System

Abstract

An object of a system according to an embodiment is to efficiently organize requests from citizens and present specific solutions and priorities.SOLUTION: A system according to an embodiment includes a request collection part, a classification and arrangement part, a proposed solution generation part, and a priority presentation part. The request collection unit collects requests from citizens. The classification and arrangement unit classifies and arranges the needs collected by the need collection unit. The proposed solution generation part generates proposed solutions for the needs classified and organized by the classification and organization part. The priority presentation part presents a priority of the demand based on the proposed solution generated by the proposed solution generation part.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 not being able to efficiently organize citizen requests and present concrete solutions and priorities.

[0005] The system according to the embodiment aims to efficiently organize requests from citizens and present specific solutions and priorities. [Means for solving the problem]

[0006] The system according to the embodiment includes a request collection unit, a classification and organization unit, a solution generation unit, and a priority presentation unit. The request collection unit collects requests from citizens. The classification and organization unit classifies and organizes the requests collected by the request collection unit. The solution generation unit generates solutions for the requests classified and organized by the classification and organization unit. The priority presentation unit presents the priority of the requests based on the solutions generated by the solution generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently organize requests from citizens and present specific solutions and priorities. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 local government DX promotion system according to an embodiment of the present invention is a system in which a generation AI organizes requests from citizens and presents specific solutions and priorities. This enables the local government DX promotion system to efficiently organize requests from citizens and present specific solutions.

[0029] A local government digital transformation promotion system according to an embodiment includes a request collection unit, a classification and organization unit, a solution generation unit, and a priority setting unit. The request collection unit collects requests from citizens. For example, the requests are collected through an online form. The request collection unit can also collect requests via email. The request collection unit can also collect requests via social media. For example, the online form may have specific input fields so that citizens can enter their requests. The email may have a dedicated email address so that citizens can send their requests. The social media may have specific hashtags so that citizens can post their requests. The classification and organization unit classifies and organizes the collected requests. For example, the classification and organization unit may classify requests into road repairs. The classification and organization unit may also classify requests into improvements to public facilities. The classification and organization unit may also classify requests into environmental protection. For example, the classification and organization unit may analyze the content of the requests and sort them into appropriate categories. The solution generation unit generates specific solutions for the classified and organized requests. For example, the solution generation unit may propose a specific repair method for a request regarding road repair. The solution generation unit may also propose a specific improvement method for a request regarding the improvement of public facilities. The solution generation unit may also propose a specific protection method for a request regarding environmental protection. For example, the solution generation unit generates an optimal solution based on past data and case studies. The priority presentation unit presents the priority of the requests based on the generated solution. For example, the priority presentation unit may prioritize requests with high urgency. The priority presentation unit may also prioritize requests received from many citizens. The priority presentation unit may also prioritize requests with high importance. For example, the priority presentation unit may evaluate the importance and urgency of the requests and present them in order of priority. This allows the local government digital transformation promotion system according to the embodiment to efficiently organize requests from citizens and present specific solutions. For example, the output unit displays the solutions and priorities to citizens and local government officials via a web application or a mobile application. If feedback on paper is desired, the results are printed using a printer.Email delivery provides immediate feedback by sending results directly to citizens and city officials.

[0030] The request collection unit can collect requests through online forms, emails, and social media. The request collection unit, for example, collects requests through an online form. For example, the online form has specific input fields so that citizens can enter their requests. The request collection unit can also collect requests through emails. For example, a dedicated email address can be set up so that citizens can send their requests. The request collection unit can also collect requests through social media. For example, a specific hashtag can be set up so that citizens can post their requests. This allows requests to be collected from a variety of channels.

[0031] The classification and sorting unit can classify requests into categories of road repair, public facility improvement, and environmental protection. For example, the classification and sorting unit classifies requests into road repair. For example, it analyzes a request related to road repair and proposes an appropriate repair method. The classification and sorting unit can also classify requests into public facility improvement. For example, it analyzes a request related to public facility improvement and proposes an appropriate improvement method. The classification and sorting unit can also classify requests into environmental protection. For example, it analyzes a request related to environmental protection and proposes an appropriate protection method. This allows requests to be classified appropriately.

