System

Abstract

An object of a system according to an embodiment is to find a problem specific to each region and present an appropriate solution and a process for realizing the solution.SOLUTION: A system according to an embodiment includes a problem finding unit, a solution presentation unit, and a process presentation unit. The problem finding unit finds and extracts a problem in an area. The solution presenting section presents an idea of a solution to the problem found by the problem finding section. The process presenting section presents a specific process for realizing the solution presented by the solution presenting section.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 had the problem of making it difficult to efficiently identify issues specific to each region and propose appropriate solutions.

[0005] The system according to the embodiment aims to discover issues specific to each region and present appropriate solutions and the process for achieving them. [Means for solving the problem]

[0006] The system according to the embodiment comprises a problem finding unit, a solution presenting unit, and a process presenting unit. The problem finding unit discovers and extracts local issues. The solution presenting unit presents solution ideas for the issues discovered by the problem finding unit. The process presenting unit presents specific processes for realizing the solutions presented by the solution presenting unit. [Effects of the Invention]

[0007] The system according to the embodiment can identify issues specific to each region and present appropriate solutions and the process for achieving them. [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 discover and extract regional issues, propose solution ideas, and then present the process for realizing them. This enables the regional revitalization system to propose optimal regional revitalization methods that take into account the characteristics and culture of each region.

[0029] A regional revitalization system according to an embodiment includes a problem discovery unit, a solution presentation unit, and a process presentation unit. The problem discovery unit discovers and extracts regional issues. For example, the generation AI analyzes regional statistical data to discover issues such as population decline and aging. The generation AI can also collect residents' opinions and extract regional problems. The generation AI can also analyze the results of past measures and identify issues based on success and failure cases. The solution presentation unit presents solution ideas for the issues discovered by the problem discovery unit. For example, the generation AI can propose new tourist routes to address a decline in tourists. The generation AI can also propose services and facilities for the elderly to address an aging population. The generation AI can also propose new agricultural technologies to address the decline of agriculture. The process presentation unit presents specific processes for implementing the solutions presented by the solution presentation unit. For example, the generation AI can select tourist destinations in response to a proposal for a new tourist route. The generation AI can also set the route. The generation AI can also plan promotional activities. As a result, the regional revitalization system according to the embodiment can propose optimal regional revitalization methods that take into account the characteristics and culture of each region. For example, it can propose specific measures that address the challenges facing each region, such as promoting tourism, improving agricultural efficiency, supporting medical services, and strengthening disaster prevention measures. This is expected to lead to the revitalization and sustainable development of the entire region.

[0030] The problem detection unit can discover and extract issues based on local statistical data, resident opinions, and information on the results of past policies. For example, the generation AI analyzes local statistical data to discover issues such as population decline and aging. The generation AI can also collect resident opinions and extract local problems. For example, resident opinions are collected through questionnaire surveys and interviews, and the generation AI analyzes the data. The generation AI can also analyze the results of past policies and identify issues based on success and failure cases. For example, evaluation data on past policies is collected, and the generation AI analyzes that data. This makes it possible to discover and extract issues based on specific local data.

[0031] The solution presentation unit can propose new tourist routes or event plans in response to the decline in tourists. In the solution presentation unit, for example, the generation AI proposes new tourist routes in response to the decline in tourists. For example, the generation AI analyzes data on local tourist destinations and sets new tourist routes. The generation AI can also present event plans to increase the appeal of tourist destinations. For example, the generation AI can propose event ideas that utilize local culture and local specialties. The generation AI can also propose new tourist plans that meet the needs of tourists. This makes it possible to present concrete solutions to the decline in tourists.

[0032] The process presentation unit can present specific steps for selecting tourist destinations, setting the route, and planning promotional activities for a proposed new tourist route. In the process presentation unit, for example, the generation AI selects tourist destinations for a proposed new tourist route. For example, the generation AI analyzes data on tourist destinations in the area and selects tourist destinations based on their attractiveness and ease of access. The generation AI can also set the route. For example, the generation AI sets the route taking into account the travel time between tourist destinations and the order in which the tourist destinations are visited. The generation AI can also plan promotional activities. For example, the generation AI plans promotional activities taking into account advertising media and target demographics. This makes it possible to present a specific realization process for a proposed new tourist route.

[0033] The problem detection unit analyzes historical data for the region and can predict current issues based on success and failure cases. For example, the generation AI analyzes historical data for the region and predicts current issues based on past success and failure cases. For example, the generation AI analyzes past demographic data to identify the causes of current population decline. The generation AI can also analyze regional economic data and predict current issues based on past economic conditions. For example, the generation AI analyzes past changes in industrial structure to identify the causes of current industrial decline. The generation AI can also analyze regional social data and predict current issues based on past social problems. For example, the generation AI analyzes past crime data to identify the causes of current security problems. This makes it possible to predict current issues based on past data.

