system

Abstract

Provide a system. 【Solution means】 Means for identifying a data source, Means for collecting data and storing it in a database, Means for preprocessing and analyzing the collected data, Means for generating an action plan based on the analysis results, Means for notifying the action plan to the person in charge of the local government, Means for soliciting opinions and participation through an application for citizens, Means for collecting and analyzing feedback from citizens, Means for providing improvement proposals based on the collected feedback, A system including

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 and includes 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 in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Local governments collect and analyze a large amount of data for the purpose of regional revitalization and improvement of resident services, and formulate various action plans based on the results. However, it is difficult to efficiently and effectively perform processes such as data collection and analysis, action plan generation, and promotion of resident participation, and it is an issue to maximize the effect of regional revitalization measures. In addition, there is a need for the ability to quickly collect feedback from residents and adjust plans based on it. The present invention aims to provide a system that automates and optimizes these processes, greatly improving the work efficiency of local governments and enhancing the effect of regional revitalization measures.

Means for Solving the Problems

[0005] To solve the above problems, the present invention provides the following means:

[0006] By providing a system that includes means for identifying data sources, collecting data and storing it in a database, pre-processing and analyzing the collected data, generating action plans based on the analysis results, notifying local government officials of the action plans, soliciting opinions and participation through a citizen application, collecting and analyzing feedback from citizens, and providing improvement proposals based on the collected feedback, it is possible to efficiently automate and optimize a series of processes for local governments, from data collection to plan formulation, citizen participation, and feedback collection and analysis.

[0007] Specifically, this includes means of conducting surveys using a resident application to collect opinions and participation intentions, and means of transmitting the collected survey results in real time. It also includes means of notifying local government officials of the analysis results and predicted effects, and means of generating action plans, including proposals for events and campaigns. This will enable local governments to implement regional development measures quickly and effectively and formulate plans that reflect the opinions of residents.

[0008] ---

[0009] A "data source" refers to the source of information that a local government uses to obtain the information it needs.

[0010] "Data collection" refers to the act of obtaining necessary information from a specified data source.

[0011] A "database" refers to a system for systematically storing and managing collected data.

[0012] "Preprocessing" refers to the process of performing actions such as imputing missing values ​​and normalizing data before analyzing the collected data.

[0013] "Analysis" refers to the calculations and application of models used to identify specific patterns or trends in collected data.

[0014] An "action plan" refers to a document that proposes specific measures and event plans based on the analysis results.

[0015] "Notification" refers to the act of communicating the generated action plan and analysis results to the relevant local government officials.

[0016] "Applications for citizens" refers to application software provided for use by citizens.

[0017] "Soliciting opinions and participation" refers to the act of collecting feedback and indications of willingness to participate from residents.

[0018] "Feedback" refers to opinions and comments provided by residents.

[0019] An "improvement proposal" refers to measures suggested to review and optimize existing plans and policies based on collected feedback.

[0020] "Real-time" refers to a situation where data and information are processed and transmitted almost instantly.

[0021] "Predictive effect" refers to the result of predicting the expected effects or outcomes of implementing a specific action plan.

[0022] "Proposals for events and campaigns" refer to specific event and campaign plans implemented for regional development. [Brief explanation of the drawing]

[0023] [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]It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0024] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0025] First, let's explain the terminology used in the following explanation.

[0026] 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), and APU (Accelerated Processing Unit).

[0027] 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.

[0028] 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.

[0029] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0030] 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0031] [First Embodiment]

[0032] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0033] As shown in Figure 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.

[0034] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0035] The smart device 14 comprises a computer 36, a reception 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 reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0036] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0037] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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.

[0038] 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.

[0039] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0040] 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.

[0041] The 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.

[0042] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0043] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0044] Patent Specification

[0045] The system of this invention aims to revitalize local governments by collecting and analyzing various data and generating concrete action plans. The system mainly consists of three components: a server, terminals, and users.

[0046] 1. Data Collection

[0047] The server is responsible for the first stage of data collection. First, the server identifies multiple data sources related to local governments. These include government open data portals, local government websites, and tourism information sites. Next, the server collects the necessary data using APIs and scraping techniques, and stores it in its internal database.

[0048] Specific example: A server uses an API to retrieve and store data on the annual number of tourists in Niigata Prefecture from the government's open data portal.

[0049] 2. Data Analysis

[0050] The server also handles data analysis. The collected data is first pre-processed. Pre-processing includes data normalization and imputation of missing values. Next, an AI model is run using the pre-processed data to analyze the strengths and weaknesses of the region. From the analysis results, areas that require particular focus on improvement are identified.

[0051] Specific example: The server analyzes data on the annual number of tourists in Niigata Prefecture and identifies that the number of tourists in autumn is lower than in other seasons.

[0052] 3. Generate an action plan

[0053] Based on the analysis results, the server uses generative AI to generate specific action plans. For example, it might suggest events or campaigns specifically tailored to periods with low tourist numbers.

[0054] Specific example: The server generates a proposal to hold an "Autumn Harvest Festival" in Niigata Prefecture to increase tourism during the autumn season.

[0055] 4. Notification to the user

[0056] The server notifies local government officials (users) of the generated action plan and analysis results. Notifications are sent via email or a dedicated dashboard.

[0057] Specific example: The server notifies the tourism official in Niigata Prefecture via email of the generated "Autumn Harvest Festival" plan and its predicted effects.

[0058] 5. Promoting citizen participation

[0059] The terminal will provide a function to inform citizens about local government plans through a citizen-oriented application. The application will allow for event announcements and surveys to collect residents' opinions and willingness to participate.

[0060] Specific example: A device uses an app for residents of Niigata Prefecture to distribute a survey about the "Autumn Harvest Festival" and collect citizens' willingness to participate.

[0061] 6. Feedback Analysis

[0062] The server analyzes feedback collected from citizens and evaluates the effectiveness of the plan. It then generates adjustments and improvements to the plan as needed and notifies the user again.

[0063] Specific example: The server analyzes the results of a survey from citizens, confirms the high level of interest in the "Autumn Harvest Festival," and proposes expanding the scale of the event.

[0064] ---

[0065] This invention aims to support local governments in effectively and efficiently planning and implementing regional development measures by automating and optimizing these processes. This is expected to lead to regional revitalization.

[0066] The following describes the processing flow.

[0067] Program processing steps

[0068] Step 1: Identify the data source

[0069] The server identifies data sources for collecting relevant local government data. These data sources include government open data portals, local government websites, and tourism information sites.

[0070] Step 2: Data Collection

[0071] The server collects data from specified data sources using APIs and scraping techniques, and stores it in its internal database.

[0072] Step 3: Data Preprocessing

[0073] The server preprocesses the collected data. Specifically, it performs data normalization, imputation of missing values, and standardization of the format.

[0074] Step 4: Data Analysis

[0075] The server uses pre-processed data to perform AI modeling and analyze the region's strengths and weaknesses. This identifies key challenges and opportunities that require particular focus.

[0076] Step 5: Generate an action plan

[0077] The server uses generative AI to generate specific action plans based on the analysis results. These plans may include proposals for events and campaigns.

[0078] Step 6: Notify the user

[0079] The server notifies local government officials (users) of the generated action plan and its analysis results. Notifications are sent via email or a dedicated dashboard.

[0080] Step 7: Promoting citizen participation

[0081] The terminals will inform citizens about the local government's plans through a citizen-oriented application. Furthermore, the application will be used to conduct surveys and collect opinions and intentions to participate.

[0082] Step 8: Gathering Feedback

[0083] The device collects survey results and other feedback and sends them to the server.

[0084] Step 9: Feedback Analysis

[0085] The server analyzes feedback from citizens and evaluates the effectiveness of the plan. Based on the analysis results, it makes necessary adjustments and generates improvement proposals.

[0086] Step 10: Notification of proposed improvements

[0087] The server optimizes the plan by cycling through each step and notifying the local government official (user) of improvement suggestions generated from the analyzed feedback.

[0088] (Example 1)

[0089] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0090] In revitalizing local governments, the insufficient effective use of data makes it difficult to create concrete action plans and effectively promote citizen participation. Furthermore, formulating improvement measures based on collected feedback requires considerable time and effort, making it difficult to implement rapid and effective regional development measures. To solve this problem, an automated and optimized process is needed that unifies everything from data collection to action plan generation, citizen participation promotion, and feedback analysis.

[0091] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0092] In this invention, the server includes means for identifying data sources, means for collecting data and storing it in a database, means for preprocessing and analyzing the collected data, means for generating an action plan using an artificial intelligence model based on the analysis results, means for notifying local government officials of the action plan, means for soliciting opinions and participation through application software for citizens, means for collecting and analyzing feedback from citizens, and means for providing improvement proposals based on the collected feedback. This enables local governments to effectively utilize data, quickly generate and implement action plans that reflect citizens' opinions and participation, and further formulate improvement measures based on feedback from citizens.

[0093] A "data source" refers to a website, portal site, or similar online information provider that offers information related to local governments.

[0094] A "database" refers to a computer system used to manage and store collected data, which is later used for analysis or other processes.

[0095] "Preprocessing" refers to a series of steps that transform collected raw data into an analyzable format, including data normalization, imputation of missing values, and data type conversion.

[0096] "Analysis" refers to data processing techniques that use statistical methods or machine learning models on pre-processed data to elucidate specific objectives or propositions.

[0097] A "generative artificial intelligence model" refers to a system that uses artificial intelligence to automatically generate proposals and plans based on input conditions, specifically employing technologies such as natural language processing.

[0098] An "action plan" is a compilation of specific action plans and proposals based on the analysis results, and includes proposals for measures and events that local governments can implement.

[0099] "Application software" refers to devices and applications provided to citizens, through which information is provided and surveys are conducted.

[0100] "Feedback" refers to opinions and evaluations collected from citizens through application software, which are used to improve the system.

[0101] The system of this invention is configured to automate data collection, analysis, action plan generation, and feedback analysis, with the aim of revitalizing local governments. The system mainly consists of three components: a server, terminals, and users.

[0102] 1. Data Collection

[0103] First, the server is responsible for data collection. Specifically, the server identifies multiple data sources related to local governments (e.g., government open data portals, local government official websites, tourism information sites, etc.) and collects data using APIs and scraping techniques. In this process, libraries such as Python's Requests, BeautifulSoup, and Scrapy are often used. The collected data is stored in databases such as MySQL® or PostgreSQL.

[0104] Specific example: A server automatically executes a script at 2 AM every day to retrieve annual tourist data for Niigata Prefecture from the government's open data portal and save it to a database.

[0105] 2. Data Analysis

[0106] Next, the server preprocesses and analyzes the collected data. Preprocessing includes data normalization, missing value imputation, and data type conversion, using the Pandas library. The preprocessed data is then analyzed using machine learning libraries such as TENSORFLOW® and PyTorch. This analysis identifies the region's strengths and weaknesses.

[0107] Specific example: The server preprocesses annual tourist data for Niigata Prefecture and uses Scikit-learn's clustering algorithm to identify that the number of tourists in autumn is lower than in other seasons.

[0108] 3. Generate an action plan

[0109] The server generates an action plan based on the analysis results using an artificial intelligence model (e.g., GPT-3® or BERT). The generated action plan includes suggestions for specific events and campaigns.

[0110] Specific example: The server generates a prompt based on the analysis results, inputs the prompt "Generate event proposals to increase the number of tourists in Niigata Prefecture during the autumn season" into GPT-3, and saves the generated "Autumn Harvest Festival" proposal as the final action plan.

[0111] 4. Notification to the user

[0112] The server notifies the local government official (user) of the generated action plan and analysis results. Notification can be sent via email using an SMTP server, or through a dedicated dashboard utilizing a web application such as Django or Flask.

[0113] Specific example: The server will notify tourism officials in Niigata Prefecture via email of the generated "Autumn Harvest Festival" plan and its predicted effects. This information will also be available for real-time monitoring via a dashboard.

[0114] 5. Promoting citizen participation

[0115] The device will use a citizen-facing application to disseminate action plans to citizens and encourage their opinions and participation. This application will be developed using Flutter® and React Native. The application will allow for event announcements and surveys, enabling the collection of residents' opinions and willingness to participate.

[0116] Specific example: A device uses an app for residents of Niigata Prefecture to distribute detailed information about the "Autumn Harvest Festival" and conducts a survey to collect participation intentions.

[0117] 6. Feedback Analysis

[0118] Finally, the server analyzes the feedback collected from citizens. The same techniques as in preprocessing are used for the analysis, and the effectiveness of the plan is evaluated based on the results. If necessary, adjustments or improvements to the plan are generated and notified to the user again.

[0119] Specific example: The server analyzes the results of a survey from citizens in real time and confirms the high level of interest in the "Autumn Harvest Festival." Based on this, it notifies users of a suggestion to expand the scale of the event.

[0120] Thus, the system of the present invention supports local governments in effectively utilizing data and in quickly and accurately formulating and implementing regional development measures.

[0121] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0122] System program processing flow

[0123] Step 1: Identify the data source

[0124] Step 2: Data Collection

[0125] Step 3: Save data

[0126] Step 4: Data preprocessing

[0127] Step 5: Data Analysis

[0128] Step 6: Generate an action plan

[0129] Step 7: Notify the user

[0130] Step 8: Distribution of information to citizens

[0131] Step 9: Gathering feedback from citizens

[0132] Step 10: Analyze feedback and notify of improvement suggestions

[0133] ---

[0134] Detailed explanation of each processing step

[0135] Step 1: Identify the data source

[0136] The server identifies the data sources necessary for data collection. First, it lists data sources related to local governments (e.g., government open data portals, local government official websites, tourism information sites, etc.) and determines the data items to be collected from these data sources. The input is the URLs or API keys of the data sources, and the output is a list of data to be collected.

[0137] Specific operation: The server identifies the URL of the government's open data portal based on a defined list and retrieves its API key.

[0138] Step 2: Data Collection

[0139] The server collects data from identified data sources. Specifically, it retrieves data using APIs or scraping techniques. Inputs include a list of data sources, API keys, and collection scripts, while output is the collected raw data.

[0140] Specific operation: The server automatically executes a script at 2 AM every day, using the API of the government's open data portal to retrieve data on the annual number of tourists in Niigata Prefecture.

[0141] Step 3: Save data

[0142] The server stores the collected data in a database. Input is raw data, and output is the data stored in the database. Database systems such as MySQL and PostgreSQL are primarily used.

[0143] Specific operation: The server collects data on the annual number of tourists in Niigata Prefecture and saves it to a MySQL database.

[0144] Step 4: Data preprocessing

[0145] The server preprocesses the collected data. This preprocessing includes data normalization, missing value imputation, and data type conversion. The input is raw data, and the output is preprocessed data. The Pandas library is commonly used for this.

[0146] Specific operation: The server uses Pandas to impute missing values ​​in the annual tourist count data for Niigata Prefecture and to format the data types.

[0147] Step 5: Data Analysis

[0148] The server analyzes the pre-processed data. Specifically, it uses machine learning libraries such as TensorFlow and PyTorch to analyze the data and identify regional strengths and weaknesses. The input is pre-processed data, and the output is the analysis results.

[0149] Specific operation: The server uses Niigata Prefecture's annual tourist data and the Scikit-learn clustering algorithm to identify that the number of tourists is low in the autumn.

[0150] Step 6: Generate an action plan

[0151] The server generates an action plan based on the analysis results using a generative AI model (e.g., GPT-3 or BERT). The input is the analysis results, and the output is the generated action plan.

[0152] Specific operation: The server creates a prompt based on the analysis results and inputs the prompt "Generate event proposals to increase the number of tourists in Niigata Prefecture during the autumn season" to GPT-3. As a result, it saves the generated "Autumn Harvest Festival" plan.

[0153] Step 7: Notify the user

[0154] The server notifies the user (local government official) of the generated action plan and analysis results. Specifically, this can be done by sending an email using an SMTP server or by using a dedicated dashboard built with Django or Flask. The inputs are the generated action plan and analysis results, and the output is the notified information.

[0155] Specific operation: The server will notify tourism officials in Niigata Prefecture via email about the "Autumn Harvest Festival" plan and its projected effects. The information will also be displayed on a dedicated dashboard.

[0156] Step 8: Distribution of information to citizens

[0157] The device uses a user-facing application to disseminate action plans to citizens and solicit their opinions and participation. The input is the action plan, and the output is the notification content for citizens. Application development using Flutter or React Native falls into this category.

[0158] Specific actions: The device will distribute detailed information about the "Autumn Harvest Festival" via a user-facing app and conduct a survey to encourage residents to participate in the event.

[0159] Step 9: Gathering feedback from citizens

[0160] The device collects feedback from citizens. The input is feedback data from citizens, and the output is the collected feedback. Specific methods include surveys and event evaluations.

[0161] Specific operation: The device sends the results of surveys conducted using the user-facing app to the server in real time and collects feedback.

[0162] Step 10: Analyze feedback and notify of improvement suggestions

[0163] The server analyzes the collected feedback and generates improvement proposals. Specifically, it uses a preprocessing, analysis, and generative AI model to generate improvement proposals. The input is feedback from citizens, and the output is the generated improvement proposals.

[0164] Specific operation: The server analyzes the results of a survey from citizens and confirms the high level of interest in the "Autumn Harvest Festival." Based on this, it notifies the local government officials of a proposal to expand the scale of the event.

[0165] (Application Example 1)

[0166] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0167] Local governments need to effectively utilize a large amount of data for regional development and attracting tourists, but collecting, analyzing, and generating appropriate action plans for this data is difficult. In particular, the lack of effective means to collect and analyze real-time feedback from citizens and tourists is reducing the effectiveness of regional development measures. Furthermore, there is a need for a system that effectively communicates information to local government officials and supports rapid decision-making.

[0168] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0169] In this invention, the server includes means for identifying data sources, collecting data and storing it in a database, preprocessing and analyzing the collected data, generating an action plan based on the analysis results, notifying local government officials of the action plan, soliciting opinions and participation through a citizen-facing application, collecting and analyzing feedback from citizens, providing improvement proposals based on the collected feedback, providing information and conducting surveys using an interface installed in a vehicle, and generating an action plan using a generative AI model. This enables local governments to collect and analyze data in real time and generate and implement effective action plans. Furthermore, by utilizing the interface in the vehicle, feedback from citizens and tourists can be collected quickly, enhancing the effectiveness of regional development measures.

[0170] "Means for identifying data sources" refers to a mechanism for finding multiple data sources that provide information related to local governments.

[0171] "Means of collecting data and storing it in a database" refers to a system for obtaining information from identified data sources, organizing it, and storing it in a database.

[0172] "Means for preprocessing and analyzing collected data" refers to a system for performing preprocessing such as normalization and imputation of missing values ​​on collected data, and then conducting data analysis.

[0173] "Means for generating action plans based on analysis results" refers to a system that automatically generates specific action plans and policy proposals using the results of analyzed data.

[0174] "Means for notifying local government officials of the action plan" refers to a system for quickly communicating the generated action plan to local government officials.

[0175] "Methods for soliciting opinions and participation through citizen-oriented applications" refers to a system that uses applications for residents to gather opinions and intentions to participate in events from citizens.

[0176] "Means for collecting and analyzing feedback from citizens" refers to a system for collecting feedback information submitted by citizens and systematically analyzing it.

[0177] "A means of providing improvement proposals based on collected feedback" refers to a system that generates improvement proposals for regional development and events based on the analyzed feedback results, and reflects them in the policies of local governments.

[0178] "Methods for providing information and conducting surveys using interfaces installed in automobiles" refers to systems that provide information to passengers through displays or voice assistants installed in autonomous vehicles and other vehicles, and collect opinions through surveys.

[0179] "Methods for generating action plans using generative AI models" refer to a system that uses artificial intelligence models to perform advanced analysis and data generation, and automatically creates effective action plans.

[0180] Modes for carrying out the invention

[0181] System Configuration

[0182] The system of this invention mainly consists of three components: a server, a terminal, and a user. Details of each component are described below.

[0183] Data collection

[0184] The server is responsible for the first stage of data collection. First, the server identifies multiple data sources related to local governments, such as the government's open data portal, local government websites, and tourism information sites. Next, the server collects the necessary data using APIs and web scraping techniques, and stores it in its internal database.

[0185] Specific example: A server uses an API to retrieve data on the annual number of tourists for a given municipality and saves it to a database.

[0186] Data Analysis

[0187] The server preprocesses the collected data and uses an AI model to analyze the region's strengths and weaknesses. Preprocessing includes data normalization and imputation of missing values. Based on the preprocessed data, the AI ​​model is run to identify areas that require focused improvement.

[0188] Specific example: The server analyzes the data and discovers that the number of tourists is low during a particular season.

[0189] Action plan generation

[0190] The server generates specific action plans using an AI model based on the analysis results. For example, it might suggest events or campaigns specifically tailored to periods with low tourist numbers.

[0191] Specific example: The server proposes a plan to hold an "Autumn Harvest Festival" using a generated AI model.

[0192] Example prompt: "Generate a specific event plan to increase tourism during the autumn season."

[0193] Notification to the user

[0194] The server notifies the local government official (user) of the generated action plan and analysis results. Notifications are sent via email or a dedicated dashboard.

[0195] Specific example: The server notifies the local government's tourism official via email of the generated "Autumn Harvest Festival" plan and its predicted effects.

[0196] Promoting citizen participation

[0197] The terminals provide a function to inform citizens about local government plans through citizen-oriented applications. An interface installed in the vehicle is used to provide information to tourists and conduct surveys. This allows for the collection of feedback from both citizens and tourists.

[0198] Specific example: A terminal uses a user-facing application to conduct a survey about the "Autumn Harvest Festival" and collect information on citizens' willingness to participate.

[0199] Feedback analysis

[0200] The server analyzes feedback collected from citizens and evaluates the effectiveness of the plan. It then generates adjustments and improvements to the plan as needed and notifies users again, thereby achieving sustainable regional development.

[0201] Specific example: If the server analyzes the survey results and confirms high interest, it will propose expanding the scale of the "Autumn Harvest Festival."

[0202] Hardware and software to use

[0203] Server: Responsible for data collection and analysis. Uses APIs, web scraping, database management systems, and AI model execution environments (e.g., Python, pandas, scikit-learn, GPT-3).

[0204] Devices: Citizen applications and interfaces installed in vehicles will utilize smartphones, tablets, and displays and voice assistants on autonomous vehicles.

[0205] Software: Python libraries (pandas, scikit-learn) are used for data preprocessing and analysis, and GPT-3 is used for generative AI models. Email systems and dashboard software are used for user notifications.

[0206] Thus, this invention is a system that supports local governments in effectively and efficiently planning and implementing regional development measures. This is expected to revitalize local communities.

[0207] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0208] Step 1:

[0209] Identify the data source.