[0032] The solution generation unit can generate specific solutions for the requests classified by the classification unit. For example, the solution generation unit proposes a specific repair method for a request regarding road repair. For example, it proposes the optimal repair method based on past data. The solution generation unit can also propose a specific improvement method for a request regarding the improvement of public facilities. For example, it proposes the optimal improvement method based on past cases. The solution generation unit can also propose a specific protection method for a request regarding environmental protection. For example, it incorporates the opinions of experts to propose the optimal protection method. In this way, specific solutions can be generated.

[0033] The priority presentation unit can evaluate the urgency and importance of requests and present the priority order. The priority presentation unit, for example, presents requests with a high level of urgency first. For example, it processes requests with a high level of urgency first. The priority presentation unit can also present requests received from many citizens first. For example, it processes requests with a high level of importance first. The priority presentation unit can also present requests with a high level of importance first. For example, it processes requests with a wide range of impact first. This makes it possible to present the priority order of requests appropriately.

[0034] The request collection unit can refer to the history of requests previously submitted by the request submitter and automatically link related requests. For example, in a request collection platform, the request collection unit can refer to the history of requests previously submitted by the request submitter and automatically link related requests. For example, the request collection unit analyzes the relevance based on past requests by the same submitter. This makes it possible to automatically link related requests.

[0035] The request collection unit can analyze the content of requests in real time and provide immediate feedback. For example, when a citizen submits a request, the generation AI analyzes the content of the request in real time and provides immediate feedback. For example, an automatic reply is sent in response to the content of the request. This allows immediate feedback to be provided in response to the request.

[0036] The requirement collection unit can accept voice input or image input and analyze the data to extract requirements. For example, when collecting requirements, the requirement collection unit also accepts voice input and image input, and the generation AI analyzes the data to extract requirements. For example, voice data can be converted into text using voice recognition technology, and the requirements can be analyzed. This makes it possible to extract requirements from voice or image data.

[0037] The request collection unit is multilingual and can automatically translate and analyze requests submitted in different languages. For example, the request collection unit makes the request collection platform multilingual and automatically translates and analyzes requests submitted in different languages. For example, requests in English, Chinese, etc. are automatically translated and analyzed. This makes it possible to analyze requests in multiple languages.

[0038] The classification and organization unit can improve classification accuracy by taking into account background information of the request (attributes or region of the submitter). For example, when the generation AI classifies a request, the classification and organization unit improves classification accuracy by taking into account background information of the request (attributes or region of the submitter). For example, classification is performed based on the submitter's age, gender, and region information. This can improve classification accuracy of requests.

[0039] When analyzing the content of a request, the classification and sorting unit can refer to relevant laws, regulations, and guidelines and classify the request into an appropriate category. For example, when analyzing the content of a request, the generation AI can refer to relevant laws, regulations, and guidelines and classify the request into an appropriate category. For example, the classification and sorting unit can classify requests related to road repairs based on laws, regulations, and guidelines. This allows requests to be appropriately classified based on laws, regulations, and guidelines.

[0040] The classification and organization unit can visualize the classification results of requests and display them on an interactive dashboard. For example, the classification and organization unit can visualize the classification results of requests and display them on an interactive dashboard, allowing local government employees to intuitively understand them. For example, the distribution of requests can be displayed using graphs and charts. This allows the classification results of requests to be intuitively understood.

[0041] The classification and organization unit can share the results of the classification of requests with other local governments and jointly consider solutions to common issues. For example, the classification and organization unit can share the results of the classification of requests with other local governments and jointly consider solutions to common issues. For example, multiple local governments can work together to propose solutions to common issues. This allows them to jointly consider solutions to common issues.

[0042] The solution generation unit can refer to past success stories or failure stories to propose the optimal solution. For example, when the generation AI generates a solution, the solution generation unit refers to past success stories or failure stories to propose the optimal solution. For example, it can propose the optimal repair method based on past data. This makes it possible to propose the optimal solution based on past cases.

