System

Abstract

An object of the system according to the embodiment is to plan a measure for increasing attractiveness by utilizing characteristics and resources of a local government and to post content on media in a timely manner.SOLUTION: A system includes an analysis part, a measure planning part, a content generation part, and a media publication part. The analysis section analyzes characteristics and resources of the local government body. The measure planning unit plans a measure based on the data analyzed by the analysis unit. The content generation unit generates content on the basis of the measure drafted by the measure drafting unit. The media publishing unit publishes the content generated by the content generation unit on media.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to devise measures to increase the attractiveness of local governments by effectively utilizing their characteristics and resources, and to publish content in the media in a timely manner.

[0005] The system according to the embodiment aims to utilize the characteristics and resources of local governments to develop measures to increase their appeal and to publish content in the media in a timely manner. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a policy planning unit, a content generation unit, and a media publishing unit. The analysis unit analyzes the characteristics and resources of a local government. The policy planning unit plans policies based on the data analyzed by the analysis unit. The content generation unit generates content based on the policies planned by the policy planning unit. The media publishing unit publishes the content generated by the content generation unit in the media. [Effects of the Invention]

[0007] The system according to the embodiment can utilize the characteristics and resources of local governments to develop measures to increase their attractiveness and publish content in the media in a timely manner. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The attractiveness enhancement system according to an embodiment of the present invention uses a generation AI to analyze the characteristics and resources of a local government, propose measures to enhance the attractiveness of the local government beyond goods and tax savings, and automatically and timely publish these measures in the media. This allows the attractiveness enhancement system to effectively communicate the attractiveness of a local government and increase the number of hometown tax donation users.

[0029] An attractiveness enhancement system according to an embodiment includes an analysis unit, a policy planning unit, a content generation unit, and a media publishing unit. The analysis unit analyzes the characteristics and resources of a local government. For example, the analysis unit collects information on the local history, culture, natural environment, specialty products, tourist spots, etc., and proposes policies to enhance the attractiveness of the local government based on the collected information. The policy planning unit plans policies based on the data analyzed by the analysis unit. For example, the policy planning unit proposes tourism plans that utilize local traditional events and festivals, gourmet events using local ingredients, art projects by local artists, etc. The content generation unit generates content based on the policies proposed by the policy planning unit. For example, the content generation unit generates tourist guide articles and promotional videos based on tourism plans proposed by the generation AI. The media publishing unit publishes the content generated by the content generation unit in the media. For example, the media publishing unit publishes the generated content on the local government's official website, social media, local news sites, etc. As a result, the attractiveness enhancement system according to an embodiment can effectively promote the attractiveness of a local government and increase the number of users of hometown tax donations.

[0030] The analysis unit can collect feedback from local government residents in real time and reflect it in the analysis. For example, the generation AI in the analysis unit collects feedback from local government residents in real time and reflects it in the analysis. For example, the generation AI collects opinions and impressions provided by residents through a smartphone app and uses them to analyze the local government's characteristics and resources. In addition, to collect feedback from residents, the analysis unit has the generation AI monitor social media and online forums and analyze the opinions and emotions of residents. For example, it collects content posted using specific hashtags and reflects the results in the analysis of the local government's characteristics and resources. In addition, the analysis unit has the generation AI conduct regular surveys to collect feedback from local government residents and reflect the results in the analysis. For example, it surveys residents' satisfaction and requests and uses the results in the analysis of the local government's characteristics and resources. In this way, by reflecting residents' opinions, more accurate analysis is possible.

[0031] The analysis unit can perform more accurate analysis by referring to past successes and failures. For example, the generation AI collects past successes and failures from a database and uses them to analyze the characteristics and resources of a local government. For example, the analysis reflects the factors that led to the success and failure of past tourism projects. In addition, to refer to past successes and failures, the generation AI analyzes the local government's archived data and extracts specific patterns and trends. For example, analysis is performed based on the number of participants and responses to past events. In addition, the generation AI collects successes and failures from other local governments and uses them to analyze the characteristics and resources of a local government. For example, successful measures from other local governments are used as reference for analysis to apply to one's own local government. In this way, by referring to past cases, the accuracy of the analysis is improved.

[0032] The analysis unit can conduct comparative analysis with other municipalities and highlight their unique attractions. For example, the generation AI collects data to conduct comparative analysis with other municipalities and highlight their unique attractions. For example, it compares tourist spots and local specialties to identify the municipality's strengths. The analysis unit also has the generation AI collect tourist ratings and reviews and use them for analysis in order to conduct comparative analysis with other municipalities. For example, it compares tourist spot rating scores to highlight the municipality's attractions. The analysis unit also has the generation AI conduct comparative analysis with other municipalities and generate a report to highlight their unique attractions. For example, it identifies the municipality's strengths based on success stories and failure stories of other municipalities and compiles them into a report. This allows the generation AI to conduct comparative analysis with other municipalities and highlight its unique attractions.

[0033] The analysis unit can collect real-time environmental data using drones and sensors. For example, the generation AI uses drones to collect environmental data from local governments in real time and uses it for analysis. For example, it collects data on the scenery and natural environment of tourist destinations and reflects it in the analysis. The analysis unit also uses sensors to collect environmental data from local governments in real time, and the generation AI uses it for analysis. For example, it collects data such as temperature, humidity, and air quality and uses it to plan tourist trips. The analysis unit also collects environmental data from local governments using drones and sensors, and builds a system for the generation AI to use for analysis. For example, it monitors the congestion and traffic conditions at tourist destinations in real time and reflects it in the analysis. This allows for more accurate analysis by collecting real-time environmental data.

[0034] The policy planning department can perform simulations on policies and predict their effects before they are implemented. For example, the policy planning department performs simulations on policies proposed by the generation AI and predicts their effects before they are implemented. For example, it simulates a tourism plan and calculates the expected number of tourists and economic impact. In addition, in order to perform simulations, the generation AI analyzes past data and trends to predict the effects of the policies. For example, it predicts the probability of success of the next event based on data from past events. In addition, the policy planning department builds a system to simulate policies proposed by the generation AI and predict their effects before they are implemented. For example, it visualizes the results of the tourism plan simulation and evaluates the effects of the policies. In this way, it is possible to increase the probability of success of the policies by predicting their effects before they are implemented.

