system

Abstract

The system according to this embodiment aims to analyze local data and propose an optimal revitalization plan. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a feedback collection unit. The data collection unit collects local data. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes an optimal revitalization plan based on the analysis results obtained by the analysis unit. The feedback collection unit collects feedback from residents.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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 regional data has not been fully utilized effectively to promote regional activation, and there is room for improvement.

[0005] [[ID=]]

Means for Solving the Problems

[0006] ​The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a feedback collection unit. The data collection unit collects local data. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes an optimal revitalization plan based on the analysis results obtained by the analysis unit. The feedback collection unit collects feedback from residents. [Effects of the Invention]

[0007] The system according to this embodiment can analyze local data and propose an optimal revitalization plan. [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 labeled 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 applied 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 regional revitalization support system according to an embodiment of the present invention is a system that uses generative AI to analyze the characteristics and resources of a region and proposes an optimal revitalization plan and event planning. The regional revitalization support system uses generative AI to analyze regional population dynamics, economic conditions, and resource data in real time and generate an optimal revitalization plan. Next, the generative AI collects and analyzes feedback from residents using natural language processing and proposes a resident-participatory plan. Furthermore, the generative AI learns from past success stories and continuously provides effective measures. For example, the regional revitalization support system collects regional data. For example, it collects regional population data, economic data, environmental data, etc. Next, the regional revitalization support system analyzes the collected data. For example, the generative AI uses a data analysis algorithm to analyze the regional population dynamics and economic conditions. Next, the regional revitalization support system proposes an optimal revitalization plan based on the analysis results. For example, the generative AI uses a proposal generation algorithm to generate an economic revitalization plan and an environmental improvement plan. Next, the regional revitalization support system collects feedback from residents. For example, it uses a survey system or feedback analysis software to collect residents' opinions. Next, the regional revitalization support system analyzes the collected feedback. For example, generative AI uses natural language processing technology to analyze residents' opinions. This allows the regional revitalization support system to propose specific revitalization plans tailored to the characteristics of the region and promote the participation of local residents. Furthermore, through high-precision data analysis by generative AI and the provision of an online platform, the aim is to rediscover and disseminate the region's appeal, revitalizing the local economy and improving residents' quality of life. This enables the regional revitalization support system to analyze the characteristics and resources of the region and propose optimal revitalization plans and event planning.

[0029] The regional revitalization support system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a feedback collection unit. The data collection unit collects regional data. For example, the data collection unit collects regional population data, economic data, environmental data, etc. The data collection unit can collect data using sensors, databases, APIs, etc. For example, the data collection unit obtains regional population data from a government statistical database. The data collection unit can also obtain regional economic data from a commercial database. The data collection unit can also collect environmental data in real time using sensors. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the data using generative AI. For example, the analysis unit uses generative AI to analyze regional population dynamics and economic conditions using a data analysis algorithm. The analysis unit can also use generative AI to analyze regional resource data using data mining technology. The analysis unit can also use generative AI to analyze data patterns using machine learning algorithms. The proposal unit proposes an optimal revitalization plan based on the analysis results obtained by the analysis unit. The proposal unit proposes a plan using generative AI. For example, the proposal unit uses a generation AI and a proposal generation algorithm to generate economic revitalization plans and environmental improvement plans. The proposal unit can also use simulation technology to predict the effectiveness of the plans. Furthermore, the proposal unit can learn from past success stories and propose effective measures. The feedback collection unit collects feedback from residents. The feedback collection unit uses AI to collect feedback. For example, the feedback collection unit collects residents' opinions using a survey system. Furthermore, the feedback collection unit can analyze residents' opinions using feedback analysis software. Furthermore, the feedback collection unit can analyze residents' opinions using natural language processing technology. As a result, the regional revitalization support system according to this embodiment can support regional revitalization by collecting and analyzing regional data, proposing optimal revitalization plans, and collecting feedback from residents.

[0030] The data collection unit collects local data. For example, it collects local population data, economic data, and environmental data. Specifically, the unit obtains local population data from government statistical databases. This includes data on population distribution by age group, birth rates, death rates, and migration / exit data. The unit can also obtain local economic data from commercial databases. This includes data on the number of businesses in the area, sales by industry, unemployment rates, and average income. Furthermore, the unit can collect environmental data in real time using sensors. For example, it can install air quality sensors and water quality sensors within the area to monitor the concentration of harmful substances in the air and fluctuations in water quality. This allows the unit to understand the local environmental conditions in real time. The unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and proposal departments. Adjusting the frequency and accuracy of data collection allows for flexible responses to specific situations and conditions. This enables the unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the collection unit. The analysis unit uses generative AI to analyze the data. Specifically, the generative AI uses data analysis algorithms to analyze the demographic trends and economic conditions of the region. For example, based on population data, the generative AI predicts future population trends and identifies issues such as aging populations and the outflow of young people from the region. The generative AI can also analyze economic data to understand the industrial structure and economic growth trends of the region. Furthermore, the generative AI can also analyze regional resource data using data mining techniques. For example, it can analyze the distribution of tourism and natural resources within the region and evaluate the potential for regional revitalization by utilizing these resources. The generative AI can also analyze data patterns using machine learning algorithms. For example, based on past data, it can learn patterns of regional economic activity and environmental changes to predict future risks and opportunities. This allows the analysis unit to quickly and accurately analyze the collected data and understand the current situation and challenges of the region. Furthermore, the analysis unit can utilize past data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on historical economic data, it can predict fluctuations in economic growth in specific industries or regions and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data, issuing early warnings. 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, improving the overall reliability and security of the system.

[0032] The proposal department proposes an optimal revitalization plan based on the analysis results obtained by the analysis department. The proposal department proposes plans using generative AI. Specifically, the generative AI uses a proposal generation algorithm to generate economic revitalization plans and environmental improvement plans. For example, the generative AI can propose measures to attract new industries or strengthen existing industries based on regional economic data. The generative AI can also propose measures to improve the region's environment based on environmental data. For example, it can propose measures to combat air pollution and promote greening based on air quality sensor data. Furthermore, the proposal department can use simulation technology to predict the effects of the plans. For example, it can simulate the impact of the proposed economic revitalization plan on the regional economy and evaluate its effects. The proposal department can also have the generative AI learn from past success stories and propose effective measures. For example, it can learn from successful economic revitalization and environmental improvement measures in other regions and make proposals based on them. As a result, the proposal department can propose concrete and feasible revitalization plans based on the analysis results and contribute to solving regional problems. Furthermore, the proposal department can evaluate the feasibility and cost-effectiveness of the proposed plans and select the optimal plan. This allows the proposal department to provide optimal revitalization plans tailored to local needs and resources, thereby supporting the sustainable development of the region.