[0210] The server identifies data sources such as government open data portals, local government websites, and tourism information sites. First, the server obtains the URLs of APIs and web pages necessary to collect information from the internet. Once the data sources have been identified, the server confirms the format and structure of the data to be collected and prepares for the next data collection step.

[0211] Input: API URL, Web page URL

[0212] Output: List of URLs for collection

[0213] Step 2:

[0214] Data acquisition and storage

[0215] The server collects data from identified data sources and stores it in a database. Data collection is performed using API requests or web scraping techniques. The retrieved data is initially stored in temporary memory and then permanently stored in the database. At this time, the database also records metadata such as the collection date and time and the source.

[0216] Input: URL list

[0217] Output: Raw data stored in the database

[0218] Step 3:

[0219] Data preprocessing

[0220] The server preprocesses the collected data. This process includes normalizing the data, imputing missing values, and removing unnecessary data. Specifically, it uses the Python pandas library to manipulate dataframes, imputing missing values ​​with SimpleImputer, and normalizing with StandardScaler. Once preprocessing is complete, the data is prepared in a format suitable for analysis.

[0221] Input: Raw data

[0222] Output: Preprocessed data

[0223] Step 4:

[0224] Data Analysis

[0225] The server analyzes the pre-processed data. In this step, an AI model is used to analyze the data and identify regional strengths and weaknesses. A Python machine learning library (e.g., scikit-learn) is used to perform data analysis, extracting specific insights using correlation matrices, regression analysis, etc.

[0226] Input: Preprocessed data

[0227] Output: Analysis results (regional strengths and weaknesses)

[0228] Step 5:

[0229] Generating an action plan

[0230] The server generates an action plan using a generative AI model based on the analysis results. Using generative AI such as GPT-3, it generates regional development measures by inputting specific prompt sentences. For example, the prompt sentence "Generate a specific event plan to increase the number of tourists in the autumn" is used. The generated action plan is output in text format.

[0231] Input: Analysis result, prompt message

[0232] Output: Action plan (event plan)

[0233] Step 6:

[0234] Notification to the user

[0235] The server notifies the local government officials of the generated action plan and analysis results. Notifications are sent via email or a dedicated dashboard. The email includes details of the generated plan and its predicted effects.

[0236] Input: Action plan, analysis results

[0237] Output: Notified action plan and analysis results (email, dashboard)

[0238] Step 7:

[0239] Promoting citizen participation

[0240] The terminals use a user-friendly application and an interface installed in the vehicle to disseminate information about local events and tourist attractions. Furthermore, they conduct surveys to collect feedback from citizens and tourists. Survey results are transmitted to a server in real time.

[0241] Input: Action plan, citizen application

[0242] Output: Collected feedback (survey results)

[0243] Step 8:

[0244] Feedback analysis and improvement plan generation

[0245] The server analyzes feedback collected from citizens and evaluates the effectiveness of the plan. It uses a Python data analysis library (e.g., pandas) to perform statistical analysis. Based on the results, it generates improvement suggestions as needed and notifies the relevant municipal officials again.

[0246] Input: Feedback data

[0247] Output: Improvement suggestions and feedback analysis results (email, dashboard)

[0248] In this way, a system is realized to comprehensively support local governments' regional development policies.

[0249] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0250] Patent Specification

[0251] The present invention aims to revitalize local governments by automating and optimizing a series of processes that collect and analyze various data and generate concrete action plans. The system mainly consists of three components: a server, terminals, and users. Furthermore, by incorporating an emotion engine to recognize user emotions, it enables more accurate analysis and feedback collection.

[0252] 1. Data Collection

[0253] The server is responsible for the first stage of data collection. First, the server identifies data sources relevant to local governments. These include government open data portals, local websites, and tourism information sites. Next, the server collects the necessary data using APIs and scraping techniques and stores it in a database.

[0254] Specific example: A server uses an API to retrieve and store data on the annual number of tourists in Niigata Prefecture from the government's open data portal.

[0255] 2. Data Analysis

[0256] The server also handles data analysis. The collected data is first pre-processed. Pre-processing includes data normalization, imputation of missing values, and standardization of format. Next, an AI model is run using the pre-processed data to analyze the strengths and weaknesses of the region. From the analysis results, areas that require particular focus on improvement are identified.

[0257] Specific example: The server analyzes data on the annual number of tourists in Niigata Prefecture and identifies that the number of tourists in autumn is lower than in other seasons.

[0258] 3. Generate an action plan

[0259] Based on the analysis results, the server uses generative AI to generate specific action plans. For example, it might suggest events or campaigns specifically tailored to periods with low tourist numbers.

[0260] Specific example: The server generates a proposal to hold an "Autumn Harvest Festival" in Niigata Prefecture to increase tourism during the autumn season.

[0261] 4. Notification to the user

[0262] The server notifies local government officials (users) of the generated action plan and analysis results. Notifications are sent via email or a dedicated dashboard.

[0263] Specific example: The server notifies the tourism official in Niigata Prefecture via email of the generated "Autumn Harvest Festival" plan and its predicted effects.

[0264] 5. Promoting citizen participation

[0265] The terminal will provide a function to inform citizens about local government plans through a citizen-oriented application. The application will allow for event announcements and surveys to collect residents' opinions and willingness to participate.

[0266] Specific example: A device uses an app for residents of Niigata Prefecture to distribute a survey about the "Autumn Harvest Festival" and collect citizens' willingness to participate.

[0267] 6. Utilizing the Emotion Engine

[0268] When the server collects feedback from citizens, it uses an emotion engine to analyze the emotional tendencies of the collected feedback. The emotion engine performs sentiment analysis on the text and determines emotional categories such as positive, negative, and neutral.

[0269] Specific example: The server uses an emotion engine to analyze citizen feedback on the "Autumn Harvest Festival" and confirms that many citizens have a positive reaction to the event.

[0270] 7. Feedback analysis and improvement

[0271] The server evaluates the effectiveness of the plan based on feedback analyzed by the emotion engine. If necessary, it generates adjustments or improvements to the action plan and notifies the user again.

[0272] Specific example: Based on the server's analysis results, a proposal to change part of the "Autumn Harvest Festival" event content is sent to the tourism official in Niigata Prefecture.

[0273] ---

[0274] This invention aims to support local governments in effectively and efficiently planning and implementing regional development measures by automating and optimizing these processes. The introduction of an emotion engine enables more advanced, emotion-based feedback analysis and optimization, which is expected to further promote regional revitalization.

[0275] The following describes the processing flow.

[0276] Program processing steps

[0277] Step 1: Identify the data source

[0278] The server identifies data sources for collecting relevant local government data. These data sources include government open data portals, local government websites, and tourism information sites.

[0279] Step 2: Data Collection

[0280] The server collects the necessary data from the specified data source using APIs and scraping technologies. The collected data is stored in the database.

[0281] Step 3: Data preprocessing

[0282] The server performs preprocessing on the collected data. Specifically, it includes filling in missing values, normalizing the data, and unifying the formats.

[0283] Step 4: Data analysis

[0284] The server runs an AI model using the preprocessed data to analyze the strengths and weaknesses of the region. Specific issues and opportunities that should be particularly focused on are identified from the analysis results.

[0285] Step 5: Generation of an action plan <​​​​​​​​​​​​​​​​​​​​​​​​​The device sends collected survey results and other feedback to the server in real time. This allows for timely data collection.

[0293] Step 9: Analysis using the emotion engine

[0294] The server uses an emotion engine to analyze the emotional tendencies of the collected feedback. The emotion engine performs text analysis and classifies the feedback as either positive, negative, or neutral.

[0295] Step 10: Analysis and evaluation of feedback

[0296] The server evaluates the effectiveness of the plan based on feedback analyzed by the emotion engine. If necessary, it generates adjustments or improvements to the action plan and notifies the user.

[0297] Step 11: Notification of proposed improvements

[0298] The server will then notify local government officials again of the new action plan, which includes the improved plan. This notification will also be sent via email or a dashboard.

[0299] ---

[0300] This detailed process enables local governments to generate data-driven, effective regional development strategies and collect and analyze resident feedback in real time. Furthermore, the use of an emotion engine allows for the analysis of emotional trends in feedback, enabling more appropriate measures to be taken.

[0301] (Example 2)

[0302] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0303] In order to activate local governments, it is necessary to collect and analyze various data and generate a specific action plan. However, this process is time-consuming and labor-intensive when done manually, and there is also a problem that subjective judgments are likely to be involved. Furthermore, it is required to efficiently analyze feedback from citizens and improve the plan based on it. The present invention provides a system that solves these problems and contributes to the activation of local governments.

[0304] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0305] In this invention, the server includes means for identifying a data source, means for collecting data and storing it in a database, means for preprocessing and analyzing the collected data, means for generating an action plan using a generated AI model based on the analysis result, means for notifying the action plan to the person in charge of the local government, means for soliciting opinions and participation through a citizen application, means for collecting feedback from citizens and analyzing it using an emotion engine, and means for providing improvement plans based on the collected feedback. Thereby, the process from data collection to analysis, generation of action plans, analysis of feedback, and provision of improvement plans is automated, enabling efficient and effective activation of local governments.

[0306] A "data source" is an external or internal information source used to provide specific information.

[0307] The "means for collecting data and storing it in a database" is a process of collecting necessary data from external and internal information sources and storing the data in a storage system for systematic accumulation.

[0308] "Preprocessing" is a process such as normalization of data, complementation of missing values, and unification of formats to convert the collected data into an analyzable form.

[0309] "Analysis" is the process of running statistical or machine learning models to detect patterns or trends based on pre-processed data in order to achieve a specific objective.

[0310] A "generative AI model" is an artificial intelligence model that generates new data and information based on pre-trained data.

[0311] An "action plan" is a specific action plan or strategy set up based on the analysis results.

[0312] "Notification methods" refer to ways of communicating generated action plans and analysis results to specific users. These include email, dashboards, and application notifications.

[0313] A "citizen-oriented application" is a mobile or web application used by local governments to provide citizens with information about their plans and events, and to gather opinions and confirm their willingness to participate.

[0314] "Methods for soliciting opinions and participation" refer to methods for collecting opinions from citizens and their intention to participate in events. This includes surveys, feedback forms, and so on.

[0315] "Feedback" refers to opinions and evaluations collected from users, representing their reactions to specific policies or events.

[0316] An "emotion engine" is software that analyzes emotional tendencies from text data and identifies emotional categories such as positive, negative, and neutral.

[0317] An "improvement proposal" is a suggestion to make existing plans or action plans more effective, based on collected data and feedback.

[0318] The present invention aims to revitalize local governments by automating and optimizing a series of processes for collecting and analyzing various data and generating concrete action plans. The system mainly consists of three components: a server, terminals, and users.

[0319] The server operates as follows: First, it identifies data sources and collects data related to local governments. Data collection is performed using APIs and scraping techniques. The collected data is stored in a database. For example, the server uses an API to retrieve and store tourism data from a government open data portal. An example of a prompt in this case would be, "Write code to retrieve tourism data from the government open data portal and save it to the database."

[0320] The server then preprocesses the collected data. This preprocessing includes data normalization, imputation of missing values, and formatting. The preprocessed data is then analyzed by an AI model. For example, it might analyze tourism data for Niigata Prefecture to identify that the number of tourists is low during certain seasons. An example prompt in this case would be, "Write code to preprocess tourism data for Niigata Prefecture and use an AI model to identify that the number of tourists is low during certain seasons."

[0321] Based on these analysis results, the server uses a generated AI model to create a specific action plan. For example, it might generate a proposal to hold an "Autumn Harvest Festival" to increase the number of tourists in the fall. An example of a prompt in this case would be, "Please generate a specific event plan to increase the number of tourists in Niigata Prefecture in the fall."

[0322] The server notifies the local government official (user) of the generated action plan and analysis results. The notification is made via email or dashboard. For example, the server will notify the tourism official in Niigata Prefecture of the generated "Autumn Harvest Festival" plan and its predicted effects via email. An example of the prompt in this case would be, "Write an email to notify the tourism official in Niigata Prefecture of the Autumn Harvest Festival plan and its predicted effects."

[0323] The terminal will inform citizens about local government plans through citizen-facing applications and collect residents' opinions and intentions to participate. For example, the terminal will distribute a survey about the "Autumn Harvest Festival" through the Niigata Prefecture resident app and collect citizens' intentions to participate. An example of a prompt message in this case would be, "Please create an app notification message to send the Autumn Harvest Festival survey to residents of Niigata Prefecture."

[0324] The server uses an emotion engine to analyze the emotional tendencies of citizens when collecting their feedback. For example, the server might use the emotion engine to analyze citizens' feedback on the "Autumn Harvest Festival" and confirm that many citizens have shown positive reactions. An example of a prompt in this case would be, "Write code to analyze citizen feedback using the emotion engine and classify it as positive, negative, or neutral."

[0325] Ultimately, the server evaluates the effectiveness of the plan based on the feedback analyzed by the emotion engine and generates adjustments or improvements to the action plan as needed. For example, the server might generate a proposal to change the content of the "Autumn Harvest Festival" based on the analysis results and notify the local government official. An example of a prompt in this case would be, "Based on the feedback results, please create a proposal to improve the Autumn Harvest Festival event."

[0326] The above describes the specific implementation method of the system of the present invention. This enables local governments to effectively and efficiently plan and implement regional development measures, and the introduction of the emotion engine allows for more advanced feedback analysis and optimization.

[0327] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0328] Step 1:

[0329] The server identifies data sources, including government open data portals, local government websites, and tourism information sites. The input is a list of data sources that may provide information relevant to the revitalization of local governments, and the output is a list of actually available data sources. Based on this identified list of data sources, data collection proceeds in the next step.

[0330] Step 2:

[0331] The server collects data from identified data sources. This process utilizes API-based data collection and scraping techniques. The input is the URL or API endpoint of the data source, and the output is the collected raw data. For example, the server might use an API to retrieve tourism data from a government open data portal and store it in a database.

[0332] Step 3:

[0333] The server preprocesses the collected data. This preprocessing includes data normalization, imputation of missing values, and formatting standardization. The input is the collected raw data, and the output is preprocessed data suitable for analysis. Specifically, the data format is standardized, and missing values ​​are imputed with the mean.

[0334] Step 4:

[0335] The server analyzes pre-processed data. It runs an AI model to analyze the strengths and weaknesses of a region. The input is pre-processed data, and the output is the analysis results. For example, it analyzes annual tourism data for Niigata Prefecture to identify that the number of tourists is low during certain seasons.

[0336] Step 5:

[0337] The server generates a concrete action plan using an AI model based on the analysis results. The input is the analysis results, and the output is a concrete action plan. Specifically, it generates a proposal to hold an "Autumn Harvest Festival" during the autumn season when tourist numbers are low.

[0338] Step 6:

[0339] The server notifies the local government official (user) of the generated action plan and analysis results. The input is the generated action plan and analysis results, and the output is the notification sent to the official. Email and dashboards are used as notification methods. For example, the generated "Autumn Harvest Festival" plan and its predicted effects are notified to the tourism official via email.

[0340] Step 7:

[0341] The terminal will inform citizens about the local government's plans through a citizen-facing application and collect residents' opinions and willingness to participate. The input is the generated action plan, and the output is feedback from citizens. Specifically, a survey about the "Autumn Harvest Festival" will be distributed through the local government's application to collect citizens' willingness to participate.

[0342] Step 8:

[0343] The server collects feedback from citizens and analyzes their emotional tendencies using an emotion engine. The input is citizen feedback, and the output is the analysis results categorized into emotional categories. Specifically, it analyzes the text of feedback on the "Autumn Harvest Festival" and classifies it into positive, negative, and neutral emotions.

[0344] Step 9:

[0345] The server evaluates the effectiveness of the plan based on feedback analyzed by the emotion engine and generates adjustments and improvements to the action plan as needed. The input is the emotion analysis result, and the output is the improved action plan. For example, it adjusts the content of the "Autumn Harvest Festival" based on the feedback and notifies the person in charge of the new proposal.

[0346] (Application Example 2)

[0347] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0348] When revitalizing local governments, traditional methods often involve manual or partially automated processes for data collection, analysis, planning, and feedback gathering, resulting in low efficiency and inaccuracies. Furthermore, analyzing citizen feedback from an emotional perspective and incorporating it into plans presents significant challenges. To address these issues, a system is needed that highly automates each process and enables real-time data analysis and feedback collection.

[0349] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for identifying data sources, means for collecting data and storing it in a database, means for pre-processing and analyzing the collected data, means for generating an action plan based on the analysis results, means for notifying the person in charge of the action plan, means for soliciting opinions and participation through a user application, means for collecting and analyzing user feedback, means for providing improvement proposals based on the collected feedback, means for executing a generative artificial intelligence model for generating and distributing region-related content, and means for notifying the user of the generated content. This enables local governments to effectively and efficiently plan and implement regional development measures, and to accurately reflect citizen feedback through sentiment analysis.

[0350] A "data source" is the source from which information is collected, and examples include government open data portals, local government websites, and tourism information websites.

[0351] "Data collection" is the process of obtaining necessary information from a specified data source and storing it in a database.

[0352] A "database" is an aggregate of information that systematically stores collected data and makes it accessible as needed.

[0353] "Preprocessing" is the process of converting collected data into a format suitable for analysis, and includes data normalization, imputation of missing values, and standardization of formatting.

[0354] "Analysis" is the process of extracting patterns and trends in information using certain algorithms and models based on pre-processed data, and then interpreting the results according to a specific purpose.

[0355] An "action plan" is a specific action plan created based on the analysis results, and includes proposals for events and campaigns to promote regional development.

[0356] "Notification" refers to the process of informing the responsible party about the generated action plan and analysis results, and this is done via email or a dedicated dashboard.

[0357] A "user-facing application" is software provided to citizens and stakeholders of a local government, and has functions such as collecting opinions and announcing event information.

[0358] "Feedback" refers to opinions and impressions provided by users, and is information that is useful for improving services and generating new ideas.

[0359] "Sentiment analysis" is a process that classifies the emotional tendencies of collected feedback through text analysis, determining whether they are positive, negative, neutral, etc.

[0360] A "generative artificial intelligence model" is a program that uses artificial intelligence to automatically generate new content and ideas.

[0361] "Content" refers to all information and entertainment provided to users, including local event information and tourist guides.

[0362] 1. Data Collection

[0363] The server is responsible for the first stage of data collection. First, the server identifies data sources, which include official websites of local governments and related organizations, tourism information sites, and government open data portals. Next, the server uses API-based data collection and scraping techniques to gather the necessary data and store it in a database.

[0364] Specific example: A server uses an API to retrieve and store data on the annual number of tourists in a region from a government open data portal.

[0365] 2. Data Analysis

[0366] The server also handles data analysis. The collected data is first preprocessed. Preprocessing includes data normalization, imputation of missing values, and standardization of format. Next, a generative artificial intelligence model (e.g., TensorFlow) is run on the preprocessed data to analyze the strengths and weaknesses of the region. From the analysis results, areas that require particular attention for improvement are identified.

[0367] Specific example: The server analyzes data on the annual number of tourists in a region and identifies that the number of tourists in the fall is lower than in other seasons.

[0368] 3. Generate an action plan

[0369] Based on the analysis results, the server uses a generative artificial intelligence model to generate specific action plans. For example, it might propose events or campaigns specifically tailored to periods with low tourist numbers.

[0370] Specific example: The server generates a proposal to hold an "Autumn Harvest Festival" in the region to increase tourism during the autumn season.

[0371] 4. Notification to the person in charge

[0372] The server notifies local government officials of the generated action plan and analysis results. Notifications are sent via email or a dedicated dashboard.

[0373] Specific example: The server notifies local tourism officials via email of the generated "Autumn Harvest Festival" plan and its predicted effects.

[0374] 5. User-facing applications

[0375] The device will provide a function to inform citizens about local government plans through a user-facing application. The application will allow for event announcements and surveys, enabling the collection of residents' opinions and willingness to participate.

[0376] Specific example: A device distributes a survey about the "Autumn Harvest Festival" through a local resident app, collecting citizens' willingness to participate.

[0377] 6. Emotion analysis

[0378] The server analyzes the feedback collected from users using an emotion engine (for example, OpenAI's API). The emotion engine performs sentiment analysis on the text and determines emotion categories such as positive, negative, and neutral.

[0379] Specific example: The server uses an emotion engine to analyze citizen feedback on the "Autumn Harvest Festival" and confirms that many citizens have a positive reaction to the event.

[0380] 7. Providing improvement proposals

[0381] The server evaluates the effectiveness of the plan based on feedback analyzed by the emotion engine. It then generates adjustments and improvements to the action plan as needed and notifies the responsible party again.

[0382] Specific example: Based on the server's analysis results, it notifies local tourism officials of a proposal to change part of the "Autumn Harvest Festival" event.

[0383] Example of a prompt

[0384] "Analyze the sentiment of citizen feedback regarding Niigata Prefecture's Autumn Harvest Festival and generate optimal content."

[0385] "Analyze the sentiment of the following text: 'We received positive feedback from citizens about the Autumn Harvest Festival.'"

[0386] This system will enable local governments to quickly formulate and implement effective, data-driven regional development plans. Furthermore, it will allow for accurate incorporation of citizen feedback through sentiment analysis, which is expected to further promote regional revitalization.

[0387] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0388] Step 1:

[0389] Identifying the data source

[0390] The server identifies appropriate data sources for data collection. These data sources include official websites of local governments and related organizations, tourism information sites, and government open data portals. Specifically, the server scans a pre-configured list and collects API endpoints and URLs to scrape. The input is a pre-configured list of data sources, and the output is the collected URLs and API endpoints.

[0391] Step 2:

[0392] Data collection

[0393] The server collects data from identified data sources. It uses API-based data collection and scraping techniques to obtain the necessary data and store it in a database. The input is the URL or API endpoint of the data source identified in step 1, and the output is the collected data stored in the database. Specifically, it sends an HTTP request, parses the response to extract the necessary data, and writes it to the database.

[0394] Step 3:

[0395] Data preprocessing

[0396] The server preprocesses the collected data. Preprocessing includes data normalization, imputation of missing values, and formatting. The input is the raw data collected in step 2, and the output is the preprocessed data. Specifically, a data transformation library (e.g., Pandas) is used to format the data into a standard format.

[0397] Step 4:

[0398] Data Analysis

[0399] The server runs a generative artificial intelligence model based on pre-processed data to analyze the strengths and weaknesses of the region. The input is the data pre-processed in step 3, and the output is the analysis results. Specifically, it uses a pre-trained artificial intelligence model (e.g., TensorFlow) to analyze the data and extract specific patterns and trends.

[0400] Step 5:

[0401] Action plan generation

[0402] The server generates a specific action plan using a generative artificial intelligence model based on the analysis results. The input is the analysis results obtained in step 4, and the output is the generated action plan. Specifically, it uses a generative artificial intelligence model (e.g., GPT-3) to generate event and campaign suggestions based on the analysis results. An example of a prompt message would be, "Analyze the sentiment of citizen feedback regarding Niigata Prefecture's Autumn Harvest Festival and generate optimal content."