[0043] The solution generation unit can provide a more specific solution by reflecting the opinions and suggestions of the request submitter. For example, when generating a solution, the generation AI can provide a more specific solution by reflecting the opinions and suggestions of the request submitter. For example, the generation AI can propose a specific repair method based on the submitter's opinions. This makes it possible to provide a specific solution that reflects the submitter's opinions and suggestions.

[0044] The solution generation unit can simulate the generated solution plans under different scenarios and conditions to select the optimal solution. For example, the solution generation unit can simulate the solution plans generated by the generation AI under different scenarios and conditions to select the optimal solution. For example, it can simulate repair methods under different weather conditions. This makes it possible to select the optimal solution under different scenarios and conditions.

[0045] The solution generation unit incorporates the opinions of other local governments and experts to propose solutions from a more multifaceted perspective. For example, when generating a solution, the solution generation unit incorporates the opinions of other local governments and experts to propose solutions from a more multifaceted perspective. For example, it refers to successful cases from other local governments. This makes it possible to propose solutions from a multifaceted perspective.

[0046] The priority presentation unit can quantitatively evaluate the range of impact or the degree of impact of requests and assign priorities. For example, when the generation AI assigns priorities to requests, the priority presentation unit quantitatively evaluates the range of impact and the degree of impact of requests. For example, requests with a wide range of impact are processed preferentially. This makes it possible to quantitatively evaluate the range of impact and the degree of impact of requests and assign priorities.

[0047] The priority presentation unit can evaluate the urgency and importance of requests in real time and dynamically update the priorities. For example, when presenting priorities, the priority presentation unit has the generation AI evaluate the urgency and importance of requests in real time and dynamically update the priorities. For example, requests with a high urgency are processed first. This allows the urgency and importance of requests to be evaluated in real time and the priorities to be dynamically updated.

[0048] The priority presentation unit can compare the results of the priority presentation with data from other municipalities and adjust the priorities for common issues. The priority presentation unit, for example, compares the results of the priority presentation with data from other municipalities and adjusts the priorities for common issues. For example, the priority presentation unit adjusts the priorities based on data from other municipalities. This makes it possible to adjust the priorities by comparing with data from other municipalities.

[0049] The priority presentation unit can visualize the results of the priority presentation and disclose them to citizens in a transparent manner. The priority presentation unit, for example, visualizes the results of the priority presentation and discloses them to citizens in a transparent manner. For example, the priority presentation unit displays the priorities using graphs or charts. This allows the results of the priority presentation to be disclosed to citizens in a transparent manner.

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

[0051] The local government digital transformation promotion system can further include a feedback unit that provides feedback on citizen requests. For example, the feedback unit notifies citizens that their requests have been accepted. The feedback unit can also periodically report the progress of their requests to citizens. Furthermore, the feedback unit can also notify citizens of the results when their requests are resolved. This allows citizens to understand in real time how their requests are being handled, facilitating smooth communication with the local government.

[0052] The Request Collection Unit can also be equipped with a function to collect citizen requests anonymously. For example, when collecting requests via online forms, email, or social media, the submitter's personal information can be anonymized. The Request Collection Unit can also send anonymous requests to the Classification and Sorting Unit for appropriate processing. This allows citizens to submit requests while protecting their privacy, making it easier for more citizens to submit requests.

[0053] When analyzing the content of requests, the classification and organization unit can use natural language processing technology to accurately understand the intent of the request. For example, it can analyze the text of the request, extract context and keywords, and classify it into the appropriate category. The classification and organization unit can also refer to similar past requests and related data to understand the intent of the request. This allows it to accurately understand the intent of the request and propose an appropriate solution.

[0054] The solution generator can also use AI to perform simulations when generating solutions to citizen requests. For example, it can simulate the feasibility and effectiveness of solutions and select the optimal solution. The solution generator can also revise the solution based on the simulation results and propose a more effective solution. This makes it possible to provide feasible and effective solutions.