[0034] The problem detection unit can analyze local weather data and environmental data to discover issues related to natural disasters and environmental problems. In this problem detection unit, for example, the generation AI analyzes local weather data to discover issues related to natural disasters. For example, it analyzes data on past typhoons and floods to identify disaster prevention issues. The generation AI can also analyze local environmental data to discover issues related to environmental problems. For example, it analyzes data on air pollution and water pollution to identify environmental protection issues. The generation AI can also integrate weather data and environmental data to comprehensively discover issues related to natural disasters and environmental problems. For example, it analyzes the correlation between weather data and environmental data to identify complex issues. This makes it possible to discover issues related to natural disasters and environmental problems.

[0035] The problem detection unit can analyze regional traffic data and extract issues related to traffic congestion and public transportation usage. In the problem detection unit, for example, the generation AI analyzes regional traffic data and extracts issues related to traffic congestion. For example, it analyzes congestion on major roads and identifies issues to alleviate congestion. The generation AI can also analyze regional public transportation data and extract issues related to public transportation usage. For example, it analyzes bus and train usage rates and identifies areas for improvement in public transportation. The generation AI can also integrate traffic data and public transportation data to comprehensively extract issues related to traffic congestion and public transportation usage. For example, it analyzes the correlation between traffic congestion and public transportation usage and identifies complex issues. This makes it possible to extract issues related to traffic congestion and public transportation usage.

[0036] The solution presentation unit can analyze success stories from other regions and present solution ideas based on them. For example, the generation AI in the solution presentation unit analyzes success stories from other regions and presents solution ideas based on them. For example, the generation AI analyzes success stories from the tourism industry and proposes new ideas for attracting tourists. The generation AI can also analyze success stories from other regions in economic development and present solution ideas based on them. For example, the generation AI can analyze local industry development measures and propose new business models. The generation AI can also analyze success stories from other regions in solving social problems and present solution ideas based on them. For example, the generation AI can analyze success stories from measures to combat aging and propose new ideas for supporting the elderly. This makes it possible to present solution ideas based on success stories from other regions.

[0037] The solution presentation unit can propose new business models that utilize local specialties and culture. For example, the generation AI proposes new business models that utilize local specialties. For example, the generation AI presents ideas for tourism products and online sales using local specialties. The generation AI can also propose new business models that utilize local culture. For example, the generation AI presents ideas for tourism plans and events that utilize traditional events and cultural assets. The generation AI can also propose new business models that combine local specialties and culture. For example, the generation AI presents ideas for cultural experience tours and workshops using local specialties. This makes it possible to propose new business models that utilize local specialties and culture.

[0038] The solution presentation unit can analyze local educational data and propose improvement measures for educational institutions and programs. For example, the generation AI analyzes local educational data and proposes improvement measures for educational institutions. For example, the generation AI analyzes school grade data and attendance rates to identify areas for improvement in educational programs. The generation AI can also analyze local educational data and propose improvement measures for educational programs. For example, the generation AI analyzes curriculum content and teaching methods to propose effective educational programs. The generation AI can also integrate educational data with other data to propose comprehensive improvement measures for educational institutions and programs. For example, the generation AI analyzes educational data and economic data to propose ideas for strengthening the collaboration between education and the economy. This makes it possible to propose improvement measures for educational institutions and programs based on local educational data.

[0039] The solution presentation unit can analyze local medical data and present improvement measures for medical services. In the solution presentation unit, for example, a generating AI analyzes local medical data and presents improvement measures for medical services. For example, the generating AI analyzes hospital medical data and patient satisfaction to identify areas for improvement in medical services. The generating AI can also analyze local medical data and present improvement measures for the placement and operation of medical facilities. For example, the generating AI analyzes medical facility usage and access data to propose optimal placement and operation methods. The generating AI can also integrate medical data with other data to present comprehensive improvement measures for medical services. For example, the generating AI analyzes medical data and population data to propose medical services for the elderly. This makes it possible to present improvement measures for medical services based on local medical data.

[0040] The process presentation unit can work in conjunction with a project management tool to automatically generate specific tasks and schedules. In the process presentation unit, for example, the generation AI works in conjunction with a project management tool to automatically generate specific tasks and schedules. For example, it automatically creates plans for tourist routes and promotional activities. The generation AI can also work in conjunction with a project management tool to monitor task progress in real time and adjust the schedule as necessary. For example, it can automatically reschedule tasks if they are delayed. The generation AI can also work in conjunction with a project management tool to automatically set task priorities. For example, it can sort tasks based on importance and urgency to support efficient project progress. This makes it possible to automatically generate specific tasks and schedules in conjunction with a project management tool.