[0403] Step 6:

[0404] Notification to the North

[0405] The server notifies the local government official of the generated action plan and analysis results. The input is the action plan generated in step 5, and the output is the notified official. Specifically, the official is notified via email or a dedicated dashboard.

[0406] Step 7:

[0407] User application updates

[0408] The terminal will notify citizens of the generated action plan through a user application. The input is the action plan notified in step 6, and the output is the updated application. Specifically, the application's UI will be updated to display the new action plan information.

[0409] Step 8:

[0410] Feedback Collection

[0411] This system collects user feedback on action plans provided through the application. The input is user feedback, and the output is the collected feedback data. Specifically, it uses features such as surveys and comments to gather opinions and feedback from users.

[0412] Step 9:

[0413] Emotion analysis

[0414] The server analyzes the sentiment of the feedback it has collected. The input is the feedback collected in step 8, and the output is the sentiment analysis result. Specifically, it uses a generative artificial intelligence model for sentiment analysis (e.g., OpenAI's API) to determine the sentiment category from the feedback text. An example of a prompt would be, "Analyze the sentiment of the following text: 'We received positive feedback from citizens about the Autumn Harvest Festival.'"

[0415] Step 10:

[0416] Providing improvement proposals

[0417] The server evaluates the effectiveness of the plan based on the sentiment analysis results and generates adjustments or improvements to the action plan as needed. The input is the sentiment analysis results obtained in step 9, and the output is the improvement proposals. Specifically, it uses the generative artificial intelligence model again to generate new action plans and improvement proposals and notifies the person in charge.

[0418] This series of processes enables local governments to quickly formulate and implement effective, data-driven regional development strategies. Furthermore, sentiment analysis allows for accurate incorporation of citizen feedback.

[0419] 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.

[0420] Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. 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. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0421] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0422] [Second Embodiment]

[0423] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0424] 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.

[0425] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0426] 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.

[0427] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0428] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0429] 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.

[0430] 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 using the processor 28. The storage 32 stores the specific processing program 56.

[0431] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0432] The 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.

[0433] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0434] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0435] Patent Specification

[0436] The system of this invention aims to revitalize local governments by collecting and analyzing various data and generating concrete action plans. The system mainly consists of three components: a server, terminals, and users.

[0437] 1. Data Collection

[0438] The server is responsible for the first stage of data collection. First, the server identifies multiple data sources related to local governments. These include government open data portals, local government websites, and tourism information sites. Next, the server collects the necessary data using APIs and scraping techniques, and stores it in its internal database.

[0439] Specific example: A server uses an API to retrieve and store data on the annual number of tourists in Niigata Prefecture from the government's open data portal.

[0440] 2. Data Analysis

[0441] The server also handles data analysis. The collected data is first pre-processed. Pre-processing includes data normalization and imputation of missing values. Next, an AI model is run using the pre-processed data to analyze the strengths and weaknesses of the region. From the analysis results, areas that require particular focus on improvement are identified.

[0442] Specific example: The server analyzes data on the annual number of tourists in Niigata Prefecture and identifies that the number of tourists in autumn is lower than in other seasons.

[0443] 3. Generate an action plan

[0444] Based on the analysis results, the server uses generative AI to generate specific action plans. For example, it might suggest events or campaigns specifically tailored to periods with low tourist numbers.

[0445] Specific example: The server generates a proposal to hold an "Autumn Harvest Festival" in Niigata Prefecture to increase tourism during the autumn season.

[0446] 4. Notification to the user

[0447] The server notifies local government officials (users) of the generated action plan and analysis results. Notifications are sent via email or a dedicated dashboard.

[0448] Specific example: The server notifies the tourism official in Niigata Prefecture via email of the generated "Autumn Harvest Festival" plan and its predicted effects.

[0449] 5. Promoting citizen participation

[0450] The terminal will provide a function to inform citizens about local government plans through a citizen-oriented application. The application will allow for event announcements and surveys to collect residents' opinions and willingness to participate.

[0451] Specific example: A device uses an app for residents of Niigata Prefecture to distribute a survey about the "Autumn Harvest Festival" and collect citizens' willingness to participate.

[0452] 6. Feedback Analysis

[0453] The server analyzes feedback collected from citizens and evaluates the effectiveness of the plan. It then generates adjustments and improvements to the plan as needed and notifies the user again.

[0454] Specific example: The server analyzes the results of a survey from citizens, confirms the high level of interest in the "Autumn Harvest Festival," and proposes expanding the scale of the event.

[0455] ---

[0456] This invention aims to support local governments in effectively and efficiently planning and implementing regional development measures by automating and optimizing these processes. This is expected to lead to regional revitalization.

[0457] The following describes the processing flow.

[0458] Program processing steps

[0459] Step 1: Identify the data source

[0460] The server identifies data sources for collecting relevant local government data. These data sources include government open data portals, local government websites, and tourism information sites.

[0461] Step 2: Data Collection

[0462] The server collects data from specified data sources using APIs and scraping techniques, and stores it in its internal database.

[0463] Step 3: Data Preprocessing

[0464] The server preprocesses the collected data. Specifically, it performs data normalization, imputation of missing values, and standardization of the format.

[0465] Step 4: Data Analysis

[0466] The server uses pre-processed data to perform AI modeling and analyze the region's strengths and weaknesses. This identifies key challenges and opportunities that require particular focus.

[0467] Step 5: Generate an action plan

[0468] The server uses generative AI to generate specific action plans based on the analysis results. These plans may include proposals for events and campaigns.

[0469] Step 6: Notify the user

[0470] The server notifies local government officials (users) of the generated action plan and its analysis results. Notifications are sent via email or a dedicated dashboard.

[0471] Step 7: Promoting citizen participation

[0472] The terminals will inform citizens about the local government's plans through a citizen-oriented application. Furthermore, the application will be used to conduct surveys and collect opinions and intentions to participate.

[0473] Step 8: Gathering Feedback

[0474] The device collects survey results and other feedback and sends them to the server.

[0475] Step 9: Feedback Analysis

[0476] The server analyzes feedback from citizens and evaluates the effectiveness of the plan. Based on the analysis results, it makes necessary adjustments and generates improvement proposals.

[0477] Step 10: Notification of proposed improvements

[0478] The server optimizes the plan by cycling through each step and notifying the local government official (user) of improvement suggestions generated from the analyzed feedback.

[0479] (Example 1)

[0480] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0481] In revitalizing local governments, the insufficient effective use of data makes it difficult to create concrete action plans and effectively promote citizen participation. Furthermore, formulating improvement measures based on collected feedback requires considerable time and effort, making it difficult to implement rapid and effective regional development measures. To solve this problem, an automated and optimized process is needed that unifies everything from data collection to action plan generation, citizen participation promotion, and feedback analysis.

[0482] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0483] In this invention, the server includes means for identifying data sources, means for collecting data and storing it in a database, means for preprocessing and analyzing the collected data, means for generating an action plan using an artificial intelligence model based on the analysis results, means for notifying local government officials of the action plan, means for soliciting opinions and participation through application software for citizens, means for collecting and analyzing feedback from citizens, and means for providing improvement proposals based on the collected feedback. This enables local governments to effectively utilize data, quickly generate and implement action plans that reflect citizens' opinions and participation, and further formulate improvement measures based on feedback from citizens.

[0484] A "data source" refers to a website, portal site, or similar online information provider that offers information related to local governments.

[0485] A "database" refers to a computer system used to manage and store collected data, which is later used for analysis or other processes.

[0486] "Preprocessing" refers to a series of steps that transform collected raw data into an analyzable format, including data normalization, imputation of missing values, and data type conversion.

[0487] "Analysis" refers to data processing techniques that use statistical methods or machine learning models on pre-processed data to elucidate specific objectives or propositions.

[0488] A "generative artificial intelligence model" refers to a system that uses artificial intelligence to automatically generate proposals and plans based on input conditions, specifically employing technologies such as natural language processing.

[0489] An "action plan" is a compilation of specific action plans and proposals based on the analysis results, and includes proposals for measures and events that local governments can implement.

[0490] "Application software" refers to devices and applications provided to citizens, through which information is provided and surveys are conducted.

[0491] "Feedback" refers to opinions and evaluations collected from citizens through application software, which are used to improve the system.

[0492] The system of this invention is configured to automate data collection, analysis, action plan generation, and feedback analysis, with the aim of revitalizing local governments. The system mainly consists of three components: a server, terminals, and users.

[0493] 1. Data Collection

[0494] First, the server is responsible for data collection. Specifically, the server identifies multiple data sources related to local governments (e.g., government open data portals, local government official websites, tourism information sites, etc.) and collects data using APIs and scraping techniques. In this process, libraries such as Python's Requests, BeautifulSoup, and Scrapy are often used. The collected data is stored in databases such as MySQL or PostgreSQL.

[0495] Specific example: A server automatically executes a script at 2 AM every day to retrieve annual tourist data for Niigata Prefecture from the government's open data portal and save it to a database.

[0496] 2. Data Analysis

[0497] Next, the server preprocesses and analyzes the collected data. Preprocessing includes data normalization, missing value imputation, and data type conversion, using the Pandas library. The preprocessed data is then analyzed using machine learning libraries such as TensorFlow and PyTorch. This analysis identifies the region's strengths and weaknesses.

[0498] Specific example: The server preprocesses annual tourist data for Niigata Prefecture and uses Scikit-learn's clustering algorithm to identify that the number of tourists in autumn is lower than in other seasons.

[0499] 3. Generate an action plan

[0500] The server generates an action plan using a generative artificial intelligence model (e.g., GPT-3 or BERT) based on the analysis results. The generated action plan includes suggestions for specific events and campaigns.

[0501] Specific example: The server generates a prompt based on the analysis results, inputs the prompt "Generate event proposals to increase the number of tourists in Niigata Prefecture during the autumn season" into GPT-3, and saves the generated "Autumn Harvest Festival" proposal as the final action plan.

[0502] 4. Notification to the user

[0503] The server notifies the local government official (user) of the generated action plan and analysis results. Notification can be sent via email using an SMTP server, or through a dedicated dashboard utilizing a web application such as Django or Flask.

[0504] Specific example: The server will notify tourism officials in Niigata Prefecture via email of the generated "Autumn Harvest Festival" plan and its predicted effects. This information will also be available for real-time monitoring via a dashboard.

[0505] 5. Promoting citizen participation

[0506] The device will use a citizen-facing application to disseminate action plans to citizens and encourage their opinions and participation. This application will be developed using Flutter and React Native. The application will allow for event announcements and surveys, enabling the collection of residents' opinions and willingness to participate.

[0507] Specific example: A device uses an app for residents of Niigata Prefecture to distribute detailed information about the "Autumn Harvest Festival" and conducts a survey to collect participation intentions.

[0508] 6. Feedback Analysis

[0509] Finally, the server analyzes the feedback collected from citizens. The same techniques as in preprocessing are used for the analysis, and the effectiveness of the plan is evaluated based on the results. If necessary, adjustments or improvements to the plan are generated and notified to the user again.

[0510] Specific example: The server analyzes the results of a survey from citizens in real time and confirms the high level of interest in the "Autumn Harvest Festival." Based on this, it notifies users of a suggestion to expand the scale of the event.

[0511] Thus, the system of the present invention supports local governments in effectively utilizing data and in quickly and accurately formulating and implementing regional development measures.

[0512] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0513] System program processing flow

[0514] Step 1: Identify the data source

[0515] Step 2: Data Collection

[0516] Step 3: Save data

[0517] Step 4: Data preprocessing

[0518] Step 5: Data Analysis

[0519] Step 6: Generate an action plan

[0520] Step 7: Notify the user

[0521] Step 8: Distribution of information to citizens

[0522] Step 9: Gathering feedback from citizens

[0523] Step 10: Analyze feedback and notify of improvement suggestions

[0524] ---

[0525] Detailed explanation of each processing step

[0526] Step 1: Identify the data source

[0527] The server identifies the data sources necessary for data collection. First, it lists data sources related to local governments (e.g., government open data portals, local government official websites, tourism information sites, etc.) and determines the data items to be collected from these data sources. The input is the URLs or API keys of the data sources, and the output is a list of data to be collected.

[0528] Specific operation: The server identifies the URL of the government's open data portal based on a defined list and retrieves its API key.

[0529] Step 2: Data Collection

[0530] The server collects data from identified data sources. Specifically, it retrieves data using APIs or scraping techniques. Inputs include a list of data sources, API keys, and collection scripts, while output is the collected raw data.

[0531] Specific operation: The server automatically executes a script at 2 AM every day, using the API of the government's open data portal to retrieve data on the annual number of tourists in Niigata Prefecture.

[0532] Step 3: Save data

[0533] The server stores the collected data in a database. Input is raw data, and output is the data stored in the database. Database systems such as MySQL and PostgreSQL are primarily used.

[0534] Specific operation: The server collects data on the annual number of tourists in Niigata Prefecture and saves it to a MySQL database.

[0535] Step 4: Data preprocessing

[0536] The server preprocesses the collected data. This preprocessing includes data normalization, missing value imputation, and data type conversion. The input is raw data, and the output is preprocessed data. The Pandas library is commonly used for this.

[0537] Specific operation: The server uses Pandas to impute missing values ​​in the annual tourist count data for Niigata Prefecture and to format the data types.

[0538] Step 5: Data Analysis

[0539] The server analyzes the pre-processed data. Specifically, it uses machine learning libraries such as TensorFlow and PyTorch to analyze the data and identify regional strengths and weaknesses. The input is pre-processed data, and the output is the analysis results.

[0540] Specific operation: The server uses Niigata Prefecture's annual tourist data and the Scikit-learn clustering algorithm to identify that the number of tourists is low in the autumn.

[0541] Step 6: Generate an action plan

[0542] The server generates an action plan based on the analysis results using a generative AI model (e.g., GPT-3 or BERT). The input is the analysis results, and the output is the generated action plan.

[0543] Specific operation: The server creates a prompt based on the analysis results and inputs the prompt "Generate event proposals to increase the number of tourists in Niigata Prefecture during the autumn season" to GPT-3. As a result, it saves the generated "Autumn Harvest Festival" plan.

[0544] Step 7: Notify the user

[0545] The server notifies the user (local government official) of the generated action plan and analysis results. Specifically, this can be done by sending an email using an SMTP server or by using a dedicated dashboard built with Django or Flask. The inputs are the generated action plan and analysis results, and the output is the notified information.

[0546] Specific operation: The server will notify tourism officials in Niigata Prefecture via email about the "Autumn Harvest Festival" plan and its projected effects. The information will also be displayed on a dedicated dashboard.

[0547] Step 8: Distribution of information to citizens

[0548] The device uses a user-facing application to disseminate action plans to citizens and solicit their opinions and participation. The input is the action plan, and the output is the notification content for citizens. Application development using Flutter or React Native falls into this category.

[0549] Specific actions: The device will distribute detailed information about the "Autumn Harvest Festival" via a user-facing app and conduct a survey to encourage residents to participate in the event.

[0550] Step 9: Gathering feedback from citizens

[0551] The device collects feedback from citizens. The input is feedback data from citizens, and the output is the collected feedback. Specific methods include surveys and event evaluations.

[0552] Specific operation: The device sends the results of surveys conducted using the user-facing app to the server in real time and collects feedback.

[0553] Step 10: Analyze feedback and notify of improvement suggestions

[0554] The server analyzes the collected feedback and generates improvement proposals. Specifically, it uses a preprocessing, analysis, and generative AI model to generate improvement proposals. The input is feedback from citizens, and the output is the generated improvement proposals.

[0555] Specific operation: The server analyzes the results of a survey from citizens and confirms the high level of interest in the "Autumn Harvest Festival." Based on this, it notifies the local government officials of a proposal to expand the scale of the event.

[0556] (Application Example 1)

[0557] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0558] Local governments need to effectively utilize a large amount of data for regional development and attracting tourists, but collecting, analyzing, and generating appropriate action plans for this data is difficult. In particular, the lack of effective means to collect and analyze real-time feedback from citizens and tourists is reducing the effectiveness of regional development measures. Furthermore, there is a need for a system that effectively communicates information to local government officials and supports rapid decision-making.

[0559] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0560] In this invention, the server includes means for identifying data sources, collecting data and storing it in a database, preprocessing and analyzing the collected data, generating an action plan based on the analysis results, notifying local government officials of the action plan, soliciting opinions and participation through a citizen-facing application, collecting and analyzing feedback from citizens, providing improvement proposals based on the collected feedback, providing information and conducting surveys using an interface installed in a vehicle, and generating an action plan using a generative AI model. This enables local governments to collect and analyze data in real time and generate and implement effective action plans. Furthermore, by utilizing the interface in the vehicle, feedback from citizens and tourists can be collected quickly, enhancing the effectiveness of regional development measures.

[0561] "Means for identifying data sources" refers to a mechanism for finding multiple data sources that provide information related to local governments.

[0562] "Means of collecting data and storing it in a database" refers to a system for obtaining information from identified data sources, organizing it, and storing it in a database.

[0563] "Means for preprocessing and analyzing collected data" refers to a system for performing preprocessing such as normalization and imputation of missing values ​​on collected data, and then conducting data analysis.

[0564] "Means for generating action plans based on analysis results" refers to a system that automatically generates specific action plans and policy proposals using the results of analyzed data.

[0565] "Means for notifying local government officials of the action plan" refers to a system for quickly communicating the generated action plan to local government officials.

[0566] "Methods for soliciting opinions and participation through citizen-oriented applications" refers to a system that uses applications for residents to gather opinions and intentions to participate in events from citizens.

[0567] "Means for collecting and analyzing feedback from citizens" refers to a system for collecting feedback information submitted by citizens and systematically analyzing it.

[0568] "A means of providing improvement proposals based on collected feedback" refers to a system that generates improvement proposals for regional development and events based on the analyzed feedback results, and reflects them in the policies of local governments.

[0569] "Methods for providing information and conducting surveys using interfaces installed in automobiles" refers to systems that provide information to passengers through displays or voice assistants installed in autonomous vehicles and other vehicles, and collect opinions through surveys.

[0570] "Methods for generating action plans using generative AI models" refer to a system that uses artificial intelligence models to perform advanced analysis and data generation, and automatically creates effective action plans.

[0571] Modes for carrying out the invention

[0572] System Configuration

[0573] The system of this invention mainly consists of three components: a server, a terminal, and a user. Details of each component are described below.

[0574] Data collection

[0575] The server is responsible for the first stage of data collection. First, the server identifies multiple data sources related to local governments, such as the government's open data portal, local government websites, and tourism information sites. Next, the server collects the necessary data using APIs and web scraping techniques, and stores it in its internal database.

[0576] Specific example: A server uses an API to retrieve data on the annual number of tourists for a given municipality and saves it to a database.

[0577] Data Analysis

[0578] The server preprocesses the collected data and uses an AI model to analyze the region's strengths and weaknesses. Preprocessing includes data normalization and imputation of missing values. Based on the preprocessed data, the AI ​​model is run to identify areas that require focused improvement.

[0579] Specific example: The server analyzes the data and discovers that the number of tourists is low during a particular season.

[0580] Action plan generation

[0581] The server generates specific action plans using an AI model based on the analysis results. For example, it might suggest events or campaigns specifically tailored to periods with low tourist numbers.

[0582] Specific example: The server proposes a plan to hold an "Autumn Harvest Festival" using a generated AI model.

[0583] Example prompt: "Generate a specific event plan to increase tourism during the autumn season."

[0584] Notification to the user

[0585] The server notifies the local government official (user) of the generated action plan and analysis results. Notifications are sent via email or a dedicated dashboard.

[0586] Specific example: The server notifies the local government's tourism official via email of the generated "Autumn Harvest Festival" plan and its predicted effects.

[0587] Promoting citizen participation

[0588] The terminals provide a function to inform citizens about local government plans through citizen-oriented applications. An interface installed in the vehicle is used to provide information to tourists and conduct surveys. This allows for the collection of feedback from both citizens and tourists.

[0589] Specific example: A terminal uses a user-facing application to conduct a survey about the "Autumn Harvest Festival" and collect information on citizens' willingness to participate.

[0590] Feedback analysis

[0591] The server analyzes feedback collected from citizens and evaluates the effectiveness of the plan. It then generates adjustments and improvements to the plan as needed and notifies users again, thereby achieving sustainable regional development.

[0592] Specific example: If the server analyzes the survey results and confirms high interest, it will propose expanding the scale of the "Autumn Harvest Festival."

[0593] Hardware and software to use

[0594] Server: Responsible for data collection and analysis. Uses APIs, web scraping, database management systems, and AI model execution environments (e.g., Python, pandas, scikit-learn, GPT-3).

[0595] Devices: Citizen applications and interfaces installed in vehicles will utilize smartphones, tablets, and displays and voice assistants on autonomous vehicles.

[0596] Software: Python libraries (pandas, scikit-learn) are used for data preprocessing and analysis, and GPT-3 is used for generative AI models. Email systems and dashboard software are used for user notifications.

[0597] Thus, this invention is a system that supports local governments in effectively and efficiently planning and implementing regional development measures. This is expected to revitalize local communities.

[0598] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0599] Step 1:

[0600] Identify the data source.

[0601] The server identifies data sources such as government open data portals, local government websites, and tourism information sites. First, the server obtains the URLs of APIs and web pages necessary to collect information from the internet. Once the data sources have been identified, the server confirms the format and structure of the data to be collected and prepares for the next data collection step.

[0602] Input: API URL, Web page URL

[0603] Output: List of URLs for collection

[0604] Step 2:

[0605] Data acquisition and storage

[0606] The server collects data from identified data sources and stores it in a database. Data collection is performed using API requests or web scraping techniques. The retrieved data is initially stored in temporary memory and then permanently stored in the database. At this time, the database also records metadata such as the collection date and time and the source.

[0607] Input: URL list

[0608] Output: Raw data stored in the database

[0609] Step 3:

[0610] Data preprocessing

[0611] The server preprocesses the collected data. This process includes normalizing the data, imputing missing values, and removing unnecessary data. Specifically, it uses the Python pandas library to manipulate dataframes, imputing missing values ​​with SimpleImputer, and normalizing with StandardScaler. Once preprocessing is complete, the data is prepared in a format suitable for analysis.

[0612] Input: Raw data

[0613] Output: Preprocessed data

[0614] Step 4:

[0615] Data Analysis

[0616] The server analyzes the pre-processed data. In this step, an AI model is used to analyze the data and identify regional strengths and weaknesses. A Python machine learning library (e.g., scikit-learn) is used to perform data analysis, extracting specific insights using correlation matrices, regression analysis, etc.