[0055] The priority presentation unit can also quantitatively evaluate the extent and degree of impact of requests when presenting priorities for citizen requests. For example, it evaluates the extent to which a request will affect citizens, and prioritizes requests with a wide impact. The priority presentation unit can also evaluate the impact of requests, and prioritize requests with a high impact. This makes it possible to present priorities that take into account the extent and degree of impact of requests.

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

[0057] Step 1: The Request Collection Department collects requests from citizens. For example, requests can be collected through online forms, emails, or social media. Online forms have specific input fields for citizens to enter their requests, emails have a dedicated email address for citizens to send requests, and social media has specific hashtags for citizens to post requests. Step 2: The classification and organization unit classifies and organizes the collected requests. For example, requests are sorted into categories such as road repairs, improvements to public facilities, and environmental protection. The content of the requests is analyzed and classified into the appropriate category. Step 3: The solution generator generates specific solutions for the classified and organized requests. For example, it proposes specific repair methods for requests regarding road repairs, specific improvement methods for requests regarding public facility improvements, and specific protection methods for requests regarding environmental protection. It generates optimal solutions based on past data and examples. Step 4: The priority presentation unit presents the priority of requests based on the generated solutions. For example, it presents requests with high urgency, requests received from many citizens, and requests with high importance first. It evaluates the importance and urgency of requests and presents the priority.

[0058] (Example 2) The local government DX promotion system according to an embodiment of the present invention is a system in which a generation AI organizes requests from citizens and presents specific solutions and priorities. This enables the local government DX promotion system to efficiently organize requests from citizens and present specific solutions.

[0059] A local government digital transformation promotion system according to an embodiment includes a request collection unit, a classification and organization unit, a solution generation unit, and a priority setting unit. The request collection unit collects requests from citizens. For example, the requests are collected through an online form. The request collection unit can also collect requests via email. The request collection unit can also collect requests via social media. For example, the online form may have specific input fields so that citizens can enter their requests. The email may have a dedicated email address so that citizens can send their requests. The social media may have specific hashtags so that citizens can post their requests. The classification and organization unit classifies and organizes the collected requests. For example, the classification and organization unit may classify requests into road repairs. The classification and organization unit may also classify requests into improvements to public facilities. The classification and organization unit may also classify requests into environmental protection. For example, the classification and organization unit may analyze the content of the requests and sort them into appropriate categories. The solution generation unit generates specific solutions for the classified and organized requests. For example, the solution generation unit may propose a specific repair method for a request regarding road repair. The solution generation unit may also propose a specific improvement method for a request regarding the improvement of public facilities. The solution generation unit may also propose a specific protection method for a request regarding environmental protection. For example, the solution generation unit generates an optimal solution based on past data and case studies. The priority presentation unit presents the priority of the requests based on the generated solution. For example, the priority presentation unit may prioritize requests with high urgency. The priority presentation unit may also prioritize requests received from many citizens. The priority presentation unit may also prioritize requests with high importance. For example, the priority presentation unit may evaluate the importance and urgency of the requests and present them in order of priority. This allows the local government digital transformation promotion system according to the embodiment to efficiently organize requests from citizens and present specific solutions. For example, the output unit displays the solutions and priorities to citizens and local government officials via a web application or a mobile application. If feedback on paper is desired, the results are printed using a printer.Email delivery provides immediate feedback by sending results directly to citizens and city officials.

[0060] The request collection unit can collect requests through online forms, emails, and social media. The request collection unit, for example, collects requests through an online form. For example, the online form has specific input fields so that citizens can enter their requests. The request collection unit can also collect requests through emails. For example, a dedicated email address can be set up so that citizens can send their requests. The request collection unit can also collect requests through social media. For example, a specific hashtag can be set up so that citizens can post their requests. This allows requests to be collected from a variety of channels.

