System

Abstract

An object of a system according to an embodiment is to make processing of information and exchange of money transparent and to efficiently manage them.SOLUTION: A system according to an embodiment includes an information reception part, an information processing part, a money recording part, and a visualization part. The information reception unit receives information. The information processing unit processes the information received by the information receiving unit. The money recording part records the exchange of money relating to the information processed by the information processing part. A visualization part visualizes the exchange of the money recorded by the money recording part on a homepage.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 does not ensure transparency in petition processing or financial transactions, and there is room for improvement.

[0005] The system according to the embodiment aims to make the processing of petitions and monetary transactions transparent and efficiently managed. [Means for solving the problem]

[0006] The system according to the embodiment includes a petition receiving unit, a petition processing unit, a money recording unit, and a visualization unit. The petition receiving unit receives petitions. The petition processing unit processes petitions received by the petition receiving unit. The money recording unit records monetary transactions related to petitions processed by the petition processing unit. The visualization unit visualizes the monetary transactions recorded by the money recording unit on a homepage. [Effects of the Invention]

[0007] The system according to the embodiment can make the processing of petitions and monetary transactions transparent and efficiently managed. [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 petition processing system according to an embodiment of the present invention utilizes AI to enable assembly members to efficiently process petitions from citizens and ensure financial transparency. This system automates the entire process, from accepting petitions to processing them, recording financial transactions, and visualizing their contents on a website. As a result, the petition processing system eliminates the need for assembly members to hire numerous secretaries, reducing expenses and allowing them to focus on solving citizen problems.

[0029] A petition processing system according to an embodiment includes a petition reception unit, a petition processing unit, a financial recording unit, and a visualization unit. The petition reception unit receives petitions. For example, petitions from citizens are received via a dedicated web form or email. The petition reception unit analyzes the petition content using a generation AI and classifies and organizes the content. For example, the generation AI analyzes the petition content using a text generation AI (e.g., LLM) and classifies the petition content into categories such as petitions regarding road repairs and petitions regarding improvements to the educational environment. The generation AI analyzes the petition content and classifies it into the appropriate category using natural language processing technology. The petition processing unit processes the petition received by the petition reception unit. For example, a petition regarding road repairs is automatically forwarded to the relevant department of the local government. Furthermore, a petition regarding improvements to the educational environment is notified to the relevant board of education. The petition processing unit uses the generation AI to automatically generate specific countermeasures based on the petition content and notify the relevant departments. For example, the generation AI learns from past response data and proposes optimal countermeasures. The financial recording unit records financial transactions related to petitions processed by the petition processing unit. For example, it records details of donations and expenditures for a specific project and organizes the content. The financial recording unit uses a generation AI to automatically generate detailed reports on financial transactions to ensure transparency. For example, it records details of donation receipts and expenditures and outputs them as reports. The visualization unit visualizes the financial transactions recorded by the financial recording unit on a website. For example, it allows users to check the use and progress of donations for a specific project in real time. The visualization unit uses a generation AI to automatically generate an interface that visually displays data on petition content and financial transactions in an easy-to-understand manner. For example, it visualizes the data using graphs and charts. As a result, the petition processing system according to the embodiment enables assembly members to efficiently process petitions from citizens and ensure financial transparency. For example, assembly members no longer need to hire multiple secretaries, allowing them to focus on solving citizen issues while reducing expenses.

[0030] The petition reception unit can compare the petition content with a database of similar petitions from the past and automatically generate an initial response plan that references past response results. The petition reception unit, for example, analyzes the petition content and compares it with a database of similar petitions from the past. For example, it identifies past petitions with high similarity using keyword matching or natural language processing technology. The petition reception unit also automatically generates an initial response plan based on past response results. For example, it generates specific response procedures and proposals by reference to response measures that have been successful in the past. The petition reception unit also takes into account evaluation data of past response results when generating an initial response plan. For example, if a past response was highly rated by citizens, it will prioritize the adoption of that response measure. In this way, the accuracy of the initial response plan is improved by referring to past response results.

