system

Abstract

The system according to this embodiment aims to specifically convey the appeal of a region and make it easier for prospective migrants to access information from local governments. [Solution] The system according to the embodiment comprises a collection unit, a generation unit, a reflection unit, and a provision unit. The collection unit collects data on local government information and events, the interests of prospective residents, and local specialties and traditions. The generation unit generates stories based on the data collected by the collection unit. The reflection unit reflects the experiences of those who have lived there and real-time local information. The provision unit provides the information generated by the generation unit and the reflection unit to prospective residents.

Description

Technical Field

[0004] ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that means for specifically conveying the charm of a region are insufficient, and it is difficult for potential migrants to access information of local governments.

[0005] The system according to the embodiment aims to specifically convey the charm of a region and facilitate access by potential migrants to information of local governments.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a generation unit, a reflection unit, and a provision unit. The collection unit collects data on local government information and events, the interests of prospective residents, and local specialties and traditions. The generation unit generates stories based on the data collected by the collection unit. The reflection unit reflects the experiences of those who have lived there and real-time local information. The provision unit provides the information generated by the generation unit and the reflection unit to prospective residents. [Effects of the Invention]

[0007] The system according to this embodiment can effectively convey the appeal of a region and make it easier for prospective residents to access information from local governments. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between a plurality of 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), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The regional revitalization support system according to an embodiment of the present invention is a system that connects prospective migrants with local governments. This system uses AI to create narratives based on information and events of local governments, the interests of prospective migrants, and local specialties and traditions. It also incorporates experiences from participants and real-time local information. As a result, prospective migrants can experience local lifestyles and culture, and gain a deeper understanding by engaging with local specialties and traditional performing arts. This system connects local governments with prospective migrants, deepens understanding of regional culture by utilizing local specialties, crafts, and traditional performing arts, and aims to revitalize Japan as a whole by creating a sense of unity. In this way, the regional revitalization support system can effectively connect prospective migrants with local governments and concretely convey the appeal of the region.

[0029] The regional revitalization support system according to this embodiment comprises a collection unit, a generation unit, a reflection unit, and a provision unit. The collection unit collects data on local government information and events, the interests of prospective migrants, and local specialties and traditions. For example, the collection unit collects information such as local government tourism information, local festivals, and administrative services. The collection unit can also identify the interests of prospective migrants through questionnaire surveys and past behavioral history. Furthermore, the collection unit can also collect data on local specialties and traditional events. The generation unit generates stories based on the data collected by the collection unit. For example, the generation unit generates stories using storytelling techniques or generation algorithms. The generation unit can generate stories that include introductions to local festivals, events, and specialties. The reflection unit reflects the experiences of those who have lived in the area and real-time local information. For example, the reflection unit reflects the success stories of migrants and the latest local event information. The reflection unit can collect the experiences of those who have lived in the area through interviews and questionnaires and reflect them in the stories. The provision unit provides the information generated by the generation unit and the reflection unit to prospective migrants. The service provider, for example, delivers generated stories and real-time information through websites and applications. The service provider can also provide prospective migrants with information on local specialties and traditional performing arts. This allows the regional revitalization support system, according to this embodiment, to effectively connect prospective migrants with local governments and concretely convey the region's appeal.

[0030] The data collection department gathers information and events from local governments, the interests of prospective residents, and data on local specialties and traditions. Specifically, it obtains information from local government websites, tourism association databases, and local news sites to collect information on local tourism, local festivals, and administrative services. The data collection department can also identify the interests of prospective residents through surveys and past activity records. Surveys are conducted using methods such as online forms, mail questionnaires, and telephone interviews to collect detailed information on prospective residents' hobbies, interests, and desired conditions for a place to live. Past activity records are identified by analyzing data on areas visited, events attended, and local specialties purchased by prospective residents. Furthermore, the data collection department can also collect data on local specialties and traditional events. This includes receiving information from local chambers of commerce, agricultural cooperatives, and cultural organizations, as well as collecting feedback from local residents and businesses. The data collection department centrally collects information from these diverse data sources and stores it in a database. The collected data is used for processing in subsequent generation and reflection units, and is therefore regularly updated to maintain its accuracy and timeliness. This allows the collection unit to provide a rich and diverse dataset that forms the foundation of the regional revitalization support system, supporting the effective operation of the entire system.

[0031] The generation unit generates stories based on data collected by the collection unit. Specifically, it generates stories using storytelling techniques and generation algorithms. The generation unit can generate stories that include introductions to local festivals, events, and local products. For example, the generation unit can create stories with local history, culture, and natural landscapes as a backdrop to convey the region's appeal to prospective residents. The generation algorithm analyzes the collected data and automatically generates the story's theme, characters, and storyline. The generated stories are customized to the interests of prospective residents. For example, a prospective resident who loves nature will have a story generated that introduces the region's beautiful natural landscapes and outdoor activities. On the other hand, a prospective resident interested in culture and history will have a story generated that introduces local traditional events and historical buildings. The generation unit can also use AI technology to update the story content in real time. For example, if a new event is held in the region, that information is immediately reflected in the story, providing the latest information. In this way, the generation unit can always provide prospective residents with fresh and engaging information, increasing their interest in the region.