[0033] The Feedback Collection Department collects feedback from residents. The Feedback Collection Department uses AI to collect feedback. Specifically, the Feedback Collection Department collects residents' opinions using a survey system. For example, it collects residents' opinions and requests through online surveys and mobile apps. The Feedback Collection Department can also analyze residents' opinions using feedback analysis software. For example, it analyzes collected feedback using text mining techniques to identify trends in residents' opinions and common issues. Furthermore, the Feedback Collection Department can analyze residents' opinions using natural language processing techniques. For example, it analyzes opinions provided by residents in free-form text using natural language processing techniques to extract important keywords and phrases. This allows the Feedback Collection Department to efficiently and accurately collect and analyze residents' opinions. In addition, based on the collected feedback, the Feedback Collection Department provides feedback to the Proposal Department and Analysis Department, which can be used to improve plans and identify new issues. For example, it can revise proposed revitalization plans or propose new measures based on residents' opinions. The Feedback Collection Department can also strengthen communication with residents and build a collaborative system for solving local issues. This will enable the feedback collection unit to realize a regional revitalization support system that reflects the opinions of residents, thereby supporting the sustainable development of the region.

[0034] The analysis unit can analyze regional demographics, economic conditions, and resource data in real time. For example, the analysis unit uses a generative AI with data analysis algorithms to analyze regional demographics in real time. The analysis unit can also use a generative AI with economic models to analyze regional economic conditions in real time. Furthermore, the analysis unit can use a generative AI with sensor data to analyze regional resource data in real time. This allows for the proposal of revitalization plans based on the latest information by analyzing regional demographics, economic conditions, and resource data in real time. Real-time analysis is performed, for example, in seconds, minutes, or hours. Some or all of the above-mentioned processes in the analysis unit are performed using generative AI.

[0035] The proposal unit can generate an optimal revitalization plan based on the analysis results. For example, the proposal unit uses a generation AI and a proposal generation algorithm to generate an economic revitalization plan based on the analysis results. The proposal unit can also use a generation AI and simulation technology to generate an environmental improvement plan based on the analysis results. Furthermore, the proposal unit can have the generation AI learn from past success stories and propose effective measures based on the analysis results. By generating an optimal revitalization plan based on the analysis results, it is possible to provide specific plans that are tailored to the characteristics of the region. An optimal revitalization plan may include, for example, an economic revitalization plan or an environmental improvement plan. Some or all of the above processes in the proposal unit are performed using a generation AI.

[0036] The feedback collection unit can collect and analyze feedback from residents using natural language processing. For example, the feedback collection unit can collect residents' opinions using a survey system. Furthermore, the feedback collection unit can analyze residents' opinions using feedback analysis software. It can also analyze residents' opinions using natural language processing technology. This allows for the proposal of resident-participatory plans by collecting and analyzing feedback from residents using natural language processing. Natural language processing is performed using techniques such as morphological analysis, grammatical analysis, and semantic analysis. Some or all of the above-described processes in the feedback collection unit may be performed using AI, or they may not.

[0037] The learning unit can learn from past success stories. For example, the learning unit can use a generative AI with machine learning algorithms to learn from past success stories. The learning unit can also use a generative AI with data mining techniques to learn from past success stories. Furthermore, the learning unit can use a generative AI with natural language processing techniques to learn from past success stories. This allows the learning unit to continuously provide effective measures by learning from past success stories. Past success stories include, for example, economic revitalization cases in specific regions. Some or all of the above processing in the learning unit is performed using a generative AI.

[0038] The platform provider can provide an online platform. For example, the platform provider can provide an online platform using a web server. Furthermore, the platform provider can also provide functions for collecting residents' opinions and sharing information using a user interface. Additionally, the platform provider can provide functions for collecting residents' opinions and sharing information using a mobile application. This allows for increased participation from local residents by providing an online platform. The online platform includes, for example, functions for collecting residents' opinions and sharing information. Some or all of the above-mentioned processes in the platform provider are performed using generative AI.

[0039] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can identify the most effective collection method from past data collection history and prioritize its use. The data collection unit can also select a collection method that is effective for a specific time of day or day of the week based on past data collection history. Furthermore, the data collection unit can analyze past data collection history and select the optimal collection method for a specific event or season. In this way, the optimal collection method can be selected by analyzing past data collection history. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0040] The data collection unit can filter data based on specific local events or seasons during data collection. For example, the unit can prioritize collecting relevant data during times when local festivals or events are held. It can also consider seasonal tourist trends and collect data relevant to tourist seasons. Furthermore, it can collect relevant data based on local agricultural and fishery harvest seasons. This allows for the collection of highly relevant data by filtering it based on specific local events or seasons. Some or all of the above processing in the data collection unit may be performed using AI, or it may be performed without AI.

[0041] 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 data from the central area of ​​the region or tourist destinations. The data collection unit can also prioritize the collection of data from geographically isolated areas to analyze the impact of depopulation. Furthermore, the data collection unit can collect data in a balanced manner from commercial areas and residential areas of the region. This allows for the priority collection of highly relevant data by considering the geographical location information of the region. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0042] The data collection unit can analyze local social media activity and collect relevant data during data collection. For example, the data collection unit can collect data related to events and activities that are trending on local social media. The data collection unit can also collect opinions and impressions from local residents on social media to understand local needs. Furthermore, the data collection unit can analyze the content of social media posts to identify local trends and interests. In this way, relevant data can be collected by analyzing local social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on high-importance data and generate specific suggestions. Conversely, the analysis unit can perform a simplified analysis on low-importance data and provide only an overview. The analysis unit can also optimally allocate analysis resources according to the importance of the data. This allows for optimal resource allocation by adjusting the level of detail of the analysis based on the importance of the data. Data importance is evaluated based on criteria such as data frequency and impact. Some or all of the above processing in the analysis unit is performed using generative AI.

[0044] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, for economic data, the analysis unit applies an analysis algorithm using an economic model. It can also apply an analysis algorithm using a demographic model to demographic data. Furthermore, it can apply an analysis algorithm using a resource management model to resource data. This improves the accuracy of the analysis by applying different analysis algorithms depending on the data category. Data categories include, for example, population data, economic data, and environmental data. Some or all of the above processing in the analysis unit is performed using generative AI.

[0045] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit can prioritize the analysis of the latest data to provide real-time information. The analysis unit can also analyze long-term trends based on historical data. Furthermore, the analysis unit can prioritize the analysis of data related to specific events or seasons. This allows for the prioritization of analysis based on the data collection period, thereby ensuring that the latest information is analyzed first. The data collection period is evaluated, for example, on a monthly, seasonal, or yearly basis. Some or all of the above processing in the analysis unit is performed using generative AI.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data and generate specific suggestions. It can also postpone the analysis of less relevant data to improve overall analysis efficiency. Furthermore, the analysis unit can optimally allocate analysis resources according to the relevance of the data. This improves analysis efficiency by adjusting the order of analysis based on the relevance of the data. The relevance of the data is evaluated based on criteria such as correlation and causation. Some or all of the above processes in the analysis unit are performed using generative AI.

[0047] The proposal department can adjust the level of detail in a proposal based on its importance. For example, for highly important plans, the proposal department will provide a detailed proposal outlining specific implementation methods. For less important plans, the proposal department can provide a simplified proposal, offering only an overview. The proposal department can also optimally allocate resources to a proposal according to its importance. This allows for optimal resource allocation by adjusting the level of detail based on the plan's importance. The importance of a plan is evaluated based on criteria such as impact and feasibility. Some or all of the above processing in the proposal department is performed using generative AI.

