System

Abstract

An object of the system according to the embodiment is to plan a promotion strategy for effectively reproducing a shutter street or a depopulated area.SOLUTION: A system according to an embodiment includes a learning unit, a data providing unit, and a promotion planning unit. The learning unit uses the generated AI. The data providing unit provides data of the target area. The development strategy planning unit plans a development strategy based on the regional development strategy and the restoration strategy learned by the learning unit and the data of the target region provided by the data providing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult to devise effective revitalization measures for revitalizing shuttered shopping districts and depopulated areas.

[0005] The system according to the embodiment aims to develop promotion measures to effectively revitalize shuttered shopping districts and depopulated areas. [Means for solving the problem]

[0006] The system according to the embodiment includes a learning unit, a data providing unit, and a promotion policy planning unit. The learning unit uses a generation AI. The data providing unit provides data on a target area. The promotion policy planning unit plans promotion policies based on the regional promotion policies and reconstruction policies learned by the learning unit and the data on the target area provided by the data providing unit. [Effects of the Invention]

[0007] The system according to the embodiment can devise promotion measures to effectively revitalize shuttered shopping districts and depopulated areas. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The regional revitalization system according to the embodiment of the present invention is a system that uses generative AI to revitalize shuttered shopping districts and depopulated areas. As a result, the regional revitalization system can utilize generative AI to efficiently and effectively revitalize the area.

[0029] A regional revitalization system according to an embodiment includes a learning unit, a data providing unit, and a promotion policy planning unit. The learning unit uses a generative AI to learn regional promotion and reconstruction policies for the entire country and the world. For example, the learning unit learns data on tourism promotion, agricultural efficiency, commercial facility revitalization, and improvements to residents' living environments. The learning unit can also learn successful and unsuccessful cases, as well as policies based on the characteristics and conditions of each region. The data providing unit provides data on the target region. For example, the data providing unit provides data on the region's demographics, economic situation, current infrastructure status, and residents' needs and opinions. The data providing unit can also provide data on regions experiencing population decline or areas where shopping districts have become deserted. The promotion policy planning unit plans promotion policies based on the regional promotion and reconstruction policies learned by the learning unit and the data on the target region provided by the data providing unit. For example, in regions where tourism promotion is effective, the promotion policy planning unit proposes measures to utilize tourism resources and attract tourists. In addition, the promotion policy planning department can propose ways to improve agricultural efficiency and develop new agricultural products in areas where agriculture is the main industry. The promotion policy planning department can also propose ways to attract new stores and hold events to revitalize shopping districts. As a result, the regional revitalization system according to the embodiment can efficiently revitalize shuttered streets and depopulated areas using generative AI.

[0030] The learning unit can perform a detailed analysis of the factors that led to the success and failure of past regional development measures. For example, the learning unit trains the generation AI on data from past regional development measures and performs a detailed analysis of the factors that led to success and failure. For example, it extracts commonalities between successful development measures and problems with unsuccessful ones. The learning unit also analyzes data from past regional development measures and trains the generation AI on the factors that led to success and failure. For example, it can identify the conditions for successful measures and the causes of unsuccessful measures. The learning unit also trains the generation AI on the factors that led to success and failure of past regional development measures and uses this information to help formulate new development measures. For example, it can propose new measures that incorporate the factors that led to success. This makes it possible to formulate new development measures based on the factors that led to past success and failure.

[0031] The learning unit can study promotion measures from regions with different cultural spheres and historical backgrounds and develop promotion measures that take cultural diversity into consideration. For example, the learning unit can have the generation AI learn promotion measures from regions with different cultural spheres and historical backgrounds and develop promotion measures that take cultural diversity into consideration. For example, it can propose measures that utilize tourism resources from different cultures. The learning unit can also collect data on promotion measures from different cultural spheres and have the generation AI learn from them. For example, it can analyze successful and unsuccessful cases based on cultural background and reflect this in the development of promotion measures. The learning unit can also have the generation AI study promotion measures from regions with different cultural spheres and historical backgrounds and propose measures that take cultural diversity into consideration. For example, it can develop promotion measures that respect local traditions and customs. This makes it possible to develop promotion measures that take cultural diversity into consideration.