[0031] The petition reception unit can accept petitions via voice input or a chatbot, and automatically classify the petition content by analyzing the voice data or dialogue data. The petition reception unit, for example, builds a system that accepts petitions using voice input. For example, a user inputs the petition content by voice and converts it into text using voice recognition technology. The petition reception unit also builds a system that accepts petitions through a chatbot. For example, a user inputs the petition content while interacting with a chatbot, and the dialogue data is analyzed and automatically classified. The petition reception unit also analyzes the voice data or dialogue data to develop an algorithm that automatically classifies the petition content. For example, natural language processing technology is used to classify the petition content by category. This makes the acceptance of petitions more efficient by using voice input or a chatbot.

[0032] The petition reception unit is provided as a smartphone app and can automatically classify petitions by region based on location information. The petition reception unit, for example, develops a smartphone app and provides a petition reception system. For example, a user inputs petition content through the app and location information is automatically obtained. The petition reception unit also develops an algorithm that automatically classifies petitions by region based on location information. For example, it analyzes GPS data and classifies petition content by region. The petition reception unit also analyzes petition data collected through the smartphone app in real time to understand petition trends by region. For example, it can identify problems that occur frequently in specific regions and respond to them as a priority. This makes it possible to efficiently classify petitions by region using a smartphone app.

[0033] The petition processing unit can use generation AI to automatically generate specific countermeasures based on the petition content and notify relevant departments. For example, the petition processing unit uses generation AI to analyze the petition content and automatically generate specific countermeasures. For example, if the petition is about road repairs, it will generate a repair plan and budget proposal. The petition processing unit also builds a system to notify relevant departments based on the automatically generated countermeasures. For example, it sends the generated countermeasures to the relevant department via email or a notification system. The petition processing unit also uses generation AI to learn from past response data and automatically generate more effective countermeasures. For example, it will propose the optimal countermeasure based on past success stories. In this way, generation AI can be used to quickly generate specific countermeasures and notify relevant departments.

[0034] The petition processing unit can provide a dashboard that tracks the petition processing process in real time and automatically updates the progress. The petition processing unit, for example, builds a system that tracks the petition processing process in real time. For example, the progress of each step is automatically updated and displayed on a dashboard. The petition processing unit also visually displays the progress of the petition through the dashboard. For example, graphs and charts can be used to allow the processing progress to be understood at a glance. The petition processing unit also provides a dashboard that is updated in real time, allowing relevant parties to always check the latest information. For example, delays and problems in processing can be immediately identified and countermeasures can be taken. This improves the efficiency of petition processing by allowing progress to be understood in real time.

[0035] The petition processing unit can build a platform for sharing petition processing data between different local governments and organizations and learning from best practices. The petition processing unit, for example, builds a platform for sharing petition processing data between different local governments and organizations. For example, it may integrate databases and make them accessible to each organization. The petition processing unit also develops a system for learning best practices based on the shared data. For example, it may extract success stories and effective countermeasures and provide them to other organizations. The petition processing unit also allows different local governments and organizations to exchange information on the platform, thereby improving the efficiency of petition processing. For example, it may hold regular online meetings and forums. This allows data sharing between different local governments and organizations, thereby improving the efficiency of petition processing.

[0036] The petition processing unit can use blockchain technology to increase the transparency of petition processing and ensure the reliability of processing. The petition processing unit, for example, introduces blockchain technology into a petition processing system to ensure the transparency of processing. For example, each processing step is recorded on the blockchain to prevent tampering. The petition processing unit also uses blockchain technology to improve the reliability of petition processing. For example, the processing history is made public so that citizens can check it. The petition processing unit also builds a system on the blockchain to track the progress of petition processing in real time. For example, the completion of each step is recorded on the blockchain to visualize the progress. In this way, the use of blockchain technology improves the transparency and reliability of petition processing.

[0037] The Financial Recording Unit uses generation AI to automatically generate detailed reports on financial transactions, ensuring transparency. For example, the Financial Recording Unit uses generation AI to automatically generate detailed reports on financial transactions. For example, it records details of donation receipts and expenditures and outputs them as reports. The Financial Recording Unit also builds a system based on the automatically generated reports to make the use of money transparent. For example, it publishes the reports on a website so that citizens can check them. The Financial Recording Unit also uses generation AI to learn from past financial data and automatically generate more detailed and accurate reports. For example, it performs predictions and analysis based on past transaction history. In this way, generation AI can be used to automatically generate detailed reports on financial transactions, ensuring transparency.