[0048] The proposal unit can apply different proposal algorithms depending on the plan category when making a proposal. For example, for an economic revitalization plan, the proposal unit can apply a proposal algorithm using an economic model. Similarly, for a tourism promotion plan, it can apply a proposal algorithm using a tourism model. Furthermore, for a local community strengthening plan, it can apply a proposal algorithm using a community model. This improves the accuracy of proposals by applying different proposal algorithms depending on the plan category. Plan categories include, for example, economic plans, environmental plans, and social plans. Some or all of the above processing in the proposal unit is performed using generative AI.

[0049] The proposal department can prioritize proposals based on the submission timing. For example, it can prioritize proposals for urgent plans to encourage a quick response. It can also provide detailed proposals for long-term plans to encourage planned implementation. Furthermore, the proposal department can optimally allocate resources to proposals according to their submission timing. This allows for prioritizing proposals based on their submission timing, enabling the proposal department to prioritize urgent plans. Plan submission timing is evaluated on a monthly, seasonal, or yearly basis, for example. Some or all of the above processes in the proposal department are performed using generative AI.

[0050] The proposal department can adjust the order of proposals based on their relevance during the proposal process. For example, it can prioritize highly relevant plans and provide specific implementation methods. It can also postpone less relevant plans to improve overall proposal efficiency. Furthermore, the proposal department can optimally allocate resources to proposals according to their relevance. This improves proposal efficiency by adjusting the order of proposals based on their relevance. Plan relevance is evaluated based on criteria such as correlation and causation. Some or all of the above processing in the proposal department is performed using generative AI.

[0051] The feedback collection unit can select the optimal collection method by referring to past feedback history when collecting feedback. For example, the feedback collection unit can identify the most effective collection method from past feedback history and use that method preferentially. The feedback collection unit can also select a collection method that is effective for a specific time of day or day of the week based on past feedback history. Furthermore, the feedback collection unit can analyze past feedback history and select the optimal collection method for a specific event or season. In this way, the optimal feedback collection method can be selected by referring to past feedback history. Some or all of the above processing in the feedback collection unit may be performed using AI or not.

[0052] The feedback collection unit can select the optimal collection method when collecting feedback, taking into account the geographical location information of the region. For example, the feedback collection unit can prioritize collecting feedback from the central area or tourist destinations of the region. It can also prioritize collecting feedback from geographically isolated areas to analyze the impact of depopulation. Furthermore, the feedback collection unit can collect feedback in a balanced manner from commercial areas and residential areas of the region. In this way, the optimal feedback collection method can be selected by taking into account the geographical location information of the region. Some or all of the above processing in the feedback collection unit may be performed using AI, or it may be performed without using AI.

[0053] The learning unit can optimize its learning algorithm by referring to past learning data during the learning process. For example, the learning unit can identify the most effective learning algorithm from past learning data and prioritize its use. It can also select a learning algorithm that is effective for specific times of day or days of the week based on past learning data. Furthermore, it can analyze past learning data to select the optimal learning algorithm for specific events or seasons. This allows for the selection of the optimal learning algorithm by referring to past learning data. Past learning data includes, for example, past success stories and failure stories. Some or all of the above processes in the learning unit are performed using generative AI.

[0054] The learning unit can weight the training data based on when the data was collected during training. For example, the learning unit can give a high weight to the most recent data, emphasizing real-time information. It can also give a low weight to historical data, emphasizing long-term trends. Furthermore, the learning unit can appropriately weight data related to specific events or seasons, enabling balanced learning. This allows for balanced learning by weighting the training data based on when the data was collected. The data collection period can be evaluated, for example, on a monthly, seasonal, or yearly basis. Some or all of the above processing in the learning unit is performed using generative AI.

[0055] The platform provider can select the optimal display method by referring to the past operation history of local residents when displaying the platform. For example, the platform provider can identify the most effective display method from past operation history and use that method preferentially. The platform provider can also select a display method that is effective for a specific time of day or day of the week based on past operation history. Furthermore, the platform provider can analyze past operation history and select the optimal display method for a specific event or season. In this way, the optimal display method can be selected by referring to past operation history. Past operation history includes, for example, click history and browsing history. Some or all of the above processing in the platform provider may be performed using AI or not.

[0056] The platform provider can select the optimal display method when displaying the platform, taking into account the device information of local residents. For example, if a local resident is using a smartphone, the platform provider can provide a display method that matches the screen size. Furthermore, if a local resident is using a tablet, the platform provider can provide a display method optimized for larger screens. Also, if a local resident is using a smartwatch, the platform provider can provide a concise and highly visible display method. This allows the platform provider to select the optimal display method by considering the device information of local residents. Device information includes, for example, the device type, OS, and browser. Some or all of the above processing in the platform provider may be performed using AI, or it may be performed without AI.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The regional revitalization support system can further propose plans that take into account the local cultural background. For example, the analysis unit collects data on the region's history and traditional events and proposes unique events based on this data. The proposal unit can also generate tourism plans that utilize the region's cultural resources. Furthermore, the feedback collection unit can collect opinions based on the cultural background of residents, analyze them, and reflect them in the plan. This enables the provision of revitalization plans that leverage the region's cultural characteristics.

[0059] The proposal department can propose plans that take local climate data into consideration. For example, the analysis department collects seasonal climate data for the region and uses this to propose seasonal events and activities. The proposal department can also generate environmental improvement plans that respond to climate change. Furthermore, the feedback collection department can collect residents' opinions on the climate, analyze them, and reflect them in the plan. This makes it possible to provide revitalization plans that take into account the local climate characteristics.

[0060] The Learning Department can propose plans utilizing local educational data. For example, the Analysis Department collects data from local schools and educational institutions and uses this to propose education-related events and programs. The Proposal Department can also generate learning plans that utilize local educational resources. Furthermore, the Feedback Collection Department can collect residents' opinions on education, analyze them, and reflect them in the plans. This allows for the provision of revitalization plans that take into account the specific educational characteristics of the region.

[0061] The data collection unit can propose plans using local traffic data. For example, the analysis unit collects local traffic volume and public transport data and proposes traffic improvement plans based on this. The proposal unit can also propose tourist routes based on traffic data. Furthermore, the feedback collection unit can collect residents' opinions on traffic, analyze them, and reflect them in the plans. This makes it possible to provide revitalization plans that take into account the characteristics of local traffic.

[0062] The data collection unit can propose plans utilizing local health data. For example, the analysis unit collects local medical institutions and health-related data and proposes health promotion plans based on this data. The proposal unit can also propose sports events and health seminars based on health data. Furthermore, the feedback collection unit can collect residents' opinions on health, analyze them, and reflect them in the plans. This allows for the provision of revitalization plans that take into account the health characteristics of the region.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The data collection unit collects local data. For example, the data collection unit collects local population data, economic data, environmental data, etc. The data collection unit can collect data using sensors, databases, APIs, etc. For example, the data collection unit can obtain local population data from government statistical databases. The data collection unit can also obtain local economic data from commercial databases. Furthermore, the data collection unit can collect environmental data in real time using sensors. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses generative AI to analyze the data. For example, the analysis unit uses generative AI and data analysis algorithms to analyze regional demographics and economic conditions. The analysis unit can also use generative AI and data mining techniques to analyze regional resource data. Furthermore, the analysis unit can use generative AI and machine learning algorithms to analyze data patterns. Step 3: The proposal unit proposes an optimal revitalization plan based on the analysis results obtained by the analysis unit. The proposal unit proposes a plan using generative AI. For example, the proposal unit uses generative AI to generate economic revitalization plans and environmental improvement plans using a proposal generation algorithm. The proposal unit can also use generative AI to predict the effects of the plan using simulation technology. Furthermore, the proposal unit can have generative AI learn from past success stories and propose effective measures. Step 4: The feedback collection unit collects feedback from residents. The feedback collection unit uses AI to collect feedback. For example, the feedback collection unit collects residents' opinions using a survey system. The feedback collection unit can also analyze residents' opinions using feedback analysis software. Furthermore, the feedback collection unit can analyze residents' opinions using natural language processing technology.