[0035] The policy planning department can incorporate expert opinions into policies to improve their accuracy. For example, the policy planning department incorporates expert opinions into policies proposed by the generation AI to improve their accuracy. For example, the opinions of tourism industry experts are reflected in tourism plans. In addition, to incorporate expert opinions, the policy planning department has the generation AI collect expert opinions through online meetings and interviews and reflect them in policies. For example, the opinions of chefs are incorporated into gourmet events using local ingredients. The policy planning department also builds a system to incorporate expert opinions into policies proposed by the generation AI and improve their accuracy. For example, expert opinions are registered in a database and used in policy planning. In this way, incorporating expert opinions improves the accuracy of policies.

[0036] The policy planning department can compare the results with examples from different municipalities and regions and select the most appropriate policy. For example, the generation AI collects examples from different municipalities and regions and selects the most appropriate policy based on that. For example, it can refer to tourism plans that have been successful in other municipalities and apply them to its own municipality. In addition, the generation AI builds a database to compare examples from different municipalities and regions and evaluate the effectiveness of policies. For example, it selects policies based on the number of tourists and economic impact. In addition, the generation AI collects examples from different municipalities and regions and builds a system to select the most appropriate policy based on that. For example, it registers successful and unsuccessful cases from other municipalities in a database and uses them in policy planning. This makes it possible to select the most appropriate policy by comparing examples from different municipalities and regions.

[0037] The policy planning department can customize policies to suit different seasons and events. For example, the generation AI in the policy planning department customizes policies to suit different seasons and events. For example, it proposes beach events for the summer tourist season and ski events for the winter tourist season. In addition, in order to customize policies to suit seasons and events, the generation AI analyzes past data and trends and proposes optimal policies. For example, it predicts the success rate of the next event based on data from past events. In addition, the policy planning department builds a system that enables the generation AI to customize policies to suit different seasons and events. For example, it automatically generates suggestions for sightseeing plans and events for each season. This allows policies to be customized to suit different seasons and events, making them more effective.

[0038] The content generation unit can collect user feedback on the generated content and reflect it in the next content generation. For example, the content generation unit can collect user feedback on content generated by the generation AI and reflect it in the next content generation. For example, feedback from social media and comment sections can be collected and reflected in the next content. In addition, the content generation unit can have the generation AI conduct a questionnaire survey to collect user feedback and reflect the results in the next content generation. For example, evaluations of a tourist guide article can be collected and reflected in the next guide article. In addition, the content generation unit can build a system to collect user feedback on content generated by the generation AI and reflect it in the next content generation. For example, the feedback data can be analyzed and reflected in the next content. In this way, more effective content can be generated by collecting user feedback and reflecting it in the next content generation.

[0039] The content generation unit can optimize the generated content for different media formats (video, audio, text). For example, the content generation unit optimizes content generated by the generation AI for different media formats. For example, converting a tourist guide article into video or audio and distributing it through different media. In addition, to optimize for different media formats, the generation AI analyzes the content and selects the optimal format. For example, when converting a text article into video, important points are emphasized. The content generation unit also builds a system to optimize the content generated by the generation AI for different media formats. For example, a system can be developed that automatically converts content into video, audio, and text. This allows content to be optimized for different media formats, making it possible to deliver content to more users effectively.

[0040] The content generation unit automatically translates the generated content into different languages ​​and obtains feedback from an international perspective. For example, the content generation unit automatically translates content generated by the generation AI into different languages ​​and obtains feedback from an international perspective. For example, a tourist guide article is translated into English and Chinese and feedback from overseas users is collected. In addition, the content generation unit uses a translation engine to automatically translate content into different languages, making the content multilingual. For example, a promotional video is translated into multiple languages ​​and evaluations are obtained from an international perspective. In addition, the content generation unit builds a system to automatically translate content generated by the generation AI into different languages ​​and obtain feedback from an international perspective. For example, the translated content is posted on an international platform and feedback is collected. In this way, by automatically translating into different languages, feedback from an international perspective can be obtained.

[0041] The content generation unit can optimize the generated content for different media platforms (SNS, blogs, news sites) and post it. For example, the content generation unit optimizes the content generated by the generation AI for different media platforms and posts it. For example, a tourist guide article can be optimized and distributed for SNS, blogs, and news sites. In order to optimize the content for different media platforms, the content generation unit has the generation AI analyze the content and convert it into a format suitable for each platform. For example, it generates short videos and images for SNS. The content generation unit also builds a system for optimizing the content generated by the generation AI for posting on different media platforms. For example, a system can be developed that automatically optimizes and distributes content for each platform. This allows content to be optimized for posting on different media platforms, making it possible to effectively reach more users.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The analysis unit analyzes the characteristics and resources of the local government. For example, the analysis unit collects information on the local history, culture, natural environment, specialty products, tourist spots, etc., and proposes measures to enhance the local government's appeal based on the collected information. The policy planning unit plans policies based on the data analyzed by the analysis unit. For example, the policy planning unit proposes tourism plans utilizing local traditional events and festivals, gourmet events using local ingredients, art projects by local artists, etc. The content generation unit generates content based on the measures proposed by the policy planning unit. For example, the content generation unit generates tourist guide articles and promotional videos based on tourism plans proposed by the generation AI. The media posting unit publishes the content generated by the content generation unit in the media. For example, the media posting unit publishes the generated content on the local government's official website, social media, local news sites, etc. As a result, the attractiveness enhancement system according to the embodiment can effectively promote the appeal of the local government and increase the number of users of hometown tax donations.

[0044] The analysis unit can collect feedback from local government residents in real time and reflect it in the analysis. For example, it can collect opinions and impressions provided by residents through a smartphone app and use them to analyze the local government's characteristics and resources. To collect feedback from residents, the analysis unit has the generation AI monitor social media and online forums and analyze the residents' opinions and emotions. For example, it can collect content posted using specific hashtags and reflect this in the analysis of the local government's characteristics and resources. To collect feedback from local government residents, the analysis unit also has the generation AI conduct regular surveys and reflect the results in the analysis. For example, it can survey residents' satisfaction and requests and use the results in the analysis of the local government's characteristics and resources. This allows for more accurate analysis by incorporating residents' opinions.

[0045] The analysis unit can perform more accurate analysis by referring to past successes and failures. For example, the generation AI collects past successes and failures from a database and uses them to analyze the characteristics and resources of a local government. For example, the success and failure factors of past tourism projects are reflected in the analysis. In addition, to refer to past successes and failures, the generation AI analyzes the local government's archived data and extracts specific patterns and trends. For example, analysis is performed based on the number of participants and responses to past events. In addition, the generation AI collects successes and failures from other local governments and uses them to analyze the characteristics and resources of a local government. For example, it refers to successful measures in other local governments and performs analysis to apply them to one's own local government. In this way, by referring to past cases, the accuracy of the analysis is improved.