[0038] The financial recording department can implement a system that tracks financial transactions in real time and automatically detects fraudulent transactions. For example, the financial recording department builds a system that tracks financial transactions in real time. For example, it monitors transaction data in real time and implements an algorithm to detect fraudulent transactions. The financial recording department also develops a system that automatically detects fraudulent transactions. For example, it detects abnormal transaction patterns or signs of fraud and issues an alert. The financial recording department also takes measures to prevent fraudulent transactions based on the transaction data tracked in real time. For example, it suspends suspicious transactions and conducts detailed checks. In this way, the security of transactions is improved by tracking financial transactions in real time and automatically detecting fraudulent transactions.

[0039] The Financial Recording Department can use smart contracts to automate monetary transactions and improve the transparency and reliability of transactions. For example, the Financial Recording Department may use smart contracts to build a system that automates monetary transactions. For example, it may make payments automatically when transaction conditions are met. The Financial Recording Department may also introduce smart contracts to improve the transparency and reliability of transactions. For example, it may record transaction history on a blockchain to prevent tampering. The Financial Recording Department may also use smart contracts to develop a system that automates transactions and simultaneously monitors transaction content in real time. For example, it may issue an alert if an abnormal transaction is detected. In this way, the use of smart contracts can automate monetary transactions and improve the transparency and reliability of transactions.

[0040] The financial recording unit can safely manage data related to financial transactions on the cloud and make it accessible as needed. The financial recording unit, for example, builds a system that manages data related to financial transactions on the cloud. For example, it encrypts transaction data and stores it in the cloud to ensure security. The financial recording unit also develops a system that allows access to data managed on the cloud as needed. For example, it introduces an authentication system to ensure that only authorized users have access. The financial recording unit also updates the data managed on the cloud in real time, allowing the latest transaction information to be constantly checked. For example, it automatically updates the database every time a transaction is made. This allows data to be safely managed on the cloud and made accessible as needed, thereby improving the transparency and reliability of transactions.

[0041] The visualization unit can use a generation AI to automatically generate an interface that visually displays data on petition content and monetary transactions in an easy-to-understand manner. The visualization unit, for example, uses a generation AI to automatically generate an interface that visually displays data on petition content and monetary transactions. For example, it visualizes data using graphs and charts. The visualization unit also enables users to intuitively understand the data based on the automatically generated interface. For example, it provides an interactive dashboard. The visualization unit also uses a generation AI to learn from past data and automatically generate more effective visualization methods. For example, it improves the interface based on user feedback. In this way, by using a generation AI, it is possible to automatically generate an interface that visually displays data in an easy-to-understand manner.

[0042] The visualization unit can ensure transparency by updating the petition progress and the use of funds on the homepage in real time. The visualization unit, for example, builds a system that updates the petition progress and the use of funds on the homepage in real time. For example, the progress of each step is automatically updated and displayed on the homepage. The visualization unit also enables users to always check the latest information based on the data updated in real time. For example, the progress and the use of funds are displayed in graphs and charts. The visualization unit also automates data updates on the homepage to ensure transparency. For example, the database is automatically updated every time a transaction is made and reflected on the homepage. In this way, transparency is ensured by updating the progress and the use of funds in real time.

[0043] The Visualization Department can link the visualized data on the website with social media, allowing information to be shared widely with citizens. For example, the Visualization Department could build a system that links the visualized data on the website with social media. For example, the data on the website could be automatically posted to social media. The Visualization Department could also share information widely with citizens through social media. For example, the progress of petitions and the use of funds could be published on social media in real time. The Visualization Department could also collect citizen reactions on social media and reflect this in the visualized data on the website. For example, the data could be updated based on citizen comments and feedback. In this way, linking with social media could increase information sharing and raise citizen interest.