[0032] The Reflection Section incorporates stories from those who have experienced the area and real-time local information. Specifically, it reflects the success stories of migrants and the latest local event information. The Reflection Section can collect stories from migrants through interviews and surveys and incorporate them into the narrative. For example, it can collect specific stories about how migrants started their lives in the area, what difficulties they overcame, and how they integrated into the community. These stories are extremely useful information for prospective migrants and can be a factor in encouraging them to make a decision to move. The Reflection Section also collects the latest local event information in real time and incorporates it into the narrative. For example, by collecting information on festivals, cultural events, and harvest festivals held in the area and incorporating it into the narrative, it can convey the vibrancy and appeal of the area to prospective migrants. The Reflection Section can collect the latest information from social media, local news sites, and local bulletin boards, and always provide the most up-to-date information. In this way, the Reflection Section can convey the appeal of the area to prospective migrants in real time and strengthen their motivation to move.

[0033] The provisioning unit provides information generated by the generation and reflection units to prospective residents. Specifically, it provides generated stories and real-time information through websites and applications. The provisioning unit can also provide prospective residents with information on local specialties and traditional performing arts. For example, the website provides generated stories as reading material, allowing prospective residents to gain a deeper understanding of the region's attractions. The application allows prospective residents to easily access information that interests them and uses a notification function to provide the latest event information and introductions to local specialties in real time. The provisioning unit also puts effort into designing the user interface so that prospective residents can intuitively find information. For example, it categorizes information by region and provides a function to display the location of events and local specialties on a map. Furthermore, the provisioning unit collects feedback from prospective residents and continuously improves the quality and content of the information it provides. For example, it analyzes information that prospective residents were particularly interested in, or conversely, information that they were not very interested in, and reflects this in future information provision. In this way, the provisioning unit can always provide prospective residents with the most optimal information and increase their interest in the region.

[0034] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can prioritize collecting information sources that have received high response rates in the past. The data collection unit can also optimize the types of information collected at specific time periods based on past data collection history. The data collection unit can also adjust the collection frequency based on past data collection history. This allows the optimal collection method to be selected by analyzing past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0035] The data collection unit can filter data based on the user's current areas of interest and lifestyle. For example, the data collection unit can prioritize collecting event information that the user is currently interested in. The data collection unit can also filter information based on the user's lifestyle to ensure relevance. The data collection unit can also prioritize collecting information from specific categories based on the user's areas of interest. This allows for the collection of highly relevant data by filtering based on the user's current areas of interest and lifestyle. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user area of ​​interest and lifestyle data into a generating AI and have the generating AI perform the filtering.

[0036] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of event information in the area where the user is currently located. The data collection unit can also collect highly relevant local product information based on the user's geographical location. The data collection unit can also prioritize the collection of nearby tourist spot information based on the user's location information. This allows for the collection of more appropriate data by prioritizing the collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0037] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect event information that the user has shown interest in on social media. The data collection unit can also collect information on local products of interest from the user's social media activity. The data collection unit can also analyze the content of the user's social media posts and collect relevant regional information. In this way, relevant data can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0038] The generation unit can adjust the level of detail in a story based on the importance of the data during story generation. For example, the generation unit can generate a story that explains important event information in detail. The generation unit can also generate a story that summarizes less important information concisely. In introducing local products, the generation unit can also generate a story that emphasizes important features. By adjusting the level of detail in a story based on the importance of the data, a more appropriate story can be generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the importance of the data into a generation AI and have the generation AI perform the adjustment of the level of detail in the story based on importance.

[0039] The generation unit can apply different generation algorithms depending on the data category when generating a story. For example, the generation unit can apply a story generation algorithm that follows a timeline to event information. It can also apply a story generation algorithm that emphasizes features to local product information. It can also apply a story generation algorithm that includes historical background to traditional performing arts. By applying different generation algorithms depending on the data category, a more appropriate story can be generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the data category into a generation AI and have the generation AI execute the application of a generation algorithm appropriate to the category.

[0040] The generation unit can determine the priority of stories based on the data collection timing when generating stories. For example, the generation unit can prioritize the inclusion of the latest event information in the story. The generation unit can also generate stories based on past success stories. The generation unit can also include older information as supplementary content in the story. This allows for the generation of more appropriate stories by determining the priority of stories based on the data collection timing. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the data collection timing into a generation AI and have the generation AI perform the task of determining the priority of stories based on the collection timing.

[0041] The generation unit can adjust the order of stories based on the relevance of the data during story generation. For example, the generation unit can generate stories with highly relevant information placed first. The generation unit can also generate stories with less relevant information placed later. The generation unit can also generate stories that group highly relevant information based on a specific theme. This allows for the generation of more appropriate stories by adjusting the order of stories based on the relevance of the data. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the relevance of the data into a generation AI and have the generation AI perform story order adjustments based on relevance.

[0042] The reflection unit can improve the accuracy of the reflection process by referring to past episodes and information during the reflection process. For example, the reflection unit can reflect similar episodes based on past success stories. The reflection unit can also prioritize the reflection of highly relevant information from past episodes. The reflection unit can also improve the accuracy of the reflection process by referring to past information. In this way, the accuracy of the reflection process can be improved by referring to past episodes and information. Some or all of the above-described processes in the reflection unit may be performed using AI, for example, or without using AI. For example, the reflection unit can input past episodes and information into a generating AI and have the generating AI perform the task of improving the accuracy of the reflection process.

