The Success Stories Database Department creates a database of success stories and shares the knowledge gained. For example, the Success Stories Database Department collects regional success stories and registers them in its database. For example, the Success Stories Database Department can provide detailed information on success stories and promote their use in other regions. For example, the Success Stories Database Department can analyze success stories and identify the factors contributing to their success. For example, the Success Stories Database Department can publish the database of success stories online for wider sharing. For example, the Success Stories Database Department can use the database of success stories to formulate regional revitalization strategies. In this way, the Success Stories Database Department can create a database of success stories and share the knowledge gained, which can be used as a reference for regional revitalization.

[0099] The natural language processing unit performs natural language processing. For example, the natural language processing unit can collect the voices of local residents and analyze them as text data. For example, the natural language processing unit can perform morphological analysis to divide the text data into individual words. For example, the natural language processing unit can perform grammatical analysis to analyze the sentence structure of the text data. For example, the natural language processing unit can perform semantic analysis to understand the meaning of the text data. For example, the natural language processing unit can perform sentiment analysis to estimate the emotions of local residents from the text data. In this way, the natural language processing unit can analyze local issues and the voices of residents by performing natural language processing.

[0100] The image recognition unit performs image recognition. For example, the image recognition unit collects and analyzes images of local resources and tourist spots. For example, the image recognition unit can detect specific objects in an image using object detection technology. For example, the image recognition unit can identify people in an image using face recognition technology. For example, the image recognition unit can understand scenes in an image using scene analysis technology. For example, the image recognition unit can extract image features and classify the content of the image. As a result, the image recognition unit can perform image recognition to evaluate the value of local resources and discover tourist spots.

[0101] The Predictive Model Unit uses predictive models. For example, the Predictive Model Unit constructs a model to predict regional population dynamics. For example, the Predictive Model Unit can predict population growth and decline using regression analysis. For example, the Predictive Model Unit can also predict population fluctuation patterns using time series analysis. For example, the Predictive Model Unit can improve the accuracy of population dynamics predictions using machine learning models. For example, the Predictive Model Unit can construct a model to predict economic effects. For example, the Predictive Model Unit can predict economic effects using regional economic activity data. For example, the Predictive Model Unit can predict the number of tourists using regional tourism data. In this way, the Predictive Model Unit can predict population dynamics and economic effects by using predictive models.

[0102] The Recommendation Department makes recommendations. For example, the Recommendation Department can suggest the most suitable relocation destination to someone who wants to move. For example, the Recommendation Department can analyze the profile of someone who wants to move and suggest a relocation destination that meets the needs of the region. For example, the Recommendation Department can suggest the most suitable personnel to a company. For example, the Recommendation Department can suggest companies that can utilize local resources. For example, the Recommendation Department can suggest the most suitable tourist spots to tourists in the region. In this way, the Recommendation Department can make matching suggestions to relocators and companies by making recommendations.

[0103] The text generation unit generates text. For example, the text generation unit can generate tourism promotional text. For example, the text generation unit can generate text introducing local tourism resources. For example, the text generation unit can also generate text introducing local specialty products. For example, the text generation unit can generate text introducing local event information. For example, the text generation unit can generate text introducing local history and culture. For example, the text generation unit can generate text introducing local relocation support information. In this way, the text generation unit can create tourism promotional text and local introduction content by generating text.

[0104] The data collection unit can estimate the emotions of local residents and adjust the timing of data collection based on the estimated emotions. For example, if local residents are feeling euphoric after an event, the data collection unit can conduct a survey at that time to collect positive feedback. For example, if local residents are feeling anxious after a disaster, the data collection unit can collect data to quickly understand their support needs. For example, the data collection unit can also collect data on lifestyle habits during times when local residents are typically relaxed. By adjusting the timing of data collection according to the emotions of local residents, more appropriate data can be collected. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The data collection unit can analyze the region's past data collection history and select the optimal collection method. For example, the unit can identify the method that yielded the highest response rate from past data collection history and reuse that method. For example, the unit can analyze past data collection history to confirm that the quality of data collected during a particular season or event was high, and then collect data again at that time. For example, the unit can select an effective collection method for a specific age group or occupation based on past data collection history. In this way, the optimal collection method can be selected by analyzing past data collection history.

[0106] The data collection unit can filter data based on regional characteristics and challenges. For example, based on regional characteristics, the unit can prioritize collecting agriculture-related data in areas where agriculture is prevalent. For example, based on regional challenges, the unit can focus on collecting data on people wishing to relocate to areas experiencing serious population decline. For example, depending on regional characteristics and challenges, the unit can collect tourist behavior data in tourist areas to aid in the development of tourism strategies. In this way, more useful data can be collected by filtering data based on regional characteristics and challenges.

[0107] The data collection unit can estimate the emotions of local residents and determine the priority of data to collect based on the estimated emotions. For example, if local residents are feeling anxious, the data collection unit will prioritize collecting data that provides a sense of security. For example, if local residents are excited, the data collection unit can prioritize collecting data related to events and activities. For example, if local residents are relaxed, the data collection unit can also prioritize collecting data related to daily life. In this way, by determining the priority of data to collect according to the emotions of local residents, more important data can be collected preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0108] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of the region during data collection. For example, the data collection unit can prioritize the collection of traffic volume data in a specific area based on geographical location information. For example, the data collection unit can prioritize the collection of occupancy rate data for accommodations around tourist destinations by considering geographical location information. For example, the data collection unit can also prioritize the collection of crop production data in agricultural areas based on geographical location information. In this way, by collecting data while considering the geographical location information of the region, it is possible to prioritize the collection of more relevant data.

[0109] The data collection unit can analyze local social media activity and collect relevant data during data collection. For example, the data collection unit can analyze the response to local events on social media and collect data related to those events. For example, the data collection unit can analyze the opinions and requests of local residents on social media and collect data related to local issues. For example, the data collection unit can analyze the ratings of tourist destinations on social media and collect data useful for tourism strategies. In this way, relevant data can be collected by analyzing local social media activity.

[0110] The analysis unit can estimate the emotions of local residents and adjust the data analysis method based on the estimated emotions. For example, if local residents are feeling anxious, the analysis unit can perform data analysis to provide a sense of security. For example, if local residents are excited, the analysis unit can perform data analysis related to events and activities. For example, if local residents are relaxed, the analysis unit can also perform data analysis related to daily life. In this way, by adjusting the data analysis method according to the emotions of local residents, more appropriate analysis can be performed. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0111] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on high-importance data, and a simplified analysis on low-importance data. The analysis unit can also prioritize analyses based on the importance of the data. This allows for efficient data analysis by adjusting the level of detail based on the importance of the data.

[0112] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, it can apply a population forecasting algorithm to demographic data. For example, it can apply an industry analysis algorithm to industrial structure data. For example, it can apply a resource assessment algorithm to natural resource data. By applying different analysis algorithms depending on the data category, more accurate analysis becomes possible.