[0041] The process presentation unit can analyze regional budget data and propose optimal funding allocation and fundraising methods. In the process presentation unit, for example, the generation AI analyzes regional budget data and proposes optimal funding allocation. For example, it calculates the budget required for a tourism project and allocates funds while taking into account the balance with other projects. The generation AI can also analyze regional budget data and propose optimal funding methods. For example, it can propose methods for crowdfunding and subsidy applications, thereby improving the efficiency of fundraising. The generation AI can also integrate budget data with other data and make comprehensive proposals for funding allocation and fundraising. For example, it analyzes budget data and project progress data and adjusts the optimal funding allocation in real time. This makes it possible to propose optimal funding allocation and fundraising methods based on regional budget data.

[0042] The process presentation unit can analyze regional infrastructure data and present infrastructure development priorities and specific improvement measures. In the process presentation unit, for example, the generation AI analyzes regional infrastructure data and presents infrastructure development priorities. For example, it analyzes data on the aging of roads and bridges and identifies areas that need priority repair. The generation AI can also analyze regional infrastructure data and present specific improvement measures. For example, it can analyze water and electricity supply data and propose efficient infrastructure development methods. The generation AI can also integrate infrastructure data with other data to make comprehensive infrastructure development proposals. For example, it can analyze infrastructure data and population data and present an infrastructure development plan that takes future demand into account. This makes it possible to present infrastructure development priorities and specific improvement measures based on regional infrastructure data.

[0043] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0044] The regional revitalization system can further analyze regional energy data and make proposals for improving energy efficiency and introducing renewable energy. For example, the generation AI can analyze regional power consumption data, identify areas with low energy efficiency, and propose improvement measures. The generation AI can also evaluate the region's potential for renewable energy sources such as solar and wind power, and propose introduction plans. Furthermore, the generation AI can integrate energy consumption and environmental data to present a comprehensive strategy for sustainable energy use. This is expected to promote regional energy efficiency improvements and the introduction of renewable energy.

[0045] The regional revitalization system can also analyze local health data and make suggestions to improve residents' health. For example, the generative AI can analyze medical data from local hospitals and clinics to identify areas and age groups with high health risks and suggest preventive measures. The generative AI can also analyze residents' health checkup data and suggest programs for preventing lifestyle-related diseases and promoting health. Furthermore, the generative AI can integrate health data and environmental data to evaluate the impact of environmental factors on health and suggest improvement measures. This is expected to improve residents' health and reduce health risks.

[0046] The regional revitalization system can further analyze regional educational data and propose improvements to educational institutions and programs. For example, the generative AI can analyze school grade data and attendance rates to identify areas for improvement in educational programs. The generative AI can also analyze regional educational data and propose improvements to educational programs. For example, the generative AI can analyze curriculum content and teaching methods to propose effective educational programs. The generative AI can also integrate educational data with other data to propose comprehensive improvements to educational institutions and programs. For example, the generative AI can analyze educational data and economic data to propose ideas for strengthening the collaboration between education and the economy. This makes it possible to propose improvements to educational institutions and programs based on regional educational data.

[0047] The regional revitalization system can further analyze regional traffic data to extract issues related to traffic congestion and public transportation usage. For example, the generation AI analyzes regional traffic data to extract issues related to traffic congestion. For example, it analyzes congestion on major roads and identifies issues to alleviate congestion. The generation AI can also analyze regional public transportation data to extract issues related to public transportation usage. For example, it analyzes bus and train usage rates and identifies areas for improvement in public transportation. The generation AI can also integrate traffic data and public transportation data to comprehensively extract issues related to traffic congestion and public transportation usage. For example, it analyzes the correlation between traffic congestion and public transportation usage and identifies complex issues. This makes it possible to extract issues related to traffic congestion and public transportation usage.

[0048] The regional revitalization system can further analyze regional tourism data and propose new events and activities to enhance the attractiveness of tourist destinations. For example, the generative AI can analyze visitor data for a tourist destination and propose events based on tourists' interests. For example, it can present ideas for festivals and cultural events that tourists can enjoy. The generative AI can also analyze reviews and social media posts for tourist destinations to understand tourists' sentiments and propose activities to enhance their appeal. For example, it can present ideas for adventure tours and workshops that tourists can experience. Furthermore, the generative AI can integrate tourist destination data with other data and propose strategies to comprehensively improve the attractiveness of a tourist destination. This allows it to propose new events and activities to enhance the attractiveness of a tourist destination.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The problem detection unit discovers and extracts local issues. For example, the generation AI analyzes local statistical data to discover issues such as population decline and aging. The generation AI can also collect residents' opinions and extract local problems. Furthermore, the generation AI can analyze the results of past measures and identify issues based on success and failure cases. Step 2: The solution presentation unit presents solution ideas for the problems discovered by the problem discovery unit. For example, the generation AI can propose new tourist routes to address the decline in tourists. The generation AI can also propose services and facilities for the elderly to address the aging population. Furthermore, the generation AI can propose new agricultural technologies to address the decline of agriculture. Step 3: The process presentation unit presents a specific process for realizing the solution presented by the solution presentation unit. For example, the generation AI selects tourist destinations for a proposed new tourist route. The generation AI can also set the route. Furthermore, the generation AI can also plan promotional activities.