[0043] The reflection unit can perform reflection while considering the attribute information of the submitter of the episode and information. For example, the reflection unit can reflect episodes based on the submitter's age and gender. The reflection unit can also reflect highly relevant information based on the submitter's occupation and hobbies. The reflection unit can also reflect episodes related to the region based on the submitter's place of residence. In this way, by considering the attribute information of the submitter of the episode and information, more relevant information can be provided. Some or all of the above processing in the reflection unit may be performed using AI, for example, or without AI. For example, the reflection unit can input the submitter's attribute information into a generating AI and have the generating AI perform reflection based on the attribute information.

[0044] The reflection unit can perform reflection while considering the geographical distribution of episodes and information. For example, the reflection unit can prioritize the reflection of episodes that are geographically close. The reflection unit can also prioritize the reflection of information that is geographically relevant. The reflection unit can also reflect episodes while considering their geographical distribution. This allows for the provision of more relevant information by considering the geographical distribution of episodes and information. Some or all of the above processing in the reflection unit may be performed using AI, for example, or without AI. For example, the reflection unit can input geographical distribution data of episodes and information into a generating AI and have the generating AI perform reflection based on geographical distribution.

[0045] The reflection unit can improve the accuracy of the reflection by referring to relevant literature for episodes and information during the reflection process. For example, the reflection unit can improve the reliability of episodes by referring to relevant literature. The reflection unit can also improve the accuracy of information based on relevant literature. The reflection unit can also supplement the details of episodes by referring to relevant literature. In this way, the accuracy of the reflection can be improved by referring to relevant literature for episodes and information. Some or all of the above processing in the reflection unit may be performed using AI, for example, or without AI. For example, the reflection unit can input relevant literature data into a generating AI and have the generating AI perform accuracy improvements of the reflection based on the relevant literature.

[0046] The information delivery unit can select the optimal delivery method by referring to the user's past browsing history when providing information. For example, the information delivery unit can provide highly relevant information based on information the user has previously viewed. The information delivery unit can also select the optimal information delivery method from the user's past browsing history. The information delivery unit can also prioritize providing information in categories that the user has previously shown interest in. This allows the optimal information delivery method to be selected by referring to the user's past browsing history. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the information delivery unit can input the user's past browsing history data into a generating AI and have the generating AI select the optimal information delivery method.

[0047] The information provider can customize the content provided based on the user's current areas of interest when providing information. For example, the provider can prioritize providing event information that the user is currently interested in. The provider can also customize and provide information in specific categories based on the user's areas of interest. The provider can also provide highly relevant information according to the user's current areas of interest. This allows for the provision of more relevant information by customizing the content based on the user's current areas of interest. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can input the user's current areas of interest data into a generating AI and have the generating AI perform the customization of the content based on those areas of interest.

[0048] The information delivery unit can select the optimal delivery method by considering the user's geographical location when providing information. For example, the information delivery unit can prioritize providing event information in the area where the user is currently located. The information delivery unit can also provide highly relevant local product information based on the user's geographical location. The information delivery unit can also prioritize providing information on nearby tourist spots based on the user's location. By selecting the optimal delivery method by considering the user's geographical location, more relevant information can be provided. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the information delivery unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal delivery method.

[0049] The information provider can analyze the user's social media activity and customize the content provided. For example, the provider can provide event information that the user has shown interest in on social media. The provider can also provide information on local products that the user is interested in based on their social media activity. The provider can also analyze the content of the user's social media posts and provide relevant local information. This allows for the provision of more relevant information by analyzing the user's social media activity. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the provider can input the user's social media activity data into a generating AI and have the generating AI perform the customization of the content provided.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The regional revitalization support system can further analyze users' past travel history and customize the information provided based on that history. For example, it can prioritize providing information about regions the user has visited in the past. It can also provide information about tourist spots and events the user has shown interest in in the past. Furthermore, it can provide information about new events and activities related to the events the user has participated in and activities they have experienced in the past. In this way, by customizing the information provided based on the user's past travel history, it can provide more relevant information.

[0052] The regional revitalization support system can further customize the information provided based on the user's hobbies and preferences. For example, if a user is interested in music, it can prioritize providing information on local music events and live performances. If a user is interested in food, it can provide information on local gourmet food and specialty products. Furthermore, if a user is interested in history, it can provide information on local historical tourist spots and museums. In this way, by customizing the information provided based on the user's hobbies and preferences, it can provide more relevant information.

[0053] The regional revitalization support system can further customize the information provided based on the user's family structure. For example, if a user has children, information on family-friendly events and activities can be prioritized. Similarly, if a user lives with elderly individuals, information on services and facilities for seniors can be provided. Furthermore, if a user owns pets, information on pet-friendly tourist spots and accommodations can be provided. This allows for the provision of more relevant information by customizing it based on the user's family structure.

[0054] The regional revitalization support system can further adjust the timing of information provision by taking into account the user's geographical location. For example, when a user arrives in a specific area, it can provide real-time information on tourist attractions and events in that area. It can also provide information about the next destination when the user is on the move. Furthermore, if the user is staying at a specific tourist spot, it can provide information on nearby restaurants and shopping. By adjusting the timing of information provision based on the user's geographical location, information can be delivered at a more appropriate time.

[0055] The regional revitalization support system can further analyze users' social media activity and customize information delivery based on their responses on social media. For example, it can prioritize providing event information that users have shown interest in on social media. It can also provide information on related local products based on information about local products that users have shared on social media. Furthermore, it can provide information on local tourist spots and events that users follow on social media. In this way, by analyzing users' social media activity, it can provide more relevant information.