[0113] The strategic proposal department can estimate the emotions of local residents and adjust the presentation of strategic proposals based on those estimated emotions. For example, if local residents are feeling anxious, the department can propose strategies to provide a sense of security. For example, if local residents are excited, the department can propose strategies that are visually stimulating. For example, if local residents are relaxed, the department can propose strategies that are simple and easy to understand. By adjusting the presentation of strategic proposals according to the emotions of local residents, more effective proposals can be made. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0114] The Strategy Proposal Department can adjust the level of detail in its proposals based on the importance of the regional issues. For example, it can provide detailed strategic proposals for high-priority issues and simplified proposals for low-priority issues. The department can also prioritize strategic proposals based on the importance of the issues. This allows for more efficient strategic proposals by adjusting the level of detail based on the importance of the regional issues.

[0115] The Strategy Proposal Department can apply different proposal algorithms depending on the characteristics of the region when proposing strategies. For example, in a region where agriculture is prevalent, the Strategy Proposal Department can apply an agriculture-related strategy proposal algorithm. For example, in a tourist area, the Strategy Proposal Department can apply a tourism-related strategy proposal algorithm. For example, in an industrial area, the Strategy Proposal Department can apply an industrial strategy proposal algorithm. By applying different proposal algorithms according to the characteristics of the region, it becomes possible to make more accurate strategy proposals.

[0116] The strategic proposal department can estimate the emotions of local residents and prioritize strategic proposals based on those estimated emotions. For example, if local residents are feeling anxious, the strategic proposal department will prioritize strategic proposals that provide a sense of security. For example, if local residents are excited, the strategic proposal department can prioritize visually stimulating strategic proposals. For example, if local residents are relaxed, the strategic proposal department can also prioritize strategic proposals related to daily life. By prioritizing strategic proposals according to the emotions of local residents, more appropriate strategic proposals can be made. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0117] The Strategy Proposal Department can prioritize proposals based on the timing of regional data collection when proposing strategies. For example, the Strategy Proposal Department will prioritize strategic proposals based on the latest data. For example, the Strategy Proposal Department can make strategic proposals based on the latest data while referring to past data. For example, the Strategy Proposal Department can also prioritize strategic proposals according to the timing of data collection. This makes it possible to make strategic proposals based on the latest information by prioritizing proposals based on the timing of regional data collection.

[0118] The Strategy Proposal Department can adjust the order of proposals based on relevant regional data when proposing strategies. For example, the Department can prioritize strategic proposals based on highly relevant data. For example, the Department can postpone strategic proposals based on less relevant data. The Department can also adjust the order of strategic proposals according to the relevance of the data. This allows for more effective strategic proposals by adjusting the order of proposals based on relevant regional data.

[0119] The matching unit can estimate the emotions of local residents and adjust the matching criteria based on the estimated emotions. For example, if a local resident is feeling anxious, the matching unit can set matching criteria to provide a sense of security. For example, if a local resident is excited, the matching unit can set visually stimulating matching criteria. For example, if a local resident is relaxed, the matching unit can set simple and highly visible matching criteria. By adjusting the matching criteria according to the emotions of local residents, more appropriate matching becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0120] The matching unit can improve the accuracy of matching by considering the interrelationships between local resources and personnel during the matching process. For example, the matching unit can analyze the skill sets of local resources and personnel to perform optimal matching. For example, the matching unit can improve the accuracy of matching by referring to the past cooperation history of local resources and personnel. For example, the matching unit can also consider the interrelationships between local resources and personnel to perform matching that can build long-term cooperative relationships. In this way, considering the interrelationships between local resources and personnel enables more accurate matching.

[0121] The matching unit can perform matching while considering regional characteristics. For example, based on regional characteristics, the matching unit can match agricultural personnel and resources in areas where agriculture is thriving. For example, considering regional characteristics, the matching unit can match tourism-related personnel and resources in tourist areas. For example, depending on regional characteristics, the matching unit can match industrial personnel and resources in industrial areas. This allows for more appropriate matching by considering regional characteristics.

[0122] The matching unit can estimate the emotions of local residents and adjust the order in which matching results are displayed based on the estimated emotions. For example, if a local resident is feeling anxious, the matching unit can prioritize displaying matching results that provide a sense of security. For example, if a local resident is excited, the matching unit can prioritize displaying visually stimulating matching results. For example, if a local resident is relaxed, the matching unit can prioritize displaying simple and highly visible matching results. By adjusting the order in which matching results are displayed according to the emotions of local residents, it becomes possible to present matching results more effectively. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0123] The matching unit can perform matching while considering the geographical distribution of the region. For example, the matching unit can prioritize matching resources and personnel that are geographically close. For example, the matching unit can consider geographical distribution and perform matching in areas with good transportation access. For example, the matching unit can also prioritize matching within a specific area based on geographical distribution. This makes it possible to perform more appropriate matching by considering the geographical distribution of the region.

[0124] The matching unit can improve the accuracy of matching by referring to relevant local literature during the matching process. For example, the matching unit can refer to relevant local literature and perform matching based on past success stories. For example, the matching unit can analyze relevant local literature and select the optimal matching method. For example, the matching unit can improve the accuracy of matching by referring to relevant local literature. As a result, more accurate matching becomes possible by referring to relevant local literature.

[0125] The promotion department can estimate the emotions of local residents and adjust promotional methods based on those estimated emotions. For example, if local residents are feeling anxious, the promotion department can provide reassuring promotional methods. For example, if local residents are excited, the promotion department can provide visually stimulating promotional methods. For example, if local residents are relaxed, the promotion department can provide simple and highly visible promotional methods. By adjusting promotional methods according to the emotions of local residents, more effective promotion becomes possible. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0126] The promotion department can adjust the level of detail in promotions based on the importance of the region's attractions. For example, the promotion department can conduct detailed promotions for highly important regional attractions, and simplified promotions for less important ones. The promotion department can also prioritize promotions based on the importance of the region's attractions. This allows for more efficient promotions by adjusting the level of detail based on the importance of the region's attractions.

[0127] The Promotion Department can apply different promotional methods depending on the characteristics of the region. For example, in areas where agriculture is thriving, the Promotion Department can apply agriculture-related promotional methods. In tourist areas, for example, the Promotion Department can apply tourism-related promotional methods. In industrial areas, for example, the Promotion Department can apply industry-related promotional methods. By applying different promotional methods according to the characteristics of the region, more effective promotion becomes possible.