[0065] (Example of form 2) The regional revitalization support system according to an embodiment of the present invention is a system that uses generative AI to analyze the characteristics and resources of a region and proposes an optimal revitalization plan and event planning. The regional revitalization support system uses generative AI to analyze regional population dynamics, economic conditions, and resource data in real time and generate an optimal revitalization plan. Next, the generative AI collects and analyzes feedback from residents using natural language processing and proposes a resident-participatory plan. Furthermore, the generative AI learns from past success stories and continuously provides effective measures. For example, the regional revitalization support system collects regional data. For example, it collects regional population data, economic data, environmental data, etc. Next, the regional revitalization support system analyzes the collected data. For example, the generative AI uses a data analysis algorithm to analyze the regional population dynamics and economic conditions. Next, the regional revitalization support system proposes an optimal revitalization plan based on the analysis results. For example, the generative AI uses a proposal generation algorithm to generate an economic revitalization plan and an environmental improvement plan. Next, the regional revitalization support system collects feedback from residents. For example, it uses a survey system or feedback analysis software to collect residents' opinions. Next, the regional revitalization support system analyzes the collected feedback. For example, generative AI uses natural language processing technology to analyze residents' opinions. This allows the regional revitalization support system to propose specific revitalization plans tailored to the characteristics of the region and promote the participation of local residents. Furthermore, through high-precision data analysis by generative AI and the provision of an online platform, the aim is to rediscover and disseminate the region's appeal, revitalizing the local economy and improving residents' quality of life. This enables the regional revitalization support system to analyze the characteristics and resources of the region and propose optimal revitalization plans and event planning.

[0066] The regional revitalization support system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a feedback collection unit. The data collection unit collects regional data. For example, the data collection unit collects regional population data, economic data, environmental data, etc. The data collection unit can collect data using sensors, databases, APIs, etc. For example, the data collection unit obtains regional population data from a government statistical database. The data collection unit can also obtain regional economic data from a commercial database. The data collection unit can also collect environmental data in real time using sensors. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the data using generative AI. For example, the analysis unit uses generative AI to analyze regional population dynamics and economic conditions using a data analysis algorithm. The analysis unit can also use generative AI to analyze regional resource data using data mining technology. The analysis unit can also use generative AI to analyze data patterns using machine learning algorithms. The proposal unit proposes an optimal revitalization plan based on the analysis results obtained by the analysis unit. The proposal unit proposes a plan using generative AI. For example, the proposal unit uses a generation AI and a proposal generation algorithm to generate economic revitalization plans and environmental improvement plans. The proposal unit can also use simulation technology to predict the effectiveness of the plans. Furthermore, the proposal unit can learn from past success stories and propose effective measures. The feedback collection unit collects feedback from residents. The feedback collection unit uses AI to collect feedback. For example, the feedback collection unit collects residents' opinions using a survey system. Furthermore, the feedback collection unit can analyze residents' opinions using feedback analysis software. Furthermore, the feedback collection unit can analyze residents' opinions using natural language processing technology. As a result, the regional revitalization support system according to this embodiment can support regional revitalization by collecting and analyzing regional data, proposing optimal revitalization plans, and collecting feedback from residents.

[0067] The data collection unit collects local data. For example, it collects local population data, economic data, and environmental data. Specifically, the unit obtains local population data from government statistical databases. This includes data on population distribution by age group, birth rates, death rates, and migration / exit data. The unit can also obtain local economic data from commercial databases. This includes data on the number of businesses in the area, sales by industry, unemployment rates, and average income. Furthermore, the unit can collect environmental data in real time using sensors. For example, it can install air quality sensors and water quality sensors within the area to monitor the concentration of harmful substances in the air and fluctuations in water quality. This allows the unit to understand the local environmental conditions in real time. The unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and proposal departments. Adjusting the frequency and accuracy of data collection allows for flexible responses to specific situations and conditions. This enables the unit to collect data efficiently and effectively, improving the overall system performance.

[0068] The analysis unit analyzes the data collected by the collection unit. The analysis unit uses generative AI to analyze the data. Specifically, the generative AI uses data analysis algorithms to analyze the demographic trends and economic conditions of the region. For example, based on population data, the generative AI predicts future population trends and identifies issues such as aging populations and the outflow of young people from the region. The generative AI can also analyze economic data to understand the industrial structure and economic growth trends of the region. Furthermore, the generative AI can also analyze regional resource data using data mining techniques. For example, it can analyze the distribution of tourism and natural resources within the region and evaluate the potential for regional revitalization by utilizing these resources. The generative AI can also analyze data patterns using machine learning algorithms. For example, based on past data, it can learn patterns of regional economic activity and environmental changes to predict future risks and opportunities. This allows the analysis unit to quickly and accurately analyze the collected data and understand the current situation and challenges of the region. Furthermore, the analysis unit can utilize past data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on historical economic data, it can predict fluctuations in economic growth in specific industries or regions and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data, issuing early warnings. 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, improving the overall reliability and security of the system.

[0069] The proposal department proposes an optimal revitalization plan based on the analysis results obtained by the analysis department. The proposal department proposes plans using generative AI. Specifically, the generative AI uses a proposal generation algorithm to generate economic revitalization plans and environmental improvement plans. For example, the generative AI can propose measures to attract new industries or strengthen existing industries based on regional economic data. The generative AI can also propose measures to improve the region's environment based on environmental data. For example, it can propose measures to combat air pollution and promote greening based on air quality sensor data. Furthermore, the proposal department can use simulation technology to predict the effects of the plans. For example, it can simulate the impact of the proposed economic revitalization plan on the regional economy and evaluate its effects. The proposal department can also have the generative AI learn from past success stories and propose effective measures. For example, it can learn from successful economic revitalization and environmental improvement measures in other regions and make proposals based on them. As a result, the proposal department can propose concrete and feasible revitalization plans based on the analysis results and contribute to solving regional problems. Furthermore, the proposal department can evaluate the feasibility and cost-effectiveness of the proposed plans and select the optimal plan. This allows the proposal department to provide optimal revitalization plans tailored to local needs and resources, thereby supporting the sustainable development of the region.

[0070] The Feedback Collection Department collects feedback from residents. The Feedback Collection Department uses AI to collect feedback. Specifically, the Feedback Collection Department collects residents' opinions using a survey system. For example, it collects residents' opinions and requests through online surveys and mobile apps. The Feedback Collection Department can also analyze residents' opinions using feedback analysis software. For example, it analyzes collected feedback using text mining techniques to identify trends in residents' opinions and common issues. Furthermore, the Feedback Collection Department can analyze residents' opinions using natural language processing techniques. For example, it analyzes opinions provided by residents in free-form text using natural language processing techniques to extract important keywords and phrases. This allows the Feedback Collection Department to efficiently and accurately collect and analyze residents' opinions. In addition, based on the collected feedback, the Feedback Collection Department provides feedback to the Proposal Department and Analysis Department, which can be used to improve plans and identify new issues. For example, it can revise proposed revitalization plans or propose new measures based on residents' opinions. The Feedback Collection Department can also strengthen communication with residents and build a collaborative system for solving local issues. This will enable the feedback collection unit to realize a regional revitalization support system that reflects the opinions of residents, thereby supporting the sustainable development of the region.