[0032] The learning unit can learn a company's growth strategy and marketing strategy and propose promotion measures from a business perspective. For example, the learning unit can have the generative AI learn a company's growth strategy and marketing strategy and propose promotion measures from a business perspective. For example, it can propose marketing measures that utilize local specialties. The learning unit can also collect data on company growth strategies and marketing strategies and have the generative AI learn from them. For example, it can devise regional promotion measures based on successful business models. The learning unit can also have the generative AI learn a company's growth strategy and marketing strategy and propose promotion measures from a business perspective. For example, it can devise measures aimed at revitalizing the local economy. This makes it possible to propose promotion measures from a business perspective.

[0033] The data providing unit can provide detailed demographic data including the lifestyle habits and values ​​of local residents. For example, the data providing unit adds detailed demographic data including the lifestyle habits and values ​​of local residents to data on the target area and provides it to the generation AI. For example, it collects data such as the age group, occupation, and hobbies of residents. The data providing unit also collects demographic data including the lifestyle habits and values ​​of local residents and provides it to the generation AI. For example, it can analyze residents' lifestyles and consumption behavior. The data providing unit also adds detailed demographic data including the lifestyle habits and values ​​of local residents to data on the target area and provides it to the generation AI. For example, it collects data that reflects residents' values ​​and beliefs. This makes it possible to plan promotion measures that reflect the lifestyle habits and values ​​of local residents.

[0034] The data provision unit can provide historical data including the impact of past natural disasters and economic crises. For example, the data provision unit adds historical data including the impact of past natural disasters and economic crises to data on the target area and provides the data to the generation AI. For example, it collects information on the damage caused by past disasters and the progress of reconstruction. The data provision unit also collects historical data including the impact of past natural disasters and economic crises and provides the data to the generation AI. For example, it can analyze the economic situation and changes in infrastructure in the area. The data provision unit also adds historical data including the impact of past natural disasters and economic crises to data on the target area and provides the data to the generation AI. For example, it collects data on regional reconstruction measures and support activities. This makes it possible to plan revitalization measures that take into account the impact of past natural disasters and economic crises.

[0035] The data providing unit can provide data showing exchanges and cooperative relationships with other regions. For example, the data providing unit adds data showing exchanges and cooperative relationships with other regions to the data of the target region and provides it to the generating AI. For example, data on sister cities and affiliated regions is collected. The data providing unit also collects data showing exchanges and cooperative relationships with other regions and provides it to the generating AI. For example, data on events and joint projects between regions can be analyzed. The data providing unit also adds data showing exchanges and cooperative relationships with other regions to the data of the target region and provides it to the generating AI. For example, data on economic partnerships and cultural exchanges between regions is collected. This makes it possible to plan promotion measures that take exchanges and cooperative relationships with other regions into consideration.

[0036] The data providing unit can provide data on the natural environment and ecosystem of the region. For example, the data providing unit adds data on the natural environment and ecosystem of the region to the data of the target region and provides it to the generation AI. For example, it collects data on the flora and fauna of the region and natural landscapes. The data providing unit also collects data on the natural environment and ecosystem of the region and provides it to the generation AI. For example, it can analyze local environmental protection activities and changes in the ecosystem. The data providing unit also adds data on the natural environment and ecosystem of the region to the data of the target region and provides it to the generation AI. For example, it collects data on the region's natural resources and environmental conservation. This makes it possible to plan promotion measures that take the local natural environment and ecosystem into consideration.

[0037] The promotion policy planning unit can simulate the promotion policies that have been proposed and evaluate their predicted effects. For example, the promotion policy planning unit can have the generation AI simulate the promotion policies that have been proposed and evaluate their predicted effects. For example, it can simulate an increase in tourists and economic effects. The promotion policy planning unit can also simulate promotion policies and evaluate their predicted effects. For example, it can simulate the revitalization of shopping districts and improvements to the living environment of residents. The promotion policy planning unit can also have the generation AI simulate the promotion policies that have been proposed and evaluate their predicted effects. For example, it can simulate agricultural efficiency and the development of new agricultural products. This makes it possible to evaluate the predicted effects of the promotion policies.