[0051] (Example 2) The regional revitalization system according to the embodiment of the present invention is a system that uses generative AI to discover and extract regional issues, propose solution ideas, and then present the process for realizing them. This enables the regional revitalization system to propose optimal regional revitalization methods that take into account the characteristics and culture of each region.

[0052] A regional revitalization system according to an embodiment includes a problem discovery unit, a solution presentation unit, and a process presentation unit. The problem discovery unit discovers and extracts regional issues. For example, the generation AI analyzes regional statistical data to discover issues such as population decline and aging. The generation AI can also collect residents' opinions and extract regional problems. The generation AI can also analyze the results of past measures and identify issues based on success and failure cases. The solution presentation unit presents solution ideas for the issues discovered by the problem discovery unit. For example, the generation AI can propose new tourist routes to address a decline in tourists. The generation AI can also propose services and facilities for the elderly to address an aging population. The generation AI can also propose new agricultural technologies to address the decline of agriculture. The process presentation unit presents specific processes for implementing the solutions presented by the solution presentation unit. For example, the generation AI can select tourist destinations in response to a proposal for a new tourist route. The generation AI can also set the route. The generation AI can also plan promotional activities. As a result, the regional revitalization system according to the embodiment can propose optimal regional revitalization methods that take into account the characteristics and culture of each region. For example, it can propose specific measures that address the challenges facing each region, such as promoting tourism, improving agricultural efficiency, supporting medical services, and strengthening disaster prevention measures. This is expected to lead to the revitalization and sustainable development of the entire region.

[0053] The problem detection unit can discover and extract issues based on local statistical data, resident opinions, and information on the results of past policies. For example, the generation AI analyzes local statistical data to discover issues such as population decline and aging. The generation AI can also collect resident opinions and extract local problems. For example, resident opinions are collected through questionnaire surveys and interviews, and the generation AI analyzes the data. The generation AI can also analyze the results of past policies and identify issues based on success and failure cases. For example, evaluation data on past policies is collected, and the generation AI analyzes that data. This makes it possible to discover and extract issues based on specific local data.

[0054] The solution presentation unit can propose new tourist routes or event plans in response to the decline in tourists. In the solution presentation unit, for example, the generation AI proposes new tourist routes in response to the decline in tourists. For example, the generation AI analyzes data on local tourist destinations and sets new tourist routes. The generation AI can also present event plans to increase the appeal of tourist destinations. For example, the generation AI can propose event ideas that utilize local culture and local specialties. The generation AI can also propose new tourist plans that meet the needs of tourists. This makes it possible to present concrete solutions to the decline in tourists.

[0055] The process presentation unit can present specific steps for selecting tourist destinations, setting the route, and planning promotional activities for a proposed new tourist route. In the process presentation unit, for example, the generation AI selects tourist destinations for a proposed new tourist route. For example, the generation AI analyzes data on tourist destinations in the area and selects tourist destinations based on their attractiveness and ease of access. The generation AI can also set the route. For example, the generation AI sets the route taking into account the travel time between tourist destinations and the order in which the tourist destinations are visited. The generation AI can also plan promotional activities. For example, the generation AI plans promotional activities taking into account advertising media and target demographics. This makes it possible to present a specific realization process for a proposed new tourist route.

[0056] The problem detection unit can analyze local social media posts or blog articles, collect residents' emotions and opinions in real time, and discover issues. In the problem detection unit, for example, the generation AI analyzes local social media posts and collects residents' emotions and opinions in real time. For example, it analyzes Twitter and Facebook posts to extract residents' complaints and requests. The generation AI can also analyze local blog articles and collect residents' emotions and opinions. For example, it can analyze blog articles written about local issues and identify issues. The generation AI can also integrate data from social media and blogs to comprehensively analyze residents' emotions and opinions. For example, it can compare the content of social media posts and blog articles to find matching issues. This makes it possible to discover issues based on residents' real-time emotions and opinions.

[0057] The problem detection unit analyzes historical data for the region and can predict current issues based on success and failure cases. For example, the generation AI analyzes historical data for the region and predicts current issues based on past success and failure cases. For example, the generation AI analyzes past demographic data to identify the causes of current population decline. The generation AI can also analyze regional economic data and predict current issues based on past economic conditions. For example, the generation AI analyzes past changes in industrial structure to identify the causes of current industrial decline. The generation AI can also analyze regional social data and predict current issues based on past social problems. For example, the generation AI analyzes past crime data to identify the causes of current security problems. This makes it possible to predict current issues based on past data.