[0071] The analysis unit can analyze regional demographics, economic conditions, and resource data in real time. For example, the analysis unit uses a generative AI with data analysis algorithms to analyze regional demographics in real time. The analysis unit can also use a generative AI with economic models to analyze regional economic conditions in real time. Furthermore, the analysis unit can use a generative AI with sensor data to analyze regional resource data in real time. This allows for the proposal of revitalization plans based on the latest information by analyzing regional demographics, economic conditions, and resource data in real time. Real-time analysis is performed, for example, in seconds, minutes, or hours. Some or all of the above-mentioned processes in the analysis unit are performed using generative AI.

[0072] The proposal unit can generate an optimal revitalization plan based on the analysis results. For example, the proposal unit uses a generation AI and a proposal generation algorithm to generate an economic revitalization plan based on the analysis results. The proposal unit can also use a generation AI and simulation technology to generate an environmental improvement plan based on the analysis results. Furthermore, the proposal unit can have the generation AI learn from past success stories and propose effective measures based on the analysis results. By generating an optimal revitalization plan based on the analysis results, it is possible to provide specific plans that are tailored to the characteristics of the region. An optimal revitalization plan may include, for example, an economic revitalization plan or an environmental improvement plan. Some or all of the above processes in the proposal unit are performed using a generation AI.

[0073] The feedback collection unit can collect and analyze feedback from residents using natural language processing. For example, the feedback collection unit can collect residents' opinions using a survey system. Furthermore, the feedback collection unit can analyze residents' opinions using feedback analysis software. It can also analyze residents' opinions using natural language processing technology. This allows for the proposal of resident-participatory plans by collecting and analyzing feedback from residents using natural language processing. Natural language processing is performed using techniques such as morphological analysis, grammatical analysis, and semantic analysis. Some or all of the above-described processes in the feedback collection unit may be performed using AI, or they may not.

[0074] The learning unit can learn from past success stories. For example, the learning unit can use a generative AI with machine learning algorithms to learn from past success stories. The learning unit can also use a generative AI with data mining techniques to learn from past success stories. Furthermore, the learning unit can use a generative AI with natural language processing techniques to learn from past success stories. This allows the learning unit to continuously provide effective measures by learning from past success stories. Past success stories include, for example, economic revitalization cases in specific regions. Some or all of the above processing in the learning unit is performed using a generative AI.

[0075] The platform provider can provide an online platform. For example, the platform provider can provide an online platform using a web server. Furthermore, the platform provider can also provide functions for collecting residents' opinions and sharing information using a user interface. Additionally, the platform provider can provide functions for collecting residents' opinions and sharing information using a mobile application. This allows for increased participation from local residents by providing an online platform. The online platform includes, for example, functions for collecting residents' opinions and sharing information. Some or all of the above-mentioned processes in the platform provider are performed using generative AI.

[0076] 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 excited about an event, the data collection unit will collect data immediately after the event to obtain real-time reactions. The data collection unit can also collect data during times when local residents are less stressed if they are experiencing stress in their daily lives. Furthermore, the data collection unit can collect data on weekends and holidays when local residents are relaxed to obtain more accurate data. In this way, more accurate data can be obtained by adjusting the timing of data collection based on the emotions of local residents. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0077] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can identify the most effective collection method from past data collection history and prioritize its use. The data collection unit can also select a collection method that is effective for a specific time of day or day of the week based on past data collection history. Furthermore, the data collection unit can analyze past data collection history and select the optimal collection method for a specific event or season. In this way, the optimal collection method can be selected by analyzing past data collection history. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0078] The data collection unit can filter data based on specific local events or seasons during data collection. For example, the unit can prioritize collecting relevant data during times when local festivals or events are held. It can also consider seasonal tourist trends and collect data relevant to tourist seasons. Furthermore, it can collect relevant data based on local agricultural and fishery harvest seasons. This allows for the collection of highly relevant data by filtering it based on specific local events or seasons. Some or all of the above processing in the data collection unit may be performed using AI, or it may be performed without AI.

[0079] 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. Similarly, if local residents are excited, the data collection unit can prioritize collecting data related to events or activities that cause excitement. Furthermore, if local residents are relaxed, the data collection unit can prioritize collecting data related to daily life. This allows for the priority collection of important data by determining the priority of data to collect based on the emotions of local residents. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Some or all of the above-described processing in the data collection unit may be performed using AI or not.

[0080] 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 data from the central area of ​​the region or tourist destinations. The data collection unit can also prioritize the collection of data from geographically isolated areas to analyze the impact of depopulation. Furthermore, the data collection unit can collect data in a balanced manner from commercial areas and residential areas of the region. This allows for the priority collection of highly relevant data by considering the geographical location information of the region. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0081] The data collection unit can analyze local social media activity and collect relevant data during data collection. For example, the data collection unit can collect data related to events and activities that are trending on local social media. The data collection unit can also collect opinions and impressions from local residents on social media to understand local needs. Furthermore, the data collection unit can analyze the content of social media posts to identify local trends and interests. In this way, relevant data can be collected by analyzing local social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0082] The analysis unit can estimate the emotions of local residents and adjust the presentation of the analysis based on the estimated emotions. For example, if local residents are feeling anxious, the analysis unit can select a presentation of the analysis results that provides a sense of security. It can also select a presentation of the analysis results that maintains the excitement of the residents if they are excited, or a presentation that maintains the sense of relaxation if they are relaxed. By adjusting the presentation of the analysis based on the emotions of the local residents, the system can provide analysis results that are easy for residents to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Some or all of the above-described processing in the analysis unit is performed using generative AI.

[0083] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on high-importance data and generate specific suggestions. Conversely, the analysis unit can perform a simplified analysis on low-importance data and provide only an overview. The analysis unit can also optimally allocate analysis resources according to the importance of the data. This allows for optimal resource allocation by adjusting the level of detail of the analysis based on the importance of the data. Data importance is evaluated based on criteria such as data frequency and impact. Some or all of the above processing in the analysis unit is performed using generative AI.

[0084] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, for economic data, the analysis unit applies an analysis algorithm using an economic model. It can also apply an analysis algorithm using a demographic model to demographic data. Furthermore, it can apply an analysis algorithm using a resource management model to resource data. This improves the accuracy of the analysis by applying different analysis algorithms depending on the data category. Data categories include, for example, population data, economic data, and environmental data. Some or all of the above processing in the analysis unit is performed using generative AI.

[0085] The analysis unit can estimate the emotions of local residents and adjust the length of the analysis based on the estimated emotions. For example, if local residents are in a hurry, the analysis unit will provide a short, concise analysis. If local residents are relaxed, the analysis unit can also provide a detailed analysis. If local residents are excited, the analysis unit can also provide a visually stimulating analysis. In this way, by adjusting the length of the analysis based on the emotions of local residents, it is possible to provide an analysis of an appropriate length for the residents. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Some or all of the above processing in the analysis unit is performed using generative AI.