[0046] The analysis unit can conduct comparative analysis with other municipalities to highlight their unique attractions. For example, the generation AI conducts comparative analysis with other municipalities and collects data to highlight their unique attractions. For example, it compares tourist spots and local specialties to identify the municipality's strengths. The analysis unit also has the generation AI collect tourist ratings and reviews and use them for analysis in order to conduct comparative analysis with other municipalities. For example, it compares the rating scores of tourist spots to highlight the municipality's attractions. The analysis unit also has the generation AI conduct comparative analysis with other municipalities and generate a report to highlight their unique attractions. For example, it identifies the municipality's strengths based on examples of success and failure of other municipalities and compiles them into a report. This allows the municipality to conduct comparative analysis with other municipalities to highlight its unique attractions.

[0047] The analysis unit can collect real-time environmental data using drones and sensors. For example, the generation AI can use drones to collect environmental data from local governments in real time and use it for analysis. For example, data on the scenery and natural environment of tourist destinations can be collected and reflected in the analysis. The analysis unit can also use sensors to collect environmental data from local governments in real time, which the generation AI can use for analysis. For example, data such as temperature, humidity, and air quality can be collected and used to plan tourist trips. The analysis unit can also use drones and sensors to collect environmental data from local governments, and build a system for the generation AI to use for analysis. For example, congestion and traffic conditions at tourist destinations can be monitored in real time and reflected in the analysis. This allows for more accurate analysis by collecting real-time environmental data.

[0048] The policy planning department can perform simulations on policies to predict their effects before they are implemented. For example, it can perform simulations on policies proposed by the generation AI to predict their effects before they are implemented. For example, it can simulate a tourism plan to calculate the expected number of tourists and economic impact. In addition, in order to perform simulations, the generation AI analyzes past data and trends to predict the effects of policies. For example, it can predict the success rate of the next event based on data from past events. The policy planning department can also build a system to simulate policies proposed by the generation AI and predict their effects before they are implemented. For example, it can visualize the results of a tourism plan simulation to evaluate the effects of policies. This can increase the success rate of policies by predicting their effects before they are implemented.

[0049] The policy planning department can incorporate expert opinions into policies to improve their accuracy. For example, the policy planning department can incorporate expert opinions into policies proposed by the generation AI to improve their accuracy. For example, the opinions of tourism industry experts can be reflected in tourism plans. To incorporate expert opinions, the policy planning department has the generation AI collect expert opinions through online meetings and interviews and reflect them in policies. For example, the opinions of chefs can be incorporated into gourmet events using local ingredients. The policy planning department can also build a system to incorporate expert opinions into policies proposed by the generation AI to improve their accuracy. For example, the opinions of experts can be registered in a database and used in policy planning. In this way, incorporating expert opinions improves the accuracy of policies.

[0050] The policy planning department can compare examples of measures implemented in different municipalities and regions and select the most appropriate measures. For example, the generation AI collects examples of measures implemented in different municipalities and regions and selects the most appropriate measures based on that. For example, it can refer to tourism plans that have been successful in other municipalities and apply them to its own municipality. In addition, the generation AI builds a database to compare examples of measures implemented in different municipalities and regions and evaluate the effectiveness of measures. For example, it selects measures based on the number of tourists and economic impact. In addition, the policy planning department builds a system in which the generation AI collects examples of measures implemented in different municipalities and regions and selects the most appropriate measures based on that. For example, it registers successful and unsuccessful examples from other municipalities in a database and uses them in policy planning. This makes it possible to select the most appropriate measures by comparing them with examples implemented in different municipalities and regions.

[0051] The policy planning department can customize policies to suit different seasons and events. For example, the generation AI customizes policies to suit different seasons and events. For example, it might propose beach events for the summer tourist season and ski events for the winter tourist season. In addition, to customize policies to suit seasons and events, the generation AI analyzes past data and trends to propose optimal policies. For example, it might predict the success rate of the next event based on data from past events. The policy planning department also builds a system that enables the generation AI to customize policies to suit different seasons and events. For example, it automatically generates suggestions for sightseeing plans and events for each season. This allows policies to be customized to suit different seasons and events, making them more effective.

[0052] The content generation unit can collect user feedback on the generated content and reflect it in the next content generation. For example, user feedback on content generated by the generation AI can be collected and reflected in the next content generation. For example, feedback from social media and comment sections can be collected and reflected in the next content. In addition, the content generation unit can have the generation AI conduct a questionnaire survey to collect user feedback and reflect the results in the next content generation. For example, evaluations of a tourist guide article can be collected and reflected in the next guide article. In addition, the content generation unit can build a system to collect user feedback on content generated by the generation AI and reflect it in the next content generation. For example, the feedback data can be analyzed and reflected in the next content. In this way, more effective content can be generated by collecting user feedback and reflecting it in the next content generation.

[0053] The content generation unit can optimize the generated content for different media formats (video, audio, text). For example, content generated by the generation AI is optimized for different media formats. For example, a tourist guide article can be converted into video or audio and distributed through different media. In order to optimize for different media formats, the content generation unit has the generation AI analyze the content and select the optimal format. For example, when converting a text article into video, important points can be emphasized. The content generation unit also builds a system to optimize the content generated by the generation AI for different media formats. For example, a system can be developed that automatically converts content into video, audio, and text. This allows content to be optimized for different media formats, making it possible to deliver content to more users effectively.

[0054] The content generation unit automatically translates the generated content into different languages ​​and obtains feedback from an international perspective. For example, the content generated by the generation AI is automatically translated into different languages ​​and obtains feedback from an international perspective. For example, a tourist guide article is translated into English and Chinese and feedback from overseas users is collected. The content generation unit also uses a translation engine to automatically translate the content into different languages, making the content multilingual. For example, a promotional video is translated into multiple languages ​​and evaluations are obtained from an international perspective. The content generation unit also builds a system to automatically translate the content generated by the generation AI into different languages ​​and obtain feedback from an international perspective. For example, the translated content is posted on an international platform and feedback is collected. In this way, by automatically translating into different languages, feedback from an international perspective can be obtained.