[0058] The problem detection unit can use the emotion estimation function to analyze residents' emotions, identify areas and themes where negative emotions are prevalent, and extract issues. For example, the problem detection unit uses the emotion estimation function to analyze negative emotions from residents' social media posts and blog articles and extract issues. For example, it identifies posts where anger or sadness is prevalent and analyzes the cause. It can also use the emotion estimation function to analyze negative emotions from resident survey results and extract issues. For example, it identifies responses where dissatisfaction or anxiety is prevalent and analyzes the cause. It can also use the emotion estimation function to analyze negative emotions from the content of resident opinion exchange meetings and extract issues. For example, it identifies negative themes that frequently appear in conversations and analyzes the cause. This makes it possible to extract issues based on residents' negative emotions.

[0059] The problem detection unit can analyze local weather data and environmental data to discover issues related to natural disasters and environmental problems. In this problem detection unit, for example, the generation AI analyzes local weather data to discover issues related to natural disasters. For example, it analyzes data on past typhoons and floods to identify disaster prevention issues. The generation AI can also analyze local environmental data to discover issues related to environmental problems. For example, it analyzes data on air pollution and water pollution to identify environmental protection issues. The generation AI can also integrate weather data and environmental data to comprehensively discover issues related to natural disasters and environmental problems. For example, it analyzes the correlation between weather data and environmental data to identify complex issues. This makes it possible to discover issues related to natural disasters and environmental problems.

[0060] The problem detection unit can analyze regional traffic data and extract issues related to traffic congestion and public transportation usage. In the problem detection unit, for example, the generation AI analyzes regional traffic data and extracts issues related to traffic congestion. For example, it analyzes congestion on major roads and identifies issues to alleviate congestion. The generation AI can also analyze regional public transportation data and extract issues related to public transportation usage. For example, it analyzes bus and train usage rates and identifies areas for improvement in public transportation. The generation AI can also integrate traffic data and public transportation data to comprehensively extract issues related to traffic congestion and public transportation usage. For example, it analyzes the correlation between traffic congestion and public transportation usage and identifies complex issues. This makes it possible to extract issues related to traffic congestion and public transportation usage.

[0061] The problem detection unit can use the emotion estimation function to analyze tourists' emotions, identify their satisfaction and dissatisfaction with tourist destinations, and discover issues. For example, the problem detection unit can use the emotion estimation function to analyze emotions from tourists' social media posts and comments on review sites, and identify their satisfaction and dissatisfaction with tourist destinations. For example, it can compare tourist destinations with a high proportion of positive emotions with tourist destinations with a high proportion of negative emotions. The emotion estimation function can also be used to analyze emotions from tourist survey results and identify their satisfaction and dissatisfaction with tourist destinations. For example, it can analyze specific problems in tourist destinations with low satisfaction. The emotion estimation function can also be used to analyze emotions from the content of tourist opinion exchange meetings, and identify their satisfaction and dissatisfaction with tourist destinations. For example, it can identify dissatisfaction that frequently comes up in conversations and analyze their causes. This makes it possible to discover issues at tourist destinations based on tourists' emotions.

[0062] The solution presentation unit can analyze success stories from other regions and present solution ideas based on them. For example, the generation AI in the solution presentation unit analyzes success stories from other regions and presents solution ideas based on them. For example, the generation AI analyzes success stories from the tourism industry and proposes new ideas for attracting tourists. The generation AI can also analyze success stories from other regions in economic development and present solution ideas based on them. For example, the generation AI can analyze local industry development measures and propose new business models. The generation AI can also analyze success stories from other regions in solving social problems and present solution ideas based on them. For example, the generation AI can analyze success stories from measures to combat aging and propose new ideas for supporting the elderly. This makes it possible to present solution ideas based on success stories from other regions.

[0063] The solution presentation unit can propose new business models that utilize local specialties and culture. For example, the generation AI proposes new business models that utilize local specialties. For example, the generation AI presents ideas for tourism products and online sales using local specialties. The generation AI can also propose new business models that utilize local culture. For example, the generation AI presents ideas for tourism plans and events that utilize traditional events and cultural assets. The generation AI can also propose new business models that combine local specialties and culture. For example, the generation AI presents ideas for cultural experience tours and workshops using local specialties. This makes it possible to propose new business models that utilize local specialties and culture.

[0064] The solution presentation unit can use the emotion estimation function to analyze residents' emotions and present solution ideas that elicit positive emotions. The solution presentation unit, for example, uses the emotion estimation function to analyze residents' emotions and present solution ideas that elicit positive emotions. For example, it can propose ideas for events or services that will make residents feel happy or satisfied. The emotion estimation function can also be used to analyze residents' emotions and present ideas for community activities that will elicit positive emotions. For example, it can propose events or programs that will promote interaction between residents. The emotion estimation function can also be used to analyze residents' emotions and present ideas for public services that will elicit positive emotions. For example, it can propose medical or welfare services that will make residents feel a sense of security and satisfaction. In this way, it is possible to present solution ideas that will elicit positive emotions from residents.