[0617] Input: Preprocessed data

[0618] Output: Analysis results (regional strengths and weaknesses)

[0619] Step 5:

[0620] Generating an action plan

[0621] The server generates an action plan using a generative AI model based on the analysis results. Using generative AI such as GPT-3, it generates regional development measures by inputting specific prompt sentences. For example, the prompt sentence "Generate a specific event plan to increase the number of tourists in the autumn" is used. The generated action plan is output in text format.

[0622] Input: Analysis result, prompt message

[0623] Output: Action plan (event plan)

[0624] Step 6:

[0625] Notification to the user

[0626] The server notifies the local government officials of the generated action plan and analysis results. Notifications are sent via email or a dedicated dashboard. The email includes details of the generated plan and its predicted effects.

[0627] Input: Action plan, analysis results

[0628] Output: Notified action plan and analysis results (email, dashboard)

[0629] Step 7:

[0630] Promoting citizen participation

[0631] The terminals use a user-friendly application and an interface installed in the vehicle to disseminate information about local events and tourist attractions. Furthermore, they conduct surveys to collect feedback from citizens and tourists. Survey results are transmitted to a server in real time.

[0632] Input: Action plan, citizen application

[0633] Output: Collected feedback (survey results)

[0634] Step 8:

[0635] Feedback analysis and improvement plan generation

[0636] The server analyzes feedback collected from citizens and evaluates the effectiveness of the plan. It uses a Python data analysis library (e.g., pandas) to perform statistical analysis. Based on the results, it generates improvement suggestions as needed and notifies the relevant municipal officials again.

[0637] Input: Feedback data

[0638] Output: Improvement suggestions and feedback analysis results (email, dashboard)

[0639] In this way, a system is realized to comprehensively support local governments' regional development policies.

[0640] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0641] Patent Specification

[0642] The present invention aims to revitalize local governments by automating and optimizing a series of processes that collect and analyze various data and generate concrete action plans. The system mainly consists of three components: a server, terminals, and users. Furthermore, by incorporating an emotion engine to recognize user emotions, it enables more accurate analysis and feedback collection.

[0643] 1. Data Collection

[0644] The server is responsible for the first stage of data collection. First, the server identifies data sources relevant to local governments. These include government open data portals, local websites, and tourism information sites. Next, the server collects the necessary data using APIs and scraping techniques and stores it in a database.

[0645] Specific example: A server uses an API to retrieve and store data on the annual number of tourists in Niigata Prefecture from the government's open data portal.

[0646] 2. Data Analysis

[0647] The server also handles data analysis. The collected data is first pre-processed. Pre-processing includes data normalization, imputation of missing values, and standardization of format. Next, an AI model is run using the pre-processed data to analyze the strengths and weaknesses of the region. From the analysis results, areas that require particular focus on improvement are identified.

[0648] Specific example: The server analyzes data on the annual number of tourists in Niigata Prefecture and identifies that the number of tourists in autumn is lower than in other seasons.

[0649] 3. Generate an action plan

[0650] Based on the analysis results, the server uses generative AI to generate specific action plans. For example, it might suggest events or campaigns specifically tailored to periods with low tourist numbers.

[0651] Specific example: The server generates a proposal to hold an "Autumn Harvest Festival" in Niigata Prefecture to increase tourism during the autumn season.

[0652] 4. Notification to the user

[0653] The server notifies local government officials (users) of the generated action plan and analysis results. Notifications are sent via email or a dedicated dashboard.

[0654] Specific example: The server notifies the tourism official in Niigata Prefecture via email of the generated "Autumn Harvest Festival" plan and its predicted effects.

[0655] 5. Promoting citizen participation

[0656] The terminal will provide a function to inform citizens about local government plans through a citizen-oriented application. The application will allow for event announcements and surveys to collect residents' opinions and willingness to participate.

[0657] Specific example: A device uses an app for residents of Niigata Prefecture to distribute a survey about the "Autumn Harvest Festival" and collect citizens' willingness to participate.

[0658] 6. Utilizing the Emotion Engine

[0659] When the server collects feedback from citizens, it uses an emotion engine to analyze the emotional tendencies of the collected feedback. The emotion engine performs sentiment analysis on the text and determines emotional categories such as positive, negative, and neutral.

[0660] Specific example: The server uses an emotion engine to analyze citizen feedback on the "Autumn Harvest Festival" and confirms that many citizens have a positive reaction to the event.

[0661] 7. Feedback analysis and improvement

[0662] The server evaluates the effectiveness of the plan based on feedback analyzed by the emotion engine. If necessary, it generates adjustments or improvements to the action plan and notifies the user again.

[0663] Specific example: Based on the server's analysis results, a proposal to change part of the "Autumn Harvest Festival" event content is sent to the tourism official in Niigata Prefecture.

[0664] ---

[0665] This invention aims to support local governments in effectively and efficiently planning and implementing regional development measures by automating and optimizing these processes. The introduction of an emotion engine enables more advanced, emotion-based feedback analysis and optimization, which is expected to further promote regional revitalization.

[0666] The following describes the processing flow.

[0667] Program processing steps

[0668] Step 1: Identify the data source

[0669] The server identifies data sources for collecting relevant local government data. These data sources include government open data portals, local government websites, and tourism information sites.

[0670] Step 2: Data Collection

[0671] The server collects the necessary data from the specified data source using APIs and scraping techniques. The collected data is stored in a database.

[0672] Step 3: Data Preprocessing

[0673] The server preprocesses the collected data. Specifically, this includes imputing missing values, normalizing the data, and standardizing the format.

[0674] Step 4: Data Analysis

[0675] The server runs an AI model using pre-processed data to analyze the region's strengths and weaknesses. From the analysis results, it identifies issues and opportunities that should be given particular emphasis.

[0676] Step 5: Generate an action plan

[0677] Based on the analysis results, the server uses generative AI to generate specific action plans. For example, it might suggest events or campaigns specifically tailored to periods with low tourist numbers.

[0678] Step 6: Notify the user

[0679] The server notifies local government officials (users) of the generated action plan and its analysis results. Notifications are sent via email or a dedicated dashboard.

[0680] Step 7: Promoting citizen participation

[0681] The terminal provides a function to inform citizens about local government plans through a citizen-oriented application. The application allows for event announcements and surveys, enabling the collection of residents' opinions and willingness to participate.

[0682] Step 8: Gathering Feedback

[0683] The device sends collected survey results and other feedback to the server in real time. This allows for timely data collection.

[0684] Step 9: Analysis using the emotion engine

[0685] The server uses an emotion engine to analyze the emotional tendencies of the collected feedback. The emotion engine performs text analysis and classifies the feedback as either positive, negative, or neutral.

[0686] Step 10: Analysis and evaluation of feedback

[0687] The server evaluates the effectiveness of the plan based on feedback analyzed by the emotion engine. If necessary, it generates adjustments or improvements to the action plan and notifies the user.

[0688] Step 11: Notification of proposed improvements

[0689] The server will then notify local government officials again of the new action plan, which includes the improved plan. This notification will also be sent via email or a dashboard.

[0690] ---

[0691] This detailed process enables local governments to generate data-driven, effective regional development strategies and collect and analyze resident feedback in real time. Furthermore, the use of an emotion engine allows for the analysis of emotional trends in feedback, enabling more appropriate measures to be taken.

[0692] (Example 2)

[0693] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0694] To revitalize local governments, it is necessary to collect and analyze diverse data and generate concrete action plans. However, this process is time-consuming and labor-intensive when done manually, and is prone to subjective judgments. Furthermore, there is a need to efficiently analyze citizen feedback and improve plans based on that feedback. This invention provides a system that solves these problems and contributes to the revitalization of local governments.

[0695] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0696] In this invention, the server includes means for identifying data sources, collecting data and storing it in a database, preprocessing and analyzing the collected data, generating an action plan using a generative AI model based on the analysis results, notifying local government officials of the action plan, soliciting opinions and participation through a citizen-facing application, collecting feedback from citizens and analyzing it using an emotion engine, and providing improvement proposals based on the collected feedback. This automates the process from data collection and analysis to action plan generation, feedback analysis, and provision of improvement proposals, enabling efficient and effective revitalization of local governments.

[0697] A "data source" is an external or internal source of information used to provide specific information.

[0698] "Means of collecting data and storing it in a database" refers to the process of collecting necessary data from external and internal sources and storing that data in a storage system for systematic accumulation.

[0699] "Preprocessing" refers to processes such as data normalization, imputation of missing values, and formatting to transform collected data into an analyzable format.

[0700] "Analysis" is the process of running statistical or machine learning models to detect patterns or trends based on pre-processed data in order to achieve a specific objective.

[0701] A "generative AI model" is an artificial intelligence model that generates new data and information based on pre-trained data.

[0702] An "action plan" is a specific action plan or strategy set up based on the analysis results.

[0703] "Notification methods" refer to ways of communicating generated action plans and analysis results to specific users. These include email, dashboards, and application notifications.

[0704] A "citizen-oriented application" is a mobile or web application used by local governments to provide citizens with information about their plans and events, and to gather opinions and confirm their willingness to participate.

[0705] "Methods for soliciting opinions and participation" refer to methods for collecting opinions from citizens and their intention to participate in events. This includes surveys, feedback forms, and so on.

[0706] "Feedback" refers to opinions and evaluations collected from users, representing their reactions to specific policies or events.

[0707] An "emotion engine" is software that analyzes emotional tendencies from text data and identifies emotional categories such as positive, negative, and neutral.

[0708] An "improvement proposal" is a suggestion to make existing plans or action plans more effective, based on collected data and feedback.

[0709] The present invention aims to revitalize local governments by automating and optimizing a series of processes for collecting and analyzing various data and generating concrete action plans. The system mainly consists of three components: a server, terminals, and users.

[0710] The server operates as follows: First, it identifies data sources and collects data related to local governments. Data collection is performed using APIs and scraping techniques. The collected data is stored in a database. For example, the server uses an API to retrieve and store tourism data from a government open data portal. An example of a prompt in this case would be, "Write code to retrieve tourism data from the government open data portal and save it to the database."

[0711] The server then preprocesses the collected data. This preprocessing includes data normalization, imputation of missing values, and formatting. The preprocessed data is then analyzed by an AI model. For example, it might analyze tourism data for Niigata Prefecture to identify that the number of tourists is low during certain seasons. An example prompt in this case would be, "Write code to preprocess tourism data for Niigata Prefecture and use an AI model to identify that the number of tourists is low during certain seasons."

[0712] Based on these analysis results, the server uses a generated AI model to create a specific action plan. For example, it might generate a proposal to hold an "Autumn Harvest Festival" to increase the number of tourists in the fall. An example of a prompt in this case would be, "Please generate a specific event plan to increase the number of tourists in Niigata Prefecture in the fall."

[0713] The server notifies the local government official (user) of the generated action plan and analysis results. The notification is made via email or dashboard. For example, the server will notify the tourism official in Niigata Prefecture of the generated "Autumn Harvest Festival" plan and its predicted effects via email. An example of the prompt in this case would be, "Write an email to notify the tourism official in Niigata Prefecture of the Autumn Harvest Festival plan and its predicted effects."

[0714] The terminal will inform citizens about local government plans through citizen-facing applications and collect residents' opinions and intentions to participate. For example, the terminal will distribute a survey about the "Autumn Harvest Festival" through the Niigata Prefecture resident app and collect citizens' intentions to participate. An example of a prompt message in this case would be, "Please create an app notification message to send the Autumn Harvest Festival survey to residents of Niigata Prefecture."

[0715] The server uses an emotion engine to analyze the emotional tendencies of citizens when collecting their feedback. For example, the server might use the emotion engine to analyze citizens' feedback on the "Autumn Harvest Festival" and confirm that many citizens have shown positive reactions. An example of a prompt in this case would be, "Write code to analyze citizen feedback using the emotion engine and classify it as positive, negative, or neutral."

[0716] Ultimately, the server evaluates the effectiveness of the plan based on the feedback analyzed by the emotion engine and generates adjustments or improvements to the action plan as needed. For example, the server might generate a proposal to change the content of the "Autumn Harvest Festival" based on the analysis results and notify the local government official. An example of a prompt in this case would be, "Based on the feedback results, please create a proposal to improve the Autumn Harvest Festival event."

[0717] The above describes the specific implementation method of the system of the present invention. This enables local governments to effectively and efficiently plan and implement regional development measures, and the introduction of the emotion engine allows for more advanced feedback analysis and optimization.

[0718] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0719] Step 1:

[0720] The server identifies data sources, including government open data portals, local government websites, and tourism information sites. The input is a list of data sources that may provide information relevant to the revitalization of local governments, and the output is a list of actually available data sources. Based on this identified list of data sources, data collection proceeds in the next step.

[0721] Step 2:

[0722] The server collects data from identified data sources. This process utilizes API-based data collection and scraping techniques. The input is the URL or API endpoint of the data source, and the output is the collected raw data. For example, the server might use an API to retrieve tourism data from a government open data portal and store it in a database.

[0723] Step 3:

[0724] The server preprocesses the collected data. This preprocessing includes data normalization, imputation of missing values, and formatting standardization. The input is the collected raw data, and the output is preprocessed data suitable for analysis. Specifically, the data format is standardized, and missing values ​​are imputed with the mean.

[0725] Step 4:

[0726] The server analyzes pre-processed data. It runs an AI model to analyze the strengths and weaknesses of a region. The input is pre-processed data, and the output is the analysis results. For example, it analyzes annual tourism data for Niigata Prefecture to identify that the number of tourists is low during certain seasons.

[0727] Step 5:

[0728] The server generates a concrete action plan using an AI model based on the analysis results. The input is the analysis results, and the output is a concrete action plan. Specifically, it generates a proposal to hold an "Autumn Harvest Festival" during the autumn season when tourist numbers are low.

[0729] Step 6:

[0730] The server notifies the local government official (user) of the generated action plan and analysis results. The input is the generated action plan and analysis results, and the output is the notification sent to the official. Email and dashboards are used as notification methods. For example, the generated "Autumn Harvest Festival" plan and its predicted effects are notified to the tourism official via email.

[0731] Step 7:

[0732] The terminal will inform citizens about the local government's plans through a citizen-facing application and collect residents' opinions and willingness to participate. The input is the generated action plan, and the output is feedback from citizens. Specifically, a survey about the "Autumn Harvest Festival" will be distributed through the local government's application to collect citizens' willingness to participate.

[0733] Step 8:

[0734] The server collects feedback from citizens and analyzes their emotional tendencies using an emotion engine. The input is citizen feedback, and the output is the analysis results categorized into emotional categories. Specifically, it analyzes the text of feedback on the "Autumn Harvest Festival" and classifies it into positive, negative, and neutral emotions.

[0735] Step 9:

[0736] The server evaluates the effectiveness of the plan based on feedback analyzed by the emotion engine and generates adjustments and improvements to the action plan as needed. The input is the emotion analysis result, and the output is the improved action plan. For example, it adjusts the content of the "Autumn Harvest Festival" based on the feedback and notifies the person in charge of the new proposal.

[0737] (Application Example 2)

[0738] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0739] When revitalizing local governments, traditional methods often involve manual or partially automated processes for data collection, analysis, planning, and feedback gathering, resulting in low efficiency and inaccuracies. Furthermore, analyzing citizen feedback from an emotional perspective and incorporating it into plans presents significant challenges. To address these issues, a system is needed that highly automates each process and enables real-time data analysis and feedback collection.

[0740] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for identifying data sources, means for collecting data and storing it in a database, means for pre-processing and analyzing the collected data, means for generating an action plan based on the analysis results, means for notifying the person in charge of the action plan, means for soliciting opinions and participation through a user application, means for collecting and analyzing user feedback, means for providing improvement proposals based on the collected feedback, means for executing a generative artificial intelligence model for generating and distributing region-related content, and means for notifying the user of the generated content. This enables local governments to effectively and efficiently plan and implement regional development measures, and to accurately reflect citizen feedback through sentiment analysis.

[0741] A "data source" is the source from which information is collected, and examples include government open data portals, local government websites, and tourism information websites.

[0742] "Data collection" is the process of obtaining necessary information from a specified data source and storing it in a database.

[0743] A "database" is an aggregate of information that systematically stores collected data and makes it accessible as needed.

[0744] "Preprocessing" is the process of converting collected data into a format suitable for analysis, and includes data normalization, imputation of missing values, and standardization of formatting.

[0745] "Analysis" is the process of extracting patterns and trends in information using certain algorithms and models based on pre-processed data, and then interpreting the results according to a specific purpose.

[0746] An "action plan" is a specific action plan created based on the analysis results, and includes proposals for events and campaigns to promote regional development.

[0747] "Notification" refers to the process of informing the responsible party about the generated action plan and analysis results, and this is done via email or a dedicated dashboard.

[0748] A "user-facing application" is software provided to citizens and stakeholders of a local government, and has functions such as collecting opinions and announcing event information.

[0749] "Feedback" refers to opinions and impressions provided by users, and is information that is useful for improving services and generating new ideas.

[0750] "Sentiment analysis" is a process that classifies the emotional tendencies of collected feedback through text analysis, determining whether they are positive, negative, neutral, etc.

[0751] A "generative artificial intelligence model" is a program that uses artificial intelligence to automatically generate new content and ideas.

[0752] "Content" refers to all information and entertainment provided to users, including local event information and tourist guides.

[0753] 1. Data Collection

[0754] The server is responsible for the first stage of data collection. First, the server identifies data sources, which include official websites of local governments and related organizations, tourism information sites, and government open data portals. Next, the server uses API-based data collection and scraping techniques to gather the necessary data and store it in a database.

[0755] Specific example: A server uses an API to retrieve and store data on the annual number of tourists in a region from a government open data portal.

[0756] 2. Data Analysis

[0757] The server also handles data analysis. The collected data is first preprocessed. Preprocessing includes data normalization, imputation of missing values, and standardization of format. Next, a generative artificial intelligence model (e.g., TensorFlow) is run on the preprocessed data to analyze the strengths and weaknesses of the region. From the analysis results, areas that require particular attention for improvement are identified.

[0758] Specific example: The server analyzes data on the annual number of tourists in a region and identifies that the number of tourists in the fall is lower than in other seasons.

[0759] 3. Generate an action plan

[0760] Based on the analysis results, the server uses a generative artificial intelligence model to generate specific action plans. For example, it might propose events or campaigns specifically tailored to periods with low tourist numbers.

[0761] Specific example: The server generates a proposal to hold an "Autumn Harvest Festival" in the region to increase tourism during the autumn season.

[0762] 4. Notification to the person in charge

[0763] The server notifies local government officials of the generated action plan and analysis results. Notifications are sent via email or a dedicated dashboard.

[0764] Specific example: The server notifies local tourism officials via email of the generated "Autumn Harvest Festival" plan and its predicted effects.

[0765] 5. User-facing applications

[0766] The device will provide a function to inform citizens about local government plans through a user-facing application. The application will allow for event announcements and surveys, enabling the collection of residents' opinions and willingness to participate.

[0767] Specific example: A device distributes a survey about the "Autumn Harvest Festival" through a local resident app, collecting citizens' willingness to participate.

[0768] 6. Emotion analysis

[0769] The server analyzes the feedback collected from users using an emotion engine (for example, OpenAI's API). The emotion engine performs sentiment analysis on the text and determines emotion categories such as positive, negative, and neutral.

[0770] Specific example: The server uses an emotion engine to analyze citizen feedback on the "Autumn Harvest Festival" and confirms that many citizens have a positive reaction to the event.

[0771] 7. Providing improvement proposals

[0772] The server evaluates the effectiveness of the plan based on feedback analyzed by the emotion engine. It then generates adjustments and improvements to the action plan as needed and notifies the responsible party again.

[0773] Specific example: Based on the server's analysis results, it notifies local tourism officials of a proposal to change part of the "Autumn Harvest Festival" event.

[0774] Example of a prompt

[0775] "Analyze the sentiment of citizen feedback regarding Niigata Prefecture's Autumn Harvest Festival and generate optimal content."

[0776] "Analyze the sentiment of the following text: 'We received positive feedback from citizens about the Autumn Harvest Festival.'"

[0777] This system will enable local governments to quickly formulate and implement effective, data-driven regional development plans. Furthermore, it will allow for accurate incorporation of citizen feedback through sentiment analysis, which is expected to further promote regional revitalization.

[0778] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0779] Step 1:

[0780] Identifying the data source

[0781] The server identifies appropriate data sources for data collection. These data sources include official websites of local governments and related organizations, tourism information sites, and government open data portals. Specifically, the server scans a pre-configured list and collects API endpoints and URLs to scrape. The input is a pre-configured list of data sources, and the output is the collected URLs and API endpoints.

[0782] Step 2:

[0783] Data collection

[0784] The server collects data from identified data sources. It uses API-based data collection and scraping techniques to obtain the necessary data and store it in a database. The input is the URL or API endpoint of the data source identified in step 1, and the output is the collected data stored in the database. Specifically, it sends an HTTP request, parses the response to extract the necessary data, and writes it to the database.

[0785] Step 3:

[0786] Data preprocessing

[0787] The server preprocesses the collected data. Preprocessing includes data normalization, imputation of missing values, and formatting. The input is the raw data collected in step 2, and the output is the preprocessed data. Specifically, a data transformation library (e.g., Pandas) is used to format the data into a standard format.

[0788] Step 4:

[0789] Data Analysis

[0790] The server runs a generative artificial intelligence model based on pre-processed data to analyze the strengths and weaknesses of the region. The input is the data pre-processed in step 3, and the output is the analysis results. Specifically, it uses a pre-trained artificial intelligence model (e.g., TensorFlow) to analyze the data and extract specific patterns and trends.

[0791] Step 5:

[0792] Action plan generation

[0793] The server generates a specific action plan using a generative artificial intelligence model based on the analysis results. The input is the analysis results obtained in step 4, and the output is the generated action plan. Specifically, it uses a generative artificial intelligence model (e.g., GPT-3) to generate event and campaign suggestions based on the analysis results. An example of a prompt message would be, "Analyze the sentiment of citizen feedback regarding Niigata Prefecture's Autumn Harvest Festival and generate optimal content."

[0794] Step 6:

[0795] Notification to the North

[0796] The server notifies the local government official of the generated action plan and analysis results. The input is the action plan generated in step 5, and the output is the notified official. Specifically, the official is notified via email or a dedicated dashboard.

[0797] Step 7:

[0798] User application updates

[0799] The terminal will notify citizens of the generated action plan through a user application. The input is the action plan notified in step 6, and the output is the updated application. Specifically, the application's UI will be updated to display the new action plan information.

[0800] Step 8:

[0801] Feedback Collection

[0802] This system collects user feedback on action plans provided through the application. The input is user feedback, and the output is the collected feedback data. Specifically, it uses features such as surveys and comments to gather opinions and feedback from users.