[0038] The promotion policy planning department can perform risk analysis of the proposed promotion policy and propose measures to minimize the risks. For example, the promotion policy planning department has the generation AI perform risk analysis of the proposed promotion policy and propose measures to minimize the risks. For example, it evaluates the environmental risks associated with promoting the tourism industry and proposes measures. The promotion policy planning department can also perform risk analysis of the promotion policy and propose measures to minimize the risks. For example, it can evaluate the economic risks associated with revitalizing a shopping district and propose measures. The promotion policy planning department can also have the generation AI perform risk analysis of the proposed promotion policy and propose measures to minimize the risks. For example, it can evaluate the technological risks associated with improving agricultural efficiency and propose measures. In this way, it can propose measures to minimize the risks of the promotion policy.

[0039] The promotion policy planning unit can evaluate the feasibility in different regions and countries and propose promotion policies from a global perspective. For example, the promotion policy planning unit can have the generation AI evaluate the feasibility in different regions and countries and propose promotion policies from a global perspective. For example, it can propose policies based on successful examples from other countries. The promotion policy planning unit can also evaluate the feasibility in different regions and countries and propose promotion policies from a global perspective. For example, it can propose policies that utilize international tourism resources. The promotion policy planning unit can also have the generation AI evaluate the feasibility in different regions and countries and propose promotion policies from a global perspective. For example, it can propose policies that introduce agricultural technology from other countries. This makes it possible to propose promotion policies from a global perspective.

[0040] The promotion policy planning department can compare the promotion policies it has drafted with different scenarios and select the optimal one. For example, the promotion policy planning department can have the generation AI compare the promotion policies it has drafted with different scenarios and select the optimal one. For example, it can compare a tourism promotion scenario with an agricultural efficiency scenario. The promotion policy planning department can also compare promotion policies with different scenarios and select the optimal one. For example, it can compare a shopping district revitalization scenario with a scenario for improving the living environment of residents. The promotion policy planning department can also have the generation AI compare the promotion policies it has drafted with different scenarios and select the optimal one. For example, it can compare a tourism resource utilization scenario with a new agricultural product development scenario. This makes it possible to compare different scenarios and select the optimal promotion policy.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The regional revitalization system can also devise measures to promote ecotourism that utilize the region's natural resources. For example, it can propose tourist routes that make use of the region's natural landscapes and ecosystems. It can also develop ecotourism programs that utilize the region's specialty products. It can also collaborate with local residents to carry out environmental conservation activities and use those activities as tourism resources. This makes it possible to devise sustainable tourism promotion measures that utilize the region's natural resources.

[0043] The regional revitalization system can also devise promotional measures that make use of local culture and traditions. For example, local traditional crafts and festivals can be used as tourism resources. Local historical buildings and ruins can also be preserved and used as tourism resources. It is also possible to propose gourmet tourism that makes use of the traditional food culture of the region. This makes it possible to devise tourism promotion measures that make use of local culture and traditions.

[0044] The Regional Revitalization System can also collaborate with local educational institutions to develop educational programs related to regional revitalization. For example, classes on regional revitalization could be held at local elementary, junior high, and high schools. Research projects on regional revitalization could also be launched in collaboration with local universities and vocational schools. Furthermore, workshops and seminars on regional revitalization aimed at local residents could be held. This would allow regional revitalization measures to be developed in collaboration with local educational institutions.

[0045] The Regional Revitalization System can also devise promotional measures aimed at promoting local health. For example, it can propose health promotion programs that utilize local sports facilities and parks. It can also collaborate with local medical institutions to provide health checkups and health consultations. It can also conceivably implement campaigns to raise health awareness among local residents. This makes it possible to devise promotional measures aimed at promoting local health.

[0046] The Regional Revitalization System can also devise promotional measures to further strengthen regional disaster prevention measures. For example, it can regularly hold regional disaster prevention drills. It can also develop regional disaster prevention facilities and evacuation shelters. It can also provide disaster prevention education to local residents to raise their awareness of disaster prevention. This makes it possible to devise promotional measures to strengthen regional disaster prevention measures.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The learning unit uses generative AI to learn regional development and reconstruction measures from around the country and the world. For example, it learns data on tourism promotion, agricultural efficiency, commercial facility revitalization, and improving the living environment for residents. It also learns about success stories, failure stories, and measures based on the characteristics and conditions of each region. Step 2: The data provider provides data on the target area. For example, they provide data on the area's demographics, economic situation, current infrastructure, and the needs and opinions of residents. They also provide data on areas experiencing population decline and areas where shopping districts have become deserted. Step 3: The Promotion Policy Planning Department formulates promotion policies based on the regional promotion and reconstruction policies learned by the Learning Department and the data on the target area provided by the Data Provision Department. For example, in areas where tourism promotion is effective, the department will propose measures to utilize tourism resources and attract tourists, and in areas where agriculture is the main industry, the department will propose measures to improve agricultural efficiency and develop new agricultural products. Furthermore, to revitalize shopping districts, the department will propose measures to attract new stores and hold events.