[0065] The solution presentation unit can analyze local educational data and propose improvement measures for educational institutions and programs. For example, the generation AI analyzes local educational data and proposes improvement measures for educational institutions. For example, the generation AI analyzes school grade data and attendance rates to identify areas for improvement in educational programs. The generation AI can also analyze local educational data and propose improvement measures for educational programs. For example, the generation AI analyzes curriculum content and teaching methods to propose effective educational programs. The generation AI can also integrate educational data with other data to propose comprehensive improvement measures for educational institutions and programs. For example, the generation AI analyzes educational data and economic data to propose ideas for strengthening the collaboration between education and the economy. This makes it possible to propose improvement measures for educational institutions and programs based on local educational data.

[0066] The solution presentation unit can analyze local medical data and present improvement measures for medical services. In the solution presentation unit, for example, a generating AI analyzes local medical data and presents improvement measures for medical services. For example, the generating AI analyzes hospital medical data and patient satisfaction to identify areas for improvement in medical services. The generating AI can also analyze local medical data and present improvement measures for the placement and operation of medical facilities. For example, the generating AI analyzes medical facility usage and access data to propose optimal placement and operation methods. The generating AI can also integrate medical data with other data to present comprehensive improvement measures for medical services. For example, the generating AI analyzes medical data and population data to propose medical services for the elderly. This makes it possible to present improvement measures for medical services based on local medical data.

[0067] The solution presentation unit can use the emotion estimation function to analyze tourists' emotions and propose new events and activities to enhance the appeal of tourist destinations. The solution presentation unit can, for example, use the emotion estimation function to analyze tourists' emotions and propose new events to enhance the appeal of tourist destinations. For example, it can present ideas for festivals and cultural events that tourists can enjoy. The emotion estimation function can also be used to analyze tourists' emotions and propose new activities to enhance the appeal of tourist destinations. For example, it can present ideas for adventure tours and workshops that tourists can experience. The emotion estimation function can also be used to analyze tourists' emotions and propose new services to enhance the appeal of tourist destinations. For example, it can present ideas for spas and resort facilities where tourists can relax. In this way, it is possible to propose new events and activities to enhance the appeal of tourist destinations based on tourists' emotions.

[0068] The process presentation unit can work in conjunction with a project management tool to automatically generate specific tasks and schedules. In the process presentation unit, for example, the generation AI works in conjunction with a project management tool to automatically generate specific tasks and schedules. For example, it automatically creates plans for tourist routes and promotional activities. The generation AI can also work in conjunction with a project management tool to monitor task progress in real time and adjust the schedule as necessary. For example, it can automatically reschedule tasks if they are delayed. The generation AI can also work in conjunction with a project management tool to automatically set task priorities. For example, it can sort tasks based on importance and urgency to support efficient project progress. This makes it possible to automatically generate specific tasks and schedules in conjunction with a project management tool.

[0069] The process presentation unit can analyze regional budget data and propose optimal funding allocation and fundraising methods. In the process presentation unit, for example, the generation AI analyzes regional budget data and proposes optimal funding allocation. For example, it calculates the budget required for a tourism project and allocates funds while taking into account the balance with other projects. The generation AI can also analyze regional budget data and propose optimal funding methods. For example, it can propose methods for crowdfunding and subsidy applications, thereby improving the efficiency of fundraising. The generation AI can also integrate budget data with other data and make comprehensive proposals for funding allocation and fundraising. For example, it analyzes budget data and project progress data and adjusts the optimal funding allocation in real time. This makes it possible to propose optimal funding allocation and fundraising methods based on regional budget data.

[0070] The process presentation unit can use the emotion estimation function to analyze residents' emotions, predict their reactions to the progress of the project, and present an appropriate communication strategy. The process presentation unit, for example, uses the emotion estimation function to analyze residents' emotions and predict their reactions to the progress of the project. For example, it analyzes residents' anxieties and expectations regarding the progress of the project and presents appropriate countermeasures. It can also use the emotion estimation function to analyze residents' emotions, predict their reactions to the progress of the project, and present an appropriate communication strategy. For example, it can propose providing information or holding an explanatory meeting to alleviate residents' anxieties. It can also use the emotion estimation function to analyze residents' emotions, predict their reactions to the progress of the project, and provide appropriate feedback. For example, it can present proposed revisions to the project that reflect the opinions and requests of residents. This makes it possible to present an appropriate communication strategy for the progress of the project based on residents' emotions.

[0071] The process presentation unit can analyze regional infrastructure data and present infrastructure development priorities and specific improvement measures. In the process presentation unit, for example, the generation AI analyzes regional infrastructure data and presents infrastructure development priorities. For example, it analyzes data on the aging of roads and bridges and identifies areas that need priority repair. The generation AI can also analyze regional infrastructure data and present specific improvement measures. For example, it can analyze water and electricity supply data and propose efficient infrastructure development methods. The generation AI can also integrate infrastructure data with other data to make comprehensive infrastructure development proposals. For example, it can analyze infrastructure data and population data and present an infrastructure development plan that takes future demand into account. This makes it possible to present infrastructure development priorities and specific improvement measures based on regional infrastructure data.