[0128] The promotion department can estimate the emotions of local residents and prioritize promotions based on those estimated emotions. For example, if local residents are feeling anxious, the promotion department will prioritize promotions that provide a sense of security. For example, if local residents are excited, the promotion department can prioritize visually stimulating promotions. For example, if local residents are relaxed, the promotion department can prioritize promotions related to daily life. By prioritizing promotions according to the emotions of local residents, more effective promotions become possible. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0129] The promotion department can prioritize promotions based on the timing of regional data collection. For example, the promotion department can prioritize promotions based on the latest data. For example, the promotion department can conduct promotions based on the latest data while referring to past data. For example, the promotion department can also prioritize promotions according to the timing of data collection. This makes it possible to conduct promotions based on the latest information by prioritizing promotions based on the timing of regional data collection.

[0130] The promotions department can adjust the order of promotions based on relevant local data. For example, the promotions department can prioritize promotions based on highly relevant data. For example, the promotions department can postpone promotions based on less relevant data. The promotions department can also adjust the order of promotions according to the relevance of the data. This allows for more effective promotions by adjusting the order of promotions based on relevant local data.

[0131] The relocation support department can estimate the emotions of prospective migrants and adjust its relocation support methods based on those estimated emotions. For example, if a prospective migrant is feeling anxious, the department can provide support methods to reassure them. For example, if a prospective migrant is excited, the department can provide visually stimulating support methods. For example, if a prospective migrant is relaxed, the department can provide simple and highly visible support methods. By adjusting the relocation support methods according to the emotions of prospective migrants, more appropriate support becomes possible. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0132] The relocation support department can select the most suitable support method when providing relocation assistance by referring to the relocation history of prospective migrants. For example, the relocation support department can propose the most suitable support method based on the prospective migrants' past relocation history. For example, the relocation support department can analyze the prospective migrants' past relocation history to help them select a relocation destination. For example, the relocation support department can also determine the priority of relocation support by referring to the prospective migrants' past relocation history. In this way, the most suitable support method can be selected by referring to the prospective migrants' past relocation history.

[0133] The relocation support department can estimate the emotions of prospective migrants and determine the priority of relocation support based on those estimated emotions. For example, if a prospective migrant is feeling anxious, the department will prioritize support that provides a sense of security. For example, if a prospective migrant is excited, the department may prioritize visually stimulating support. For example, if a prospective migrant is relaxed, the department may prioritize support related to daily life. This allows for more appropriate support by determining the priority of relocation support according to the emotions of prospective migrants. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0134] The relocation support department can select the most suitable support method when providing relocation assistance, taking into account the geographical location information of the prospective migrant. For example, the relocation support department can propose the most suitable relocation destination based on the prospective migrant's geographical location information. For example, the relocation support department can use the prospective migrant's geographical location information to help select a relocation destination. For example, the relocation support department can also determine the priority of relocation support by referring to the prospective migrant's geographical location information. In this way, the most suitable support method can be selected by taking into account the prospective migrant's geographical location information.

[0135] The Industrial Creation Department can estimate the emotions of local residents and adjust the methods of industrial creation based on those estimated emotions. For example, if local residents are feeling anxious, the Industrial Creation Department can provide industrial creation methods that provide a sense of security. For example, if local residents are excited, the Industrial Creation Department can provide visually stimulating industrial creation methods. For example, if local residents are relaxed, the Industrial Creation Department can provide simple and highly visible industrial creation methods. By adjusting the methods of industrial creation according to the emotions of local residents, more appropriate industrial creation becomes possible. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0136] The Industrial Creation Department can select the optimal creation method when creating an industry by referring to the region's past industrial creation history. For example, the Industrial Creation Department can propose the optimal creation method based on the region's past industrial creation history. For example, the Industrial Creation Department can analyze the region's past industrial creation history and use that information to select industries for creation. For example, the Industrial Creation Department can also determine the priority of industries for creation by referring to the region's past industrial creation history. In this way, the optimal creation method can be selected by referring to the region's past industrial creation history.

[0137] The Industrial Creation Department can estimate the emotions of local residents and determine the priority of industrial creation based on those estimated emotions. For example, if local residents are feeling anxious, the Industrial Creation Department will prioritize industrial creation that provides a sense of security. For example, if local residents are excited, the Industrial Creation Department can prioritize industrial creation that is visually stimulating. For example, if local residents are relaxed, the Industrial Creation Department can also prioritize industrial creation related to daily life. By determining the priority of industrial creation according to the emotions of local residents, more appropriate industrial creation becomes possible. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0138] The Industrial Creation Department can select the optimal creation method when creating industries, taking into account the geographical location information of the region. For example, the Industrial Creation Department can propose the optimal industrial creation method based on geographical location information. For example, the Industrial Creation Department can use geographical location information to help select industries for creation. For example, the Industrial Creation Department can also determine the priority of industries for creation by referring to geographical location information. In this way, the optimal creation method can be selected by taking into account the geographical location information of the region.

[0139] The Collaboration Promotion Department can estimate the emotions of local residents and adjust collaboration promotion methods based on those estimated emotions. For example, if local residents are feeling anxious, the Collaboration Promotion Department can provide collaboration promotion methods to provide a sense of security. For example, if local residents are excited, the Collaboration Promotion Department can provide visually stimulating collaboration promotion methods. For example, if local residents are relaxed, the Collaboration Promotion Department can provide simple and highly visible collaboration promotion methods. By adjusting collaboration promotion methods according to the emotions of local residents, more appropriate collaboration promotion becomes possible. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0140] The Collaboration Promotion Department can select the optimal promotion method by referring to the region's past collaboration history when promoting collaboration. For example, the Collaboration Promotion Department can propose the optimal promotion method based on the region's past collaboration history. For example, the Collaboration Promotion Department can analyze the region's past collaboration history and use this to help select collaboration promotion methods. For example, the Collaboration Promotion Department can determine the priority of collaboration promotion by referring to the region's past collaboration history. In this way, the optimal promotion method can be selected by referring to the region's past collaboration history.

[0141] The Collaboration Promotion Department can estimate the emotions of local residents and determine the priority of collaboration promotion based on those estimated emotions. For example, if local residents are feeling anxious, the Collaboration Promotion Department will prioritize collaboration promotion that provides a sense of security. For example, if local residents are excited, the Collaboration Promotion Department may prioritize collaboration promotion that is visually stimulating. For example, if local residents are relaxed, the Collaboration Promotion Department may also prioritize collaboration promotion related to daily life. This allows for more appropriate collaboration promotion by determining the priority of collaboration promotion according to the emotions of local residents. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0142] The Collaboration Promotion Department can select the optimal promotion method when promoting collaboration, taking into account the geographical location information of the region. For example, the Collaboration Promotion Department can propose the optimal collaboration promotion method based on geographical location information. For example, the Collaboration Promotion Department can use geographical location information to help select collaboration promotion methods. For example, the Collaboration Promotion Department can also determine the priority of collaboration promotion by referring to geographical location information. In this way, the optimal promotion method can be selected by taking into account the geographical location information of the region.