[0056] The regional revitalization support system can further analyze users' past purchase history and customize the information provided based on that history. For example, it can prioritize providing information on local specialty products that users have purchased in the past. It can also provide information on related products and new products that users have purchased in the past. Furthermore, it can provide information on new services and facilities that are relevant to the services and facilities that users have used in the past. In this way, by customizing the information provided based on the user's past purchase history, it can provide more relevant information.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The data collection department gathers information and events from local governments, the interests of prospective residents, and data on local specialties and traditions. For example, they collect information on local tourism, local festivals, and administrative services, and identify the interests of prospective residents through surveys and past activity records. They also collect data on local specialties and traditional events. Step 2: The generation unit generates stories based on the data collected by the collection unit. For example, it uses storytelling techniques and generation algorithms to generate stories that include introductions to local festivals, events, and specialty products. Step 3: The reflection section incorporates the experiences of those who have lived there and real-time local information. For example, success stories of migrants and the latest local events are collected through interviews and surveys, and then reflected in the narrative. Step 4: The providing unit provides the information generated by the generating and reflecting units to prospective migrants. For example, it provides generated stories and real-time information through websites and applications, and provides prospective migrants with information about local specialties and traditional performing arts.

[0059] (Example of form 2) The regional revitalization support system according to an embodiment of the present invention is a system that connects prospective migrants with local governments. This system uses AI to create narratives based on information and events of local governments, the interests of prospective migrants, and local specialties and traditions. It also incorporates experiences from participants and real-time local information. As a result, prospective migrants can experience local lifestyles and culture, and gain a deeper understanding by engaging with local specialties and traditional performing arts. This system connects local governments with prospective migrants, deepens understanding of regional culture by utilizing local specialties, crafts, and traditional performing arts, and aims to revitalize Japan as a whole by creating a sense of unity. In this way, the regional revitalization support system can effectively connect prospective migrants with local governments and concretely convey the appeal of the region.

[0060] The regional revitalization support system according to this embodiment comprises a collection unit, a generation unit, a reflection unit, and a provision unit. The collection unit collects data on local government information and events, the interests of prospective migrants, and local specialties and traditions. For example, the collection unit collects information such as local government tourism information, local festivals, and administrative services. The collection unit can also identify the interests of prospective migrants through questionnaire surveys and past behavioral history. Furthermore, the collection unit can also collect data on local specialties and traditional events. The generation unit generates stories based on the data collected by the collection unit. For example, the generation unit generates stories using storytelling techniques or generation algorithms. The generation unit can generate stories that include introductions to local festivals, events, and specialties. The reflection unit reflects the experiences of those who have lived in the area and real-time local information. For example, the reflection unit reflects the success stories of migrants and the latest local event information. The reflection unit can collect the experiences of those who have lived in the area through interviews and questionnaires and reflect them in the stories. The provision unit provides the information generated by the generation unit and the reflection unit to prospective migrants. The service provider, for example, delivers generated stories and real-time information through websites and applications. The service provider can also provide prospective migrants with information on local specialties and traditional performing arts. This allows the regional revitalization support system, according to this embodiment, to effectively connect prospective migrants with local governments and concretely convey the region's appeal.

[0061] The data collection department gathers information and events from local governments, the interests of prospective residents, and data on local specialties and traditions. Specifically, it obtains information from local government websites, tourism association databases, and local news sites to collect information on local tourism, local festivals, and administrative services. The data collection department can also identify the interests of prospective residents through surveys and past activity records. Surveys are conducted using methods such as online forms, mail questionnaires, and telephone interviews to collect detailed information on prospective residents' hobbies, interests, and desired conditions for a place to live. Past activity records are identified by analyzing data on areas visited, events attended, and local specialties purchased by prospective residents. Furthermore, the data collection department can also collect data on local specialties and traditional events. This includes receiving information from local chambers of commerce, agricultural cooperatives, and cultural organizations, as well as collecting feedback from local residents and businesses. The data collection department centrally collects information from these diverse data sources and stores it in a database. The collected data is used for processing in subsequent generation and reflection units, and is therefore regularly updated to maintain its accuracy and timeliness. This allows the collection unit to provide a rich and diverse dataset that forms the foundation of the regional revitalization support system, supporting the effective operation of the entire system.

[0062] The generation unit generates stories based on data collected by the collection unit. Specifically, it generates stories using storytelling techniques and generation algorithms. The generation unit can generate stories that include introductions to local festivals, events, and local products. For example, the generation unit can create stories with local history, culture, and natural landscapes as a backdrop to convey the region's appeal to prospective residents. The generation algorithm analyzes the collected data and automatically generates the story's theme, characters, and storyline. The generated stories are customized to the interests of prospective residents. For example, a prospective resident who loves nature will have a story generated that introduces the region's beautiful natural landscapes and outdoor activities. On the other hand, a prospective resident interested in culture and history will have a story generated that introduces local traditional events and historical buildings. The generation unit can also use AI technology to update the story content in real time. For example, if a new event is held in the region, that information is immediately reflected in the story, providing the latest information. In this way, the generation unit can always provide prospective residents with fresh and engaging information, increasing their interest in the region.