[0803] Step 9:

[0804] Emotion analysis

[0805] The server analyzes the sentiment of the feedback it has collected. The input is the feedback collected in step 8, and the output is the sentiment analysis result. Specifically, it uses a generative artificial intelligence model for sentiment analysis (e.g., OpenAI's API) to determine the sentiment category from the feedback text. An example of a prompt would be, "Analyze the sentiment of the following text: 'We received positive feedback from citizens about the Autumn Harvest Festival.'"

[0806] Step 10:

[0807] Providing improvement proposals

[0808] The server evaluates the effectiveness of the plan based on the sentiment analysis results and generates adjustments or improvements to the action plan as needed. The input is the sentiment analysis results obtained in step 9, and the output is the improvement proposals. Specifically, it uses the generative artificial intelligence model again to generate new action plans and improvement proposals and notifies the person in charge.

[0809] This series of processes enables local governments to quickly formulate and implement effective, data-driven regional development strategies. Furthermore, sentiment analysis allows for accurate incorporation of citizen feedback.

[0810] 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.

[0811] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. 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. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0812] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0813] [Third Embodiment]

[0814] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0815] 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.

[0816] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0817] 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.

[0818] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0819] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0820] 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.

[0821] 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.

[0822] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0823] The 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.

[0824] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0825] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0826] Patent Specification

[0827] The system of this invention aims to revitalize local governments by collecting and analyzing various data and generating concrete action plans. The system mainly consists of three components: a server, terminals, and users.

[0828] 1. Data Collection

[0829] The server is responsible for the first stage of data collection. First, the server identifies multiple data sources related to local governments. These include government open data portals, local government websites, and tourism information sites. Next, the server collects the necessary data using APIs and scraping techniques, and stores it in its internal database.

[0830] Specific example: A server uses an API to retrieve and store data on the annual number of tourists in Niigata Prefecture from the government's open data portal.

[0831] 2. Data Analysis

[0832] The server also handles data analysis. The collected data is first pre-processed. Pre-processing includes data normalization and imputation of missing values. Next, an AI model is run using the pre-processed data to analyze the strengths and weaknesses of the region. From the analysis results, areas that require particular focus on improvement are identified.

[0833] Specific example: The server analyzes data on the annual number of tourists in Niigata Prefecture and identifies that the number of tourists in autumn is lower than in other seasons.

[0834] 3. Generate an action plan

[0835] Based on the analysis results, the server uses generative AI to generate specific action plans. For example, it might suggest events or campaigns specifically tailored to periods with low tourist numbers.

[0836] Specific example: The server generates a proposal to hold an "Autumn Harvest Festival" in Niigata Prefecture to increase tourism during the autumn season.

[0837] 4. Notification to the user

[0838] The server notifies local government officials (users) of the generated action plan and analysis results. Notifications are sent via email or a dedicated dashboard.

[0839] Specific example: The server notifies the tourism official in Niigata Prefecture via email of the generated "Autumn Harvest Festival" plan and its predicted effects.

[0840] 5. Promoting citizen participation

[0841] The terminal will provide a function to inform citizens about local government plans through a citizen-oriented application. The application will allow for event announcements and surveys to collect residents' opinions and willingness to participate.

[0842] Specific example: A device uses an app for residents of Niigata Prefecture to distribute a survey about the "Autumn Harvest Festival" and collect citizens' willingness to participate.

[0843] 6. Feedback Analysis

[0844] The server analyzes feedback collected from citizens and evaluates the effectiveness of the plan. It then generates adjustments and improvements to the plan as needed and notifies the user again.

[0845] Specific example: The server analyzes the results of a survey from citizens, confirms the high level of interest in the "Autumn Harvest Festival," and proposes expanding the scale of the event.

[0846] ---

[0847] This invention aims to support local governments in effectively and efficiently planning and implementing regional development measures by automating and optimizing these processes. This is expected to lead to regional revitalization.

[0848] The following describes the processing flow.

[0849] Program processing steps

[0850] Step 1: Identify the data source

[0851] The server identifies data sources for collecting relevant local government data. These data sources include government open data portals, local government websites, and tourism information sites.

[0852] Step 2: Data Collection

[0853] The server collects data from specified data sources using APIs and scraping techniques, and stores it in its internal database.

[0854] Step 3: Data Preprocessing

[0855] The server preprocesses the collected data. Specifically, it performs data normalization, imputation of missing values, and standardization of the format.

[0856] Step 4: Data Analysis

[0857] The server uses pre-processed data to perform AI modeling and analyze the region's strengths and weaknesses. This identifies key challenges and opportunities that require particular focus.

[0858] Step 5: Generate an action plan

[0859] The server uses generative AI to generate specific action plans based on the analysis results. These plans may include proposals for events and campaigns.

[0860] Step 6: Notify the user

[0861] The server notifies local government officials (users) of the generated action plan and its analysis results. Notifications are sent via email or a dedicated dashboard.

[0862] Step 7: Promoting citizen participation

[0863] The terminals will inform citizens about the local government's plans through a citizen-oriented application. Furthermore, the application will be used to conduct surveys and collect opinions and intentions to participate.

[0864] Step 8: Gathering Feedback

[0865] The device collects survey results and other feedback and sends them to the server.

[0866] Step 9: Feedback Analysis

[0867] The server analyzes feedback from citizens and evaluates the effectiveness of the plan. Based on the analysis results, it makes necessary adjustments and generates improvement proposals.

[0868] Step 10: Notification of proposed improvements

[0869] The server optimizes the plan by cycling through each step and notifying the local government official (user) of improvement suggestions generated from the analyzed feedback.

[0870] (Example 1)

[0871] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0872] In revitalizing local governments, the insufficient effective use of data makes it difficult to create concrete action plans and effectively promote citizen participation. Furthermore, formulating improvement measures based on collected feedback requires considerable time and effort, making it difficult to implement rapid and effective regional development measures. To solve this problem, an automated and optimized process is needed that unifies everything from data collection to action plan generation, citizen participation promotion, and feedback analysis.

[0873] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0874] In this invention, the server includes means for identifying data sources, means for collecting data and storing it in a database, means for preprocessing and analyzing the collected data, means for generating an action plan using an artificial intelligence model based on the analysis results, means for notifying local government officials of the action plan, means for soliciting opinions and participation through application software for citizens, means for collecting and analyzing feedback from citizens, and means for providing improvement proposals based on the collected feedback. This enables local governments to effectively utilize data, quickly generate and implement action plans that reflect citizens' opinions and participation, and further formulate improvement measures based on feedback from citizens.

[0875] A "data source" refers to a website, portal site, or similar online information provider that offers information related to local governments.

[0876] A "database" refers to a computer system used to manage and store collected data, which is later used for analysis or other processes.

[0877] "Preprocessing" refers to a series of steps that transform collected raw data into an analyzable format, including data normalization, imputation of missing values, and data type conversion.

[0878] "Analysis" refers to data processing techniques that use statistical methods or machine learning models on pre-processed data to elucidate specific objectives or propositions.

[0879] A "generative artificial intelligence model" refers to a system that uses artificial intelligence to automatically generate proposals and plans based on input conditions, specifically employing technologies such as natural language processing.

[0880] An "action plan" is a compilation of specific action plans and proposals based on the analysis results, and includes proposals for measures and events that local governments can implement.

[0881] "Application software" refers to devices and applications provided to citizens, through which information is provided and surveys are conducted.

[0882] "Feedback" refers to opinions and evaluations collected from citizens through application software, which are used to improve the system.

[0883] The system of this invention is configured to automate data collection, analysis, action plan generation, and feedback analysis, with the aim of revitalizing local governments. The system mainly consists of three components: a server, terminals, and users.

[0884] 1. Data Collection

[0885] First, the server is responsible for data collection. Specifically, the server identifies multiple data sources related to local governments (e.g., government open data portals, local government official websites, tourism information sites, etc.) and collects data using APIs and scraping techniques. In this process, libraries such as Python's Requests, BeautifulSoup, and Scrapy are often used. The collected data is stored in databases such as MySQL or PostgreSQL.

[0886] Specific example: A server automatically executes a script at 2 AM every day to retrieve annual tourist data for Niigata Prefecture from the government's open data portal and save it to a database.

[0887] 2. Data Analysis

[0888] Next, the server preprocesses and analyzes the collected data. Preprocessing includes data normalization, missing value imputation, and data type conversion, using the Pandas library. The preprocessed data is then analyzed using machine learning libraries such as TensorFlow and PyTorch. This analysis identifies the region's strengths and weaknesses.

[0889] Specific example: The server preprocesses annual tourist data for Niigata Prefecture and uses Scikit-learn's clustering algorithm to identify that the number of tourists in autumn is lower than in other seasons.

[0890] 3. Generate an action plan

[0891] The server generates an action plan using a generative artificial intelligence model (e.g., GPT-3 or BERT) based on the analysis results. The generated action plan includes suggestions for specific events and campaigns.

[0892] Specific example: The server generates a prompt based on the analysis results, inputs the prompt "Generate event proposals to increase the number of tourists in Niigata Prefecture during the autumn season" into GPT-3, and saves the generated "Autumn Harvest Festival" proposal as the final action plan.

[0893] 4. Notification to the user

[0894] The server notifies the local government official (user) of the generated action plan and analysis results. Notification can be sent via email using an SMTP server, or through a dedicated dashboard utilizing a web application such as Django or Flask.

[0895] Specific example: The server will notify tourism officials in Niigata Prefecture via email of the generated "Autumn Harvest Festival" plan and its predicted effects. This information will also be available for real-time monitoring via a dashboard.

[0896] 5. Promoting citizen participation

[0897] The device will use a citizen-facing application to disseminate action plans to citizens and encourage their opinions and participation. This application will be developed using Flutter and React Native. The application will allow for event announcements and surveys, enabling the collection of residents' opinions and willingness to participate.

[0898] Specific example: A device uses an app for residents of Niigata Prefecture to distribute detailed information about the "Autumn Harvest Festival" and conducts a survey to collect participation intentions.

[0899] 6. Feedback Analysis

[0900] Finally, the server analyzes the feedback collected from citizens. The same techniques as in preprocessing are used for the analysis, and the effectiveness of the plan is evaluated based on the results. If necessary, adjustments or improvements to the plan are generated and notified to the user again.

[0901] Specific example: The server analyzes the results of a survey from citizens in real time and confirms the high level of interest in the "Autumn Harvest Festival." Based on this, it notifies users of a suggestion to expand the scale of the event.

[0902] Thus, the system of the present invention supports local governments in effectively utilizing data and in quickly and accurately formulating and implementing regional development measures.

[0903] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0904] System program processing flow

[0905] Step 1: Identify the data source

[0906] Step 2: Data Collection

[0907] Step 3: Save data

[0908] Step 4: Data preprocessing

[0909] Step 5: Data Analysis

[0910] Step 6: Generate an action plan

[0911] Step 7: Notify the user

[0912] Step 8: Distribution of information to citizens

[0913] Step 9: Gathering feedback from citizens

[0914] Step 10: Analyze feedback and notify of improvement suggestions

[0915] ---

[0916] Detailed explanation of each processing step

[0917] Step 1: Identify the data source

[0918] The server identifies the data sources necessary for data collection. First, it lists data sources related to local governments (e.g., government open data portals, local government official websites, tourism information sites, etc.) and determines the data items to be collected from these data sources. The input is the URLs or API keys of the data sources, and the output is a list of data to be collected.

[0919] Specific operation: The server identifies the URL of the government's open data portal based on a defined list and retrieves its API key.

[0920] Step 2: Data Collection

[0921] The server collects data from identified data sources. Specifically, it retrieves data using APIs or scraping techniques. Inputs include a list of data sources, API keys, and collection scripts, while output is the collected raw data.

[0922] Specific operation: The server automatically executes a script at 2 AM every day, using the API of the government's open data portal to retrieve data on the annual number of tourists in Niigata Prefecture.

[0923] Step 3: Save data

[0924] The server stores the collected data in a database. Input is raw data, and output is the data stored in the database. Database systems such as MySQL and PostgreSQL are primarily used.

[0925] Specific operation: The server collects data on the annual number of tourists in Niigata Prefecture and saves it to a MySQL database.

[0926] Step 4: Data preprocessing

[0927] The server preprocesses the collected data. This preprocessing includes data normalization, missing value imputation, and data type conversion. The input is raw data, and the output is preprocessed data. The Pandas library is commonly used for this.

[0928] Specific operation: The server uses Pandas to impute missing values ​​in the annual tourist count data for Niigata Prefecture and to format the data types.

[0929] Step 5: Data Analysis

[0930] The server analyzes the pre-processed data. Specifically, it uses machine learning libraries such as TensorFlow and PyTorch to analyze the data and identify regional strengths and weaknesses. The input is pre-processed data, and the output is the analysis results.

[0931] Specific operation: The server uses Niigata Prefecture's annual tourist data and the Scikit-learn clustering algorithm to identify that the number of tourists is low in the autumn.

[0932] Step 6: Generate an action plan

[0933] The server generates an action plan based on the analysis results using a generative AI model (e.g., GPT-3 or BERT). The input is the analysis results, and the output is the generated action plan.

[0934] Specific operation: The server creates a prompt based on the analysis results and inputs the prompt "Generate event proposals to increase the number of tourists in Niigata Prefecture during the autumn season" to GPT-3. As a result, it saves the generated "Autumn Harvest Festival" plan.

[0935] Step 7: Notify the user

[0936] The server notifies the user (local government official) of the generated action plan and analysis results. Specifically, this can be done by sending an email using an SMTP server or by using a dedicated dashboard built with Django or Flask. The inputs are the generated action plan and analysis results, and the output is the notified information.

[0937] Specific operation: The server will notify tourism officials in Niigata Prefecture via email about the "Autumn Harvest Festival" plan and its projected effects. The information will also be displayed on a dedicated dashboard.

[0938] Step 8: Distribution of information to citizens

[0939] The device uses a user-facing application to disseminate action plans to citizens and solicit their opinions and participation. The input is the action plan, and the output is the notification content for citizens. Application development using Flutter or React Native falls into this category.

[0940] Specific actions: The device will distribute detailed information about the "Autumn Harvest Festival" via a user-facing app and conduct a survey to encourage residents to participate in the event.

[0941] Step 9: Gathering feedback from citizens

[0942] The device collects feedback from citizens. The input is feedback data from citizens, and the output is the collected feedback. Specific methods include surveys and event evaluations.

[0943] Specific operation: The device sends the results of surveys conducted using the user-facing app to the server in real time and collects feedback.

[0944] Step 10: Analyze feedback and notify of improvement suggestions

[0945] The server analyzes the collected feedback and generates improvement proposals. Specifically, it uses a preprocessing, analysis, and generative AI model to generate improvement proposals. The input is feedback from citizens, and the output is the generated improvement proposals.

[0946] Specific operation: The server analyzes the results of a survey from citizens and confirms the high level of interest in the "Autumn Harvest Festival." Based on this, it notifies the local government officials of a proposal to expand the scale of the event.

[0947] (Application Example 1)

[0948] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0949] Local governments need to effectively utilize a large amount of data for regional development and attracting tourists, but collecting, analyzing, and generating appropriate action plans for this data is difficult. In particular, the lack of effective means to collect and analyze real-time feedback from citizens and tourists is reducing the effectiveness of regional development measures. Furthermore, there is a need for a system that effectively communicates information to local government officials and supports rapid decision-making.

[0950] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0951] In this invention, the server includes means for identifying data sources, collecting data and storing it in a database, preprocessing and analyzing the collected data, generating an action plan based on the analysis results, notifying local government officials of the action plan, soliciting opinions and participation through a citizen-facing application, collecting and analyzing feedback from citizens, providing improvement proposals based on the collected feedback, providing information and conducting surveys using an interface installed in a vehicle, and generating an action plan using a generative AI model. This enables local governments to collect and analyze data in real time and generate and implement effective action plans. Furthermore, by utilizing the interface in the vehicle, feedback from citizens and tourists can be collected quickly, enhancing the effectiveness of regional development measures.

[0952] "Means for identifying data sources" refers to a mechanism for finding multiple data sources that provide information related to local governments.

[0953] "Means of collecting data and storing it in a database" refers to a system for obtaining information from identified data sources, organizing it, and storing it in a database.

[0954] "Means for preprocessing and analyzing collected data" refers to a system for performing preprocessing such as normalization and imputation of missing values ​​on collected data, and then conducting data analysis.

[0955] "Means for generating action plans based on analysis results" refers to a system that automatically generates specific action plans and policy proposals using the results of analyzed data.

[0956] "Means for notifying local government officials of the action plan" refers to a system for quickly communicating the generated action plan to local government officials.

[0957] "Methods for soliciting opinions and participation through citizen-oriented applications" refers to a system that uses applications for residents to gather opinions and intentions to participate in events from citizens.

[0958] "Means for collecting and analyzing feedback from citizens" refers to a system for collecting feedback information submitted by citizens and systematically analyzing it.

[0959] "A means of providing improvement proposals based on collected feedback" refers to a system that generates improvement proposals for regional development and events based on the analyzed feedback results, and reflects them in the policies of local governments.

[0960] "Methods for providing information and conducting surveys using interfaces installed in automobiles" refers to systems that provide information to passengers through displays or voice assistants installed in autonomous vehicles and other vehicles, and collect opinions through surveys.

[0961] "Methods for generating action plans using generative AI models" refer to a system that uses artificial intelligence models to perform advanced analysis and data generation, and automatically creates effective action plans.

[0962] Modes for carrying out the invention

[0963] System Configuration

[0964] The system of this invention mainly consists of three components: a server, a terminal, and a user. Details of each component are described below.

[0965] Data collection

[0966] The server is responsible for the first stage of data collection. First, the server identifies multiple data sources related to local governments, such as the government's open data portal, local government websites, and tourism information sites. Next, the server collects the necessary data using APIs and web scraping techniques, and stores it in its internal database.

[0967] Specific example: A server uses an API to retrieve data on the annual number of tourists for a given municipality and saves it to a database.

[0968] Data Analysis

[0969] The server preprocesses the collected data and uses an AI model to analyze the region's strengths and weaknesses. Preprocessing includes data normalization and imputation of missing values. Based on the preprocessed data, the AI ​​model is run to identify areas that require focused improvement.

[0970] Specific example: The server analyzes the data and discovers that the number of tourists is low during a particular season.

[0971] Action plan generation

[0972] The server generates specific action plans using an AI model based on the analysis results. For example, it might suggest events or campaigns specifically tailored to periods with low tourist numbers.

[0973] Specific example: The server proposes a plan to hold an "Autumn Harvest Festival" using a generated AI model.

[0974] Example prompt: "Generate a specific event plan to increase tourism during the autumn season."

[0975] Notification to the user

[0976] The server notifies the local government official (user) of the generated action plan and analysis results. Notifications are sent via email or a dedicated dashboard.

[0977] Specific example: The server notifies the local government's tourism official via email of the generated "Autumn Harvest Festival" plan and its predicted effects.

[0978] Promoting citizen participation

[0979] The terminals provide a function to inform citizens about local government plans through citizen-oriented applications. An interface installed in the vehicle is used to provide information to tourists and conduct surveys. This allows for the collection of feedback from both citizens and tourists.

[0980] Specific example: A terminal uses a user-facing application to conduct a survey about the "Autumn Harvest Festival" and collect information on citizens' willingness to participate.

[0981] Feedback analysis

[0982] The server analyzes feedback collected from citizens and evaluates the effectiveness of the plan. It then generates adjustments and improvements to the plan as needed and notifies users again, thereby achieving sustainable regional development.

[0983] Specific example: If the server analyzes the survey results and confirms high interest, it will propose expanding the scale of the "Autumn Harvest Festival."

[0984] Hardware and software to use

[0985] Server: Responsible for data collection and analysis. Uses APIs, web scraping, database management systems, and AI model execution environments (e.g., Python, pandas, scikit-learn, GPT-3).

[0986] Devices: Citizen applications and interfaces installed in vehicles will utilize smartphones, tablets, and displays and voice assistants on autonomous vehicles.

[0987] Software: Python libraries (pandas, scikit-learn) are used for data preprocessing and analysis, and GPT-3 is used for generative AI models. Email systems and dashboard software are used for user notifications.

[0988] Thus, this invention is a system that supports local governments in effectively and efficiently planning and implementing regional development measures. This is expected to revitalize local communities.

[0989] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0990] Step 1:

[0991] Identify the data source.

[0992] The server identifies data sources such as government open data portals, local government websites, and tourism information sites. First, the server obtains the URLs of APIs and web pages necessary to collect information from the internet. Once the data sources have been identified, the server confirms the format and structure of the data to be collected and prepares for the next data collection step.

[0993] Input: API URL, Web page URL

[0994] Output: List of URLs for collection

[0995] Step 2:

[0996] Data acquisition and storage

[0997] The server collects data from identified data sources and stores it in a database. Data collection is performed using API requests or web scraping techniques. The retrieved data is initially stored in temporary memory and then permanently stored in the database. At this time, the database also records metadata such as the collection date and time and the source.

[0998] Input: URL list

[0999] Output: Raw data stored in the database

[1000] Step 3:

[1001] Data preprocessing

[1002] The server preprocesses the collected data. This process includes normalizing the data, imputing missing values, and removing unnecessary data. Specifically, it uses the Python pandas library to manipulate dataframes, imputing missing values ​​with SimpleImputer, and normalizing with StandardScaler. Once preprocessing is complete, the data is prepared in a format suitable for analysis.

[1003] Input: Raw data

[1004] Output: Preprocessed data

[1005] Step 4:

[1006] Data Analysis

[1007] The server analyzes the pre-processed data. In this step, an AI model is used to analyze the data and identify regional strengths and weaknesses. A Python machine learning library (e.g., scikit-learn) is used to perform data analysis, extracting specific insights using correlation matrices, regression analysis, etc.

[1008] Input: Preprocessed data

[1009] Output: Analysis results (regional strengths and weaknesses)

[1010] Step 5:

[1011] Generating an action plan

[1012] The server generates an action plan using a generative AI model based on the analysis results. Using generative AI such as GPT-3, it generates regional development measures by inputting specific prompt sentences. For example, the prompt sentence "Generate a specific event plan to increase the number of tourists in the autumn" is used. The generated action plan is output in text format.

[1013] Input: Analysis result, prompt message

[1014] Output: Action plan (event plan)

[1015] Step 6:

[1016] Notification to the user

[1017] The server notifies the local government officials of the generated action plan and analysis results. Notifications are sent via email or a dedicated dashboard. The email includes details of the generated plan and its predicted effects.

[1018] Input: Action plan, analysis results

[1019] Output: Notified action plan and analysis results (email, dashboard)

[1020] Step 7:

[1021] Promoting citizen participation

[1022] The terminals use a user-friendly application and an interface installed in the vehicle to disseminate information about local events and tourist attractions. Furthermore, they conduct surveys to collect feedback from citizens and tourists. Survey results are transmitted to a server in real time.