[0055] The content generation unit can optimize the generated content for different media platforms (SNS, blogs, news sites) and post it. For example, the content generated by the generation AI is optimized for different media platforms and posted. For example, a tourist guide article can be optimized and distributed for SNS, blogs, and news sites. In order to optimize for different media platforms, the content generation unit has the generation AI analyze the content and convert it into a format suitable for each platform. For example, it generates short videos and images for SNS. The content generation unit also builds a system to optimize the content generated by the generation AI for posting on different media platforms. For example, a system can be developed that automatically optimizes and distributes content for each platform. This allows content to be posted on different media platforms and delivered more effectively to more users.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The analysis department analyzes the characteristics and resources of the local government. For example, the analysis department collects information on the local history, culture, natural environment, local specialties, tourist spots, etc., and uses this information to propose measures to enhance the local government's appeal. Step 2: The Policy Planning Department formulates policies based on the data analyzed by the Analysis Department. For example, the Policy Planning Department may propose tourism plans that utilize local traditional events and festivals, gourmet events using local ingredients, and art projects by local artists. Step 3: The content generation unit generates content based on the measures proposed by the measure planning unit. For example, the content generation unit generates tourist guide articles and promotional videos based on the sightseeing plans proposed by the generation AI. Step 4: The media publishing department publishes the content generated by the content generation department in the media. For example, the media publishing department publishes the generated content on the local government's official website, social media, local news sites, etc.

[0058] (Example 2) The attractiveness enhancement system according to an embodiment of the present invention uses a generation AI to analyze the characteristics and resources of a local government, propose measures to enhance the attractiveness of the local government beyond goods and tax savings, and automatically and timely publish these measures in the media. This allows the attractiveness enhancement system to effectively communicate the attractiveness of a local government and increase the number of hometown tax donation users.

[0059] An attractiveness enhancement system according to an embodiment includes an analysis unit, a policy planning unit, a content generation unit, and a media publishing unit. The analysis unit analyzes the characteristics and resources of a local government. For example, the analysis unit collects information on the local history, culture, natural environment, specialty products, tourist spots, etc., and proposes policies to enhance the attractiveness of the local government based on the collected information. The policy planning unit plans policies based on the data analyzed by the analysis unit. For example, the policy planning unit proposes tourism plans that utilize local traditional events and festivals, gourmet events using local ingredients, art projects by local artists, etc. The content generation unit generates content based on the policies proposed by the policy planning unit. For example, the content generation unit generates tourist guide articles and promotional videos based on tourism plans proposed by the generation AI. The media publishing unit publishes the content generated by the content generation unit in the media. For example, the media publishing unit publishes the generated content on the local government's official website, social media, local news sites, etc. As a result, the attractiveness enhancement system according to an embodiment can effectively promote the attractiveness of a local government and increase the number of users of hometown tax donations.

[0060] The analysis unit can collect feedback from local government residents in real time and reflect it in the analysis. For example, the generation AI in the analysis unit collects feedback from local government residents in real time and reflects it in the analysis. For example, the generation AI collects opinions and impressions provided by residents through a smartphone app and uses them to analyze the local government's characteristics and resources. In addition, to collect feedback from residents, the analysis unit has the generation AI monitor social media and online forums and analyze the opinions and emotions of residents. For example, it collects content posted using specific hashtags and reflects the results in the analysis of the local government's characteristics and resources. In addition, the analysis unit has the generation AI conduct regular surveys to collect feedback from local government residents and reflect the results in the analysis. For example, it surveys residents' satisfaction and requests and uses the results in the analysis of the local government's characteristics and resources. In this way, by reflecting residents' opinions, more accurate analysis is possible.

[0061] The analysis unit can perform more accurate analysis by referring to past successes and failures. For example, the generation AI collects past successes and failures from a database and uses them to analyze the characteristics and resources of a local government. For example, the analysis reflects the factors that led to the success and failure of past tourism projects. In addition, to refer to past successes and failures, the generation AI analyzes the local government's archived data and extracts specific patterns and trends. For example, analysis is performed based on the number of participants and responses to past events. In addition, the generation AI collects successes and failures from other local governments and uses them to analyze the characteristics and resources of a local government. For example, successful measures from other local governments are used as reference for analysis to apply to one's own local government. In this way, by referring to past cases, the accuracy of the analysis is improved.

[0062] The analysis unit can use the emotion estimation function to analyze the emotions of residents and tourists and identify characteristics and resources that elicit positive emotions. For example, the generation AI uses the emotion estimation function to analyze the emotions of residents and tourists and identify characteristics and resources that elicit positive emotions. For example, it analyzes social media posts and comments on review sites to identify characteristics and resources that contain a lot of positive emotions. The analysis unit also uses the emotion estimation function to analyze the emotions of residents and tourists in real time and identify characteristics and resources that elicit positive emotions. For example, it collects real-time emotion data at tourist destinations and reflects it in the analysis. The analysis unit also uses the emotion estimation function to conduct a questionnaire survey to analyze the emotions of residents and tourists and identify characteristics and resources that elicit positive emotions. For example, it surveys emotional reactions at tourist destinations and events and uses the results in the analysis. This allows more effective measures to be developed by identifying characteristics and resources that elicit positive emotions.

[0063] The analysis unit can conduct comparative analysis with other municipalities and highlight their unique attractions. For example, the generation AI collects data to conduct comparative analysis with other municipalities and highlight their unique attractions. For example, it compares tourist spots and local specialties to identify the municipality's strengths. The analysis unit also has the generation AI collect tourist ratings and reviews and use them for analysis in order to conduct comparative analysis with other municipalities. For example, it compares tourist spot rating scores to highlight the municipality's attractions. The analysis unit also has the generation AI conduct comparative analysis with other municipalities and generate a report to highlight their unique attractions. For example, it identifies the municipality's strengths based on success stories and failure stories of other municipalities and compiles them into a report. This allows the generation AI to conduct comparative analysis with other municipalities and highlight its unique attractions.

[0064] The analysis unit can collect real-time environmental data using drones and sensors. For example, the generation AI uses drones to collect environmental data from local governments in real time and uses it for analysis. For example, it collects data on the scenery and natural environment of tourist destinations and reflects it in the analysis. The analysis unit also uses sensors to collect environmental data from local governments in real time, and the generation AI uses it for analysis. For example, it collects data such as temperature, humidity, and air quality and uses it to plan tourist trips. The analysis unit also collects environmental data from local governments using drones and sensors, and builds a system for the generation AI to use for analysis. For example, it monitors the congestion and traffic conditions at tourist destinations in real time and reflects it in the analysis. This allows for more accurate analysis by collecting real-time environmental data.