[0049] (Example 2) The regional revitalization system according to the embodiment of the present invention is a system that uses generative AI to revitalize shuttered shopping districts and depopulated areas. As a result, the regional revitalization system can utilize generative AI to efficiently and effectively revitalize the area.

[0050] A regional revitalization system according to an embodiment includes a learning unit, a data providing unit, and a promotion policy planning unit. The learning unit uses a generative AI to learn regional promotion and reconstruction policies for the entire country and the world. For example, the learning unit learns data on tourism promotion, agricultural efficiency, commercial facility revitalization, and improvements to residents' living environments. The learning unit can also learn successful and unsuccessful cases, as well as policies based on the characteristics and conditions of each region. The data providing unit provides data on the target region. For example, the data providing unit provides data on the region's demographics, economic situation, current infrastructure status, and residents' needs and opinions. The data providing unit can also provide data on regions experiencing population decline or areas where shopping districts have become deserted. The promotion policy planning unit plans promotion policies based on the regional promotion and reconstruction policies learned by the learning unit and the data on the target region provided by the data providing unit. For example, in regions where tourism promotion is effective, the promotion policy planning unit proposes measures to utilize tourism resources and attract tourists. In addition, the promotion policy planning department can propose ways to improve agricultural efficiency and develop new agricultural products in areas where agriculture is the main industry. The promotion policy planning department can also propose ways to attract new stores and hold events to revitalize shopping districts. As a result, the regional revitalization system according to the embodiment can efficiently revitalize shuttered streets and depopulated areas using generative AI.

[0051] The learning unit can learn from social media posts containing the emotions and opinions of local residents. For example, the learning unit includes the opinions and emotions posted by local residents on social media in the dataset used to train the generation AI. For example, it collects posts from Twitter and Facebook and extracts local issues and needs. The learning unit also analyzes social media posts and teaches the generation AI the emotions and opinions of local residents. For example, it can classify positive and negative opinions and identify local issues. The learning unit also teaches the generation AI social media posts containing the emotions and opinions of local residents to gain a deeper understanding of local needs. For example, it can analyze residents' complaints and requests and reflect them in the development of promotion measures. This makes it possible to develop promotion measures that reflect the emotions and opinions of local residents.

[0052] The learning unit can perform a detailed analysis of the factors that led to the success and failure of past regional development measures. For example, the learning unit trains the generation AI on data from past regional development measures and performs a detailed analysis of the factors that led to success and failure. For example, it extracts commonalities between successful development measures and problems with unsuccessful ones. The learning unit also analyzes data from past regional development measures and trains the generation AI on the factors that led to success and failure. For example, it can identify the conditions for successful measures and the causes of unsuccessful measures. The learning unit also trains the generation AI on the factors that led to success and failure of past regional development measures and uses this information to help formulate new development measures. For example, it can propose new measures that incorporate the factors that led to success. This makes it possible to formulate new development measures based on the factors that led to past success and failure.

[0053] The learning unit can use the emotion estimation function to analyze the emotions of local residents and prioritize learning of promotion measures that elicit positive emotions. For example, the learning unit can use the emotion estimation function to analyze the emotions of local residents and have the generation AI learn promotion measures that elicit positive emotions. For example, it prioritizes learning of measures that result in high resident satisfaction. The learning unit also collects emotion data of local residents and analyzes it using the emotion estimation function. For example, it can identify measures that elicit positive emotions and have the generation AI learn them. The learning unit also uses the emotion estimation function to monitor the emotions of local residents in real time and reflect that data in the learning of the generation AI. For example, it prioritizes learning of measures that will make residents feel positive. This makes it possible to plan promotion measures that elicit positive emotions in local residents.