[0061] The classification and sorting unit can classify requests into categories of road repair, public facility improvement, and environmental protection. For example, the classification and sorting unit classifies requests into road repair. For example, it analyzes a request related to road repair and proposes an appropriate repair method. The classification and sorting unit can also classify requests into public facility improvement. For example, it analyzes a request related to public facility improvement and proposes an appropriate improvement method. The classification and sorting unit can also classify requests into environmental protection. For example, it analyzes a request related to environmental protection and proposes an appropriate protection method. This allows requests to be classified appropriately.

[0062] The solution generation unit can generate specific solutions for the requests classified by the classification unit. For example, the solution generation unit proposes a specific repair method for a request regarding road repair. For example, it proposes the optimal repair method based on past data. The solution generation unit can also propose a specific improvement method for a request regarding the improvement of public facilities. For example, it proposes the optimal improvement method based on past cases. The solution generation unit can also propose a specific protection method for a request regarding environmental protection. For example, it incorporates the opinions of experts to propose the optimal protection method. In this way, specific solutions can be generated.

[0063] The priority presentation unit can evaluate the urgency and importance of requests and present the priority order. The priority presentation unit, for example, presents requests with a high level of urgency first. For example, it processes requests with a high level of urgency first. The priority presentation unit can also present requests received from many citizens first. For example, it processes requests with a high level of importance first. The priority presentation unit can also present requests with a high level of importance first. For example, it processes requests with a wide range of impact first. This makes it possible to present the priority order of requests appropriately.

[0064] The request collection unit can analyze the emotional tone of the request and prioritize requests that are emotionally strong. For example, when collecting requests from citizens, the request collection unit uses the generation AI to analyze the emotional tone of the request and prioritize requests that are emotionally strong. For example, requests that express strong emotions such as anger or sadness can be analyzed with priority and responded to quickly. This allows requests that express strong emotions to be prioritized.

[0065] The request collection unit can refer to the history of requests previously submitted by the request submitter and automatically link related requests. For example, in a request collection platform, the request collection unit can refer to the history of requests previously submitted by the request submitter and automatically link related requests. For example, the request collection unit analyzes the relevance based on past requests by the same submitter. This makes it possible to automatically link related requests.

[0066] The request collection unit can analyze the content of requests in real time and provide immediate feedback. For example, when a citizen submits a request, the generation AI analyzes the content of the request in real time and provides immediate feedback. For example, an automatic reply is sent in response to the content of the request. This allows immediate feedback to be provided in response to the request.

[0067] The requirement collection unit can accept voice input or image input and analyze the data to extract requirements. For example, when collecting requirements, the requirement collection unit also accepts voice input and image input, and the generation AI analyzes the data to extract requirements. For example, voice data can be converted into text using voice recognition technology, and the requirements can be analyzed. This makes it possible to extract requirements from voice or image data.

[0068] The request collection unit can use the emotion estimation function to estimate the citizen's emotions and make suggestions that elicit positive emotions. For example, when a citizen submits a request, the request collection unit uses the emotion estimation function to estimate the citizen's emotions and make suggestions that elicit positive emotions. For example, positive suggestions are made based on the emotion score. This allows for proposals that take the citizen's emotions into consideration.

[0069] The request collection unit is multilingual and can automatically translate and analyze requests submitted in different languages. For example, the request collection unit makes the request collection platform multilingual and automatically translates and analyzes requests submitted in different languages. For example, requests in English, Chinese, etc. are automatically translated and analyzed. This makes it possible to analyze requests in multiple languages.

[0070] The classification and organization unit can improve classification accuracy by taking into account background information of the request (attributes or region of the submitter). For example, when the generation AI classifies a request, the classification and organization unit improves classification accuracy by taking into account background information of the request (attributes or region of the submitter). For example, classification is performed based on the submitter's age, gender, and region information. This can improve classification accuracy of requests.

[0071] When analyzing the content of a request, the classification and sorting unit can refer to relevant laws, regulations, and guidelines and classify the request into an appropriate category. For example, when analyzing the content of a request, the generation AI can refer to relevant laws, regulations, and guidelines and classify the request into an appropriate category. For example, the classification and sorting unit can classify requests related to road repairs based on laws, regulations, and guidelines. This allows requests to be appropriately classified based on laws, regulations, and guidelines.