[0065] The analysis unit can use the emotion estimation function to analyze residents' emotions toward the characteristics and resources of the municipality and propose measures to elicit positive emotions. For example, the generation AI in the analysis unit uses the emotion estimation function to analyze residents' emotions toward the characteristics and resources of the municipality and propose measures to elicit positive emotions. For example, the analysis unit proposes local events and projects based on residents' emotion data. The analysis unit also uses the emotion estimation function to analyze residents' emotions toward the characteristics and resources of the municipality in real time and propose measures to elicit positive emotions. For example, the analysis unit proposes sightseeing plans and gourmet events based on residents' emotional responses. The analysis unit also uses the emotion estimation function to analyze residents' emotions toward the characteristics and resources of the municipality and conducts a questionnaire survey to propose measures to elicit positive emotions. For example, the analysis unit proposes a local art project based on residents' emotion data. This makes it possible to improve resident satisfaction by proposing measures to elicit positive emotions.

[0066] The policy planning department can perform simulations on policies and predict their effects before they are implemented. For example, the policy planning department performs simulations on policies proposed by the generation AI and predicts their effects before they are implemented. For example, it simulates a tourism plan and calculates the expected number of tourists and economic impact. In addition, in order to perform simulations, the generation AI analyzes past data and trends to predict the effects of the policies. For example, it predicts the probability of success of the next event based on data from past events. In addition, the policy planning department builds a system to simulate policies proposed by the generation AI and predict their effects before they are implemented. For example, it visualizes the results of the tourism plan simulation and evaluates the effects of the policies. In this way, it is possible to increase the probability of success of the policies by predicting their effects before they are implemented.

[0067] The policy planning department can incorporate expert opinions into policies to improve their accuracy. For example, the policy planning department incorporates expert opinions into policies proposed by the generation AI to improve their accuracy. For example, the opinions of tourism industry experts are reflected in tourism plans. In addition, to incorporate expert opinions, the policy planning department has the generation AI collect expert opinions through online meetings and interviews and reflect them in policies. For example, the opinions of chefs are incorporated into gourmet events using local ingredients. The policy planning department also builds a system to incorporate expert opinions into policies proposed by the generation AI and improve their accuracy. For example, expert opinions are registered in a database and used in policy planning. In this way, incorporating expert opinions improves the accuracy of policies.

[0068] The policy planning unit can use the emotion estimation function to analyze the emotions of residents and tourists and plan policies to elicit positive emotions. For example, the generation AI in the policy planning unit uses the emotion estimation function to analyze the emotions of residents and tourists and plan policies to elicit positive emotions. For example, the policy planning unit proposes local events and projects based on the emotion data of residents. The policy planning unit also uses the emotion estimation function to analyze the emotions of residents and tourists in real time and plan policies to elicit positive emotions. For example, the policy planning unit proposes sightseeing plans and gourmet events based on the emotional responses of residents. The generation AI in the policy planning unit also uses the emotion estimation function to analyze the emotions of residents and tourists and conducts a questionnaire survey to plan policies to elicit positive emotions. For example, the policy planning unit proposes a local art project based on the emotion data of residents. In this way, by planning policies to elicit positive emotions, the satisfaction of residents and tourists can be improved.

[0069] The policy planning department can compare the results with examples from different municipalities and regions and select the most appropriate policy. For example, the generation AI collects examples from different municipalities and regions and selects the most appropriate policy based on that. For example, it can refer to tourism plans that have been successful in other municipalities and apply them to its own municipality. In addition, the generation AI builds a database to compare examples from different municipalities and regions and evaluate the effectiveness of policies. For example, it selects policies based on the number of tourists and economic impact. In addition, the generation AI collects examples from different municipalities and regions and builds a system to select the most appropriate policy based on that. For example, it registers successful and unsuccessful cases from other municipalities in a database and uses them in policy planning. This makes it possible to select the most appropriate policy by comparing examples from different municipalities and regions.

[0070] The policy planning department can customize policies to suit different seasons and events. For example, the generation AI in the policy planning department customizes policies to suit different seasons and events. For example, it proposes beach events for the summer tourist season and ski events for the winter tourist season. In addition, in order to customize policies to suit seasons and events, the generation AI analyzes past data and trends and proposes optimal policies. For example, it predicts the success rate of the next event based on data from past events. In addition, the policy planning department builds a system that enables the generation AI to customize policies to suit different seasons and events. For example, it automatically generates suggestions for sightseeing plans and events for each season. This allows policies to be customized to suit different seasons and events, making them more effective.

[0071] The policy planning department can use the emotion estimation function to monitor the emotions of residents and tourists regarding policies in real time, maximizing the effectiveness of the policies. For example, the generation AI in the policy planning department uses the emotion estimation function to monitor the emotions of residents and tourists regarding policies in real time, maximizing the effectiveness of the policies. For example, emotional data during an event is collected and policies are adjusted in real time. The policy planning department also uses the emotion estimation function to monitor the emotions of residents and tourists regarding policies in real time, making adjustments to elicit positive emotions. For example, music and lighting are adjusted according to the progress of the event. The policy planning department also builds a system in which the generation AI uses the emotion estimation function to monitor the emotions of residents and tourists regarding policies in real time, maximizing the effectiveness of the policies. For example, the content and progress of the event are adjusted based on the emotional data. In this way, the effectiveness of the policies can be maximized by monitoring emotions regarding policies in real time.

[0072] The content generation unit can collect user feedback on the generated content and reflect it in the next content generation. For example, the content generation unit can collect user feedback on content generated by the generation AI and reflect it in the next content generation. For example, feedback from social media and comment sections can be collected and reflected in the next content. In addition, the content generation unit can have the generation AI conduct a questionnaire survey to collect user feedback and reflect the results in the next content generation. For example, evaluations of a tourist guide article can be collected and reflected in the next guide article. In addition, the content generation unit can build a system to collect user feedback on content generated by the generation AI and reflect it in the next content generation. For example, the feedback data can be analyzed and reflected in the next content. In this way, more effective content can be generated by collecting user feedback and reflecting it in the next content generation.