[0054] The learning unit can study promotion measures from regions with different cultural spheres and historical backgrounds and develop promotion measures that take cultural diversity into consideration. For example, the learning unit can have the generation AI learn promotion measures from regions with different cultural spheres and historical backgrounds and develop promotion measures that take cultural diversity into consideration. For example, it can propose measures that utilize tourism resources from different cultures. The learning unit can also collect data on promotion measures from different cultural spheres and have the generation AI learn from them. For example, it can analyze successful and unsuccessful cases based on cultural background and reflect this in the development of promotion measures. The learning unit can also have the generation AI study promotion measures from regions with different cultural spheres and historical backgrounds and propose measures that take cultural diversity into consideration. For example, it can develop promotion measures that respect local traditions and customs. This makes it possible to develop promotion measures that take cultural diversity into consideration.

[0055] The learning unit can learn a company's growth strategy and marketing strategy and propose promotion measures from a business perspective. For example, the learning unit can have the generative AI learn a company's growth strategy and marketing strategy and propose promotion measures from a business perspective. For example, it can propose marketing measures that utilize local specialties. The learning unit can also collect data on company growth strategies and marketing strategies and have the generative AI learn from them. For example, it can devise regional promotion measures based on successful business models. The learning unit can also have the generative AI learn a company's growth strategy and marketing strategy and propose promotion measures from a business perspective. For example, it can devise measures aimed at revitalizing the local economy. This makes it possible to propose promotion measures from a business perspective.

[0056] The learning unit can use the emotion estimation function to monitor the emotions of local residents in real time and reflect that data in the learning. For example, the learning unit can use the emotion estimation function to monitor the emotions of local residents in real time and reflect that data in the learning of the generation AI. For example, it can prioritize learning measures that will make residents feel positive. The learning unit can also collect emotion data of local residents in real time and have the generation AI learn from it. For example, it can update the learning data every time the emotions of residents change. The learning unit can also use the emotion estimation function to monitor the emotions of local residents in real time and reflect that data in the learning of the generation AI. For example, it can prioritize learning measures that will make residents feel positive. This makes it possible to plan revitalization measures that reflect the emotions of local residents in real time.

[0057] The data providing unit can provide detailed demographic data including the lifestyle habits and values ​​of local residents. For example, the data providing unit adds detailed demographic data including the lifestyle habits and values ​​of local residents to data on the target area and provides it to the generation AI. For example, it collects data such as the age group, occupation, and hobbies of residents. The data providing unit also collects demographic data including the lifestyle habits and values ​​of local residents and provides it to the generation AI. For example, it can analyze residents' lifestyles and consumption behavior. The data providing unit also adds detailed demographic data including the lifestyle habits and values ​​of local residents to data on the target area and provides it to the generation AI. For example, it collects data that reflects residents' values ​​and beliefs. This makes it possible to plan promotion measures that reflect the lifestyle habits and values ​​of local residents.

[0058] The data provision unit can provide historical data including the impact of past natural disasters and economic crises. For example, the data provision unit adds historical data including the impact of past natural disasters and economic crises to data on the target area and provides the data to the generation AI. For example, it collects information on the damage caused by past disasters and the progress of reconstruction. The data provision unit also collects historical data including the impact of past natural disasters and economic crises and provides the data to the generation AI. For example, it can analyze the economic situation and changes in infrastructure in the area. The data provision unit also adds historical data including the impact of past natural disasters and economic crises to data on the target area and provides the data to the generation AI. For example, it collects data on regional reconstruction measures and support activities. This makes it possible to plan revitalization measures that take into account the impact of past natural disasters and economic crises.

[0059] The data providing unit can use the emotion estimation function to collect emotion data of local residents and provide it to the generation AI. The data providing unit can, for example, use the emotion estimation function to collect emotion data of local residents and provide it to the generation AI. For example, it can plan revitalization measures based on the emotion scores of residents. The data providing unit can also collect emotion data of local residents and provide it to the generation AI. For example, it can propose measures that will make residents more positive. The data providing unit can also use the emotion estimation function to collect emotion data of local residents and provide it to the generation AI. For example, it can propose improvement measures if residents' emotions are negative. This makes it possible to plan revitalization measures that reflect the emotions of local residents.