[0063] The Reflection Section incorporates stories from those who have experienced the area and real-time local information. Specifically, it reflects the success stories of migrants and the latest local event information. The Reflection Section can collect stories from migrants through interviews and surveys and incorporate them into the narrative. For example, it can collect specific stories about how migrants started their lives in the area, what difficulties they overcame, and how they integrated into the community. These stories are extremely useful information for prospective migrants and can be a factor in encouraging them to make a decision to move. The Reflection Section also collects the latest local event information in real time and incorporates it into the narrative. For example, by collecting information on festivals, cultural events, and harvest festivals held in the area and incorporating it into the narrative, it can convey the vibrancy and appeal of the area to prospective migrants. The Reflection Section can collect the latest information from social media, local news sites, and local bulletin boards, and always provide the most up-to-date information. In this way, the Reflection Section can convey the appeal of the area to prospective migrants in real time and strengthen their motivation to move.

[0064] The provisioning unit provides information generated by the generation and reflection units to prospective residents. Specifically, it provides generated stories and real-time information through websites and applications. The provisioning unit can also provide prospective residents with information on local specialties and traditional performing arts. For example, the website provides generated stories as reading material, allowing prospective residents to gain a deeper understanding of the region's attractions. The application allows prospective residents to easily access information that interests them and uses a notification function to provide the latest event information and introductions to local specialties in real time. The provisioning unit also puts effort into designing the user interface so that prospective residents can intuitively find information. For example, it categorizes information by region and provides a function to display the location of events and local specialties on a map. Furthermore, the provisioning unit collects feedback from prospective residents and continuously improves the quality and content of the information it provides. For example, it analyzes information that prospective residents were particularly interested in, or conversely, information that they were not very interested in, and reflects this in future information provision. In this way, the provisioning unit can always provide prospective residents with the most optimal information and increase their interest in the region.

[0065] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is excited, the data collection unit can collect the latest event information in real time. If the user is relaxed, the data collection unit can also collect past success stories and episodes. If the user is feeling anxious, the data collection unit can prioritize collecting local safety information that provides a sense of security. This allows for data collection at a more appropriate time by adjusting the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of data collection timing based on emotions.

[0066] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can prioritize collecting information sources that have received high response rates in the past. The data collection unit can also optimize the types of information collected at specific time periods based on past data collection history. The data collection unit can also adjust the collection frequency based on past data collection history. This allows the optimal collection method to be selected by analyzing past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0067] The data collection unit can filter data based on the user's current areas of interest and lifestyle. For example, the data collection unit can prioritize collecting event information that the user is currently interested in. The data collection unit can also filter information based on the user's lifestyle to ensure relevance. The data collection unit can also prioritize collecting information from specific categories based on the user's areas of interest. This allows for the collection of highly relevant data by filtering based on the user's current areas of interest and lifestyle. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user area of ​​interest and lifestyle data into a generating AI and have the generating AI perform the filtering.

[0068] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, if the user is excited, the data collection unit may prioritize collecting the latest event information. If the user is relaxed, the data collection unit may also prioritize collecting past success stories and episodes. If the user is feeling anxious, the data collection unit may also prioritize collecting information that provides a sense of security. This allows for the collection of more appropriate data by prioritizing data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion-based data prioritization.

[0069] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of event information in the area where the user is currently located. The data collection unit can also collect highly relevant local product information based on the user's geographical location. The data collection unit can also prioritize the collection of nearby tourist spot information based on the user's location information. This allows for the collection of more appropriate data by prioritizing the collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0070] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect event information that the user has shown interest in on social media. The data collection unit can also collect information on local products of interest from the user's social media activity. The data collection unit can also analyze the content of the user's social media posts and collect relevant regional information. In this way, relevant data can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0071] The generation unit can estimate the user's emotions and adjust the way the story is presented based on those emotions. For example, if the user is relaxed, the generation unit will generate a story in a calm tone. If the user is excited, the generation unit can also generate a story using lively expressions. If the user is feeling anxious, the generation unit can also generate a story using reassuring expressions. This allows for the generation of more appropriate stories by adjusting the way the story is presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the way the story is presented based on those emotions.

[0072] The generation unit can adjust the level of detail in a story based on the importance of the data during story generation. For example, the generation unit can generate a story that explains important event information in detail. The generation unit can also generate a story that summarizes less important information concisely. In introducing local products, the generation unit can also generate a story that emphasizes important features. By adjusting the level of detail in a story based on the importance of the data, a more appropriate story can be generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the importance of the data into a generation AI and have the generation AI perform the adjustment of the level of detail in the story based on importance.

[0073] The generation unit can apply different generation algorithms depending on the data category when generating a story. For example, the generation unit can apply a story generation algorithm that follows a timeline to event information. It can also apply a story generation algorithm that emphasizes features to local product information. It can also apply a story generation algorithm that includes historical background to traditional performing arts. By applying different generation algorithms depending on the data category, a more appropriate story can be generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the data category into a generation AI and have the generation AI execute the application of a generation algorithm appropriate to the category.

[0074] The generation unit can estimate the user's emotions and adjust the length of the story based on those emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise story. If the user is relaxed, the generation unit can also generate a longer story with more detailed explanations. If the user is excited, the generation unit can also generate a story with visually stimulating effects. This allows for the generation of more appropriate stories by adjusting the length based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into a generation AI and have the generation AI adjust the story length based on the emotion.

[0075] The generation unit can determine the priority of stories based on the data collection timing when generating stories. For example, the generation unit can prioritize the inclusion of the latest event information in the story. The generation unit can also generate stories based on past success stories. The generation unit can also include older information as supplementary content in the story. This allows for the generation of more appropriate stories by determining the priority of stories based on the data collection timing. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the data collection timing into a generation AI and have the generation AI perform the task of determining the priority of stories based on the collection timing.