[1023] Input: Action plan, citizen application

[1024] Output: Collected feedback (survey results)

[1025] Step 8:

[1026] Feedback analysis and improvement plan generation

[1027] The server analyzes feedback collected from citizens and evaluates the effectiveness of the plan. It uses a Python data analysis library (e.g., pandas) to perform statistical analysis. Based on the results, it generates improvement suggestions as needed and notifies the relevant municipal officials again.

[1028] Input: Feedback data

[1029] Output: Improvement suggestions and feedback analysis results (email, dashboard)

[1030] In this way, a system is realized to comprehensively support local governments' regional development policies.

[1031] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1032] Patent Specification

[1033] The present invention aims to revitalize local governments by automating and optimizing a series of processes that collect and analyze various data and generate concrete action plans. The system mainly consists of three components: a server, terminals, and users. Furthermore, by incorporating an emotion engine to recognize user emotions, it enables more accurate analysis and feedback collection.

[1034] 1. Data Collection

[1035] The server is responsible for the first stage of data collection. First, the server identifies data sources relevant to local governments. These include government open data portals, local websites, and tourism information sites. Next, the server collects the necessary data using APIs and scraping techniques and stores it in a database.

[1036] Specific example: A server uses an API to retrieve and store data on the annual number of tourists in Niigata Prefecture from the government's open data portal.

[1037] 2. Data Analysis

[1038] The server also handles data analysis. The collected data is first pre-processed. Pre-processing includes data normalization, imputation of missing values, and standardization of format. Next, an AI model is run using the pre-processed data to analyze the strengths and weaknesses of the region. From the analysis results, areas that require particular focus on improvement are identified.

[1039] Specific example: The server analyzes data on the annual number of tourists in Niigata Prefecture and identifies that the number of tourists in autumn is lower than in other seasons.

[1040] 3. Generate an action plan

[1041] Based on the analysis results, the server uses generative AI to generate specific action plans. For example, it might suggest events or campaigns specifically tailored to periods with low tourist numbers.

[1042] Specific example: The server generates a proposal to hold an "Autumn Harvest Festival" in Niigata Prefecture to increase tourism during the autumn season.

[1043] 4. Notification to the user

[1044] The server notifies local government officials (users) of the generated action plan and analysis results. Notifications are sent via email or a dedicated dashboard.

[1045] Specific example: The server notifies the tourism official in Niigata Prefecture via email of the generated "Autumn Harvest Festival" plan and its predicted effects.

[1046] 5. Promoting citizen participation

[1047] The terminal will provide a function to inform citizens about local government plans through a citizen-oriented application. The application will allow for event announcements and surveys to collect residents' opinions and willingness to participate.

[1048] Specific example: A device uses an app for residents of Niigata Prefecture to distribute a survey about the "Autumn Harvest Festival" and collect citizens' willingness to participate.

[1049] 6. Utilizing the Emotion Engine

[1050] When the server collects feedback from citizens, it uses an emotion engine to analyze the emotional tendencies of the collected feedback. The emotion engine performs sentiment analysis on the text and determines emotional categories such as positive, negative, and neutral.

[1051] Specific example: The server uses an emotion engine to analyze citizen feedback on the "Autumn Harvest Festival" and confirms that many citizens have a positive reaction to the event.

[1052] 7. Feedback analysis and improvement

[1053] The server evaluates the effectiveness of the plan based on feedback analyzed by the emotion engine. If necessary, it generates adjustments or improvements to the action plan and notifies the user again.

[1054] Specific example: Based on the server's analysis results, a proposal to change part of the "Autumn Harvest Festival" event content is sent to the tourism official in Niigata Prefecture.

[1055] ---

[1056] This invention aims to support local governments in effectively and efficiently planning and implementing regional development measures by automating and optimizing these processes. The introduction of an emotion engine enables more advanced, emotion-based feedback analysis and optimization, which is expected to further promote regional revitalization.

[1057] The following describes the processing flow.

[1058] Program processing steps

[1059] Step 1: Identify the data source

[1060] The server identifies data sources for collecting relevant local government data. These data sources include government open data portals, local government websites, and tourism information sites.

[1061] Step 2: Data Collection

[1062] The server collects the necessary data from the specified data source using APIs and scraping techniques. The collected data is stored in a database.

[1063] Step 3: Data Preprocessing

[1064] The server preprocesses the collected data. Specifically, this includes imputing missing values, normalizing the data, and standardizing the format.

[1065] Step 4: Data Analysis

[1066] The server runs an AI model using pre-processed data to analyze the region's strengths and weaknesses. From the analysis results, it identifies issues and opportunities that should be given particular emphasis.

[1067] Step 5: Generate an action plan

[1068] Based on the analysis results, the server uses generative AI to generate specific action plans. For example, it might suggest events or campaigns specifically tailored to periods with low tourist numbers.

[1069] Step 6: Notify the user

[1070] The server notifies local government officials (users) of the generated action plan and its analysis results. Notifications are sent via email or a dedicated dashboard.

[1071] Step 7: Promoting citizen participation

[1072] The terminal provides a function to inform citizens about local government plans through a citizen-oriented application. The application allows for event announcements and surveys, enabling the collection of residents' opinions and willingness to participate.

[1073] Step 8: Gathering Feedback

[1074] The device sends collected survey results and other feedback to the server in real time. This allows for timely data collection.

[1075] Step 9: Analysis using the emotion engine

[1076] The server uses an emotion engine to analyze the emotional tendencies of the collected feedback. The emotion engine performs text analysis and classifies the feedback as either positive, negative, or neutral.

[1077] Step 10: Analysis and evaluation of feedback

[1078] The server evaluates the effectiveness of the plan based on feedback analyzed by the emotion engine. If necessary, it generates adjustments or improvements to the action plan and notifies the user.

[1079] Step 11: Notification of proposed improvements

[1080] The server will then notify local government officials again of the new action plan, which includes the improved plan. This notification will also be sent via email or a dashboard.

[1081] ---

[1082] This detailed process enables local governments to generate data-driven, effective regional development strategies and collect and analyze resident feedback in real time. Furthermore, the use of an emotion engine allows for the analysis of emotional trends in feedback, enabling more appropriate measures to be taken.

[1083] (Example 2)

[1084] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1085] To revitalize local governments, it is necessary to collect and analyze diverse data and generate concrete action plans. However, this process is time-consuming and labor-intensive when done manually, and is prone to subjective judgments. Furthermore, there is a need to efficiently analyze citizen feedback and improve plans based on that feedback. This invention provides a system that solves these problems and contributes to the revitalization of local governments.

[1086] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1087] In this invention, the server includes means for identifying data sources, collecting data and storing it in a database, preprocessing and analyzing the collected data, generating an action plan using a generative AI model based on the analysis results, notifying local government officials of the action plan, soliciting opinions and participation through a citizen-facing application, collecting feedback from citizens and analyzing it using an emotion engine, and providing improvement proposals based on the collected feedback. This automates the process from data collection and analysis to action plan generation, feedback analysis, and provision of improvement proposals, enabling efficient and effective revitalization of local governments.

[1088] A "data source" is an external or internal source of information used to provide specific information.

[1089] "Means of collecting data and storing it in a database" refers to the process of collecting necessary data from external and internal sources and storing that data in a storage system for systematic accumulation.

[1090] "Preprocessing" refers to processes such as data normalization, imputation of missing values, and formatting to transform collected data into an analyzable format.

[1091] "Analysis" is the process of running statistical or machine learning models to detect patterns or trends based on pre-processed data in order to achieve a specific objective.

[1092] A "generative AI model" is an artificial intelligence model that generates new data and information based on pre-trained data.

[1093] An "action plan" is a specific action plan or strategy set up based on the analysis results.

[1094] "Notification methods" refer to ways of communicating generated action plans and analysis results to specific users. These include email, dashboards, and application notifications.

[1095] A "citizen-oriented application" is a mobile or web application used by local governments to provide citizens with information about their plans and events, and to gather opinions and confirm their willingness to participate.

[1096] "Methods for soliciting opinions and participation" refer to methods for collecting opinions from citizens and their intention to participate in events. This includes surveys, feedback forms, and so on.

[1097] "Feedback" refers to opinions and evaluations collected from users, representing their reactions to specific policies or events.

[1098] An "emotion engine" is software that analyzes emotional tendencies from text data and identifies emotional categories such as positive, negative, and neutral.

[1099] An "improvement proposal" is a suggestion to make existing plans or action plans more effective, based on collected data and feedback.

[1100] The present invention aims to revitalize local governments by automating and optimizing a series of processes for collecting and analyzing various data and generating concrete action plans. The system mainly consists of three components: a server, terminals, and users.

[1101] The server operates as follows: First, it identifies data sources and collects data related to local governments. Data collection is performed using APIs and scraping techniques. The collected data is stored in a database. For example, the server uses an API to retrieve and store tourism data from a government open data portal. An example of a prompt in this case would be, "Write code to retrieve tourism data from the government open data portal and save it to the database."

[1102] The server then preprocesses the collected data. This preprocessing includes data normalization, imputation of missing values, and formatting. The preprocessed data is then analyzed by an AI model. For example, it might analyze tourism data for Niigata Prefecture to identify that the number of tourists is low during certain seasons. An example prompt in this case would be, "Write code to preprocess tourism data for Niigata Prefecture and use an AI model to identify that the number of tourists is low during certain seasons."

[1103] Based on these analysis results, the server uses a generated AI model to create a specific action plan. For example, it might generate a proposal to hold an "Autumn Harvest Festival" to increase the number of tourists in the fall. An example of a prompt in this case would be, "Please generate a specific event plan to increase the number of tourists in Niigata Prefecture in the fall."

[1104] The server notifies the local government official (user) of the generated action plan and analysis results. The notification is made via email or dashboard. For example, the server will notify the tourism official in Niigata Prefecture of the generated "Autumn Harvest Festival" plan and its predicted effects via email. An example of the prompt in this case would be, "Write an email to notify the tourism official in Niigata Prefecture of the Autumn Harvest Festival plan and its predicted effects."

[1105] The terminal will inform citizens about local government plans through citizen-facing applications and collect residents' opinions and intentions to participate. For example, the terminal will distribute a survey about the "Autumn Harvest Festival" through the Niigata Prefecture resident app and collect citizens' intentions to participate. An example of a prompt message in this case would be, "Please create an app notification message to send the Autumn Harvest Festival survey to residents of Niigata Prefecture."

[1106] The server uses an emotion engine to analyze the emotional tendencies of citizens when collecting their feedback. For example, the server might use the emotion engine to analyze citizens' feedback on the "Autumn Harvest Festival" and confirm that many citizens have shown positive reactions. An example of a prompt in this case would be, "Write code to analyze citizen feedback using the emotion engine and classify it as positive, negative, or neutral."

[1107] Ultimately, the server evaluates the effectiveness of the plan based on the feedback analyzed by the emotion engine and generates adjustments or improvements to the action plan as needed. For example, the server might generate a proposal to change the content of the "Autumn Harvest Festival" based on the analysis results and notify the local government official. An example of a prompt in this case would be, "Based on the feedback results, please create a proposal to improve the Autumn Harvest Festival event."

[1108] The above describes the specific implementation method of the system of the present invention. This enables local governments to effectively and efficiently plan and implement regional development measures, and the introduction of the emotion engine allows for more advanced feedback analysis and optimization.

[1109] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1110] Step 1:

[1111] The server identifies data sources, including government open data portals, local government websites, and tourism information sites. The input is a list of data sources that may provide information relevant to the revitalization of local governments, and the output is a list of actually available data sources. Based on this identified list of data sources, data collection proceeds in the next step.

[1112] Step 2:

[1113] The server collects data from identified data sources. This process utilizes API-based data collection and scraping techniques. The input is the URL or API endpoint of the data source, and the output is the collected raw data. For example, the server might use an API to retrieve tourism data from a government open data portal and store it in a database.

[1114] Step 3:

[1115] The server preprocesses the collected data. This preprocessing includes data normalization, imputation of missing values, and formatting standardization. The input is the collected raw data, and the output is preprocessed data suitable for analysis. Specifically, the data format is standardized, and missing values ​​are imputed with the mean.

[1116] Step 4:

[1117] The server analyzes pre-processed data. It runs an AI model to analyze the strengths and weaknesses of a region. The input is pre-processed data, and the output is the analysis results. For example, it analyzes annual tourism data for Niigata Prefecture to identify that the number of tourists is low during certain seasons.

[1118] Step 5:

[1119] The server generates a concrete action plan using an AI model based on the analysis results. The input is the analysis results, and the output is a concrete action plan. Specifically, it generates a proposal to hold an "Autumn Harvest Festival" during the autumn season when tourist numbers are low.

[1120] Step 6:

[1121] The server notifies the local government official (user) of the generated action plan and analysis results. The input is the generated action plan and analysis results, and the output is the notification sent to the official. Email and dashboards are used as notification methods. For example, the generated "Autumn Harvest Festival" plan and its predicted effects are notified to the tourism official via email.

[1122] Step 7:

[1123] The terminal will inform citizens about the local government's plans through a citizen-facing application and collect residents' opinions and willingness to participate. The input is the generated action plan, and the output is feedback from citizens. Specifically, a survey about the "Autumn Harvest Festival" will be distributed through the local government's application to collect citizens' willingness to participate.

[1124] Step 8:

[1125] The server collects feedback from citizens and analyzes their emotional tendencies using an emotion engine. The input is citizen feedback, and the output is the analysis results categorized into emotional categories. Specifically, it analyzes the text of feedback on the "Autumn Harvest Festival" and classifies it into positive, negative, and neutral emotions.

[1126] Step 9:

[1127] The server evaluates the effectiveness of the plan based on feedback analyzed by the emotion engine and generates adjustments and improvements to the action plan as needed. The input is the emotion analysis result, and the output is the improved action plan. For example, it adjusts the content of the "Autumn Harvest Festival" based on the feedback and notifies the person in charge of the new proposal.

[1128] (Application Example 2)

[1129] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1130] When revitalizing local governments, traditional methods often involve manual or partially automated processes for data collection, analysis, planning, and feedback gathering, resulting in low efficiency and inaccuracies. Furthermore, analyzing citizen feedback from an emotional perspective and incorporating it into plans presents significant challenges. To address these issues, a system is needed that highly automates each process and enables real-time data analysis and feedback collection.

[1131] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for identifying data sources, means for collecting data and storing it in a database, means for pre-processing and analyzing the collected data, means for generating an action plan based on the analysis results, means for notifying the person in charge of the action plan, means for soliciting opinions and participation through a user application, means for collecting and analyzing user feedback, means for providing improvement proposals based on the collected feedback, means for executing a generative artificial intelligence model for generating and distributing region-related content, and means for notifying the user of the generated content. This enables local governments to effectively and efficiently plan and implement regional development measures, and to accurately reflect citizen feedback through sentiment analysis.

[1132] A "data source" is the source from which information is collected, and examples include government open data portals, local government websites, and tourism information websites.

[1133] "Data collection" is the process of obtaining necessary information from a specified data source and storing it in a database.

[1134] A "database" is an aggregate of information that systematically stores collected data and makes it accessible as needed.

[1135] "Preprocessing" is the process of converting collected data into a format suitable for analysis, and includes data normalization, imputation of missing values, and standardization of formatting.

[1136] "Analysis" is the process of extracting patterns and trends in information using certain algorithms and models based on pre-processed data, and then interpreting the results according to a specific purpose.

[1137] An "action plan" is a specific action plan created based on the analysis results, and includes proposals for events and campaigns to promote regional development.

[1138] "Notification" refers to the process of informing the responsible party about the generated action plan and analysis results, and this is done via email or a dedicated dashboard.

[1139] A "user-facing application" is software provided to citizens and stakeholders of a local government, and has functions such as collecting opinions and announcing event information.

[1140] "Feedback" refers to opinions and impressions provided by users, and is information that is useful for improving services and generating new ideas.

[1141] "Sentiment analysis" is a process that classifies the emotional tendencies of collected feedback through text analysis, determining whether they are positive, negative, neutral, etc.

[1142] A "generative artificial intelligence model" is a program that uses artificial intelligence to automatically generate new content and ideas.

[1143] "Content" refers to all information and entertainment provided to users, including local event information and tourist guides.

[1144] 1. Data Collection

[1145] The server is responsible for the first stage of data collection. First, the server identifies data sources, which include official websites of local governments and related organizations, tourism information sites, and government open data portals. Next, the server uses API-based data collection and scraping techniques to gather the necessary data and store it in a database.

[1146] Specific example: A server uses an API to retrieve and store data on the annual number of tourists in a region from a government open data portal.

[1147] 2. Data Analysis

[1148] The server also handles data analysis. The collected data is first preprocessed. Preprocessing includes data normalization, imputation of missing values, and standardization of format. Next, a generative artificial intelligence model (e.g., TensorFlow) is run on the preprocessed data to analyze the strengths and weaknesses of the region. From the analysis results, areas that require particular attention for improvement are identified.

[1149] Specific example: The server analyzes data on the annual number of tourists in a region and identifies that the number of tourists in the fall is lower than in other seasons.

[1150] 3. Generate an action plan

[1151] Based on the analysis results, the server uses a generative artificial intelligence model to generate specific action plans. For example, it might propose events or campaigns specifically tailored to periods with low tourist numbers.

[1152] Specific example: The server generates a proposal to hold an "Autumn Harvest Festival" in the region to increase tourism during the autumn season.

[1153] 4. Notification to the person in charge

[1154] The server notifies local government officials of the generated action plan and analysis results. Notifications are sent via email or a dedicated dashboard.

[1155] Specific example: The server notifies local tourism officials via email of the generated "Autumn Harvest Festival" plan and its predicted effects.

[1156] 5. User-facing applications

[1157] The device will provide a function to inform citizens about local government plans through a user-facing application. The application will allow for event announcements and surveys, enabling the collection of residents' opinions and willingness to participate.

[1158] Specific example: A device distributes a survey about the "Autumn Harvest Festival" through a local resident app, collecting citizens' willingness to participate.

[1159] 6. Emotion analysis

[1160] The server analyzes the feedback collected from users using an emotion engine (for example, OpenAI's API). The emotion engine performs sentiment analysis on the text and determines emotion categories such as positive, negative, and neutral.

[1161] Specific example: The server uses an emotion engine to analyze citizen feedback on the "Autumn Harvest Festival" and confirms that many citizens have a positive reaction to the event.

[1162] 7. Providing improvement proposals

[1163] The server evaluates the effectiveness of the plan based on feedback analyzed by the emotion engine. It then generates adjustments and improvements to the action plan as needed and notifies the responsible party again.

[1164] Specific example: Based on the server's analysis results, it notifies local tourism officials of a proposal to change part of the "Autumn Harvest Festival" event.

[1165] Example of a prompt

[1166] "Analyze the sentiment of citizen feedback regarding Niigata Prefecture's Autumn Harvest Festival and generate optimal content."

[1167] "Analyze the sentiment of the following text: 'We received positive feedback from citizens about the Autumn Harvest Festival.'"

[1168] This system will enable local governments to quickly formulate and implement effective, data-driven regional development plans. Furthermore, it will allow for accurate incorporation of citizen feedback through sentiment analysis, which is expected to further promote regional revitalization.

[1169] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1170] Step 1:

[1171] Identifying the data source

[1172] The server identifies appropriate data sources for data collection. These data sources include official websites of local governments and related organizations, tourism information sites, and government open data portals. Specifically, the server scans a pre-configured list and collects API endpoints and URLs to scrape. The input is a pre-configured list of data sources, and the output is the collected URLs and API endpoints.

[1173] Step 2:

[1174] Data collection

[1175] The server collects data from identified data sources. It uses API-based data collection and scraping techniques to obtain the necessary data and store it in a database. The input is the URL or API endpoint of the data source identified in step 1, and the output is the collected data stored in the database. Specifically, it sends an HTTP request, parses the response to extract the necessary data, and writes it to the database.

[1176] Step 3:

[1177] Data preprocessing

[1178] The server preprocesses the collected data. Preprocessing includes data normalization, imputation of missing values, and formatting. The input is the raw data collected in step 2, and the output is the preprocessed data. Specifically, a data transformation library (e.g., Pandas) is used to format the data into a standard format.

[1179] Step 4:

[1180] Data Analysis

[1181] The server runs a generative artificial intelligence model based on pre-processed data to analyze the strengths and weaknesses of the region. The input is the data pre-processed in step 3, and the output is the analysis results. Specifically, it uses a pre-trained artificial intelligence model (e.g., TensorFlow) to analyze the data and extract specific patterns and trends.

[1182] Step 5:

[1183] Action plan generation

[1184] The server generates a specific action plan using a generative artificial intelligence model based on the analysis results. The input is the analysis results obtained in step 4, and the output is the generated action plan. Specifically, it uses a generative artificial intelligence model (e.g., GPT-3) to generate event and campaign suggestions based on the analysis results. An example of a prompt message would be, "Analyze the sentiment of citizen feedback regarding Niigata Prefecture's Autumn Harvest Festival and generate optimal content."

[1185] Step 6:

[1186] Notification to the North

[1187] The server notifies the local government official of the generated action plan and analysis results. The input is the action plan generated in step 5, and the output is the notified official. Specifically, the official is notified via email or a dedicated dashboard.

[1188] Step 7:

[1189] User application updates

[1190] The terminal will notify citizens of the generated action plan through a user application. The input is the action plan notified in step 6, and the output is the updated application. Specifically, the application's UI will be updated to display the new action plan information.

[1191] Step 8:

[1192] Feedback Collection

[1193] This system collects user feedback on action plans provided through the application. The input is user feedback, and the output is the collected feedback data. Specifically, it uses features such as surveys and comments to gather opinions and feedback from users.

[1194] Step 9:

[1195] Emotion analysis

[1196] The server analyzes the sentiment of the feedback it has collected. The input is the feedback collected in step 8, and the output is the sentiment analysis result. Specifically, it uses a generative artificial intelligence model for sentiment analysis (e.g., OpenAI's API) to determine the sentiment category from the feedback text. An example of a prompt would be, "Analyze the sentiment of the following text: 'We received positive feedback from citizens about the Autumn Harvest Festival.'"

[1197] Step 10:

[1198] Providing improvement proposals

[1199] The server evaluates the effectiveness of the plan based on the sentiment analysis results and generates adjustments or improvements to the action plan as needed. The input is the sentiment analysis results obtained in step 9, and the output is the improvement proposals. Specifically, it uses the generative artificial intelligence model again to generate new action plans and improvement proposals and notifies the person in charge.

[1200] This series of processes enables local governments to quickly formulate and implement effective, data-driven regional development strategies. Furthermore, sentiment analysis allows for accurate incorporation of citizen feedback.

[1201] 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.

[1202] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1203] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1204] [Fourth Embodiment]

[1205] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1206] 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.

[1207] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[1208] 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.