[0060] The data providing unit can provide data showing exchanges and cooperative relationships with other regions. For example, the data providing unit adds data showing exchanges and cooperative relationships with other regions to the data of the target region and provides it to the generating AI. For example, data on sister cities and affiliated regions is collected. The data providing unit also collects data showing exchanges and cooperative relationships with other regions and provides it to the generating AI. For example, data on events and joint projects between regions can be analyzed. The data providing unit also adds data showing exchanges and cooperative relationships with other regions to the data of the target region and provides it to the generating AI. For example, data on economic partnerships and cultural exchanges between regions is collected. This makes it possible to plan promotion measures that take exchanges and cooperative relationships with other regions into consideration.

[0061] The data providing unit can provide data on the natural environment and ecosystem of the region. For example, the data providing unit adds data on the natural environment and ecosystem of the region to the data of the target region and provides it to the generation AI. For example, it collects data on the flora and fauna of the region and natural landscapes. The data providing unit also collects data on the natural environment and ecosystem of the region and provides it to the generation AI. For example, it can analyze local environmental protection activities and changes in the ecosystem. The data providing unit also adds data on the natural environment and ecosystem of the region to the data of the target region and provides it to the generation AI. For example, it collects data on the region's natural resources and environmental conservation. This makes it possible to plan promotion measures that take the local natural environment and ecosystem into consideration.

[0062] The data providing unit can use the emotion estimation function to collect the emotions of local residents in real time and provide the data to the generation AI. For example, the data providing unit can use the emotion estimation function to collect the emotions of local residents in real time and provide the data to the generation AI. For example, it can plan revitalization measures based on the residents' emotion scores. The data providing unit can also collect emotion data of local residents in real time and provide it to the generation AI. For example, it can propose measures to make residents' emotions more positive. The data providing unit can also use the emotion estimation function to collect the emotions of local residents in real time and provide the data to the generation AI. For example, it can propose improvement measures if residents' emotions are negative. This makes it possible to plan revitalization measures that reflect the emotions of local residents in real time.

[0063] The promotion policy planning unit can simulate the promotion policies that have been proposed and evaluate their predicted effects. For example, the promotion policy planning unit can have the generation AI simulate the promotion policies that have been proposed and evaluate their predicted effects. For example, it can simulate an increase in tourists and economic effects. The promotion policy planning unit can also simulate promotion policies and evaluate their predicted effects. For example, it can simulate the revitalization of shopping districts and improvements to the living environment of residents. The promotion policy planning unit can also have the generation AI simulate the promotion policies that have been proposed and evaluate their predicted effects. For example, it can simulate agricultural efficiency and the development of new agricultural products. This makes it possible to evaluate the predicted effects of the promotion policies.

[0064] The promotion policy planning department can perform risk analysis of the proposed promotion policy and propose measures to minimize the risks. For example, the promotion policy planning department has the generation AI perform risk analysis of the proposed promotion policy and propose measures to minimize the risks. For example, it evaluates the environmental risks associated with promoting the tourism industry and proposes measures. The promotion policy planning department can also perform risk analysis of the promotion policy and propose measures to minimize the risks. For example, it can evaluate the economic risks associated with revitalizing a shopping district and propose measures. The promotion policy planning department can also have the generation AI perform risk analysis of the proposed promotion policy and propose measures to minimize the risks. For example, it can evaluate the technological risks associated with improving agricultural efficiency and propose measures. In this way, it can propose measures to minimize the risks of the promotion policy.

[0065] The promotion policy planning unit can use the emotion estimation function to evaluate the emotional impact of the proposed promotion policy on local residents and prioritize promotion policies that elicit positive emotions. The promotion policy planning unit, for example, uses the emotion estimation function to evaluate the emotional impact of the proposed promotion policy on local residents and prioritize promotion policies that elicit positive emotions. For example, it proposes policies that result in high resident satisfaction. The promotion policy planning unit can also evaluate the emotional impact of the proposed promotion policy on local residents and prioritize promotion policies that elicit positive emotions. For example, it can propose policies that improve resident happiness. The promotion policy planning unit can also use the emotion estimation function to evaluate the emotional impact of the proposed promotion policy on local residents and prioritize promotion policies that elicit positive emotions. For example, it proposes policies that will make residents feel positive. This makes it possible to prioritize promotion policies that elicit positive emotions from local residents.