[0143] The success story database unit can estimate the emotions of those involved in success stories and adjust the database construction method based on the estimated emotions. For example, if those involved in a success story are feeling anxious, the success story database unit can provide a database construction method that provides a sense of security. For example, if those involved in a success story are excited, the success story database unit can provide a visually stimulating database construction method. For example, if those involved in a success story are relaxed, the success story database unit can also provide a simple and highly visible database construction method. This allows for more appropriate database construction by adjusting the database construction method according to the emotions of those involved in success stories. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0144] The Success Stories Database Department can select the optimal construction method by referring to past success stories when building a database. For example, the Success Stories Database Department can propose the optimal database construction method based on past success stories. For example, the Success Stories Database Department can analyze past success stories and use that information to select a database construction method. For example, the Success Stories Database Department can also determine the priority of database construction by referring to past success stories. This allows for the selection of the optimal database construction method by referring to past success stories.

[0145] The success story database unit can estimate the emotions of those involved in success stories and prioritize databases based on these estimated emotions. For example, if those involved in a success story are feeling anxious, the success story database unit will prioritize creating databases that provide a sense of security. For example, if those involved in a success story are excited, the success story database unit can prioritize creating visually stimulating databases. For example, if those involved in a success story are relaxed, the success story database unit can also prioritize creating databases related to daily life. This allows for the creation of more appropriate databases by prioritizing databases according to the emotions of those involved in success stories. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0146] The Success Stories Database Department can select the optimal construction method when building a database, taking into account the geographical location information of success stories. For example, the Success Stories Database Department can propose the optimal database construction method based on geographical location information. For example, the Success Stories Database Department can use geographical location information to help select database construction methods. For example, the Success Stories Database Department can also determine the priority of database construction by referring to geographical location information. This allows for the selection of the optimal database construction method by considering the geographical location information of success stories.

[0147] The natural language processing unit can estimate the emotions of local residents and adjust its natural language processing methods based on the estimated emotions. For example, if local residents are feeling anxious, the natural language processing unit can provide natural language processing methods to provide a sense of security. For example, if local residents are excited, the natural language processing unit can provide visually stimulating natural language processing methods. For example, if local residents are relaxed, the natural language processing unit can provide simple and highly visible natural language processing methods. By adjusting the natural language processing methods according to the emotions of local residents, more appropriate natural language processing becomes possible. Emotion estimation is achieved using emotion estimation functions, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0148] The natural language processing unit (NLP) can select the optimal processing method based on regional characteristics during natural language processing. For example, based on regional characteristics, the NLP can apply agriculture-related natural language processing methods to areas where agriculture is prevalent. For example, it can apply tourism-related natural language processing methods to tourist destinations. For example, it can apply industry-related natural language processing methods to industrial areas. By selecting the optimal processing method based on regional characteristics, more accurate natural language processing becomes possible.

[0149] The natural language processing unit can estimate the emotions of local residents and determine the priority of natural language processing based on the estimated emotions. For example, if local residents are feeling anxious, the natural language processing unit will prioritize natural language processing that provides a sense of security. For example, if local residents are excited, the natural language processing unit can prioritize visually stimulating natural language processing. For example, if local residents are relaxed, the natural language processing unit can also prioritize natural language processing related to daily life. This allows for more appropriate natural language processing by determining the priority of natural language processing according to the emotions of local residents. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0150] The natural language processing unit can select the optimal processing method by considering the geographical location information of the region during natural language processing. For example, the natural language processing unit can propose the optimal natural language processing method based on geographical location information. For example, the natural language processing unit can consider geographical location information and use it to help select natural language processing methods. For example, the natural language processing unit can also determine the priority of natural language processing by referring to geographical location information. In this way, the optimal processing method can be selected by considering the geographical location information of the region.

[0151] The image recognition unit can estimate the emotions of local residents and adjust the image recognition method based on the estimated emotions. For example, if local residents are feeling anxious, the image recognition unit can provide an image recognition method that provides a sense of security. For example, if local residents are excited, the image recognition unit can provide a visually stimulating image recognition method. For example, if local residents are relaxed, the image recognition unit can also provide a simple and highly visible image recognition method. By adjusting the image recognition method according to the emotions of local residents, more appropriate image recognition becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0152] The image recognition unit can select the optimal recognition method based on the characteristics of the region during image recognition. For example, based on the characteristics of the region, the image recognition unit can apply an agriculture-related image recognition method in areas where agriculture is prevalent. For example, the image recognition unit can apply a tourism-related image recognition method to a tourist destination. For example, the image recognition unit can apply an industrial-related image recognition method to an industrial area. By selecting the optimal recognition method based on the characteristics of the region, more accurate image recognition becomes possible.

[0153] The image recognition unit can estimate the emotions of local residents and determine the priority of image recognition based on the estimated emotions. For example, if a local resident is feeling anxious, the image recognition unit will prioritize images that provide a sense of security. For example, if a local resident is excited, the image recognition unit can prioritize visually stimulating images. For example, if a local resident is relaxed, the image recognition unit can also prioritize images related to daily life. By determining the priority of image recognition according to the emotions of local residents, more appropriate image recognition becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0154] The image recognition unit can select the optimal recognition method by considering the geographical location information of the region during image recognition. For example, the image recognition unit can propose the optimal image recognition method based on geographical location information. For example, the image recognition unit can consider geographical location information and use it to help select image recognition methods. For example, the image recognition unit can also determine the priority of image recognition by referring to geographical location information. In this way, the optimal recognition method can be selected by considering the geographical location information of the region.

[0155] The predictive model unit can estimate the emotions of local residents and adjust the predictive model method based on the estimated emotions. For example, if local residents are feeling anxious, the predictive model unit can provide a predictive model method to provide a sense of security. For example, if local residents are excited, the predictive model unit can provide a visually stimulating predictive model method. For example, if local residents are relaxed, the predictive model unit can also provide a simple and highly visible predictive model method. By adjusting the predictive model method according to the emotions of local residents, more appropriate predictions become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0156] The prediction model unit can select the optimal model based on regional characteristics when building a prediction model. For example, based on regional characteristics, the prediction model unit can apply an agriculture-related prediction model to areas where agriculture is prevalent. For example, the prediction model unit can apply a tourism-related prediction model to tourist destinations. For example, the prediction model unit can apply an industry-related prediction model to industrial areas. By selecting the optimal model based on regional characteristics, more accurate predictions become possible.

[0157] The predictive model unit can estimate the emotions of local residents and determine the priority of predictive models based on the estimated emotions. For example, if local residents are feeling anxious, the predictive model unit will prioritize predictive models that provide a sense of security. For example, if local residents are excited, the predictive model unit can prioritize visually stimulating predictive models. For example, if local residents are relaxed, the predictive model unit can also prioritize predictive models related to daily life. By determining the priority of predictive models according to the emotions of local residents, more appropriate predictions become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0158] The prediction model unit can select the optimal model by considering the geographical location information of the region when constructing a prediction model. For example, the prediction model unit proposes the optimal prediction model based on geographical location information. For example, the prediction model unit can consider geographical location information and use it to help select a prediction model. For example, the prediction model unit can also determine the priority of prediction models by referring to geographical location information. In this way, the optimal model can be selected by considering the geographical location information of the region.