[0076] The generation unit can adjust the order of stories based on the relevance of the data during story generation. For example, the generation unit can generate stories with highly relevant information placed first. The generation unit can also generate stories with less relevant information placed later. The generation unit can also generate stories that group highly relevant information based on a specific theme. This allows for the generation of more appropriate stories by adjusting the order of stories based on the relevance of the data. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the relevance of the data into a generation AI and have the generation AI perform story order adjustments based on relevance.

[0077] The reflection unit can estimate the user's emotions and adjust how episodes and information are reflected based on the estimated user emotions. For example, if the user is relaxed, the reflection unit will reflect the episode in a calm tone. If the user is excited, the reflection unit may also reflect the episode in a lively expression. If the user is feeling anxious, the reflection unit may also reflect the episode in a reassuring expression. This allows for the provision of more appropriate information by adjusting how episodes and information are reflected based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reflection unit may be performed using AI, for example, or not using AI. For example, the reflection unit can input user emotion data into a generative AI and have the generative AI adjust how episodes and information are reflected based on emotions.

[0078] The reflection unit can improve the accuracy of the reflection process by referring to past episodes and information during the reflection process. For example, the reflection unit can reflect similar episodes based on past success stories. The reflection unit can also prioritize the reflection of highly relevant information from past episodes. The reflection unit can also improve the accuracy of the reflection process by referring to past information. In this way, the accuracy of the reflection process can be improved by referring to past episodes and information. Some or all of the above-described processes in the reflection unit may be performed using AI, for example, or without using AI. For example, the reflection unit can input past episodes and information into a generating AI and have the generating AI perform the task of improving the accuracy of the reflection process.

[0079] The reflection unit can perform reflection while considering the attribute information of the submitter of the episode and information. For example, the reflection unit can reflect episodes based on the submitter's age and gender. The reflection unit can also reflect highly relevant information based on the submitter's occupation and hobbies. The reflection unit can also reflect episodes related to the region based on the submitter's place of residence. In this way, by considering the attribute information of the submitter of the episode and information, more relevant information can be provided. Some or all of the above processing in the reflection unit may be performed using AI, for example, or without AI. For example, the reflection unit can input the submitter's attribute information into a generating AI and have the generating AI perform reflection based on the attribute information.

[0080] The reflection unit can estimate the user's emotions and determine the priority of information to reflect based on the estimated user emotions. For example, if the user is excited, the reflection unit may prioritize reflecting the latest event information. If the user is relaxed, the reflection unit may also prioritize reflecting past success stories. If the user is feeling anxious, the reflection unit may also prioritize reflecting information that provides a sense of security. In this way, more appropriate information can be provided by determining the priority of information to reflect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reflection unit may be performed using AI, for example, or not using AI. For example, the reflection unit can input user emotion data into a generative AI and have the generative AI perform the emotion-based information prioritization.

[0081] The reflection unit can perform reflection while considering the geographical distribution of episodes and information. For example, the reflection unit can prioritize the reflection of episodes that are geographically close. The reflection unit can also prioritize the reflection of information that is geographically relevant. The reflection unit can also reflect episodes while considering their geographical distribution. This allows for the provision of more relevant information by considering the geographical distribution of episodes and information. Some or all of the above processing in the reflection unit may be performed using AI, for example, or without AI. For example, the reflection unit can input geographical distribution data of episodes and information into a generating AI and have the generating AI perform reflection based on geographical distribution.

[0082] The reflection unit can improve the accuracy of the reflection by referring to relevant literature for episodes and information during the reflection process. For example, the reflection unit can improve the reliability of episodes by referring to relevant literature. The reflection unit can also improve the accuracy of information based on relevant literature. The reflection unit can also supplement the details of episodes by referring to relevant literature. In this way, the accuracy of the reflection can be improved by referring to relevant literature for episodes and information. Some or all of the above processing in the reflection unit may be performed using AI, for example, or without AI. For example, the reflection unit can input relevant literature data into a generating AI and have the generating AI perform accuracy improvements of the reflection based on the relevant literature.

[0083] The information provider can estimate the user's emotions and adjust the method of information delivery based on the estimated emotions. For example, if the user is relaxed, the information provider can deliver information in a calm tone. If the user is excited, the information provider can deliver information in a lively manner. If the user is feeling anxious, the information provider can deliver information in a reassuring manner. This allows for the delivery of more appropriate information by adjusting the method of information delivery based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input user emotion data into a generative AI and have the generative AI perform the adjustment of the information delivery method based on emotions.

[0084] The information delivery unit can select the optimal delivery method by referring to the user's past browsing history when providing information. For example, the information delivery unit can provide highly relevant information based on information the user has previously viewed. The information delivery unit can also select the optimal information delivery method from the user's past browsing history. The information delivery unit can also prioritize providing information in categories that the user has previously shown interest in. This allows the optimal information delivery method to be selected by referring to the user's past browsing history. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the information delivery unit can input the user's past browsing history data into a generating AI and have the generating AI select the optimal information delivery method.

[0085] The information provider can customize the content provided based on the user's current areas of interest when providing information. For example, the provider can prioritize providing event information that the user is currently interested in. The provider can also customize and provide information in specific categories based on the user's areas of interest. The provider can also provide highly relevant information according to the user's current areas of interest. This allows for the provision of more relevant information by customizing the content based on the user's current areas of interest. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can input the user's current areas of interest data into a generating AI and have the generating AI perform the customization of the content based on those areas of interest.