[0066] The promotion policy planning unit can evaluate the feasibility in different regions and countries and propose promotion policies from a global perspective. For example, the promotion policy planning unit can have the generation AI evaluate the feasibility in different regions and countries and propose promotion policies from a global perspective. For example, it can propose policies based on successful examples from other countries. The promotion policy planning unit can also evaluate the feasibility in different regions and countries and propose promotion policies from a global perspective. For example, it can propose policies that utilize international tourism resources. The promotion policy planning unit can also have the generation AI evaluate the feasibility in different regions and countries and propose promotion policies from a global perspective. For example, it can propose policies that introduce agricultural technology from other countries. This makes it possible to propose promotion policies from a global perspective.

[0067] The promotion policy planning department can compare the promotion policies it has drafted with different scenarios and select the optimal one. For example, the promotion policy planning department can have the generation AI compare the promotion policies it has drafted with different scenarios and select the optimal one. For example, it can compare a tourism promotion scenario with an agricultural efficiency scenario. The promotion policy planning department can also compare promotion policies with different scenarios and select the optimal one. For example, it can compare a shopping district revitalization scenario with a scenario for improving the living environment of residents. The promotion policy planning department can also have the generation AI compare the promotion policies it has drafted with different scenarios and select the optimal one. For example, it can compare a tourism resource utilization scenario with a new agricultural product development scenario. This makes it possible to compare different scenarios and select the optimal promotion policy.

[0068] The promotion policy planning unit can use the emotion estimation function to monitor the emotional reactions of local residents to the promotion policy that has been proposed in real time and revise the promotion policy based on the feedback. For example, the promotion policy planning unit can use the emotion estimation function to monitor the emotional reactions of local residents to the promotion policy that has been proposed in real time and revise the promotion policy based on the feedback. For example, the promotion policy planning unit can improve the policy if the residents' emotions are negative. The promotion policy planning unit can also monitor the emotional reactions of local residents to the promotion policy that has been proposed in real time and revise the promotion policy based on the feedback. For example, the promotion policy planning unit can propose a policy that reflects the residents' opinions. The promotion policy planning unit can also use the emotion estimation function to monitor the emotional reactions of local residents to the promotion policy that has been proposed in real time and revise the promotion policy based on the feedback. For example, the promotion policy planning unit can adjust the policy so that the residents' emotions become positive. This makes it possible to revise the promotion policy that reflects the emotional reactions of local residents in real time.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The regional revitalization system can also devise measures to promote ecotourism that utilize the region's natural resources. For example, it can propose tourist routes that make use of the region's natural landscapes and ecosystems. It can also develop ecotourism programs that utilize the region's specialty products. It can also collaborate with local residents to carry out environmental conservation activities and use those activities as tourism resources. This makes it possible to devise sustainable tourism promotion measures that utilize the region's natural resources.

[0071] The regional revitalization system can also devise promotional measures that make use of local culture and traditions. For example, local traditional crafts and festivals can be used as tourism resources. Local historical buildings and ruins can also be preserved and used as tourism resources. It is also possible to propose gourmet tourism that makes use of the traditional food culture of the region. This makes it possible to devise tourism promotion measures that make use of local culture and traditions.

[0072] The Regional Revitalization System can also collaborate with local educational institutions to develop educational programs related to regional revitalization. For example, classes on regional revitalization could be held at local elementary, junior high, and high schools. Research projects on regional revitalization could also be launched in collaboration with local universities and vocational schools. Furthermore, workshops and seminars on regional revitalization aimed at local residents could be held. This would allow regional revitalization measures to be developed in collaboration with local educational institutions.

[0073] The Regional Revitalization System can also devise promotional measures aimed at promoting local health. For example, it can propose health promotion programs that utilize local sports facilities and parks. It can also collaborate with local medical institutions to provide health checkups and health consultations. It can also conceivably implement campaigns to raise health awareness among local residents. This makes it possible to devise promotional measures aimed at promoting local health.

[0074] The Regional Revitalization System can also devise promotional measures to further strengthen regional disaster prevention measures. For example, it can regularly hold regional disaster prevention drills. It can also develop regional disaster prevention facilities and evacuation shelters. It can also provide disaster prevention education to local residents to raise their awareness of disaster prevention. This makes it possible to devise promotional measures to strengthen regional disaster prevention measures.

[0075] The regional revitalization system can also use its emotion estimation function to analyze the emotions of local residents and develop promotional measures to alleviate negative emotions. For example, it can propose relaxation programs to reduce stress among local residents. It can also provide counseling services to alleviate anxiety among local residents. It can also promote community activities to reduce feelings of loneliness among local residents. This makes it possible to develop promotional measures to alleviate negative emotions among local residents.