[0159] The recommendation unit can estimate the emotions of local residents and adjust its recommendation methods based on those estimated emotions. For example, if local residents are feeling anxious, the recommendation unit can provide recommendations that provide a sense of security. For example, if local residents are excited, the recommendation unit can provide visually stimulating recommendations. For example, if local residents are relaxed, the recommendation unit can provide simple and highly visible recommendations. By adjusting the recommendation method according to the emotions of local residents, more appropriate suggestions become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0160] The recommendation unit can select the most suitable recommendation method based on regional characteristics during the recommendation process. For example, based on regional characteristics, the recommendation unit can apply agriculture-related recommendation methods to areas where agriculture is prevalent. For example, the recommendation unit can apply tourism-related recommendation methods to tourist destinations. For example, the recommendation unit can apply industry-related recommendation methods to industrial areas. By selecting the most suitable recommendation method based on regional characteristics, more accurate recommendations become possible.

[0161] The recommendation unit can estimate the emotions of local residents and determine the priority of recommendations based on those estimated emotions. For example, if local residents are feeling anxious, the recommendation unit will prioritize recommendations that provide a sense of security. For example, if local residents are excited, the recommendation unit can prioritize visually stimulating recommendations. For example, if local residents are relaxed, the recommendation unit can also prioritize recommendations related to daily life. By determining the priority of recommendations according to the emotions of local residents, more appropriate suggestions become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0162] The recommendation unit can select the optimal suggestion method by considering the geographical location information of the region during the recommendation process. For example, the recommendation unit can propose the optimal recommendation method based on geographical location information. For example, the recommendation unit can consider geographical location information and use it to help select recommendations. For example, the recommendation unit can also determine the priority of recommendations by referring to geographical location information. In this way, the optimal suggestion method can be selected by considering the geographical location information of the region.

[0163] The text generation unit can estimate the emotions of local residents and adjust the text generation method based on the estimated emotions. For example, if local residents are feeling anxious, the text generation unit can provide a text generation method that provides a sense of security. For example, if local residents are excited, the text generation unit can provide a visually stimulating text generation method. For example, if local residents are relaxed, the text generation unit can also provide a simple and highly visible text generation method. By adjusting the text generation method according to the emotions of local residents, more appropriate text generation becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0164] The text generation unit can select the optimal generation method based on regional characteristics during text generation. For example, based on regional characteristics, the text generation unit can apply agriculture-related text generation methods in areas where agriculture is prevalent. For example, the text generation unit can apply tourism-related text generation methods in tourist areas. For example, the text generation unit can apply industry-related text generation methods in industrial areas. By selecting the optimal generation method based on regional characteristics, more accurate text generation becomes possible.

[0165] The text generation unit can estimate the emotions of local residents and determine the priority of text generation based on the estimated emotions. For example, if local residents are feeling anxious, the text generation unit will prioritize generating text that provides a sense of security. For example, if local residents are excited, the text generation unit can prioritize generating visually stimulating text. For example, if local residents are relaxed, the text generation unit can also prioritize generating text related to daily life. By determining the priority of text generation according to the emotions of local residents, more appropriate text generation becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0166] The text generation unit can select the optimal generation method by considering the geographical location information of the region during text generation. For example, the text generation unit can propose the optimal text generation method based on geographical location information. For example, the text generation unit can consider geographical location information and use it to select the text generation method. For example, the text generation unit can also determine the priority of text generation by referring to geographical location information. This allows for the selection of the optimal generation method by considering the geographical location information of the region.

[0167] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0168] The data collection unit can estimate the emotions of local residents and adjust the timing of data collection based on those estimated emotions. For example, if local residents are feeling elated after an event, a survey can be conducted at that time to collect positive feedback. Also, if local residents are feeling anxious after a disaster, data can be collected to quickly understand their support needs. Furthermore, data on lifestyle habits can be collected during times when local residents are typically relaxed. By adjusting the timing of data collection according to the emotions of local residents, more appropriate data can be collected.

[0169] The analysis department can estimate the emotions of local residents and adjust the data analysis method based on those estimated emotions. For example, if local residents are feeling anxious, the analysis can be conducted to provide them with a sense of security. If local residents are excited, the analysis can be conducted on events and activities. Furthermore, if local residents are relaxed, the analysis can be conducted on daily life. By adjusting the data analysis method according to the emotions of local residents, more appropriate analysis can be performed.

[0170] The Strategic Proposal Department can estimate the emotions of local residents and adjust the presentation of strategic proposals based on those estimates. For example, if local residents are feeling anxious, strategic proposals can be made to provide a sense of security. If local residents are excited, visually stimulating strategic proposals can be made. Furthermore, if local residents are relaxed, simple and highly visible strategic proposals can be made. By adjusting the presentation of strategic proposals according to the emotions of local residents, more effective proposals can be made.

[0171] The matching unit can estimate the emotions of local residents and adjust the matching criteria based on those estimated emotions. For example, if local residents are feeling anxious, matching criteria designed to provide a sense of security can be set. If local residents are excited, visually stimulating matching criteria can be set. Furthermore, if local residents are relaxed, simple and easily recognizable matching criteria can be set. By adjusting the matching criteria according to the emotions of local residents, more appropriate matching becomes possible.

[0172] The promotion department can estimate the emotions of local residents and adjust promotional methods based on those estimates. For example, if local residents are feeling anxious, they can provide promotional methods that provide a sense of security. If local residents are excited, they can provide visually stimulating promotional methods. Furthermore, if local residents are relaxed, they can provide simple and highly visible promotional methods. By adjusting promotional methods according to the emotions of local residents, more effective promotion becomes possible.

[0173] The data collection unit can analyze the region's past data collection history and select the optimal collection method. For example, it can identify the method that yielded the highest response rate from past data collection history and reuse that method. It can also analyze past data collection history to confirm that the quality of data collected during specific seasons or events was high, and then collect data again at those times. Furthermore, based on past data collection history, it can select collection methods that are effective for specific age groups or occupational groups. In this way, the optimal collection method can be selected by analyzing past data collection history.

[0174] The analysis department can adjust the level of detail of the analysis based on the importance of the data. For example, it can perform a detailed analysis on high-importance data, and a simplified analysis on low-importance data. Furthermore, it can determine the priority of the analysis based on the importance of the data. This allows for efficient data analysis by adjusting the level of detail based on the importance of the data.