[0072] The classification and organization unit performs sentiment analysis on the request classification results, identifies negative requests, and proposes improvement measures. For example, the classification and organization unit uses a generation AI to perform sentiment analysis on the request classification results, identifies negative requests, and proposes improvement measures. For example, it proposes specific improvement measures for requests that express strong emotions such as anger or sadness. This makes it possible to propose appropriate improvement measures for negative requests.

[0073] The classification and organization unit can visualize the classification results of requests and display them on an interactive dashboard. For example, the classification and organization unit can visualize the classification results of requests and display them on an interactive dashboard, allowing local government employees to intuitively understand them. For example, the distribution of requests can be displayed using graphs and charts. This allows the classification results of requests to be intuitively understood.

[0074] The classification and organization unit can share the results of the classification of requests with other local governments and jointly consider solutions to common issues. For example, the classification and organization unit can share the results of the classification of requests with other local governments and jointly consider solutions to common issues. For example, multiple local governments can work together to propose solutions to common issues. This allows them to jointly consider solutions to common issues.

[0075] The classification and organization unit can collect citizens' emotional responses to the requests classified using the emotion estimation function, thereby improving classification accuracy. The classification and organization unit can, for example, use the emotion estimation function to collect citizens' emotional responses to the requests classified, thereby improving classification accuracy. For example, the classification result is reevaluated based on the emotion score. This allows citizens' emotional responses to be collected, thereby improving classification accuracy.

[0076] The solution generation unit can refer to past success stories or failure stories to propose the optimal solution. For example, when the generation AI generates a solution, the solution generation unit refers to past success stories or failure stories to propose the optimal solution. For example, it can propose the optimal repair method based on past data. This makes it possible to propose the optimal solution based on past cases.

[0077] The solution generation unit can provide a more specific solution by reflecting the opinions and suggestions of the request submitter. For example, when generating a solution, the generation AI can provide a more specific solution by reflecting the opinions and suggestions of the request submitter. For example, the generation AI can propose a specific repair method based on the submitter's opinions. This makes it possible to provide a specific solution that reflects the submitter's opinions and suggestions.

[0078] The solution generation unit incorporates an emotion estimation function and can propose solutions that take into consideration the emotions of citizens. The solution generation unit, for example, incorporates an emotion estimation function into the generation of solutions and proposes solutions that take into consideration the emotions of citizens. For example, the solution generation unit proposes a repair method that takes into consideration the emotions of citizens based on the emotion score. This makes it possible to propose solutions that take into consideration the emotions of citizens.

[0079] The solution generation unit can simulate the generated solution plans under different scenarios and conditions to select the optimal solution. For example, the solution generation unit can simulate the solution plans generated by the generation AI under different scenarios and conditions to select the optimal solution. For example, it can simulate repair methods under different weather conditions. This makes it possible to select the optimal solution under different scenarios and conditions.

[0080] The solution generation unit incorporates the opinions of other local governments and experts to propose solutions from a more multifaceted perspective. For example, when generating a solution, the solution generation unit incorporates the opinions of other local governments and experts to propose solutions from a more multifaceted perspective. For example, it refers to successful cases from other local governments. This makes it possible to propose solutions from a multifaceted perspective.

[0081] The solution generation unit can use the emotion estimation function to predict citizens' emotional reactions to the solution proposals and select a solution that will elicit the most positive reaction. The solution generation unit can, for example, use the emotion estimation function to predict citizens' emotional reactions to the solution proposals and select a solution that will elicit the most positive reaction. For example, the solution can be selected based on an emotion score. This makes it possible to predict citizens' emotional reactions and select the most positive solution.

[0082] The priority presentation unit can quantitatively evaluate the range of impact or the degree of impact of requests and assign priorities. For example, when the generation AI assigns priorities to requests, the priority presentation unit quantitatively evaluates the range of impact and the degree of impact of requests. For example, requests with a wide range of impact are processed preferentially. This makes it possible to quantitatively evaluate the range of impact and the degree of impact of requests and assign priorities.