[0072] The process presentation unit can use the emotion estimation function to analyze tourists' emotions, predict tourists' reactions to the progress of the tourism project, and propose an appropriate marketing strategy. The process presentation unit can, for example, use the emotion estimation function to analyze tourists' emotions and predict tourists' reactions to the progress of the tourism project. For example, the process presentation unit can analyze tourists' expectations and anxieties regarding the establishment of a new tourist route and propose appropriate countermeasures. The process presentation unit can also use the emotion estimation function to analyze tourists' emotions, predict tourists' reactions to the progress of the tourism project, and propose an appropriate marketing strategy. For example, the process presentation unit can propose promotional activities and advertising campaigns that will attract tourists' interest. The process presentation unit can also use the emotion estimation function to analyze tourists' emotions, predict tourists' reactions to the progress of the tourism project, and provide appropriate feedback. For example, the process presentation unit can propose proposed revisions to the project that reflect the opinions and requests of tourists. This makes it possible to propose an appropriate marketing strategy for the progress of the tourism project based on tourists' emotions.

[0073] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0074] The regional revitalization system can further analyze regional energy data and make proposals for improving energy efficiency and introducing renewable energy. For example, the generation AI can analyze regional power consumption data, identify areas with low energy efficiency, and propose improvement measures. The generation AI can also evaluate the region's potential for renewable energy sources such as solar and wind power, and propose introduction plans. Furthermore, the generation AI can integrate energy consumption and environmental data to present a comprehensive strategy for sustainable energy use. This is expected to promote regional energy efficiency improvements and the introduction of renewable energy.

[0075] The regional revitalization system can also analyze local health data and make suggestions to improve residents' health. For example, the generative AI can analyze medical data from local hospitals and clinics to identify areas and age groups with high health risks and suggest preventive measures. The generative AI can also analyze residents' health checkup data and suggest programs for preventing lifestyle-related diseases and promoting health. Furthermore, the generative AI can integrate health data and environmental data to evaluate the impact of environmental factors on health and suggest improvement measures. This is expected to improve residents' health and reduce health risks.

[0076] The regional revitalization system can further analyze regional educational data and propose improvements to educational institutions and programs. For example, the generative AI can analyze school grade data and attendance rates to identify areas for improvement in educational programs. The generative AI can also analyze regional educational data and propose improvements to educational programs. For example, the generative AI can analyze curriculum content and teaching methods to propose effective educational programs. The generative AI can also integrate educational data with other data to propose comprehensive improvements to educational institutions and programs. For example, the generative AI can analyze educational data and economic data to propose ideas for strengthening the collaboration between education and the economy. This makes it possible to propose improvements to educational institutions and programs based on regional educational data.

[0077] The regional revitalization system can further analyze regional traffic data to extract issues related to traffic congestion and public transportation usage. For example, the generation AI analyzes regional traffic data to extract issues related to traffic congestion. For example, it analyzes congestion on major roads and identifies issues to alleviate congestion. The generation AI can also analyze regional public transportation data to extract issues related to public transportation usage. For example, it analyzes bus and train usage rates and identifies areas for improvement in public transportation. The generation AI can also integrate traffic data and public transportation data to comprehensively extract issues related to traffic congestion and public transportation usage. For example, it analyzes the correlation between traffic congestion and public transportation usage and identifies complex issues. This makes it possible to extract issues related to traffic congestion and public transportation usage.

[0078] The regional revitalization system can further analyze regional tourism data and propose new events and activities to enhance the attractiveness of tourist destinations. For example, the generative AI can analyze visitor data for a tourist destination and propose events based on tourists' interests. For example, it can present ideas for festivals and cultural events that tourists can enjoy. The generative AI can also analyze reviews and social media posts for tourist destinations to understand tourists' sentiments and propose activities to enhance their appeal. For example, it can present ideas for adventure tours and workshops that tourists can experience. Furthermore, the generative AI can integrate tourist destination data with other data and propose strategies to comprehensively improve the attractiveness of a tourist destination. This allows it to propose new events and activities to enhance the attractiveness of a tourist destination.

[0079] The regional revitalization system can also analyze local social media posts and blog articles to collect residents' emotions and opinions in real time and identify issues. For example, the generative AI analyzes local social media posts to collect residents' emotions and opinions in real time. For example, it analyzes Twitter and Facebook posts to extract residents' complaints and requests. The generative AI can also analyze local blog articles to collect residents' emotions and opinions. For example, it can analyze blog articles about local issues and identify issues. The generative AI can also integrate data from social media and blogs to comprehensively analyze residents' emotions and opinions. For example, it can compare the content of social media posts and blog articles to find matching issues. This makes it possible to identify issues based on residents' real-time emotions and opinions.