[0175] The Strategy Proposal Department can adjust the level of detail in its proposals based on the importance of the regional issues. For example, it can provide detailed strategic proposals for high-priority issues and simplified proposals for low-priority issues. Furthermore, it can prioritize strategic proposals according to the importance of the issues. This allows for more efficient strategic proposals by adjusting the level of detail based on the importance of the regional issues.

[0176] The matching function can improve the accuracy of matching by considering the interrelationships between local resources and personnel. For example, it can analyze the skill sets of local resources and personnel to make the optimal match. It can also improve matching accuracy by referring to the past cooperation history of local resources and personnel. Furthermore, it can make matches that allow for the establishment of long-term cooperative relationships by considering the interrelationships between local resources and personnel. In this way, by considering the interrelationships between local resources and personnel, more accurate matching becomes possible.

[0177] The promotion department can adjust the level of detail in promotions based on the importance of each region's attractions. For example, they can conduct detailed promotions for highly important regional attractions, and simplified promotions for less important ones. Furthermore, they can determine the priority of promotions based on the importance of each region's attractions. This allows for more efficient promotions by adjusting the level of detail based on the importance of each region's attractions.

[0178] The following briefly describes the processing flow for example form 2.

[0179] Step 1: The data collection unit collects data. The data collection unit can collect big data such as regional demographics, industrial structure, and natural resources. The data collection unit can also collect sensor data and social media data. The data collection unit can also collect survey data from local residents. Step 2: The analysis department analyzes the data collected by the collection department. The analysis department may, for example, use AI to analyze the data and identify regional characteristics and challenges. The analysis department may, for example, use machine learning algorithms to cluster the data and classify regional characteristics. The analysis department may also, for example, use natural language processing technology to analyze the voices of local residents. Step 3: The Strategy Proposal Department proposes the optimal revitalization strategy based on the analysis results obtained by the Analysis Department. For example, the Strategy Proposal Department can formulate a revitalization strategy tailored to the characteristics of the region. For example, the Strategy Proposal Department can propose solutions to regional challenges. For example, the Strategy Proposal Department can propose the creation of new industries utilizing regional resources. Step 4: The matching department matches local underutilized assets with individuals and companies that can utilize them. For example, the matching department can match vacant houses with people who wish to relocate. For example, the matching department can match abandoned farmland with agricultural workers. For example, the matching department can also match local companies with external talent.

[0180] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0181] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0182] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0183] Each of the multiple elements mentioned above, including the data collection unit, analysis unit, strategy proposal unit, matching unit, promotion unit, migration support unit, industry creation unit, collaboration promotion unit, success story database unit, natural language processing unit, image recognition unit, predictive model unit, recommendation unit, and text generation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects data using the sensors and cameras of the smart device 14 and analyzes it using the specific processing unit 290 of the data processing unit 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. For example, the strategy proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes an revitalization strategy based on the analysis results. For example, the matching unit is implemented by the control unit 46A of the smart device 14 and matches local idle assets with people and companies. For example, the promotion unit is implemented by the control unit 46A of the smart device 14 and conducts tourism promotion. For example, the relocation support department is implemented by the control unit 46A of the smart device 14 and matches prospective migrants with the needs of the region. For example, the industrial creation department is implemented by the specific processing unit 290 of the data processing device 12 and proposes the creation of new industries utilizing local resources. For example, the collaboration promotion department is implemented by the control unit 46A of the smart device 14 and promotes collaboration among residents, government, and businesses. For example, the success story database department is implemented by the specific processing unit 290 of the data processing device 12 and databases success stories to share knowledge. For example, the natural language processing unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the voices of local residents. For example, the image recognition department collects images using the camera of the smart device 14 and analyzes them using the specific processing unit 290 of the data processing device 12. For example, the predictive model department is implemented by the specific processing unit 290 of the data processing device 12 and predicts population dynamics and economic effects. For example, the recommendation department is implemented by the specific processing unit 290 of the data processing device 12 and makes suggestions to prospective migrants and businesses. For example, the text generation unit is implemented by the specific processing unit 290 of the data processing device 12, and generates tourism PR text and regional introduction content.The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0184] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0185] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0186] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0187] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0188] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0189] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0190] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0191] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0192] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0193] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0194] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0195] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0196] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0197] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0198] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0199] Each of the multiple elements mentioned above, including the data collection unit, analysis unit, strategy proposal unit, matching unit, promotion unit, migration support unit, industry creation unit, collaboration promotion unit, success story database unit, natural language processing unit, image recognition unit, predictive model unit, recommendation unit, and text generation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects data using the sensors and cameras of the smart glasses 214 and analyzes it using the specific processing unit 290 of the data processing unit 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. For example, the strategy proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes an revitalization strategy based on the analysis results. For example, the matching unit is implemented by the control unit 46A of the smart glasses 214 and matches local idle assets with people and companies. For example, the promotion unit is implemented by the control unit 46A of the smart glasses 214 and conducts tourism promotion. For example, the relocation support unit is implemented by the control unit 46A of the smart glasses 214 and matches prospective migrants with the needs of the region. For example, the industrial creation unit is implemented by the specific processing unit 290 of the data processing device 12 and proposes the creation of new industries utilizing local resources. For example, the collaboration promotion unit is implemented by the control unit 46A of the smart glasses 214 and promotes collaboration among residents, government, and businesses. For example, the success story database unit is implemented by the specific processing unit 290 of the data processing device 12 and databases success stories to share knowledge. For example, the natural language processing unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the voices of local residents. For example, the image recognition unit collects images using the camera of the smart glasses 214 and analyzes them using the specific processing unit 290 of the data processing device 12. For example, the predictive model unit is implemented by the specific processing unit 290 of the data processing device 12 and predicts population dynamics and economic effects. For example, the recommendation unit is implemented by the specific processing unit 290 of the data processing device 12 and makes suggestions to prospective migrants and businesses. For example, the text generation unit is implemented by the specific processing unit 290 of the data processing device 12, and generates tourism PR text and regional introduction content.The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0200] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0201] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0202] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0203] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0204] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0205] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0206] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0207] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0208] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0209] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0210] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0211] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0212] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0213] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0214] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0215] Each of the multiple elements mentioned above, including the data collection unit, analysis unit, strategy proposal unit, matching unit, promotion unit, migration support unit, industry creation unit, collaboration promotion unit, success story database unit, natural language processing unit, image recognition unit, predictive model unit, recommendation unit, and text generation unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects data using the sensors and camera of the headset terminal 314 and analyzes it using the specific processing unit 290 of the data processing unit 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. For example, the strategy proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes an revitalization strategy based on the analysis results. For example, the matching unit is implemented by the control unit 46A of the headset terminal 314 and matches local idle assets with personnel and companies. For example, the promotion unit is implemented by the control unit 46A of the headset terminal 314 and conducts tourism promotion. For example, the relocation support unit is implemented by the control unit 46A of the headset terminal 314 and matches prospective migrants with the needs of the region. For example, the industrial creation unit is implemented by the specific processing unit 290 of the data processing device 12 and proposes the creation of new industries utilizing local resources. For example, the collaboration promotion unit is implemented by the control unit 46A of the headset terminal 314 and promotes collaboration among residents, government, and businesses. For example, the success story database unit is implemented by the specific processing unit 290 of the data processing device 12 and databases success stories to share knowledge. For example, the natural language processing unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the voices of local residents. For example, the image recognition unit collects images using the camera of the headset terminal 314 and analyzes them using the specific processing unit 290 of the data processing device 12. For example, the prediction model unit is implemented by the specific processing unit 290 of the data processing device 12 and makes predictions about population dynamics and economic effects. For example, the recommendation unit is implemented by the specific processing unit 290 of the data processing device 12 and makes suggestions to prospective residents and companies. For example, the text generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates tourism PR text and regional introduction content.The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0216] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0217] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0218] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0219] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0220] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0221] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0222] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0223] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0224] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0225] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0226] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0227] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0228] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0229] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0230] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0231] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0232] Each of the multiple elements mentioned above, including the data collection unit, analysis unit, strategy proposal unit, matching unit, promotion unit, migration support unit, industry creation unit, collaboration promotion unit, success story database unit, natural language processing unit, image recognition unit, predictive model unit, recommendation unit, and text generation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects data using the sensors and cameras of the robot 414 and analyzes it using the specific processing unit 290 of the data processing unit 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. For example, the strategy proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes an revitalization strategy based on the analysis results. For example, the matching unit is implemented by the control unit 46A of the robot 414 and matches local idle assets with people and companies. For example, the promotion unit is implemented by the control unit 46A of the robot 414 and conducts tourism promotion. For example, the relocation support unit is implemented by the control unit 46A of the robot 414 and matches prospective migrants with the needs of the region. For example, the industrial creation unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the creation of new industries utilizing local resources. For example, the collaboration promotion unit is implemented by the control unit 46A of the robot 414 and promotes collaboration among residents, government, and businesses. For example, the success story database unit is implemented by the specific processing unit 290 of the data processing unit 12 and databases success stories to share knowledge. For example, the natural language processing unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the voices of local residents. For example, the image recognition unit collects images using the camera of the robot 414 and analyzes them using the specific processing unit 290 of the data processing unit 12. For example, the predictive model unit is implemented by the specific processing unit 290 of the data processing unit 12 and predicts population dynamics and economic effects. For example, the recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes suggestions to prospective migrants and businesses. For example, the text generation unit is implemented by the specific processing unit 290 of the data processing device 12, and generates tourism PR text and regional introduction content. The correspondence between each unit and the device and control unit is not limited to the example described above, and various modifications are possible.