[0086] The information delivery unit can estimate the user's emotions and determine the priority of information delivery based on the estimated user emotions. For example, if the user is excited, the information delivery unit can prioritize providing the latest event information. If the user is relaxed, the information delivery unit can also prioritize providing past success stories. If the user is feeling anxious, the information delivery unit can also prioritize providing reassuring information. In this way, more appropriate information can be provided by determining the priority of information delivery based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or not using AI. For example, the information delivery unit can input user emotion data into a generative AI and have the generative AI perform emotion-based information delivery priority determination.

[0087] The information delivery unit can select the optimal delivery method by considering the user's geographical location when providing information. For example, the information delivery unit can prioritize providing event information in the area where the user is currently located. The information delivery unit can also provide highly relevant local product information based on the user's geographical location. The information delivery unit can also prioritize providing information on nearby tourist spots based on the user's location. By selecting the optimal delivery method by considering the user's geographical location, more relevant information can be provided. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the information delivery unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal delivery method.

[0088] The information provider can analyze the user's social media activity and customize the content provided. For example, the provider can provide event information that the user has shown interest in on social media. The provider can also provide information on local products that the user is interested in based on their social media activity. The provider can also analyze the content of the user's social media posts and provide relevant local information. This allows for the provision of more relevant information by analyzing the user's social media activity. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the provider can input the user's social media activity data into a generating AI and have the generating AI perform the customization of the content provided.

[0089] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0090] The regional revitalization support system can further monitor the user's health status and tailor the information provided based on that status. For example, if a user is tired, it can prioritize providing information on relaxing tourist spots and hot springs. If the user is active, it can provide information on hiking and sporting events. Furthermore, if a user has health concerns, it can provide information on local medical facilities and health support services. By tailoring information based on the user's health status, it can provide more appropriate information.

[0091] The regional revitalization support system can further analyze users' past travel history and customize the information provided based on that history. For example, it can prioritize providing information about regions the user has visited in the past. It can also provide information about tourist spots and events the user has shown interest in in the past. Furthermore, it can provide information about new events and activities related to the events the user has participated in and activities they have experienced in the past. In this way, by customizing the information provided based on the user's past travel history, it can provide more relevant information.

[0092] The regional revitalization support system can further customize the information provided based on the user's hobbies and preferences. For example, if a user is interested in music, it can prioritize providing information on local music events and live performances. If a user is interested in food, it can provide information on local gourmet food and specialty products. Furthermore, if a user is interested in history, it can provide information on local historical tourist spots and museums. In this way, by customizing the information provided based on the user's hobbies and preferences, it can provide more relevant information.

[0093] The regional revitalization support system can further customize the information provided based on the user's family structure. For example, if a user has children, information on family-friendly events and activities can be prioritized. Similarly, if a user lives with elderly individuals, information on services and facilities for seniors can be provided. Furthermore, if a user owns pets, information on pet-friendly tourist spots and accommodations can be provided. This allows for the provision of more relevant information by customizing it based on the user's family structure.

[0094] The regional revitalization support system can further estimate the user's emotions and adjust the timing of information delivery based on those emotions. For example, if the user is excited, it can provide real-time information on the latest events. If the user is relaxed, it can provide past success stories and anecdotes. Furthermore, if the user is feeling anxious, it can provide reassuring information about local safety. By adjusting the timing of information delivery based on the user's emotions, information can be delivered at a more appropriate time.

[0095] The regional revitalization support system can further adjust the timing of information provision by taking into account the user's geographical location. For example, when a user arrives in a specific area, it can provide real-time information on tourist attractions and events in that area. It can also provide information about the next destination when the user is on the move. Furthermore, if the user is staying at a specific tourist spot, it can provide information on nearby restaurants and shopping. By adjusting the timing of information provision based on the user's geographical location, information can be delivered at a more appropriate time.

[0096] The regional revitalization support system can further estimate the user's emotions and adjust the format of information delivery based on those emotions. For example, if the user is relaxed, information can be delivered in a calm tone. If the user is excited, information can be delivered in a lively manner. Furthermore, if the user is feeling anxious, information can be delivered in a reassuring manner. In this way, by adjusting the format of information delivery based on the user's emotions, information can be delivered in a more appropriate format.

[0097] The regional revitalization support system can further analyze users' social media activity and customize information delivery based on their responses on social media. For example, it can prioritize providing event information that users have shown interest in on social media. It can also provide information on related local products based on information about local products that users have shared on social media. Furthermore, it can provide information on local tourist spots and events that users follow on social media. In this way, by analyzing users' social media activity, it can provide more relevant information.

[0098] The regional revitalization support system can further estimate the user's emotions and adjust the frequency of information delivery based on those emotions. For example, if the user is excited, the frequency of information delivery can be increased. Conversely, if the user is relaxed, the frequency of information delivery can be decreased. Furthermore, if the user is feeling anxious, reassuring information can be provided more frequently. In this way, by adjusting the frequency of information delivery based on the user's emotions, information can be delivered at a more appropriate frequency.

[0099] The regional revitalization support system can further analyze users' past purchase history and customize the information provided based on that history. For example, it can prioritize providing information on local specialty products that users have purchased in the past. It can also provide information on related products and new products that users have purchased in the past. Furthermore, it can provide information on new services and facilities that are relevant to the services and facilities that users have used in the past. In this way, by customizing the information provided based on the user's past purchase history, it can provide more relevant information.

[0100] The following briefly describes the processing flow for example form 2.