[0073] The content generation unit can optimize the generated content for different media formats (video, audio, text). For example, the content generation unit optimizes content generated by the generation AI for different media formats. For example, converting a tourist guide article into video or audio and distributing it through different media. In addition, to optimize for different media formats, the generation AI analyzes the content and selects the optimal format. For example, when converting a text article into video, important points are emphasized. The content generation unit also builds a system to optimize the content generated by the generation AI for different media formats. For example, a system can be developed that automatically converts content into video, audio, and text. This allows content to be optimized for different media formats, making it possible to deliver content to more users effectively.

[0074] The content generation unit can use the emotion estimation function to analyze the user's emotion toward the content and generate content that elicits positive emotions. For example, the content generation unit uses the emotion estimation function to analyze the user's emotion toward the content and generate content that elicits positive emotions. For example, the content generation unit generates a tourist guide article that elicits positive emotions based on the user's emotion data. The content generation unit also uses the emotion estimation function to analyze the user's emotion toward the content in real time and makes adjustments to elicit positive emotions. For example, it edits videos and adjusts audio. The content generation unit also builds a system in which the generation AI uses the emotion estimation function to analyze the user's emotion toward the content and generate content that elicits positive emotions. For example, it adjusts the content and expression of the content based on the emotion data. This allows the generation of content that elicits positive emotions, thereby improving user satisfaction.

[0075] The content generation unit automatically translates the generated content into different languages ​​and obtains feedback from an international perspective. For example, the content generation unit automatically translates content generated by the generation AI into different languages ​​and obtains feedback from an international perspective. For example, a tourist guide article is translated into English and Chinese and feedback from overseas users is collected. In addition, the content generation unit uses a translation engine to automatically translate content into different languages, making the content multilingual. For example, a promotional video is translated into multiple languages ​​and evaluations are obtained from an international perspective. In addition, the content generation unit builds a system to automatically translate content generated by the generation AI into different languages ​​and obtain feedback from an international perspective. For example, the translated content is posted on an international platform and feedback is collected. In this way, by automatically translating into different languages, feedback from an international perspective can be obtained.

[0076] The content generation unit can optimize the generated content for different media platforms (SNS, blogs, news sites) and post it. For example, the content generation unit optimizes the content generated by the generation AI for different media platforms and posts it. For example, a tourist guide article can be optimized and distributed for SNS, blogs, and news sites. In order to optimize the content for different media platforms, the content generation unit has the generation AI analyze the content and convert it into a format suitable for each platform. For example, it generates short videos and images for SNS. The content generation unit also builds a system for optimizing the content generated by the generation AI for posting on different media platforms. For example, a system can be developed that automatically optimizes and distributes content for each platform. This allows content to be optimized for posting on different media platforms, making it possible to effectively reach more users.

[0077] The content generation unit can use the emotion estimation function to monitor user emotions toward content in real time and select the optimal media platform. For example, the generation AI in the content generation unit uses the emotion estimation function to monitor user emotions toward content in real time and select the optimal media platform. For example, the optimal platform is selected from social media, blogs, and news sites based on user emotion data. The content generation unit also uses the emotion estimation function to analyze user emotions toward content in real time and select the optimal media platform that will elicit positive emotions. For example, videos and images are distributed to the optimal platform based on the emotion data. The content generation unit also builds a system in which the generation AI uses the emotion estimation function to monitor user emotions toward content in real time and select the optimal media platform. For example, a system is developed that automatically selects content distribution destinations based on emotion data. This makes it possible to select the optimal media platform by monitoring user emotions in real time.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The analysis unit analyzes the characteristics and resources of the local government. For example, the analysis unit collects information on the local history, culture, natural environment, specialty products, tourist spots, etc., and proposes measures to enhance the local government's appeal based on the collected information. The policy planning unit plans policies based on the data analyzed by the analysis unit. For example, the policy planning unit proposes tourism plans utilizing local traditional events and festivals, gourmet events using local ingredients, art projects by local artists, etc. The content generation unit generates content based on the measures proposed by the policy planning unit. For example, the content generation unit generates tourist guide articles and promotional videos based on tourism plans proposed by the generation AI. The media posting unit publishes the content generated by the content generation unit in the media. For example, the media posting unit publishes the generated content on the local government's official website, social media, local news sites, etc. As a result, the attractiveness enhancement system according to the embodiment can effectively promote the appeal of the local government and increase the number of users of hometown tax donations.

[0080] The analysis unit can collect feedback from local government residents in real time and reflect it in the analysis. For example, it can collect opinions and impressions provided by residents through a smartphone app and use them to analyze the local government's characteristics and resources. To collect feedback from residents, the analysis unit has the generation AI monitor social media and online forums and analyze the residents' opinions and emotions. For example, it can collect content posted using specific hashtags and reflect this in the analysis of the local government's characteristics and resources. To collect feedback from local government residents, the analysis unit also has the generation AI conduct regular surveys and reflect the results in the analysis. For example, it can survey residents' satisfaction and requests and use the results in the analysis of the local government's characteristics and resources. This allows for more accurate analysis by incorporating residents' opinions.

[0081] The analysis unit can perform more accurate analysis by referring to past successes and failures. For example, the generation AI collects past successes and failures from a database and uses them to analyze the characteristics and resources of a local government. For example, the success and failure factors of past tourism projects are reflected in the analysis. In addition, to refer to past successes and failures, the generation AI analyzes the local government's archived data and extracts specific patterns and trends. For example, analysis is performed based on the number of participants and responses to past events. In addition, the generation AI collects successes and failures from other local governments and uses them to analyze the characteristics and resources of a local government. For example, it refers to successful measures in other local governments and performs analysis to apply them to one's own local government. In this way, by referring to past cases, the accuracy of the analysis is improved.

[0082] The analysis unit can use the emotion estimation function to analyze the emotions of residents and tourists and identify characteristics and resources that elicit positive emotions. For example, the generation AI uses the emotion estimation function to analyze the emotions of residents and tourists and identify characteristics and resources that elicit positive emotions. For example, it analyzes social media posts and comments on review sites to identify characteristics and resources that contain a lot of positive emotions. The analysis unit also uses the emotion estimation function to analyze the emotions of residents and tourists in real time and identify characteristics and resources that elicit positive emotions. For example, it collects real-time emotion data at tourist destinations and reflects it in the analysis. The analysis unit also uses the emotion estimation function to analyze the emotions of residents and tourists and conducts questionnaire surveys to identify characteristics and resources that elicit positive emotions. For example, it surveys emotional responses at tourist destinations and events and uses the results in the analysis. This allows more effective measures to be developed by identifying characteristics and resources that elicit positive emotions.