[0233] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0234] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0235] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0236] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0237] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0238] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0239] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0240] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0241] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0242] 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.

[0243] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0244] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0245] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0246] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0247] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0248] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0249] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0250] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0251] (Note 1) A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A strategy proposal unit proposes an optimal activation strategy based on the analysis results obtained by the aforementioned analysis unit, The Matching Department connects local underutilized assets with individuals and companies that can utilize them, Equipped with A system characterized by the following features. (Note 2) It has a promotion department that handles tourism promotion. The system described in Appendix 1, characterized by the features described herein. (Note 3) It has a relocation support department that matches prospective residents with the needs of the local community. The system described in Appendix 1, characterized by the features described herein. (Note 4) It has an Industry Creation Department that proposes the creation of new industries utilizing local resources. The system described in Appendix 1, characterized by the features described herein. (Note 5) It has a Collaboration Promotion Department that facilitates cooperation among residents, government, and businesses. The system described in Appendix 1, characterized by the features described herein. (Note 6) It has a success story database department that creates a database of successful cases and shares knowledge. The system described in Appendix 1, characterized by the features described herein. (Note 7) It includes a natural language processing unit that performs natural language processing. The system described in Appendix 1, characterized by the features described herein. (Note 8) It includes an image recognition unit that performs image recognition. The system described in Appendix 1, characterized by the features described herein. (Note 9) It includes a predictive model section that uses a predictive model. The system described in Appendix 1, characterized by the features described herein. (Note 10) It includes a recommendation unit that performs recommendations. The system described in Appendix 1, characterized by the features described herein. (Note 11) It includes a text generation unit that generates text. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is We estimate the sentiments of local residents and adjust the timing of data collection based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is Analyze the region's past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When collecting data, filtering is performed based on regional characteristics and challenges. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is We estimate the sentiments of local residents and prioritize the data to collect based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the geographical location information of the region. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned collection unit is During data collection, analyze local social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is We estimate the sentiments of local residents and adjust the data analysis method based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the data. The system according to Appendix 1, characterized in that... (Appendix 20) The analysis unit applies different analysis algorithms according to the category of data during analysis The system according to Appendix 1, characterized in that... (Appendix 21) The strategic proposal unit estimates the feelings of local residents and adjusts the expression method of strategic proposals based on the estimated feelings The system according to Appendix 1, characterized in that... (Appendix 22) The strategic proposal unit adjusts the detail level of the proposal based on the importance of local issues during strategic proposal The system according to Appendix 1, characterized in that... (Appendix 23) The strategic proposal unit applies different proposal algorithms according to the characteristics of the region during strategic proposal The system according to Appendix 1, characterized in that... (Appendix 24) The strategic proposal unit estimates the feelings of local residents and determines the priority of strategic proposals based on the estimated feelings The system according to Appendix 1, characterized in that... (Appendix 25) The strategic proposal unit determines the priority of the proposal based on the local data collection time during strategic proposal The system according to Appendix 1, characterized in that... (Appendix 26) The matching unit estimates the feelings of local residents and adjusts the matching criteria based on the estimated feelings The system according to Appendix 1, characterized in that... (Appendix 27) The matching unit estimates the feelings of local residents and adjusts the matching criteria based on the estimated feelings The system according to Appendix 1, characterized in that... (Appendix 28) The matching unit improves the accuracy of matching by considering the interrelationship between regional resources and personnel during matching. The system according to Appendix 1, characterized in that. (Appendix 29) The matching unit performs matching by considering regional characteristic information during matching. The system according to Appendix 1, characterized in that. (Appendix 30) The matching unit estimates the feelings of local residents and adjusts the order of displaying the matching results based on the estimated feelings. The system according to Appendix 1, characterized in that. (Appendix 31) The matching unit performs matching by considering the geographical distribution of the region during matching. The system according to Appendix 1, characterized in that. (Appendix 32) The matching unit improves the accuracy of matching by referring to relevant literature of the region during matching. The system according to Appendix 1, characterized in that. (Appendix 33) The promotion unit estimates the feelings of local residents and adjusts the promotion method based on the estimated feelings. The system according to Appendix 1, characterized in that. (Appendix 34) The promotion unit adjusts the detail level of promotion based on the importance of the charm of the region during promotion. The system according to Appendix 1, characterized in that. (Appendix 35) The promotion unit applies different promotion methods according to the characteristics of the region during promotion. The system according to Appendix 1, characterized in that. (Appendix 36) The promotion department estimates the feelings of local residents and determines the priority order of promotion based on the estimated feelings The system according to Appendix 1, characterized in that (Appendix 37) The promotion department determines the priority order of promotion based on the data collection period of the region during promotion The system according to Appendix 1, characterized in that (Appendix 38) The promotion department adjusts the order of promotion based on the relevant data of the region during promotion The system according to Appendix 1, characterized in that (Appendix 39) The relocation support department estimates the feelings of relocation applicants and adjusts the relocation support method based on the estimated feelings The system according to Appendix 1, characterized in that (Appendix 40) The relocation support department selects the optimal support method by referring to the past relocation history of relocation applicants during relocation support The system according to Appendix 1, characterized in that (Appendix 41) The relocation support department estimates the feelings of relocation applicants and determines the priority order of relocation support based on the estimated feelings The system according to Appendix 1, characterized in that (Appendix 42) The relocation support department selects the optimal support method by considering the geographical location information of relocation applicants during relocation support The system according to Appendix 1, characterized in that (Appendix 43) The industrial creation