[0101] Step 1: The data collection department gathers information and events from local governments, the interests of prospective residents, and data on local specialties and traditions. For example, they collect information on local tourism, local festivals, and administrative services, and identify the interests of prospective residents through surveys and past activity records. They also collect data on local specialties and traditional events. Step 2: The generation unit generates stories based on the data collected by the collection unit. For example, it uses storytelling techniques and generation algorithms to generate stories that include introductions to local festivals, events, and specialty products. Step 3: The reflection section incorporates the experiences of those who have lived there and real-time local information. For example, success stories of migrants and the latest local events are collected through interviews and surveys, and then reflected in the narrative. Step 4: The providing unit provides the information generated by the generating and reflecting units to prospective migrants. For example, it provides generated stories and real-time information through websites and applications, and provides prospective migrants with information about local specialties and traditional performing arts.

[0102] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0103] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0104] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0105] Each of the multiple elements described above, including the collection unit, generation unit, reflection unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 38B of the smart device 14 to collect information on local government, events, and the interests of prospective residents, and transmits this information to the data processing unit 12 via the control unit 46A. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates a story based on the collected data. The reflection unit is implemented, for example, by the control unit 46A of the smart device 14, and reflects the experiences of participants and real-time local information. The provision unit provides the generated story and real-time information to prospective residents, for example, through the output device 40 of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0106] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0107] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0112] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0113] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0114] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0115] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0116] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0121] Each of the multiple elements described above, including the collection unit, generation unit, reflection unit, and provision unit, is implemented in, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the smart glasses 214 to collect information on local government, events, and the interests of prospective residents, and transmits it to the data processing unit 12 via the control unit 46A. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and generates a story based on the collected data. The reflection unit is implemented, for example, by the control unit 46A of the smart glasses 214, and reflects the experiences of participants and real-time local information. The provision unit provides the generated story and real-time information to prospective residents, for example, through the speaker 240 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0122] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0123] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0125] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0126] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0128] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0129] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0130] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0131] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0132] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0133] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0136] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0137] Each of the multiple elements described above, including the collection unit, generation unit, reflection unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the headset terminal 314 to collect information on local government, events, and the interests of prospective residents, and transmits it to the data processing unit 12 via the control unit 46A. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and generates a story based on the collected data. The reflection unit is implemented, for example, by the control unit 46A of the headset terminal 314, and reflects the experiences of participants and real-time local information. The provision unit provides the generated story and real-time information to prospective residents, for example, through the display 343 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0138] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0139] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0140] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0141] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0142] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0144] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0145] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0146] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0147] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0148] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0149] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0151] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0153] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0154] Each of the multiple elements described above, including the collection unit, generation unit, reflection unit, and provision unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the robot 414 to collect information on local government, events, and the interests of prospective residents, and transmits it to the data processing unit 12 via the control unit 46A. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates a story based on the collected data. The reflection unit is implemented, for example, by the control unit 46A of the robot 414, and reflects the experiences of participants and real-time local information. The provision unit provides the generated story and real-time information to prospective residents, for example, through the speaker 240 of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0155] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0156] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0157] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0158] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0159] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0160] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0161] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0162] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0163] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0164] 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.

[0165] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0166] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0167] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0168] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0169] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0170] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0171] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0172] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0173] (Note 1) The collection department collects data on local government information and events, the interests of prospective residents, and local specialties and traditions. A generation unit that generates a story based on the data collected by the collection unit, A section that reflects the experiences of those who have participated and real-time local information, The system includes a provisioning unit that provides the information generated by the generation unit and the reflection unit to prospective migrants. A system characterized by the following features. (Note 2) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Analyze past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current areas of interest and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 8) The generating unit is It estimates the user's emotions and adjusts the way the story is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is When generating a story, adjust the level of detail in the story based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is When generating stories, different generation algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is It estimates the user's emotions and adjusts the length of the story based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is When generating a story, prioritize the story based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When generating a story, adjust the order of the stories based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reflection unit is, It estimates the user's emotions and adjusts how episodes and information are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned reflection unit is, When implementing changes, we refer to past episodes and information to improve the accuracy of the implementation. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned reflection unit is, When updating, the attribute information of the person who submitted the episode or information will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned reflection unit is, It estimates the user's emotions and determines the priority of information to reflect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned reflection unit is, When updating, the geographical distribution of episodes and information will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned reflection unit is, When implementing the changes, we refer to relevant literature related to the episodes and information to improve the accuracy of the implementation. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way information is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing information, the system selects the most suitable method of delivery by referring to the user's past browsing history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing information, customize the content based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, The system estimates the user's emotions and prioritizes information provision based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing information, the optimal method of delivery will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing information, we analyze the user's social media activity to customize the content provided. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0174] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection department collects data on local government information and events, the interests of prospective residents, and local specialties and traditions. A generation unit that generates a story based on the data collected by the collection unit, A section that reflects the experiences of those who have participated and real-time local information, The system includes a provisioning unit that provides the information generated by the generation unit and the reflection unit to prospective migrants. A system characterized by the following features.

2. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

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

4. The aforementioned collection unit is When collecting data, filtering is performed based on the user's current areas of interest and lifestyle. The system according to feature 1.

5. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

6. The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system according to feature 1.

7. The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system according to feature 1.

8. The generating unit is It estimates the user's emotions and adjusts the way the story is presented based on those estimated emotions. The system according to feature 1.

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