[0080] The regional revitalization system can also use the emotion estimation function to analyze residents' emotions, identify areas and themes with a high level of negative emotions, and extract issues. For example, the emotion estimation function can be used to analyze negative emotions from residents' social media posts and blog articles and extract issues. For example, posts with a high level of anger or sadness can be identified and the causes analyzed. The emotion estimation function can also be used to analyze negative emotions from resident survey results and extract issues. For example, responses with a high level of dissatisfaction or anxiety can be identified and the causes analyzed. The emotion estimation function can also be used to analyze negative emotions from the content of resident opinion exchange meetings and extract issues. For example, negative themes that frequently appear in conversations can be identified and the causes analyzed. This makes it possible to extract issues based on residents' negative emotions.

[0081] The regional revitalization system can also use its emotion estimation function to analyze tourists' emotions, identify their satisfaction and dissatisfaction with tourist destinations, and discover issues. For example, the emotion estimation function can be used to analyze emotions from tourists' social media posts and comments on review sites to identify their satisfaction and dissatisfaction with tourist destinations. For example, tourist destinations with a high percentage of positive emotions can be compared with tourist destinations with a high percentage of negative emotions. The emotion estimation function can also be used to analyze emotions from tourist survey results to identify their satisfaction and dissatisfaction with tourist destinations. For example, specific problems with tourist destinations with low satisfaction can be analyzed. The emotion estimation function can also be used to analyze emotions from the content of tourist opinion exchange meetings to identify their satisfaction and dissatisfaction with tourist destinations. For example, it can identify dissatisfaction that frequently comes up in conversations and analyze their causes. This makes it possible to discover issues at tourist destinations based on tourists' emotions.

[0082] The regional revitalization system can further use its emotion estimation function to analyze residents' emotions and present ideas for solutions that elicit positive emotions. For example, it can propose ideas for events and services that will make residents feel happy and satisfied. The emotion estimation function can also be used to analyze residents' emotions and present ideas for community activities that will elicit positive emotions. For example, it can propose events and programs that will promote interaction between residents. The emotion estimation function can also be used to analyze residents' emotions and present ideas for public services that will elicit positive emotions. For example, it can propose medical and welfare services that will make residents feel safe and satisfied. This makes it possible to present ideas for solutions that will elicit positive emotions in residents.

[0083] The regional revitalization system can further use the emotion estimation function to analyze tourists' emotions and propose new events and activities to enhance the attractiveness of tourist destinations. For example, the emotion estimation function can be used to analyze tourists' emotions and propose new events to enhance the attractiveness of tourist destinations. For example, ideas for festivals and cultural events that tourists can enjoy can be presented. The emotion estimation function can also be used to analyze tourists' emotions and propose new activities to enhance the attractiveness of tourist destinations. For example, ideas for adventure tours and workshops that tourists can experience can be presented. The emotion estimation function can also be used to analyze tourists' emotions and propose new services to enhance the attractiveness of tourist destinations. For example, ideas for spas and resort facilities where tourists can relax can be presented. This makes it possible to propose new events and activities to enhance the attractiveness of tourist destinations based on tourists' emotions.

[0084] The processing flow of the second embodiment will be briefly explained below.

[0085] Step 1: The problem detection unit discovers and extracts local issues. For example, the generation AI analyzes local statistical data to discover issues such as population decline and aging. The generation AI can also collect residents' opinions and extract local problems. Furthermore, the generation AI can analyze the results of past measures and identify issues based on success and failure cases. Step 2: The solution presentation unit presents solution ideas for the problems discovered by the problem discovery unit. For example, the generation AI can propose new tourist routes to address the decline in tourists. The generation AI can also propose services and facilities for the elderly to address the aging population. Furthermore, the generation AI can propose new agricultural technologies to address the decline of agriculture. Step 3: The process presentation unit presents a specific process for realizing the solution presented by the solution presentation unit. For example, the generation AI selects tourist destinations for a proposed new tourist route. The generation AI can also set the route. Furthermore, the generation AI can also plan promotional activities.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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).

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0103] 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.

[0104] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0105] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0118] 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.

[0119] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0131] 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.

[0132] 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.

[0133] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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."

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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]

[0153] 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. The problem discovery department discovers and extracts local issues, a solution presentation unit that presents ideas for solutions to the problems discovered by the problem discovery unit; a process presentation unit that presents a specific process for realizing the solution presented by the solution presentation unit; A system characterized by:

2. The problem finding unit Identify and extract issues based on statistical data for the area, residents' opinions, and information on the results of past measures.

2. The system of claim 1.

3. The solution presentation unit To address the decline in tourists, propose new tourist routes or plan events.

2. The system of claim 1.

4. The process presentation unit For the proposed new tourist route, specific steps will be presented for selecting tourist destinations, setting routes, and planning promotional activities.

2. The system of claim 1.

5. The problem finding unit Analyze social media posts or blog posts in the area to gather residents' sentiments and opinions in real time and identify issues.

2. The system of claim 1.

Images