[0083] The priority presentation unit can evaluate the urgency and importance of requests in real time and dynamically update the priorities. For example, when presenting priorities, the priority presentation unit has the generation AI evaluate the urgency and importance of requests in real time and dynamically update the priorities. For example, requests with a high urgency are processed first. This allows the urgency and importance of requests to be evaluated in real time and the priorities to be dynamically updated.

[0084] The priority order presentation unit incorporates an emotion estimation function and can adjust the priority order based on the emotion of the citizen. The priority order presentation unit, for example, incorporates an emotion estimation function into the presentation of the priority order and adjusts the priority order based on the emotion of the citizen. For example, the priority order presentation unit adjusts the priority order based on the emotion score. This makes it possible to adjust the priority order based on the emotion of the citizen.

[0085] The priority presentation unit can compare the results of the priority presentation with data from other municipalities and adjust the priorities for common issues. The priority presentation unit, for example, compares the results of the priority presentation with data from other municipalities and adjusts the priorities for common issues. For example, the priority presentation unit adjusts the priorities based on data from other municipalities. This makes it possible to adjust the priorities by comparing with data from other municipalities.

[0086] The priority presentation unit can visualize the results of the priority presentation and disclose them to citizens in a transparent manner. The priority presentation unit, for example, visualizes the results of the priority presentation and discloses them to citizens in a transparent manner. For example, the priority presentation unit displays the priorities using graphs or charts. This allows the results of the priority presentation to be disclosed to citizens in a transparent manner.

[0087] The priority presentation unit can use the emotion estimation function to collect citizens' emotional reactions to the priority presentation results and improve the accuracy of the priorities. The priority presentation unit can use the emotion estimation function to collect citizens' emotional reactions to the priority presentation results and improve the accuracy of the priorities. For example, the priority presentation unit re-evaluates the priorities based on the emotion scores. This allows citizens' emotional reactions to be collected and the accuracy of the priorities to be improved.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The local government digital transformation promotion system can further include a feedback unit that provides feedback on citizen requests. For example, the feedback unit notifies citizens that their requests have been accepted. The feedback unit can also periodically report the progress of their requests to citizens. Furthermore, the feedback unit can also notify citizens of the results when their requests are resolved. This allows citizens to understand in real time how their requests are being handled, facilitating smooth communication with the local government.

[0090] The Request Collection Unit can also be equipped with a function to collect citizen requests anonymously. For example, when collecting requests via online forms, email, or social media, the submitter's personal information can be anonymized. The Request Collection Unit can also send anonymous requests to the Classification and Sorting Unit for appropriate processing. This allows citizens to submit requests while protecting their privacy, making it easier for more citizens to submit requests.

[0091] When analyzing the content of requests, the classification and organization unit can use natural language processing technology to accurately understand the intent of the request. For example, it can analyze the text of the request, extract context and keywords, and classify it into the appropriate category. The classification and organization unit can also refer to similar past requests and related data to understand the intent of the request. This allows it to accurately understand the intent of the request and propose an appropriate solution.

[0092] The solution generator can also use AI to perform simulations when generating solutions to citizen requests. For example, it can simulate the feasibility and effectiveness of solutions and select the optimal solution. The solution generator can also revise the solution based on the simulation results and propose a more effective solution. This makes it possible to provide feasible and effective solutions.

[0093] The priority presentation unit can also quantitatively evaluate the extent and degree of impact of requests when presenting priorities for citizen requests. For example, it evaluates the extent to which a request will affect citizens, and prioritizes requests with a wide impact. The priority presentation unit can also evaluate the impact of requests, and prioritize requests with a high impact. This makes it possible to present priorities that take into account the extent and degree of impact of requests.

[0094] The request collection unit can further estimate the emotions of citizens and prioritize requests that express strong emotions. For example, it can analyze the emotions of citizens from the text of the request and prioritize requests that express strong emotions such as anger or sadness. The request collection unit can also quickly respond to requests that express strong emotions and reduce citizen dissatisfaction. This allows requests that express strong emotions to be prioritized and improves citizen satisfaction.