[0086] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit can prioritize the analysis of the latest data to provide real-time information. The analysis unit can also analyze long-term trends based on historical data. Furthermore, the analysis unit can prioritize the analysis of data related to specific events or seasons. This allows for the prioritization of analysis based on the data collection period, thereby ensuring that the latest information is analyzed first. The data collection period is evaluated, for example, on a monthly, seasonal, or yearly basis. Some or all of the above processing in the analysis unit is performed using generative AI.

[0087] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data and generate specific suggestions. It can also postpone the analysis of less relevant data to improve overall analysis efficiency. Furthermore, the analysis unit can optimally allocate analysis resources according to the relevance of the data. This improves analysis efficiency by adjusting the order of analysis based on the relevance of the data. The relevance of the data is evaluated based on criteria such as correlation and causation. Some or all of the above processes in the analysis unit are performed using generative AI.

[0088] The proposal unit can estimate the emotions of local residents and adjust the way the proposal is presented based on those estimated emotions. For example, if local residents are feeling anxious, the proposal unit will select a way of presenting the proposal that provides a sense of security. It can also select a way of presenting the proposal that maintains the excitement of the residents if they are excited, or a way of presenting the proposal that maintains their relaxed state if they are relaxed. By adjusting the presentation of the proposal based on the emotions of the residents, the proposal can be made easier for them to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Some or all of the above-described processing in the proposal unit is performed using generative AI.

[0089] The proposal department can adjust the level of detail in a proposal based on its importance. For example, for highly important plans, the proposal department will provide a detailed proposal outlining specific implementation methods. For less important plans, the proposal department can provide a simplified proposal, offering only an overview. The proposal department can also optimally allocate resources to a proposal according to its importance. This allows for optimal resource allocation by adjusting the level of detail based on the plan's importance. The importance of a plan is evaluated based on criteria such as impact and feasibility. Some or all of the above processing in the proposal department is performed using generative AI.

[0090] The proposal unit can apply different proposal algorithms depending on the plan category when making a proposal. For example, for an economic revitalization plan, the proposal unit can apply a proposal algorithm using an economic model. Similarly, for a tourism promotion plan, it can apply a proposal algorithm using a tourism model. Furthermore, for a local community strengthening plan, it can apply a proposal algorithm using a community model. This improves the accuracy of proposals by applying different proposal algorithms depending on the plan category. Plan categories include, for example, economic plans, environmental plans, and social plans. Some or all of the above processing in the proposal unit is performed using generative AI.

[0091] The suggestion unit can estimate the emotions of local residents and adjust the length of the suggestion based on those emotions. For example, if a resident is in a hurry, the suggestion unit will provide a short, concise suggestion. If a resident is relaxed, the suggestion unit can provide a detailed suggestion. If a resident is excited, the suggestion unit can provide a visually stimulating suggestion. By adjusting the length of the suggestion based on the resident's emotions, the suggestion unit can provide suggestions of an appropriate length for the residents. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Some or all of the processing described above in the suggestion unit is performed using generative AI.

[0092] The proposal department can prioritize proposals based on the submission timing. For example, it can prioritize proposals for urgent plans to encourage a quick response. It can also provide detailed proposals for long-term plans to encourage planned implementation. Furthermore, the proposal department can optimally allocate resources to proposals according to their submission timing. This allows for prioritizing proposals based on their submission timing, enabling the proposal department to prioritize urgent plans. Plan submission timing is evaluated on a monthly, seasonal, or yearly basis, for example. Some or all of the above processes in the proposal department are performed using generative AI.

[0093] The proposal department can adjust the order of proposals based on their relevance during the proposal process. For example, it can prioritize highly relevant plans and provide specific implementation methods. It can also postpone less relevant plans to improve overall proposal efficiency. Furthermore, the proposal department can optimally allocate resources to proposals according to their relevance. This improves proposal efficiency by adjusting the order of proposals based on their relevance. Plan relevance is evaluated based on criteria such as correlation and causation. Some or all of the above processing in the proposal department is performed using generative AI.

[0094] The feedback collection unit can estimate the emotions of local residents and adjust the feedback collection method based on the estimated emotions. For example, if local residents are feeling anxious, the feedback collection unit can select a feedback collection method that provides a sense of security. It can also select a feedback collection method that maintains excitement if local residents are excited, or a feedback collection method that maintains relaxation if local residents are relaxed. By adjusting the feedback collection method based on the emotions of local residents, an appropriate feedback collection method can be provided for the residents. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Some or all of the above-described processing in the feedback collection unit may be performed using AI or not.

[0095] The feedback collection unit can select the optimal collection method by referring to past feedback history when collecting feedback. For example, the feedback collection unit can identify the most effective collection method from past feedback history and use that method preferentially. The feedback collection unit can also select a collection method that is effective for a specific time of day or day of the week based on past feedback history. Furthermore, the feedback collection unit can analyze past feedback history and select the optimal collection method for a specific event or season. In this way, the optimal feedback collection method can be selected by referring to past feedback history. Some or all of the above processing in the feedback collection unit may be performed using AI or not.

[0096] The feedback collection unit can estimate the emotions of local residents and determine the priority of feedback based on the estimated emotions. For example, if a local resident is feeling anxious, the feedback collection unit will prioritize collecting feedback that helps alleviate that anxiety. Similarly, if a local resident is agitated, the feedback collection unit can prioritize collecting feedback related to the events or activities that caused the agitation. Furthermore, if a local resident is relaxed, the feedback collection unit can prioritize collecting feedback related to daily life. This allows for the priority collection of important feedback by determining the priority of feedback based on the emotions of local residents. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Some or all of the above-described processes in the feedback collection unit may be performed using AI or not.

[0097] The feedback collection unit can select the optimal collection method when collecting feedback, taking into account the geographical location information of the region. For example, the feedback collection unit can prioritize collecting feedback from the central area or tourist destinations of the region. It can also prioritize collecting feedback from geographically isolated areas to analyze the impact of depopulation. Furthermore, the feedback collection unit can collect feedback in a balanced manner from commercial areas and residential areas of the region. In this way, the optimal feedback collection method can be selected by taking into account the geographical location information of the region. Some or all of the above processing in the feedback collection unit may be performed using AI, or it may be performed without using AI.

[0098] The learning unit can estimate the emotions of local residents and select training data based on those estimated emotions. For example, if local residents are feeling anxious, the learning unit will prioritize learning data that helps alleviate that anxiety. Similarly, if local residents are excited, the learning unit can prioritize learning data related to events or activities that cause that excitement. Furthermore, if local residents are relaxed, the learning unit can prioritize learning data related to their daily lives. This allows for effective learning by selecting training data based on the emotions of local residents. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Some or all of the above-described processes in the learning unit are performed using generative AI.

[0099] The learning unit can optimize its learning algorithm by referring to past learning data during the learning process. For example, the learning unit can identify the most effective learning algorithm from past learning data and prioritize its use. It can also select a learning algorithm that is effective for specific times of day or days of the week based on past learning data. Furthermore, it can analyze past learning data to select the optimal learning algorithm for specific events or seasons. This allows for the selection of the optimal learning algorithm by referring to past learning data. Past learning data includes, for example, past success stories and failure stories. Some or all of the above processes in the learning unit are performed using generative AI.