[1209] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[1210] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1211] 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.

[1212] 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1213] 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.

[1214] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[1215] The 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.

[1216] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1217] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1218] Patent Specification

[1219] The system of this invention aims to revitalize local governments by collecting and analyzing various data and generating concrete action plans. The system mainly consists of three components: a server, terminals, and users.

[1220] 1. Data Collection

[1221] The server is responsible for the first stage of data collection. First, the server identifies multiple data sources related to local governments. These include government open data portals, local government websites, and tourism information sites. Next, the server collects the necessary data using APIs and scraping techniques, and stores it in its internal database.

[1222] Specific example: A server uses an API to retrieve and store data on the annual number of tourists in Niigata Prefecture from the government's open data portal.

[1223] 2. Data Analysis

[1224] The server also handles data analysis. The collected data is first pre-processed. Pre-processing includes data normalization and imputation of missing values. Next, an AI model is run using the pre-processed data to analyze the strengths and weaknesses of the region. From the analysis results, areas that require particular focus on improvement are identified.

[1225] Specific example: The server analyzes data on the annual number of tourists in Niigata Prefecture and identifies that the number of tourists in autumn is lower than in other seasons.

[1226] 3. Generate an action plan

[1227] Based on the analysis results, the server uses generative AI to generate specific action plans. For example, it might suggest events or campaigns specifically tailored to periods with low tourist numbers.

[1228] Specific example: The server generates a proposal to hold an "Autumn Harvest Festival" in Niigata Prefecture to increase tourism during the autumn season.

[1229] 4. Notification to the user

[1230] The server notifies local government officials (users) of the generated action plan and analysis results. Notifications are sent via email or a dedicated dashboard.

[1231] Specific example: The server notifies the tourism official in Niigata Prefecture via email of the generated "Autumn Harvest Festival" plan and its predicted effects.

[1232] 5. Promoting citizen participation

[1233] The terminal will provide a function to inform citizens about local government plans through a citizen-oriented application. The application will allow for event announcements and surveys to collect residents' opinions and willingness to participate.

[1234] Specific example: A device uses an app for residents of Niigata Prefecture to distribute a survey about the "Autumn Harvest Festival" and collect citizens' willingness to participate.

[1235] 6. Feedback Analysis

[1236] The server analyzes feedback collected from citizens and evaluates the effectiveness of the plan. It then generates adjustments and improvements to the plan as needed and notifies the user again.

[1237] Specific example: The server analyzes the results of a survey from citizens, confirms the high level of interest in the "Autumn Harvest Festival," and proposes expanding the scale of the event.

[1238] ---

[1239] This invention aims to support local governments in effectively and efficiently planning and implementing regional development measures by automating and optimizing these processes. This is expected to lead to regional revitalization.

[1240] The following describes the processing flow.

[1241] Program processing steps

[1242] Step 1: Identify the data source

[1243] The server identifies data sources for collecting relevant local government data. These data sources include government open data portals, local government websites, and tourism information sites.

[1244] Step 2: Data Collection

[1245] The server collects data from specified data sources using APIs and scraping techniques, and stores it in its internal database.

[1246] Step 3: Data Preprocessing

[1247] The server preprocesses the collected data. Specifically, it performs data normalization, imputation of missing values, and standardization of the format.

[1248] Step 4: Data Analysis

[1249] The server uses pre-processed data to perform AI modeling and analyze the region's strengths and weaknesses. This identifies key challenges and opportunities that require particular focus.

[1250] Step 5: Generate an action plan

[1251] The server uses generative AI to generate specific action plans based on the analysis results. These plans may include proposals for events and campaigns.

[1252] Step 6: Notify the user

[1253] The server notifies local government officials (users) of the generated action plan and its analysis results. Notifications are sent via email or a dedicated dashboard.

[1254] Step 7: Promoting citizen participation

[1255] The terminals will inform citizens about the local government's plans through a citizen-oriented application. Furthermore, the application will be used to conduct surveys and collect opinions and intentions to participate.

[1256] Step 8: Gathering Feedback

[1257] The device collects survey results and other feedback and sends them to the server.

[1258] Step 9: Feedback Analysis

[1259] The server analyzes feedback from citizens and evaluates the effectiveness of the plan. Based on the analysis results, it makes necessary adjustments and generates improvement proposals.

[1260] Step 10: Notification of proposed improvements

[1261] The server optimizes the plan by cycling through each step and notifying the local government official (user) of improvement suggestions generated from the analyzed feedback.

[1262] (Example 1)

[1263] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1264] In revitalizing local governments, the insufficient effective use of data makes it difficult to create concrete action plans and effectively promote citizen participation. Furthermore, formulating improvement measures based on collected feedback requires considerable time and effort, making it difficult to implement rapid and effective regional development measures. To solve this problem, an automated and optimized process is needed that unifies everything from data collection to action plan generation, citizen participation promotion, and feedback analysis.

[1265] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1266] In this invention, the server includes means for identifying data sources, means for collecting data and storing it in a database, means for preprocessing and analyzing the collected data, means for generating an action plan using an artificial intelligence model based on the analysis results, means for notifying local government officials of the action plan, means for soliciting opinions and participation through application software for citizens, means for collecting and analyzing feedback from citizens, and means for providing improvement proposals based on the collected feedback. This enables local governments to effectively utilize data, quickly generate and implement action plans that reflect citizens' opinions and participation, and further formulate improvement measures based on feedback from citizens.

[1267] A "data source" refers to a website, portal site, or similar online information provider that offers information related to local governments.

[1268] A "database" refers to a computer system used to manage and store collected data, which is later used for analysis or other processes.

[1269] "Preprocessing" refers to a series of steps that transform collected raw data into an analyzable format, including data normalization, imputation of missing values, and data type conversion.

[1270] "Analysis" refers to data processing techniques that use statistical methods or machine learning models on pre-processed data to elucidate specific objectives or propositions.

[1271] A "generative artificial intelligence model" refers to a system that uses artificial intelligence to automatically generate proposals and plans based on input conditions, specifically employing technologies such as natural language processing.

[1272] An "action plan" is a compilation of specific action plans and proposals based on the analysis results, and includes proposals for measures and events that local governments can implement.

[1273] "Application software" refers to devices and applications provided to citizens, through which information is provided and surveys are conducted.

[1274] "Feedback" refers to opinions and evaluations collected from citizens through application software, which are used to improve the system.

[1275] The system of this invention is configured to automate data collection, analysis, action plan generation, and feedback analysis, with the aim of revitalizing local governments. The system mainly consists of three components: a server, terminals, and users.

[1276] 1. Data Collection

[1277] First, the server is responsible for data collection. Specifically, the server identifies multiple data sources related to local governments (e.g., government open data portals, local government official websites, tourism information sites, etc.) and collects data using APIs and scraping techniques. In this process, libraries such as Python's Requests, BeautifulSoup, and Scrapy are often used. The collected data is stored in databases such as MySQL or PostgreSQL.

[1278] Specific example: A server automatically executes a script at 2 AM every day to retrieve annual tourist data for Niigata Prefecture from the government's open data portal and save it to a database.

[1279] 2. Data Analysis

[1280] Next, the server preprocesses and analyzes the collected data. Preprocessing includes data normalization, missing value imputation, and data type conversion, using the Pandas library. The preprocessed data is then analyzed using machine learning libraries such as TensorFlow and PyTorch. This analysis identifies the region's strengths and weaknesses.

[1281] Specific example: The server preprocesses annual tourist data for Niigata Prefecture and uses Scikit-learn's clustering algorithm to identify that the number of tourists in autumn is lower than in other seasons.

[1282] 3. Generate an action plan

[1283] The server generates an action plan using a generative artificial intelligence model (e.g., GPT-3 or BERT) based on the analysis results. The generated action plan includes suggestions for specific events and campaigns.

[1284] Specific example: The server generates a prompt based on the analysis results, inputs the prompt "Generate event proposals to increase the number of tourists in Niigata Prefecture during the autumn season" into GPT-3, and saves the generated "Autumn Harvest Festival" proposal as the final action plan.

[1285] 4. Notification to the user

[1286] The server notifies the local government official (user) of the generated action plan and analysis results. Notification can be sent via email using an SMTP server, or through a dedicated dashboard utilizing a web application such as Django or Flask.

[1287] Specific example: The server will notify tourism officials in Niigata Prefecture via email of the generated "Autumn Harvest Festival" plan and its predicted effects. This information will also be available for real-time monitoring via a dashboard.

[1288] 5. Promoting citizen participation

[1289] The device will use a citizen-facing application to disseminate action plans to citizens and encourage their opinions and participation. This application will be developed using Flutter and React Native. The application will allow for event announcements and surveys, enabling the collection of residents' opinions and willingness to participate.

[1290] Specific example: A device uses an app for residents of Niigata Prefecture to distribute detailed information about the "Autumn Harvest Festival" and conducts a survey to collect participation intentions.

[1291] 6. Feedback Analysis

[1292] Finally, the server analyzes the feedback collected from citizens. The same techniques as in preprocessing are used for the analysis, and the effectiveness of the plan is evaluated based on the results. If necessary, adjustments or improvements to the plan are generated and notified to the user again.

[1293] Specific example: The server analyzes the results of a survey from citizens in real time and confirms the high level of interest in the "Autumn Harvest Festival." Based on this, it notifies users of a suggestion to expand the scale of the event.

[1294] Thus, the system of the present invention supports local governments in effectively utilizing data and in quickly and accurately formulating and implementing regional development measures.

[1295] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1296] System program processing flow

[1297] Step 1: Identify the data source

[1298] Step 2: Data Collection

[1299] Step 3: Save data

[1300] Step 4: Data preprocessing

[1301] Step 5: Data Analysis

[1302] Step 6: Generate an action plan

[1303] Step 7: Notify the user

[1304] Step 8: Distribution of information to citizens

[1305] Step 9: Gathering feedback from citizens

[1306] Step 10: Analyze feedback and notify of improvement suggestions

[1307] ---

[1308] Detailed explanation of each processing step

[1309] Step 1: Identify the data source

[1310] The server identifies the data sources necessary for data collection. First, it lists data sources related to local governments (e.g., government open data portals, local government official websites, tourism information sites, etc.) and determines the data items to be collected from these data sources. The input is the URLs or API keys of the data sources, and the output is a list of data to be collected.

[1311] Specific operation: The server identifies the URL of the government's open data portal based on a defined list and retrieves its API key.

[1312] Step 2: Data Collection

[1313] The server collects data from identified data sources. Specifically, it retrieves data using APIs or scraping techniques. Inputs include a list of data sources, API keys, and collection scripts, while output is the collected raw data.

[1314] Specific operation: The server automatically executes a script at 2 AM every day, using the API of the government's open data portal to retrieve data on the annual number of tourists in Niigata Prefecture.

[1315] Step 3: Save data

[1316] The server stores the collected data in a database. Input is raw data, and output is the data stored in the database. Database systems such as MySQL and PostgreSQL are primarily used.

[1317] Specific operation: The server collects data on the annual number of tourists in Niigata Prefecture and saves it to a MySQL database.

[1318] Step 4: Data preprocessing

[1319] The server preprocesses the collected data. This preprocessing includes data normalization, missing value imputation, and data type conversion. The input is raw data, and the output is preprocessed data. The Pandas library is commonly used for this.

[1320] Specific operation: The server uses Pandas to impute missing values ​​in the annual tourist count data for Niigata Prefecture and to format the data types.

[1321] Step 5: Data Analysis

[1322] The server analyzes the pre-processed data. Specifically, it uses machine learning libraries such as TensorFlow and PyTorch to analyze the data and identify regional strengths and weaknesses. The input is pre-processed data, and the output is the analysis results.

[1323] Specific operation: The server uses Niigata Prefecture's annual tourist data and the Scikit-learn clustering algorithm to identify that the number of tourists is low in the autumn.

[1324] Step 6: Generate an action plan

[1325] The server generates an action plan based on the analysis results using a generative AI model (e.g., GPT-3 or BERT). The input is the analysis results, and the output is the generated action plan.

[1326] Specific operation: The server creates a prompt based on the analysis results and inputs the prompt "Generate event proposals to increase the number of tourists in Niigata Prefecture during the autumn season" to GPT-3. As a result, it saves the generated "Autumn Harvest Festival" plan.

[1327] Step 7: Notify the user

[1328] The server notifies the user (local government official) of the generated action plan and analysis results. Specifically, this can be done by sending an email using an SMTP server or by using a dedicated dashboard built with Django or Flask. The inputs are the generated action plan and analysis results, and the output is the notified information.

[1329] Specific operation: The server will notify tourism officials in Niigata Prefecture via email about the "Autumn Harvest Festival" plan and its projected effects. The information will also be displayed on a dedicated dashboard.

[1330] Step 8: Distribution of information to citizens

[1331] The device uses a user-facing application to disseminate action plans to citizens and solicit their opinions and participation. The input is the action plan, and the output is the notification content for citizens. Application development using Flutter or React Native falls into this category.

[1332] Specific actions: The device will distribute detailed information about the "Autumn Harvest Festival" via a user-facing app and conduct a survey to encourage residents to participate in the event.

[1333] Step 9: Gathering feedback from citizens

[1334] The device collects feedback from citizens. The input is feedback data from citizens, and the output is the collected feedback. Specific methods include surveys and event evaluations.

[1335] Specific operation: The device sends the results of surveys conducted using the user-facing app to the server in real time and collects feedback.

[1336] Step 10: Analyze feedback and notify of improvement suggestions

[1337] The server analyzes the collected feedback and generates improvement proposals. Specifically, it uses a preprocessing, analysis, and generative AI model to generate improvement proposals. The input is feedback from citizens, and the output is the generated improvement proposals.

[1338] Specific operation: The server analyzes the results of a survey from citizens and confirms the high level of interest in the "Autumn Harvest Festival." Based on this, it notifies the local government officials of a proposal to expand the scale of the event.

[1339] (Application Example 1)

[1340] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1341] Local governments need to effectively utilize a large amount of data for regional development and attracting tourists, but collecting, analyzing, and generating appropriate action plans for this data is difficult. In particular, the lack of effective means to collect and analyze real-time feedback from citizens and tourists is reducing the effectiveness of regional development measures. Furthermore, there is a need for a system that effectively communicates information to local government officials and supports rapid decision-making.

[1342] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1343] In this invention, the server includes means for identifying data sources, collecting data and storing it in a database, preprocessing and analyzing the collected data, generating an action plan based on the analysis results, notifying local government officials of the action plan, soliciting opinions and participation through a citizen-facing application, collecting and analyzing feedback from citizens, providing improvement proposals based on the collected feedback, providing information and conducting surveys using an interface installed in a vehicle, and generating an action plan using a generative AI model. This enables local governments to collect and analyze data in real time and generate and implement effective action plans. Furthermore, by utilizing the interface in the vehicle, feedback from citizens and tourists can be collected quickly, enhancing the effectiveness of regional development measures.

[1344] "Means for identifying data sources" refers to a mechanism for finding multiple data sources that provide information related to local governments.

[1345] "Means of collecting data and storing it in a database" refers to a system for obtaining information from identified data sources, organizing it, and storing it in a database.

[1346] "Means for preprocessing and analyzing collected data" refers to a system for performing preprocessing such as normalization and imputation of missing values ​​on collected data, and then conducting data analysis.

[1347] "Means for generating action plans based on analysis results" refers to a system that automatically generates specific action plans and policy proposals using the results of analyzed data.

[1348] "Means for notifying local government officials of the action plan" refers to a system for quickly communicating the generated action plan to local government officials.

[1349] "Methods for soliciting opinions and participation through citizen-oriented applications" refers to a system that uses applications for residents to gather opinions and intentions to participate in events from citizens.

[1350] "Means for collecting and analyzing feedback from citizens" refers to a system for collecting feedback information submitted by citizens and systematically analyzing it.

[1351] "A means of providing improvement proposals based on collected feedback" refers to a system that generates improvement proposals for regional development and events based on the analyzed feedback results, and reflects them in the policies of local governments.

[1352] "Methods for providing information and conducting surveys using interfaces installed in automobiles" refers to systems that provide information to passengers through displays or voice assistants installed in autonomous vehicles and other vehicles, and collect opinions through surveys.

[1353] "Methods for generating action plans using generative AI models" refer to a system that uses artificial intelligence models to perform advanced analysis and data generation, and automatically creates effective action plans.

[1354] Modes for carrying out the invention

[1355] System Configuration

[1356] The system of this invention mainly consists of three components: a server, a terminal, and a user. Details of each component are described below.

[1357] Data collection

[1358] The server is responsible for the first stage of data collection. First, the server identifies multiple data sources related to local governments, such as the government's open data portal, local government websites, and tourism information sites. Next, the server collects the necessary data using APIs and web scraping techniques, and stores it in its internal database.

[1359] Specific example: A server uses an API to retrieve data on the annual number of tourists for a given municipality and saves it to a database.

[1360] Data Analysis

[1361] The server preprocesses the collected data and uses an AI model to analyze the region's strengths and weaknesses. Preprocessing includes data normalization and imputation of missing values. Based on the preprocessed data, the AI ​​model is run to identify areas that require focused improvement.

[1362] Specific example: The server analyzes the data and discovers that the number of tourists is low during a particular season.

[1363] Action plan generation

[1364] The server generates specific action plans using an AI model based on the analysis results. For example, it might suggest events or campaigns specifically tailored to periods with low tourist numbers.

[1365] Specific example: The server proposes a plan to hold an "Autumn Harvest Festival" using a generated AI model.

[1366] Example prompt: "Generate a specific event plan to increase tourism during the autumn season."

[1367] Notification to the user

[1368] The server notifies the local government official (user) of the generated action plan and analysis results. Notifications are sent via email or a dedicated dashboard.

[1369] Specific example: The server notifies the local government's tourism official via email of the generated "Autumn Harvest Festival" plan and its predicted effects.

[1370] Promoting citizen participation

[1371] The terminals provide a function to inform citizens about local government plans through citizen-oriented applications. An interface installed in the vehicle is used to provide information to tourists and conduct surveys. This allows for the collection of feedback from both citizens and tourists.

[1372] Specific example: A terminal uses a user-facing application to conduct a survey about the "Autumn Harvest Festival" and collect information on citizens' willingness to participate.

[1373] Feedback analysis

[1374] The server analyzes feedback collected from citizens and evaluates the effectiveness of the plan. It then generates adjustments and improvements to the plan as needed and notifies users again, thereby achieving sustainable regional development.

[1375] Specific example: If the server analyzes the survey results and confirms high interest, it will propose expanding the scale of the "Autumn Harvest Festival."

[1376] Hardware and software to use

[1377] Server: Responsible for data collection and analysis. Uses APIs, web scraping, database management systems, and AI model execution environments (e.g., Python, pandas, scikit-learn, GPT-3).

[1378] Devices: Citizen applications and interfaces installed in vehicles will utilize smartphones, tablets, and displays and voice assistants on autonomous vehicles.

[1379] Software: Python libraries (pandas, scikit-learn) are used for data preprocessing and analysis, and GPT-3 is used for generative AI models. Email systems and dashboard software are used for user notifications.

[1380] Thus, this invention is a system that supports local governments in effectively and efficiently planning and implementing regional development measures. This is expected to revitalize local communities.

[1381] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1382] Step 1:

[1383] Identify the data source.

[1384] The server identifies data sources such as government open data portals, local government websites, and tourism information sites. First, the server obtains the URLs of APIs and web pages necessary to collect information from the internet. Once the data sources have been identified, the server confirms the format and structure of the data to be collected and prepares for the next data collection step.

[1385] Input: API URL, Web page URL

[1386] Output: List of URLs for collection

[1387] Step 2:

[1388] Data acquisition and storage

[1389] The server collects data from identified data sources and stores it in a database. Data collection is performed using API requests or web scraping techniques. The retrieved data is initially stored in temporary memory and then permanently stored in the database. At this time, the database also records metadata such as the collection date and time and the source.

[1390] Input: URL list

[1391] Output: Raw data stored in the database

[1392] Step 3:

[1393] Data preprocessing

[1394] The server preprocesses the collected data. This process includes normalizing the data, imputing missing values, and removing unnecessary data. Specifically, it uses the Python pandas library to manipulate dataframes, imputing missing values ​​with SimpleImputer, and normalizing with StandardScaler. Once preprocessing is complete, the data is prepared in a format suitable for analysis.

[1395] Input: Raw data

[1396] Output: Preprocessed data

[1397] Step 4:

[1398] Data Analysis

[1399] The server analyzes the pre-processed data. In this step, an AI model is used to analyze the data and identify regional strengths and weaknesses. A Python machine learning library (e.g., scikit-learn) is used to perform data analysis, extracting specific insights using correlation matrices, regression analysis, etc.

[1400] Input: Preprocessed data

[1401] Output: Analysis results (regional strengths and weaknesses)

[1402] Step 5:

[1403] Generating an action plan

[1404] The server generates an action plan using a generative AI model based on the analysis results. Using generative AI such as GPT-3, it generates regional development measures by inputting specific prompt sentences. For example, the prompt sentence "Generate a specific event plan to increase the number of tourists in the autumn" is used. The generated action plan is output in text format.

[1405] Input: Analysis result, prompt message

[1406] Output: Action plan (event plan)

[1407] Step 6:

[1408] Notification to the user

[1409] The server notifies the local government officials of the generated action plan and analysis results. Notifications are sent via email or a dedicated dashboard. The email includes details of the generated plan and its predicted effects.

[1410] Input: Action plan, analysis results

[1411] Output: Notified action plan and analysis results (email, dashboard)

[1412] Step 7:

[1413] Promoting citizen participation

[1414] The terminals use a user-friendly application and an interface installed in the vehicle to disseminate information about local events and tourist attractions. Furthermore, they conduct surveys to collect feedback from citizens and tourists. Survey results are transmitted to a server in real time.

[1415] Input: Action plan, citizen application

[1416] Output: Collected feedback (survey results)

[1417] Step 8:

[1418] Feedback analysis and improvement plan generation

[1419] The server analyzes feedback collected from citizens and evaluates the effectiveness of the plan. It uses a Python data analysis library (e.g., pandas) to perform statistical analysis. Based on the results, it generates improvement suggestions as needed and notifies the relevant municipal officials again.

[1420] Input: Feedback data

[1421] Output: Improvement suggestions and feedback analysis results (email, dashboard)

[1422] In this way, a system is realized to comprehensively support local governments' regional development policies.

[1423] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1424] Patent Specification

[1425] The present invention aims to revitalize local governments by automating and optimizing a series of processes that collect and analyze various data and generate concrete action plans. The system mainly consists of three components: a server, terminals, and users. Furthermore, by incorporating an emotion engine to recognize user emotions, it enables more accurate analysis and feedback collection.