[0083] The analysis unit can conduct comparative analysis with other municipalities to highlight their unique attractions. For example, the generation AI conducts comparative analysis with other municipalities and collects data to highlight their unique attractions. For example, it compares tourist spots and local specialties to identify the municipality's strengths. The analysis unit also has the generation AI collect tourist ratings and reviews and use them for analysis in order to conduct comparative analysis with other municipalities. For example, it compares the rating scores of tourist spots to highlight the municipality's attractions. The analysis unit also has the generation AI conduct comparative analysis with other municipalities and generate a report to highlight their unique attractions. For example, it identifies the municipality's strengths based on examples of success and failure of other municipalities and compiles them into a report. This allows the municipality to conduct comparative analysis with other municipalities to highlight its unique attractions.

[0084] The analysis unit can collect real-time environmental data using drones and sensors. For example, the generation AI can use drones to collect environmental data from local governments in real time and use it for analysis. For example, data on the scenery and natural environment of tourist destinations can be collected and reflected in the analysis. The analysis unit can also use sensors to collect environmental data from local governments in real time, which the generation AI can use for analysis. For example, data such as temperature, humidity, and air quality can be collected and used to plan tourist trips. The analysis unit can also use drones and sensors to collect environmental data from local governments, and build a system for the generation AI to use for analysis. For example, congestion and traffic conditions at tourist destinations can be monitored in real time and reflected in the analysis. This allows for more accurate analysis by collecting real-time environmental data.

[0085] The analysis unit can use the emotion estimation function to analyze residents' emotions toward the municipality's characteristics and resources, and propose measures to elicit positive emotions. For example, the generation AI can use the emotion estimation function to analyze residents' emotions toward the municipality's characteristics and resources, and propose measures to elicit positive emotions. For example, it can propose local events and projects based on residents' emotional data. The analysis unit can also use the emotion estimation function to analyze residents' emotions toward the municipality's characteristics and resources in real time, and propose measures to elicit positive emotions. For example, it can propose sightseeing plans and gourmet events based on residents' emotional responses. The analysis unit can also use the emotion estimation function to analyze residents' emotions toward the municipality's characteristics and resources, and conduct a questionnaire survey to propose measures to elicit positive emotions. For example, it can propose a local art project based on residents' emotional data. This makes it possible to improve resident satisfaction by proposing measures to elicit positive emotions.

[0086] The policy planning department can perform simulations on policies to predict their effects before they are implemented. For example, it can perform simulations on policies proposed by the generation AI to predict their effects before they are implemented. For example, it can simulate a tourism plan to calculate the expected number of tourists and economic impact. In addition, in order to perform simulations, the generation AI analyzes past data and trends to predict the effects of policies. For example, it can predict the success rate of the next event based on data from past events. The policy planning department can also build a system to simulate policies proposed by the generation AI and predict their effects before they are implemented. For example, it can visualize the results of a tourism plan simulation to evaluate the effects of policies. This can increase the success rate of policies by predicting their effects before they are implemented.

[0087] The policy planning department can incorporate expert opinions into policies to improve their accuracy. For example, the policy planning department can incorporate expert opinions into policies proposed by the generation AI to improve their accuracy. For example, the opinions of tourism industry experts can be reflected in tourism plans. To incorporate expert opinions, the policy planning department has the generation AI collect expert opinions through online meetings and interviews and reflect them in policies. For example, the opinions of chefs can be incorporated into gourmet events using local ingredients. The policy planning department can also build a system to incorporate expert opinions into policies proposed by the generation AI to improve their accuracy. For example, the opinions of experts can be registered in a database and used in policy planning. In this way, incorporating expert opinions improves the accuracy of policies.

[0088] The policy planning department can use the emotion estimation function to analyze the emotions of residents and tourists and develop policies that elicit positive emotions. For example, the generation AI uses the emotion estimation function to analyze the emotions of residents and tourists and develop policies that elicit positive emotions. For example, local events and projects are proposed based on the residents' emotional data. The policy planning department also uses the emotion estimation function to analyze the emotions of residents and tourists in real time and develop policies that elicit positive emotions. For example, sightseeing plans and gourmet events are proposed based on the residents' emotional responses. The policy planning department also uses the emotion estimation function to analyze the emotions of residents and tourists and conduct questionnaire surveys to develop policies that elicit positive emotions. For example, a local art project is proposed based on the residents' emotional data. In this way, policies that elicit positive emotions can be developed, thereby improving the satisfaction of residents and tourists.

[0089] The policy planning department can compare examples of measures implemented in different municipalities and regions and select the most appropriate measures. For example, the generation AI collects examples of measures implemented in different municipalities and regions and selects the most appropriate measures based on that. For example, it can refer to tourism plans that have been successful in other municipalities and apply them to its own municipality. In addition, the generation AI builds a database to compare examples of measures implemented in different municipalities and regions and evaluate the effectiveness of measures. For example, it selects measures based on the number of tourists and economic impact. In addition, the policy planning department builds a system in which the generation AI collects examples of measures implemented in different municipalities and regions and selects the most appropriate measures based on that. For example, it registers successful and unsuccessful examples from other municipalities in a database and uses them in policy planning. This makes it possible to select the most appropriate measures by comparing them with examples implemented in different municipalities and regions.

[0090] The policy planning department can customize policies to suit different seasons and events. For example, the generation AI customizes policies to suit different seasons and events. For example, it might propose beach events for the summer tourist season and ski events for the winter tourist season. In addition, to customize policies to suit seasons and events, the generation AI analyzes past data and trends to propose optimal policies. For example, it might predict the success rate of the next event based on data from past events. The policy planning department also builds a system that enables the generation AI to customize policies to suit different seasons and events. For example, it automatically generates suggestions for sightseeing plans and events for each season. This allows policies to be customized to suit different seasons and events, making them more effective.