[0095] The request collection unit can further estimate the emotions of citizens and prioritize requests that are emotionally positive. For example, it can analyze the emotions of citizens from the text of the request and prioritize requests that express strong feelings of joy or gratitude. The request collection unit can also quickly respond to emotionally positive requests and improve citizen satisfaction. This allows emotionally positive requests to be prioritized and citizen satisfaction to be improved.

[0096] The classification and organization unit can further estimate citizens' emotions, identify requests that evoke strong emotions, and propose improvement measures. For example, it can analyze citizens' emotions from the request text and propose specific improvement measures for requests that evoke strong emotions such as anger or sadness. The classification and organization unit can also quickly respond to requests that evoke strong emotions and reduce citizens' dissatisfaction. This makes it possible to propose appropriate improvement measures for requests that evoke strong emotions, thereby improving citizen satisfaction.

[0097] The solution generation unit can further estimate the emotions of citizens and propose emotionally positive solutions. For example, it can analyze the emotions of citizens from the text of their requests and propose solutions that evoke emotions such as joy and gratitude. The solution generation unit can also improve citizen satisfaction by proposing emotionally positive solutions. This makes it possible to propose emotionally positive solutions and improve citizen satisfaction.

[0098] The priority presentation unit can further estimate the emotions of citizens and prioritize requests that express strong emotions. For example, it can analyze the emotions of citizens from the text of the requests and prioritize requests that express strong emotions such as anger or sadness. The priority presentation unit can also quickly respond to requests that express strong emotions and reduce citizen dissatisfaction. This allows requests that express strong emotions to be prioritized and improves citizen satisfaction.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The Request Collection Department collects requests from citizens. For example, requests can be collected through online forms, emails, or social media. Online forms have specific input fields for citizens to enter their requests, emails have a dedicated email address for citizens to send requests, and social media has specific hashtags for citizens to post requests. Step 2: The classification and organization unit classifies and organizes the collected requests. For example, requests are sorted into categories such as road repairs, improvements to public facilities, and environmental protection. The content of the requests is analyzed and classified into the appropriate category. Step 3: The solution generator generates specific solutions for the classified and organized requests. For example, it proposes specific repair methods for requests regarding road repairs, specific improvement methods for requests regarding public facility improvements, and specific protection methods for requests regarding environmental protection. It generates optimal solutions based on past data and examples. Step 4: The priority presentation unit presents the priority of requests based on the generated solutions. For example, it presents requests with high urgency, requests received from many citizens, and requests with high importance first. It evaluates the importance and urgency of requests and presents the priority.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 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.

[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 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.

[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. 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.

[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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0114] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 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.

[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 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.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[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 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.

[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 (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).

[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] 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.

[0127] 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.

[0128] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0129] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0145] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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]

[0168] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A request collection department that collects requests from citizens; a classification and organization unit that classifies and organizes the requests collected by the request collection unit; a solution generation unit that generates solutions for the requests classified and organized by the classification and organization unit; a priority presentation unit that presents the priority of requests based on the solutions generated by the solution generation unit. A system characterized by:

2. The request collection unit Collect requests through online forms, emails, and social media 2. The system of claim 1.

3. The classification and organization unit Categorize the requests into road repairs, public facility improvements, and environmental protection categories.

2. The system of claim 1.

4. The priority presentation unit Evaluate the urgency and importance of the request and provide a priority list 2. The system of claim 1.

5. The solution generation unit Referencing past successes and failures to propose optimal solutions 2. The system of claim 1.

6. The request collection unit Analyze the emotional tone of the requests and prioritize emotionally intense requests 2. The system of claim 1.

7. The classification and organization unit Sentiment analysis is performed on the classification results of the requests, negative requests are identified, and improvements are proposed.

2. The system of claim 1.

8. The priority presentation unit Incorporating a sentiment estimation function to adjust the priorities based on the sentiment of the citizens 2. The system of claim 1.

Images