[0100] The learning unit can estimate the emotions of local residents and adjust the frequency of learning based on the estimated emotions. For example, if a local resident is feeling anxious, the learning unit will increase the frequency of learning to alleviate the anxiety. The learning unit can also frequently learn data related to events or activities that cause excitement if the local resident is excited. Furthermore, if the local resident is relaxed, the learning unit can regularly learn data related to daily life. This allows for effective learning by adjusting the frequency of learning based on the emotions of the local residents. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Some or all of the above-described processes in the learning unit are performed using generative AI.

[0101] The learning unit can weight the training data based on when the data was collected during training. For example, the learning unit can give a high weight to the most recent data, emphasizing real-time information. It can also give a low weight to historical data, emphasizing long-term trends. Furthermore, the learning unit can appropriately weight data related to specific events or seasons, enabling balanced learning. This allows for balanced learning by weighting the training data based on when the data was collected. The data collection period can be evaluated, for example, on a monthly, seasonal, or yearly basis. Some or all of the above processing in the learning unit is performed using generative AI.

[0102] The platform provider can estimate the emotions of local residents and adjust the platform's display method based on the estimated emotions. For example, if local residents are feeling anxious, the platform provider can select a display method that provides a sense of security. It can also select a display method that maintains excitement if local residents are excited, or a display method that maintains relaxation if local residents are relaxed. By adjusting the platform's display method based on the emotions of local residents, the platform provider can provide an appropriate display method for them. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Some or all of the above-described processing in the platform provider is performed using generative AI.

[0103] The platform provider can select the optimal display method by referring to the past operation history of local residents when displaying the platform. For example, the platform provider can identify the most effective display method from past operation history and use that method preferentially. The platform provider can also select a display method that is effective for a specific time of day or day of the week based on past operation history. Furthermore, the platform provider can analyze past operation history and select the optimal display method for a specific event or season. In this way, the optimal display method can be selected by referring to past operation history. Past operation history includes, for example, click history and browsing history. Some or all of the above processing in the platform provider may be performed using AI or not.

[0104] The platform provider can estimate the emotions of local residents and adjust the platform's operating procedures based on those estimated emotions. For example, if a local resident is feeling anxious, the platform provider can select operating procedures that provide a sense of security. It can also select operating procedures that maintain excitement if the local resident is excited, or that maintain relaxation if the local resident is relaxed. By adjusting the platform's operating procedures based on the local resident's emotions, the platform provider can provide appropriate procedures for the residents. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Some or all of the above-described processing in the platform provider is performed using generative AI.

[0105] The platform provider can select the optimal display method when displaying the platform, taking into account the device information of local residents. For example, if a local resident is using a smartphone, the platform provider can provide a display method that matches the screen size. Furthermore, if a local resident is using a tablet, the platform provider can provide a display method optimized for larger screens. Also, if a local resident is using a smartwatch, the platform provider can provide a concise and highly visible display method. This allows the platform provider to select the optimal display method by considering the device information of local residents. Device information includes, for example, the device type, OS, and browser. Some or all of the above processing in the platform provider may be performed using AI, or it may be performed without AI.

[0106] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0107] The regional revitalization support system can further propose plans that take into account the local cultural background. For example, the analysis unit collects data on the region's history and traditional events and proposes unique events based on this data. The proposal unit can also generate tourism plans that utilize the region's cultural resources. Furthermore, the feedback collection unit can collect opinions based on the cultural background of residents, analyze them, and reflect them in the plan. This enables the provision of revitalization plans that leverage the region's cultural characteristics.

[0108] The analysis unit can estimate the emotions of local residents and adjust the analysis priority based on those estimated emotions. For example, if residents are feeling anxious, it will prioritize analyzing data that helps alleviate that anxiety. Similarly, if residents are agitated, it can prioritize analyzing data that may be causing that agitation. Furthermore, if residents are relaxed, it can prioritize analyzing data related to their daily lives. By adjusting the analysis priority based on the emotions of local residents, the system can prioritize providing information that is important to them.

[0109] The proposal department can propose plans that take local climate data into consideration. For example, the analysis department collects seasonal climate data for the region and uses this to propose seasonal events and activities. The proposal department can also generate environmental improvement plans that respond to climate change. Furthermore, the feedback collection department can collect residents' opinions on the climate, analyze them, and reflect them in the plan. This makes it possible to provide revitalization plans that take into account the local climate characteristics.

[0110] The feedback collection unit can estimate the emotions of local residents and adjust the feedback collection method based on those estimated emotions. For example, if local residents are feeling anxious, it can select a feedback collection method that provides a sense of security. Similarly, if local residents are excited, it can select a feedback collection method that maintains their excitement. Furthermore, if local residents are relaxed, it can select a feedback collection method that maintains their relaxed state. By adjusting the feedback collection method based on the emotions of local residents, the system can provide them with an appropriate feedback collection method.

[0111] The Learning Department can propose plans utilizing local educational data. For example, the Analysis Department collects data from local schools and educational institutions and uses this to propose education-related events and programs. The Proposal Department can also generate learning plans that utilize local educational resources. Furthermore, the Feedback Collection Department can collect residents' opinions on education, analyze them, and reflect them in the plans. This allows for the provision of revitalization plans that take into account the specific educational characteristics of the region.

[0112] The platform provider can estimate the emotions of local residents and adjust the platform's display methods based on those estimates. For example, if local residents are feeling anxious, it can select a display method that provides a sense of security. Similarly, if local residents are excited, it can select a display method that maintains that excitement. Furthermore, if local residents are relaxed, it can select a display method that maintains that relaxed state. By adjusting the platform's display methods based on the emotions of local residents, it is possible to provide them with a display method that is appropriate for them.

[0113] The data collection unit can propose plans using local traffic data. For example, the analysis unit collects local traffic volume and public transport data and proposes traffic improvement plans based on this. The proposal unit can also propose tourist routes based on traffic data. Furthermore, the feedback collection unit can collect residents' opinions on traffic, analyze them, and reflect them in the plans. This makes it possible to provide revitalization plans that take into account the characteristics of local traffic.

[0114] 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 excited about an event, data collection can be performed immediately after the event to obtain real-time reactions. Alternatively, if local residents are experiencing stress in their daily lives, data collection can be performed during times when stress levels are lower. Furthermore, data collection can be performed on weekends or holidays when local residents are more relaxed to obtain more accurate data. In this way, by adjusting the timing of data collection based on the emotions of local residents, more accurate data can be obtained.

[0115] The data collection unit can propose plans utilizing local health data. For example, the analysis unit collects local medical institutions and health-related data and proposes health promotion plans based on this data. The proposal unit can also propose sports events and health seminars based on health data. Furthermore, the feedback collection unit can collect residents' opinions on health, analyze them, and reflect them in the plans. This allows for the provision of revitalization plans that take into account the health characteristics of the region.