[0091] The policy planning department can use the emotion estimation function to monitor the emotions of residents and tourists regarding policies in real time, maximizing the effectiveness of the policies. For example, the generation AI can use the emotion estimation function to monitor the emotions of residents and tourists regarding policies in real time, maximizing the effectiveness of the policies. For example, emotional data can be collected during an event and policies can be adjusted in real time. The policy planning department can also use the emotion estimation function to monitor the emotions of residents and tourists regarding policies in real time and make adjustments to elicit positive emotions. For example, music and lighting can be adjusted according to the progress of the event. The policy planning department can also use the emotion estimation function to monitor the emotions of residents and tourists regarding policies in real time, building a system to maximize the effectiveness of policies. For example, the content and progress of the event can be adjusted based on the emotional data. In this way, the effectiveness of policies can be maximized by monitoring emotions regarding policies in real time.

[0092] The content generation unit can collect user feedback on the generated content and reflect it in the next content generation. For example, user feedback on content generated by the generation AI can be collected and reflected in the next content generation. For example, feedback from social media and comment sections can be collected and reflected in the next content. In addition, the content generation unit can have the generation AI conduct a questionnaire survey to collect user feedback and reflect the results in the next content generation. For example, evaluations of a tourist guide article can be collected and reflected in the next guide article. In addition, the content generation unit can build a system to collect user feedback on content generated by the generation AI and reflect it in the next content generation. For example, the feedback data can be analyzed and reflected in the next content. In this way, more effective content can be generated by collecting user feedback and reflecting it in the next content generation.

[0093] The content generation unit can optimize the generated content for different media formats (video, audio, text). For example, content generated by the generation AI is optimized for different media formats. For example, a tourist guide article can be converted into video or audio and distributed through different media. In order to optimize for different media formats, the content generation unit has the generation AI analyze the content and select the optimal format. For example, when converting a text article into video, important points can be emphasized. The content generation unit also builds a system to optimize the content generated by the generation AI for different media formats. For example, a system can be developed that automatically converts content into video, audio, and text. This allows content to be optimized for different media formats, making it possible to deliver content to more users effectively.

[0094] The content generation unit can use the emotion estimation function to analyze a user's emotions toward the content and generate content that elicits positive emotions. For example, the generation AI uses the emotion estimation function to analyze a user's emotions toward the content and generate content that elicits positive emotions. For example, a tourist guide article that elicits positive emotions is generated based on the user's emotion data. The content generation unit also uses the emotion estimation function to analyze a user's emotions toward the content in real time and make adjustments to elicit positive emotions. For example, it edits videos and adjusts audio. The content generation unit also builds a system in which the generation AI uses the emotion estimation function to analyze a user's emotions toward the content and generate content that elicits positive emotions. For example, it adjusts the content and expression of the content based on the emotion data. This allows the generation of content that elicits positive emotions, thereby improving user satisfaction.

[0095] The content generation unit automatically translates the generated content into different languages ​​and obtains feedback from an international perspective. For example, the content generated by the generation AI is automatically translated into different languages ​​and obtains feedback from an international perspective. For example, a tourist guide article is translated into English and Chinese and feedback from overseas users is collected. The content generation unit also uses a translation engine to automatically translate the content into different languages, making the content multilingual. For example, a promotional video is translated into multiple languages ​​and evaluations are obtained from an international perspective. The content generation unit also builds a system to automatically translate the content generated by the generation AI into different languages ​​and obtain feedback from an international perspective. For example, the translated content is posted on an international platform and feedback is collected. In this way, by automatically translating into different languages, feedback from an international perspective can be obtained.

[0096] The content generation unit can optimize the generated content for different media platforms (SNS, blogs, news sites) and post it. For example, the content generated by the generation AI is optimized for different media platforms and posted. For example, a tourist guide article can be optimized and distributed for SNS, blogs, and news sites. In order to optimize for different media platforms, the content generation unit has the generation AI analyze the content and convert it into a format suitable for each platform. For example, it generates short videos and images for SNS. The content generation unit also builds a system to optimize the content generated by the generation AI for posting on different media platforms. For example, a system can be developed that automatically optimizes and distributes content for each platform. This allows content to be posted on different media platforms and delivered more effectively to more users.

[0097] The content generation unit can use the emotion estimation function to monitor user emotions toward content in real time and select the optimal media platform. For example, the generation AI can use the emotion estimation function to monitor user emotions toward content in real time and select the optimal media platform. For example, the optimal platform can be selected from social media, blogs, and news sites based on user emotion data. The content generation unit can also use the emotion estimation function to analyze user emotions toward content in real time and select the optimal media platform that will elicit positive emotions. For example, videos and images can be distributed to the optimal platform based on the emotion data. The content generation unit can also build a system in which the generation AI can use the emotion estimation function to monitor user emotions toward content in real time and select the optimal media platform. For example, a system can be developed that automatically selects the distribution destination for content based on emotion data. This makes it possible to select the optimal media platform by monitoring user emotions in real time.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The analysis department analyzes the characteristics and resources of the local government. For example, the analysis department collects information on the local history, culture, natural environment, local specialties, tourist spots, etc., and uses this information to propose measures to enhance the local government's appeal. Step 2: The Policy Planning Department formulates policies based on the data analyzed by the Analysis Department. For example, the Policy Planning Department may propose tourism plans that utilize local traditional events and festivals, gourmet events using local ingredients, and art projects by local artists. Step 3: The content generation unit generates content based on the measures proposed by the measure planning unit. For example, the content generation unit generates tourist guide articles and promotional videos based on the sightseeing plans proposed by the generation AI. Step 4: The media publishing department publishes the content generated by the content generation department in the media. For example, the media publishing department publishes the generated content on the local government's official website, social media, local news sites, etc.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0110] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0111] 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.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] 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.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0140] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0141] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0142] 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.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0144] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0145] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0146] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0148] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0149] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0150] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0151] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0152] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0153] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0154] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0155] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0156] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0157] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0158] 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.

[0159] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0160] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0161] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0162] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0163] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0164] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0165] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0166] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0167] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An analysis department that analyzes the characteristics and resources of local governments; a policy planning unit that plans a policy based on the data analyzed by the analysis unit; a content generation unit that generates content based on the measures formulated by the measure formulation unit; a media publishing unit that publishes the content generated by the content generating unit on media. A system characterized by:

2. The analysis unit Gather real-time feedback from local residents and incorporate it into your analysis 2. The system of claim 1.

3. The analysis unit Refer to past successes and failures to perform more accurate analysis 2. The system of claim 1.

4. The analysis unit Analyze the emotions of residents and tourists and identify the characteristics and resources that elicit positive emotions 2. The system of claim 1.

5. The analysis unit Conduct comparative analysis with other municipalities to highlight their unique appeal 2. The system of claim 1.

Images