[1426] 1. Data Collection

[1427] The server is responsible for the first stage of data collection. First, the server identifies data sources relevant to local governments. These include government open data portals, local websites, and tourism information sites. Next, the server collects the necessary data using APIs and scraping techniques and stores it in a database.

[1428] Specific example: A server uses an API to retrieve and store data on the annual number of tourists in Niigata Prefecture from the government's open data portal.

[1429] 2. Data Analysis

[1430] The server also handles data analysis. The collected data is first pre-processed. Pre-processing includes data normalization, imputation of missing values, and standardization of format. Next, an AI model is run using the pre-processed data to analyze the strengths and weaknesses of the region. From the analysis results, areas that require particular focus on improvement are identified.

[1431] Specific example: The server analyzes data on the annual number of tourists in Niigata Prefecture and identifies that the number of tourists in autumn is lower than in other seasons.

[1432] 3. Generate an action plan

[1433] Based on the analysis results, the server uses generative AI to generate specific action plans. For example, it might suggest events or campaigns specifically tailored to periods with low tourist numbers.

[1434] Specific example: The server generates a proposal to hold an "Autumn Harvest Festival" in Niigata Prefecture to increase tourism during the autumn season.

[1435] 4. Notification to the user

[1436] The server notifies local government officials (users) of the generated action plan and analysis results. Notifications are sent via email or a dedicated dashboard.

[1437] Specific example: The server notifies the tourism official in Niigata Prefecture via email of the generated "Autumn Harvest Festival" plan and its predicted effects.

[1438] 5. Promoting citizen participation

[1439] The terminal will provide a function to inform citizens about local government plans through a citizen-oriented application. The application will allow for event announcements and surveys to collect residents' opinions and willingness to participate.

[1440] Specific example: A device uses an app for residents of Niigata Prefecture to distribute a survey about the "Autumn Harvest Festival" and collect citizens' willingness to participate.

[1441] 6. Utilizing the Emotion Engine

[1442] When the server collects feedback from citizens, it uses an emotion engine to analyze the emotional tendencies of the collected feedback. The emotion engine performs sentiment analysis on the text and determines emotional categories such as positive, negative, and neutral.

[1443] Specific example: The server uses an emotion engine to analyze citizen feedback on the "Autumn Harvest Festival" and confirms that many citizens have a positive reaction to the event.

[1444] 7. Feedback analysis and improvement

[1445] The server evaluates the effectiveness of the plan based on feedback analyzed by the emotion engine. If necessary, it generates adjustments or improvements to the action plan and notifies the user again.

[1446] Specific example: Based on the server's analysis results, a proposal to change part of the "Autumn Harvest Festival" event content is sent to the tourism official in Niigata Prefecture.

[1447] ---

[1448] This invention aims to support local governments in effectively and efficiently planning and implementing regional development measures by automating and optimizing these processes. The introduction of an emotion engine enables more advanced, emotion-based feedback analysis and optimization, which is expected to further promote regional revitalization.

[1449] The following describes the processing flow.

[1450] Program processing steps

[1451] Step 1: Identify the data source

[1452] The server identifies data sources for collecting relevant local government data. These data sources include government open data portals, local government websites, and tourism information sites.

[1453] Step 2: Data Collection

[1454] The server collects the necessary data from the specified data source using APIs and scraping techniques. The collected data is stored in a database.

[1455] Step 3: Data Preprocessing

[1456] The server preprocesses the collected data. Specifically, this includes imputing missing values, normalizing the data, and standardizing the format.

[1457] Step 4: Data Analysis

[1458] The server runs an AI model using pre-processed data to analyze the region's strengths and weaknesses. From the analysis results, it identifies issues and opportunities that should be given particular emphasis.

[1459] Step 5: Generate an action plan

[1460] Based on the analysis results, the server uses generative AI to generate specific action plans. For example, it might suggest events or campaigns specifically tailored to periods with low tourist numbers.

[1461] Step 6: Notify the user

[1462] The server notifies local government officials (users) of the generated action plan and its analysis results. Notifications are sent via email or a dedicated dashboard.

[1463] Step 7: Promoting citizen participation

[1464] The terminal provides a function to inform citizens about local government plans through a citizen-oriented application. The application allows for event announcements and surveys, enabling the collection of residents' opinions and willingness to participate.

[1465] Step 8: Gathering Feedback

[1466] The device sends collected survey results and other feedback to the server in real time. This allows for timely data collection.

[1467] Step 9: Analysis using the emotion engine

[1468] The server uses an emotion engine to analyze the emotional tendencies of the collected feedback. The emotion engine performs text analysis and classifies the feedback as either positive, negative, or neutral.

[1469] Step 10: Analysis and evaluation of feedback

[1470] The server evaluates the effectiveness of the plan based on feedback analyzed by the emotion engine. If necessary, it generates adjustments or improvements to the action plan and notifies the user.

[1471] Step 11: Notification of proposed improvements

[1472] The server will then notify local government officials again of the new action plan, which includes the improved plan. This notification will also be sent via email or a dashboard.

[1473] ---

[1474] This detailed process enables local governments to generate data-driven, effective regional development strategies and collect and analyze resident feedback in real time. Furthermore, the use of an emotion engine allows for the analysis of emotional trends in feedback, enabling more appropriate measures to be taken.

[1475] (Example 2)

[1476] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1477] To revitalize local governments, it is necessary to collect and analyze diverse data and generate concrete action plans. However, this process is time-consuming and labor-intensive when done manually, and is prone to subjective judgments. Furthermore, there is a need to efficiently analyze citizen feedback and improve plans based on that feedback. This invention provides a system that solves these problems and contributes to the revitalization of local governments.

[1478] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1479] In this invention, the server includes means for identifying data sources, collecting data and storing it in a database, preprocessing and analyzing the collected data, generating an action plan using a generative AI model based on the analysis results, notifying local government officials of the action plan, soliciting opinions and participation through a citizen-facing application, collecting feedback from citizens and analyzing it using an emotion engine, and providing improvement proposals based on the collected feedback. This automates the process from data collection and analysis to action plan generation, feedback analysis, and provision of improvement proposals, enabling efficient and effective revitalization of local governments.

[1480] A "data source" is an external or internal source of information used to provide specific information.

[1481] "Means of collecting data and storing it in a database" refers to the process of collecting necessary data from external and internal sources and storing that data in a storage system for systematic accumulation.

[1482] "Preprocessing" refers to processes such as data normalization, imputation of missing values, and formatting to transform collected data into an analyzable format.

[1483] "Analysis" is the process of running statistical or machine learning models to detect patterns or trends based on pre-processed data in order to achieve a specific objective.

[1484] A "generative AI model" is an artificial intelligence model that generates new data and information based on pre-trained data.

[1485] An "action plan" is a specific action plan or strategy set up based on the analysis results.

[1486] "Notification methods" refer to ways of communicating generated action plans and analysis results to specific users. These include email, dashboards, and application notifications.

[1487] A "citizen-oriented application" is a mobile or web application used by local governments to provide citizens with information about their plans and events, and to gather opinions and confirm their willingness to participate.

[1488] "Methods for soliciting opinions and participation" refer to methods for collecting opinions from citizens and their intention to participate in events. This includes surveys, feedback forms, and so on.

[1489] "Feedback" refers to opinions and evaluations collected from users, representing their reactions to specific policies or events.

[1490] An "emotion engine" is software that analyzes emotional tendencies from text data and identifies emotional categories such as positive, negative, and neutral.

[1491] An "improvement proposal" is a suggestion to make existing plans or action plans more effective, based on collected data and feedback.

[1492] The present invention aims to revitalize local governments by automating and optimizing a series of processes for collecting and analyzing various data and generating concrete action plans. The system mainly consists of three components: a server, terminals, and users.

[1493] The server operates as follows: First, it identifies data sources and collects data related to local governments. Data collection is performed using APIs and scraping techniques. The collected data is stored in a database. For example, the server uses an API to retrieve and store tourism data from a government open data portal. An example of a prompt in this case would be, "Write code to retrieve tourism data from the government open data portal and save it to the database."

[1494] The server then preprocesses the collected data. This preprocessing includes data normalization, imputation of missing values, and formatting. The preprocessed data is then analyzed by an AI model. For example, it might analyze tourism data for Niigata Prefecture to identify that the number of tourists is low during certain seasons. An example prompt in this case would be, "Write code to preprocess tourism data for Niigata Prefecture and use an AI model to identify that the number of tourists is low during certain seasons."

[1495] Based on these analysis results, the server uses a generated AI model to create a specific action plan. For example, it might generate a proposal to hold an "Autumn Harvest Festival" to increase the number of tourists in the fall. An example of a prompt in this case would be, "Please generate a specific event plan to increase the number of tourists in Niigata Prefecture in the fall."

[1496] The server notifies the local government official (user) of the generated action plan and analysis results. The notification is made via email or dashboard. For example, the server will notify the tourism official in Niigata Prefecture of the generated "Autumn Harvest Festival" plan and its predicted effects via email. An example of the prompt in this case would be, "Write an email to notify the tourism official in Niigata Prefecture of the Autumn Harvest Festival plan and its predicted effects."

[1497] The terminal will inform citizens about local government plans through citizen-facing applications and collect residents' opinions and intentions to participate. For example, the terminal will distribute a survey about the "Autumn Harvest Festival" through the Niigata Prefecture resident app and collect citizens' intentions to participate. An example of a prompt message in this case would be, "Please create an app notification message to send the Autumn Harvest Festival survey to residents of Niigata Prefecture."

[1498] The server uses an emotion engine to analyze the emotional tendencies of citizens when collecting their feedback. For example, the server might use the emotion engine to analyze citizens' feedback on the "Autumn Harvest Festival" and confirm that many citizens have shown positive reactions. An example of a prompt in this case would be, "Write code to analyze citizen feedback using the emotion engine and classify it as positive, negative, or neutral."

[1499] Ultimately, the server evaluates the effectiveness of the plan based on the feedback analyzed by the emotion engine and generates adjustments or improvements to the action plan as needed. For example, the server might generate a proposal to change the content of the "Autumn Harvest Festival" based on the analysis results and notify the local government official. An example of a prompt in this case would be, "Based on the feedback results, please create a proposal to improve the Autumn Harvest Festival event."

[1500] The above describes the specific implementation method of the system of the present invention. This enables local governments to effectively and efficiently plan and implement regional development measures, and the introduction of the emotion engine allows for more advanced feedback analysis and optimization.

[1501] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1502] Step 1:

[1503] The server identifies data sources, including government open data portals, local government websites, and tourism information sites. The input is a list of data sources that may provide information relevant to the revitalization of local governments, and the output is a list of actually available data sources. Based on this identified list of data sources, data collection proceeds in the next step.

[1504] Step 2:

[1505] The server collects data from identified data sources. This process utilizes API-based data collection and scraping techniques. The input is the URL or API endpoint of the data source, and the output is the collected raw data. For example, the server might use an API to retrieve tourism data from a government open data portal and store it in a database.

[1506] Step 3:

[1507] The server preprocesses the collected data. This preprocessing includes data normalization, imputation of missing values, and formatting standardization. The input is the collected raw data, and the output is preprocessed data suitable for analysis. Specifically, the data format is standardized, and missing values ​​are imputed with the mean.

[1508] Step 4:

[1509] The server analyzes pre-processed data. It runs an AI model to analyze the strengths and weaknesses of a region. The input is pre-processed data, and the output is the analysis results. For example, it analyzes annual tourism data for Niigata Prefecture to identify that the number of tourists is low during certain seasons.

[1510] Step 5:

[1511] The server generates a concrete action plan using an AI model based on the analysis results. The input is the analysis results, and the output is a concrete action plan. Specifically, it generates a proposal to hold an "Autumn Harvest Festival" during the autumn season when tourist numbers are low.

[1512] Step 6:

[1513] The server notifies the local government official (user) of the generated action plan and analysis results. The input is the generated action plan and analysis results, and the output is the notification sent to the official. Email and dashboards are used as notification methods. For example, the generated "Autumn Harvest Festival" plan and its predicted effects are notified to the tourism official via email.

[1514] Step 7:

[1515] The terminal will inform citizens about the local government's plans through a citizen-facing application and collect residents' opinions and willingness to participate. The input is the generated action plan, and the output is feedback from citizens. Specifically, a survey about the "Autumn Harvest Festival" will be distributed through the local government's application to collect citizens' willingness to participate.

[1516] Step 8:

[1517] The server collects feedback from citizens and analyzes their emotional tendencies using an emotion engine. The input is citizen feedback, and the output is the analysis results categorized into emotional categories. Specifically, it analyzes the text of feedback on the "Autumn Harvest Festival" and classifies it into positive, negative, and neutral emotions.

[1518] Step 9:

[1519] The server evaluates the effectiveness of the plan based on feedback analyzed by the emotion engine and generates adjustments and improvements to the action plan as needed. The input is the emotion analysis result, and the output is the improved action plan. For example, it adjusts the content of the "Autumn Harvest Festival" based on the feedback and notifies the person in charge of the new proposal.

[1520] (Application Example 2)

[1521] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1522] When revitalizing local governments, traditional methods often involve manual or partially automated processes for data collection, analysis, planning, and feedback gathering, resulting in low efficiency and inaccuracies. Furthermore, analyzing citizen feedback from an emotional perspective and incorporating it into plans presents significant challenges. To address these issues, a system is needed that highly automates each process and enables real-time data analysis and feedback collection.

[1523] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for identifying data sources, means for collecting data and storing it in a database, means for pre-processing and analyzing the collected data, means for generating an action plan based on the analysis results, means for notifying the person in charge of the action plan, means for soliciting opinions and participation through a user application, means for collecting and analyzing user feedback, means for providing improvement proposals based on the collected feedback, means for executing a generative artificial intelligence model for generating and distributing region-related content, and means for notifying the user of the generated content. This enables local governments to effectively and efficiently plan and implement regional development measures, and to accurately reflect citizen feedback through sentiment analysis.

[1524] A "data source" is the source from which information is collected, and examples include government open data portals, local government websites, and tourism information websites.

[1525] "Data collection" is the process of obtaining necessary information from a specified data source and storing it in a database.

[1526] A "database" is an aggregate of information that systematically stores collected data and makes it accessible as needed.

[1527] "Preprocessing" is the process of converting collected data into a format suitable for analysis, and includes data normalization, imputation of missing values, and standardization of formatting.

[1528] "Analysis" is the process of extracting patterns and trends in information using certain algorithms and models based on pre-processed data, and then interpreting the results according to a specific purpose.

[1529] An "action plan" is a specific action plan created based on the analysis results, and includes proposals for events and campaigns to promote regional development.

[1530] "Notification" refers to the process of informing the responsible party about the generated action plan and analysis results, and this is done via email or a dedicated dashboard.

[1531] A "user-facing application" is software provided to citizens and stakeholders of a local government, and has functions such as collecting opinions and announcing event information.

[1532] "Feedback" refers to opinions and impressions provided by users, and is information that is useful for improving services and generating new ideas.

[1533] "Sentiment analysis" is a process that classifies the emotional tendencies of collected feedback through text analysis, determining whether they are positive, negative, neutral, etc.

[1534] A "generative artificial intelligence model" is a program that uses artificial intelligence to automatically generate new content and ideas.

[1535] "Content" refers to all information and entertainment provided to users, including local event information and tourist guides.

[1536] 1. Data Collection

[1537] The server is responsible for the first stage of data collection. First, the server identifies data sources, which include official websites of local governments and related organizations, tourism information sites, and government open data portals. Next, the server uses API-based data collection and scraping techniques to gather the necessary data and store it in a database.

[1538] Specific example: A server uses an API to retrieve and store data on the annual number of tourists in a region from a government open data portal.

[1539] 2. Data Analysis

[1540] The server also handles data analysis. The collected data is first preprocessed. Preprocessing includes data normalization, imputation of missing values, and standardization of format. Next, a generative artificial intelligence model (e.g., TensorFlow) is run on the preprocessed data to analyze the strengths and weaknesses of the region. From the analysis results, areas that require particular attention for improvement are identified.

[1541] Specific example: The server analyzes data on the annual number of tourists in a region and identifies that the number of tourists in the fall is lower than in other seasons.

[1542] 3. Generate an action plan

[1543] Based on the analysis results, the server uses a generative artificial intelligence model to generate specific action plans. For example, it might propose events or campaigns specifically tailored to periods with low tourist numbers.

[1544] Specific example: The server generates a proposal to hold an "Autumn Harvest Festival" in the region to increase tourism during the autumn season.

[1545] 4. Notification to the person in charge

[1546] The server notifies local government officials of the generated action plan and analysis results. Notifications are sent via email or a dedicated dashboard.

[1547] Specific example: The server notifies local tourism officials via email of the generated "Autumn Harvest Festival" plan and its predicted effects.

[1548] 5. User-facing applications

[1549] The device will provide a function to inform citizens about local government plans through a user-facing application. The application will allow for event announcements and surveys, enabling the collection of residents' opinions and willingness to participate.

[1550] Specific example: A device distributes a survey about the "Autumn Harvest Festival" through a local resident app, collecting citizens' willingness to participate.

[1551] 6. Emotion analysis

[1552] The server analyzes the feedback collected from users using an emotion engine (for example, OpenAI's API). The emotion engine performs sentiment analysis on the text and determines emotion categories such as positive, negative, and neutral.

[1553] Specific example: The server uses an emotion engine to analyze citizen feedback on the "Autumn Harvest Festival" and confirms that many citizens have a positive reaction to the event.

[1554] 7. Providing improvement proposals

[1555] The server evaluates the effectiveness of the plan based on feedback analyzed by the emotion engine. It then generates adjustments and improvements to the action plan as needed and notifies the responsible party again.

[1556] Specific example: Based on the server's analysis results, it notifies local tourism officials of a proposal to change part of the "Autumn Harvest Festival" event.

[1557] Example of a prompt

[1558] "Analyze the sentiment of citizen feedback regarding Niigata Prefecture's Autumn Harvest Festival and generate optimal content."

[1559] "Analyze the sentiment of the following text: 'We received positive feedback from citizens about the Autumn Harvest Festival.'"

[1560] This system will enable local governments to quickly formulate and implement effective, data-driven regional development plans. Furthermore, it will allow for accurate incorporation of citizen feedback through sentiment analysis, which is expected to further promote regional revitalization.

[1561] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1562] Step 1:

[1563] Identifying the data source

[1564] The server identifies appropriate data sources for data collection. These data sources include official websites of local governments and related organizations, tourism information sites, and government open data portals. Specifically, the server scans a pre-configured list and collects API endpoints and URLs to scrape. The input is a pre-configured list of data sources, and the output is the collected URLs and API endpoints.

[1565] Step 2:

[1566] Data collection

[1567] The server collects data from identified data sources. It uses API-based data collection and scraping techniques to obtain the necessary data and store it in a database. The input is the URL or API endpoint of the data source identified in step 1, and the output is the collected data stored in the database. Specifically, it sends an HTTP request, parses the response to extract the necessary data, and writes it to the database.

[1568] Step 3:

[1569] Data preprocessing

[1570] The server preprocesses the collected data. Preprocessing includes data normalization, imputation of missing values, and formatting. The input is the raw data collected in step 2, and the output is the preprocessed data. Specifically, a data transformation library (e.g., Pandas) is used to format the data into a standard format.

[1571] Step 4:

[1572] Data Analysis

[1573] The server runs a generative artificial intelligence model based on pre-processed data to analyze the strengths and weaknesses of the region. The input is the data pre-processed in step 3, and the output is the analysis results. Specifically, it uses a pre-trained artificial intelligence model (e.g., TensorFlow) to analyze the data and extract specific patterns and trends.

[1574] Step 5:

[1575] Action plan generation

[1576] The server generates a specific action plan using a generative artificial intelligence model based on the analysis results. The input is the analysis results obtained in step 4, and the output is the generated action plan. Specifically, it uses a generative artificial intelligence model (e.g., GPT-3) to generate event and campaign suggestions based on the analysis results. An example of a prompt message would be, "Analyze the sentiment of citizen feedback regarding Niigata Prefecture's Autumn Harvest Festival and generate optimal content."

[1577] Step 6:

[1578] Notification to the North

[1579] The server notifies the local government official of the generated action plan and analysis results. The input is the action plan generated in step 5, and the output is the notified official. Specifically, the official is notified via email or a dedicated dashboard.

[1580] Step 7:

[1581] User application updates

[1582] The terminal will notify citizens of the generated action plan through a user application. The input is the action plan notified in step 6, and the output is the updated application. Specifically, the application's UI will be updated to display the new action plan information.

[1583] Step 8:

[1584] Feedback Collection

[1585] This system collects user feedback on action plans provided through the application. The input is user feedback, and the output is the collected feedback data. Specifically, it uses features such as surveys and comments to gather opinions and feedback from users.

[1586] Step 9:

[1587] Emotion analysis

[1588] The server analyzes the sentiment of the feedback it has collected. The input is the feedback collected in step 8, and the output is the sentiment analysis result. Specifically, it uses a generative artificial intelligence model for sentiment analysis (e.g., OpenAI's API) to determine the sentiment category from the feedback text. An example of a prompt would be, "Analyze the sentiment of the following text: 'We received positive feedback from citizens about the Autumn Harvest Festival.'"

[1589] Step 10:

[1590] Providing improvement proposals

[1591] The server evaluates the effectiveness of the plan based on the sentiment analysis results and generates adjustments or improvements to the action plan as needed. The input is the sentiment analysis results obtained in step 9, and the output is the improvement proposals. Specifically, it uses the generative artificial intelligence model again to generate new action plans and improvement proposals and notifies the person in charge.

[1592] This series of processes enables local governments to quickly formulate and implement effective, data-driven regional development strategies. Furthermore, sentiment analysis allows for accurate incorporation of citizen feedback.

[1593] 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.

[1594] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1595] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1596] 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.

[1597] Figure 9 shows an 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.

[1598] 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.

[1599] 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.

[1600] 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, motorcycles, etc., 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, for example, based 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.

[1601] 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."

[1602] 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.

[1603] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1604] 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1605] 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.

[1606] 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.

[1607] 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.

[1608] 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.

[1609] 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.

[1610] 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.

[1611] 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.

[1612] The descriptions and illustrations presented above are detailed explanations of the techni...

Claims

1. Means for identifying data sources, Means for collecting data and storing it in a database, A means of preprocessing and analyzing the collected data, A means for generating an action plan based on the analysis results, Means of notifying local government officials of the action plan, A means of soliciting opinions and participation through applications for citizens, A means of collecting and analyzing feedback from citizens, A means of providing improvement suggestions based on collected feedback, A system that includes this.

2. A means of collecting opinions and intentions to participate by conducting surveys using a citizen-oriented application, A means of transmitting the collected survey results in real time, The system according to claim 1, including the following:

3. A means of notifying local government officials of the analysis results and predicted effects, A means of generating an action plan that includes proposals for events and campaigns, The system according to claim 1, including the following:

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