[0116] The proposal team can estimate the emotions of local residents and adjust the way the proposal is presented based on those estimated emotions. For example, if local residents are feeling anxious, they can select a way of presenting the proposal that provides a sense of security. If local residents are excited, they can select a way of presenting the proposal that maintains that excitement. Furthermore, if local residents are relaxed, they can select a way of presenting the proposal that maintains that relaxed feeling. By adjusting the way the proposal is presented based on the emotions of local residents, it becomes possible to provide proposals that are easy for residents to understand.

[0117] The following briefly describes the processing flow for example form 2.

[0118] Step 1: The data collection unit collects local data. For example, the data collection unit collects local population data, economic data, environmental data, etc. The data collection unit can collect data using sensors, databases, APIs, etc. For example, the data collection unit can obtain local population data from government statistical databases. The data collection unit can also obtain local economic data from commercial databases. Furthermore, the data collection unit can collect environmental data in real time using sensors. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses generative AI to analyze the data. For example, the analysis unit uses generative AI and data analysis algorithms to analyze regional demographics and economic conditions. The analysis unit can also use generative AI and data mining techniques to analyze regional resource data. Furthermore, the analysis unit can use generative AI and machine learning algorithms to analyze data patterns. Step 3: The proposal unit proposes an optimal revitalization plan based on the analysis results obtained by the analysis unit. The proposal unit proposes a plan using generative AI. For example, the proposal unit uses generative AI to generate economic revitalization plans and environmental improvement plans using a proposal generation algorithm. The proposal unit can also use generative AI to predict the effects of the plan using simulation technology. Furthermore, the proposal unit can have generative AI learn from past success stories and propose effective measures. Step 4: The feedback collection unit collects feedback from residents. The feedback collection unit uses AI to collect feedback. For example, the feedback collection unit collects residents' opinions using a survey system. The feedback collection unit can also analyze residents' opinions using feedback analysis software. Furthermore, the feedback collection unit can analyze residents' opinions using natural language processing technology.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, feedback collection unit, learning unit, and platform provision 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 local data using the sensors and database of the smart device 14. The analysis unit analyzes the data using generated AI by the specific processing unit 290 of the data processing unit 12. The proposal unit proposes an optimal activation plan using generated AI by the specific processing unit 290 of the data processing unit 12. The feedback collection unit collects residents' opinions using the questionnaire system of the smart device 14. The learning unit learns past success stories using generated AI by the specific processing unit 290 of the data processing unit 12. The platform provision unit provides an online platform using the web server of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0123] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, feedback collection unit, learning unit, and platform provision 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 local data using the sensors and database of the smart glasses 214. The analysis unit analyzes the data using generated AI by the specific processing unit 290 of the data processing unit 12. The proposal unit proposes an optimal activation plan using generated AI by the specific processing unit 290 of the data processing unit 12. The feedback collection unit collects residents' opinions using the questionnaire system of the smart glasses 214. The learning unit learns past success stories using generated AI by the specific processing unit 290 of the data processing unit 12. The platform provision unit provides an online platform using the web server of the smart glasses 214. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0139] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, feedback collection unit, learning unit, and platform provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects local data using the sensors and database of the headset terminal 314. The analysis unit analyzes the data using generated AI by the specific processing unit 290 of the data processing unit 12. The proposal unit proposes an optimal activation plan using generated AI by the specific processing unit 290 of the data processing unit 12. The feedback collection unit collects residents' opinions using the questionnaire system of the headset terminal 314. The learning unit learns past success stories using generated AI by the specific processing unit 290 of the data processing unit 12. The platform provision unit provides an online platform using the web server of the headset terminal 314. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0155] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, feedback collection unit, learning unit, and platform provision unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit collects local data using the sensors and database of the robot 414. The analysis unit analyzes the data using generated AI by the specific processing unit 290 of the data processing unit 12. The proposal unit proposes an optimal activation plan using generated AI by the specific processing unit 290 of the data processing unit 12. The feedback collection unit collects residents' opinions using the survey system of the robot 414. The learning unit learns past success stories using generated AI by the specific processing unit 290 of the data processing unit 12. The platform provision unit provides an online platform using the web server of the robot 414. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] (Note 1) The data collection department collects local data, An analysis unit analyzes the data collected by the aforementioned collection unit, A proposal unit proposes an optimal activation plan based on the analysis results obtained by the aforementioned analysis unit, It includes a feedback collection unit that collects feedback from residents. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Analyze local demographics, economic conditions, and resource data in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, Based on the analysis results, an optimal activation plan is generated. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned feedback collection unit is Collect and analyze feedback from residents using natural language processing. The system described in Appendix 1, characterized by the features described herein. (Note 5) It includes a learning section for studying past success stories. The system described in Appendix 1, characterized by the features described herein. (Note 6) It has a platform provider department that provides an online platform. The system described in Appendix 1, characterized by the features described herein. (Note 7) 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 8) The aforementioned collection unit is Analyze past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filter it based on specific local events or seasons. The system described in Appendix 1, characterized by the features described herein. (Note 10) 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 11) 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 12) 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 13) The aforementioned analysis unit, We estimate the sentiments of local residents and adjust the representation of the analysis based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the sentiments of local residents and adjusts the length of the analysis based on these estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, We estimate the sentiments of local residents and adjust the way the proposal is expressed based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail in the proposal based on the importance of the plan. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When submitting a proposal, different proposal algorithms are applied depending on the plan category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, The system estimates the sentiments of local residents and adjusts the length of the proposal based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When submitting proposals, we will prioritize them based on when the plans were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the plan. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned feedback collection unit is We estimate the sentiments of local residents and adjust the feedback collection method based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned feedback collection unit is When collecting feedback, refer to past feedback history to select the most suitable collection method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned feedback collection unit is The system estimates the sentiments of local residents and prioritizes feedback based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned feedback collection unit is When collecting feedback, the optimal collection method will be selected considering the geographical location information of the region. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned learning unit, The system estimates the sentiments of local residents and selects training data based on these estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned learning unit, The system estimates the sentiments of local residents and adjusts the frequency of learning based on these estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned learning unit, During training, the training data is weighted based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned platform provider unit, The platform estimates the sentiments of local residents and adjusts how the platform is displayed based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned platform provider unit, When displaying the platform, the system selects the optimal display method by referring to the past operation history of local residents. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned platform provider unit, The system estimates the sentiments of local residents and adjusts the platform's operating procedures based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned platform provider unit, When displaying the platform, the optimal display method is selected considering the device information of local residents. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0191] 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. The data collection department collects local data, An analysis unit analyzes the data collected by the aforementioned collection unit, A proposal unit proposes an optimal activation plan based on the analysis results obtained by the aforementioned analysis unit, It includes a feedback collection unit that collects feedback from residents. A system characterized by the following features.

2. The aforementioned analysis unit, Analyze local demographics, economic conditions, and resource data in real time. The system according to feature 1.

3. The aforementioned proposal section is, Based on the analysis results, an optimal activation plan is generated. The system according to feature 1.

4. The aforementioned feedback collection unit is Collect and analyze feedback from residents using natural language processing. The system according to feature 1.

5. It includes a learning section for studying past success stories. The system according to feature 1.

6. It has a platform provider department that provides an online platform. The system according to feature 1.

7. 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 according to feature 1.

8. The aforementioned collection unit is Analyze past data collection history and select the optimal collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting data, filter it based on specific local events or seasons. The system according to feature 1.

10. 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 according to feature 1.

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