department estimates the feelings of local residents and adjusts the industrial creation method based on the estimated feelings The system described in Appendix 1, characterized by the features described herein. (Note 44) The aforementioned Industrial Creation Department When creating an industry, the optimal method of creation is selected by referring to the region's past industrial creation history. The system described in Appendix 1, characterized by the features described herein. (Note 45) The aforementioned Industrial Creation Department Estimate the sentiments of local residents and determine the priority of industrial creation based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 46) The aforementioned Industrial Creation Department When creating an industry, the optimal method of creation is selected by considering the geographical location information of the region. The system described in Appendix 1, characterized by the features described herein. (Note 47) The aforementioned Collaboration Promotion Department, Estimate the sentiments of local residents and adjust methods for promoting collaboration based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 48) The aforementioned Collaboration Promotion Department, When promoting collaboration, refer to the region's past collaboration history to select the most suitable promotion method. The system described in Appendix 1, characterized by the features described herein. (Note 49) The aforementioned Collaboration Promotion Department, Estimate the sentiments of local residents and determine priorities for promoting collaboration based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 50) The aforementioned Collaboration Promotion Department, When promoting collaboration, the most suitable promotion method will be selected by considering the geographical location information of the region. The system described in Appendix 1, characterized by the features described herein. (Note 51) The aforementioned success story database unit is We estimate the emotions of those involved in success stories and adjust the database construction method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 52) The aforementioned success story database unit is When building a database, refer to past success stories to select the optimal construction method. The system described in Appendix 1, characterized by the features described herein. (Note 53) The aforementioned success story database unit is The system estimates the emotions of stakeholders in success stories and prioritizes the database based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 54) The aforementioned success story database unit is When building the database, the optimal construction method will be selected by considering the geographical location information of successful cases. The system described in Appendix 1, characterized by the features described herein. (Note 55) The aforementioned natural language processing unit, We estimate the sentiments of local residents and adjust the natural language processing method based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 56) The aforementioned natural language processing unit, When processing natural language, the optimal processing method is selected based on regional characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 57) The aforementioned natural language processing unit, The system estimates the sentiments of local residents and determines the priority of natural language processing based on these estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 58) The aforementioned natural language processing unit, When processing natural language, the optimal processing method is selected by considering the geographical location information of the region. The system described in Appendix 1, characterized by the features described herein. (Note 59) The image recognition unit, The system estimates the emotions of local residents and adjusts the image recognition method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 60) The image recognition unit, During image recognition, the optimal recognition method is selected based on the characteristics of the region. The system described in Appendix 1, characterized by the features described herein. (Note 61) The image recognition unit, The system estimates the emotions of local residents and determines the priority of image recognition based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 62) The image recognition unit, During image recognition, the optimal recognition method is selected by considering the geographical location information of the region. The system described in Appendix 1, characterized by the features described herein. (Note 63) The aforementioned prediction model unit, We estimate the sentiments of local residents and adjust the predictive model's methods based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 64) The aforementioned prediction model unit, When building a predictive model, select the optimal model based on the characteristics of the region. The system described in Appendix 1, characterized by the features described herein. (Note 65) The aforementioned prediction model unit, The system estimates the sentiments of local residents and prioritizes predictive models based on these estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 66) The aforementioned prediction model unit is When building a predictive model, the optimal model is selected by considering the geographical location information of the region. The system described in Appendix 1, characterized by the features described herein. (Note 67) The aforementioned recommendation unit, The system estimates the sentiments of local residents and adjusts the recommendation method based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 68) The aforementioned recommendation unit, When making recommendations, the most suitable suggestion method is selected based on the characteristics of the region. The system described in Appendix 1, characterized by the features described herein. (Note 69) The aforementioned recommendation unit, The system estimates the sentiments of local residents and determines the priority of recommendations based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 70) The aforementioned recommendation unit, When making recommendations, the system selects the optimal suggestion method by considering the geographical location information of the region. The system described in Appendix 1, characterized by the features described herein. (Note 71) The aforementioned text generation unit, We estimate the sentiments of local residents and adjust the text generation method based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 72) The aforementioned text generation unit, When generating text, the optimal generation method is selected based on the characteristics of the region. The system described in Appendix 1, characterized by the features described herein. (Note 73) The aforementioned text generation unit, The system estimates the sentiments of local residents and determines the priority of text generation based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 74) The aforementioned text generation unit, When generating text, the optimal generation method is selected by considering the geographical location information of the region. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0252] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A strategy proposal unit proposes an optimal activation strategy based on the analysis results obtained by the aforementioned analysis unit, The Matching Department connects local underutilized assets with individuals and companies that can utilize them, Equipped with A system characterized by the following features.

2. It has a promotion department that handles tourism promotion. The system according to feature 1.

3. It has a relocation support department that matches prospective residents with the needs of the local community. The system according to feature 1.

4. It has an Industry Creation Department that proposes the creation of new industries utilizing local resources. The system according to feature 1.

5. It has a Collaboration Promotion Department that facilitates cooperation among residents, government, and businesses. The system according to feature 1.

6. It has a success story database department that creates a database of successful cases and shares knowledge. The system according to feature 1.

7. It includes a natural language processing unit that performs natural language processing. The system according to feature 1.

8. It includes an image recognition unit that performs image recognition. The system according to feature 1.

9. It includes a predictive model section that uses a predictive model. The system according to feature 1.

10. It includes a recommendation unit that performs recommendations. The system according to feature 1.

Images