[0076] The regional revitalization system can also use its emotion estimation function to analyze the emotions of local residents and reflect this in the planning of local events and festivals. For example, events that residents can enjoy can be planned based on the emotional data of local residents. The system can also adjust the content and schedule of festivals taking into account the emotions of local residents. It is also possible to promote events that reflect the emotions of local residents. This makes it possible to plan events and festivals that reflect the emotions of local residents.

[0077] The regional revitalization system can also use its emotion estimation function to analyze the emotions of local residents and use this information to revitalize local shopping districts. For example, the system can adjust the store layout and product lineup in a shopping district based on the emotional data of local residents. It can also plan events and sales in the shopping district taking into account the emotions of local residents. It can also promote the shopping district in a way that reflects the emotions of local residents. This makes it possible to plan revitalization measures for shopping districts that reflect the emotions of local residents.

[0078] The regional revitalization system can also use its emotion estimation function to analyze the emotions of local residents and use the results to improve local public services. For example, the system can adjust the delivery method and content of public services based on the emotional data of local residents. It can also propose measures to improve user satisfaction with public services by taking into account the emotions of local residents. It can also promote public services that reflect the emotions of local residents. This makes it possible to plan improvements to public services that reflect the emotions of local residents.

[0079] The regional revitalization system can also use its emotion estimation function to analyze the emotions of local residents and use the results to improve the local educational environment. For example, school curricula and educational programs can be adjusted based on the emotional data of local residents. It can also improve the equipment and environment of educational facilities by taking the emotions of local residents into consideration. It is also possible to promote the educational environment in a way that reflects the emotions of local residents. This makes it possible to plan improvements to the educational environment that reflect the emotions of local residents.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The learning unit uses generative AI to learn regional development and reconstruction measures from around the country and the world. For example, it learns data on tourism promotion, agricultural efficiency, commercial facility revitalization, and improving the living environment for residents. It also learns about success stories, failure stories, and measures based on the characteristics and conditions of each region. Step 2: The data provider provides data on the target area. For example, they provide data on the area's demographics, economic situation, current infrastructure, and the needs and opinions of residents. They also provide data on areas experiencing population decline and areas where shopping districts have become deserted. Step 3: The Promotion Policy Planning Department formulates promotion policies based on the regional promotion and reconstruction policies learned by the Learning Department and the data on the target area provided by the Data Provision Department. For example, in areas where tourism promotion is effective, the department will propose measures to utilize tourism resources and attract tourists, and in areas where agriculture is the main industry, the department will propose measures to improve agricultural efficiency and develop new agricultural products. Furthermore, to revitalize shopping districts, the department will propose measures to attract new stores and hold events.

[0082] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0084] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0087] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0088] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0089] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0090] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0091] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0092] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0093] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0094] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0095] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0096] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0097] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0098] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0102] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0103] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0104] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0105] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0106] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0107] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0108] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0109] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0110] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0111] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0112] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 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.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0123] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0126] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0131] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0132] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0133] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0134] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0135] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0136] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0137] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0138] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0139] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0140] 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.

[0141] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0142] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0143] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0144] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0145] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0146] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0147] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0148] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0149] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A learning unit using generative AI, a data providing unit that provides data on the target area; a promotion plan planning unit that plans promotion plans based on the regional promotion plans and reconstruction plans learned by the learning unit and the data of the target area provided by the data providing unit. A system characterized by:

2. The learning unit Study social media posts containing local residents' sentiments and opinions 2. The system of claim 1.

3. The learning unit A detailed analysis of the success and failure factors of past regional development policies 2. The system of claim 1.

4. The learning unit Analyze the emotions of local residents and prioritize learning about promotion measures that will elicit positive emotions.

2. The system of claim 1.

5. The learning unit Learn about the promotion measures in regions with different cultural and historical backgrounds, and develop promotion measures that take cultural diversity into consideration.

2. The system of claim 1.

6. The learning unit Learn about corporate growth and marketing strategies and propose promotion measures from a business perspective 2. The system of claim 1.

7. The learning unit Monitor the emotions of local residents in real time and incorporate that data into learning.

2. The system of claim 1.

8. The data providing unit Providing detailed demographic data on local residents, including their lifestyles and values 2. The system of claim 1.

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