[0044] The visualization unit automatically translates the visualized data of the homepage into different languages, thereby obtaining feedback from an international perspective. The visualization unit, for example, builds a system that automatically translates the visualized data of the homepage into different languages. For example, it translates into multiple languages ​​such as English, French, and Chinese. The visualization unit also collects feedback from an international perspective based on the translated data. For example, it collects comments and opinions from users in different languages. The visualization unit also reflects improvement suggestions from an international perspective based on the automatically translated data. For example, it reflects feedback that takes cultural and market differences into account on the homepage. In this way, by automatically translating into different languages, feedback from an international perspective can be obtained.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] When accepting petition content, the petition reception unit translates the user's input in real time, enabling multilingual support. For example, it supports multiple languages, including English, French, and Chinese. The petition reception unit also develops algorithms to classify the translated content into appropriate categories. For example, it uses natural language processing technology to analyze the translated text and classify it into categories such as road repairs or improvements to the educational environment. The petition reception unit also accumulates translation data and compares it with past data to improve translation accuracy. This enables multilingual support, making it possible to efficiently process petitions from foreign citizens.

[0047] The petition reception unit can compare petitions with a database of similar petitions from the past and automatically generate an initial response plan that references past response results. For example, it can use keyword matching or natural language processing technology to identify past petitions with high similarity. The petition reception unit also automatically generates an initial response plan based on past response results. For example, it can reference responses that have been successful in the past to generate specific response procedures and proposals. The petition reception unit also takes into account evaluation data of past response results when generating an initial response plan. For example, if a past response received high praise from citizens, it will prioritize the adoption of that response plan. In this way, the accuracy of the initial response plan can be improved by referring to past response results.

[0048] The petition reception unit can accept petitions via voice input or a chatbot, and automatically classify the petition content by analyzing the voice data or dialogue data. For example, a system can be built that accepts petitions using voice input. For example, a user can input the petition content by voice and convert it into text using voice recognition technology. The petition reception unit can also build a system that accepts petitions through a chatbot. For example, a user can input the petition content while interacting with a chatbot, and the dialogue data can be analyzed and automatically classified. The petition reception unit can also analyze the voice data or dialogue data and develop an algorithm that automatically classifies the petition content. For example, natural language processing technology can be used to classify the petition content by category. This makes the acceptance of petitions more efficient by using voice input or a chatbot.

[0049] The petition reception unit is provided as a smartphone app and can automatically classify petitions by region based on location information. For example, a smartphone app is developed to provide a petition reception system. For example, a user enters petition details through the app and location information is automatically obtained. The petition reception unit also develops an algorithm to automatically classify petitions by region based on location information. For example, it analyzes GPS data and classifies petition details by region. The petition reception unit also analyzes petition data collected through the smartphone app in real time to understand petition trends by region. For example, it can identify problems that occur frequently in specific regions and respond to them as a priority. This makes it possible to efficiently classify petitions by region using a smartphone app.

[0050] The petition processing unit can use generation AI to automatically generate specific countermeasures based on the petition content and notify relevant departments. For example, generation AI can be used to analyze the petition content and automatically generate specific countermeasures. For example, if the petition is about road repairs, a repair plan and budget proposal can be generated. The petition processing unit also builds a system to notify relevant departments based on the automatically generated countermeasures. For example, the generated countermeasures can be sent to the relevant department via email or a notification system. The petition processing unit also uses generation AI to learn from past response data and automatically generate more effective countermeasures. For example, it can propose optimal countermeasures based on past success stories. In this way, generation AI can be used to quickly generate specific countermeasures and notify relevant departments.

[0051] The petition processing unit can provide a dashboard that tracks the petition processing process in real time and automatically updates the progress. For example, a system can be built that tracks the petition processing process in real time. For example, the progress of each step can be automatically updated and displayed on the dashboard. The petition processing unit can also visually display the progress of the petition through the dashboard. For example, graphs and charts can be used to allow the processing progress to be understood at a glance. The petition processing unit can also provide a dashboard that is updated in real time, allowing relevant parties to always check the latest information. For example, delays and problems in processing can be immediately identified and countermeasures can be taken. This improves the efficiency of petition processing by allowing progress to be understood in real time.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The petition reception unit accepts petitions. For example, petitions from citizens are accepted via a dedicated web form or email. The petition reception unit also uses generation AI to analyze the petition content and classify and organize it. For example, the generation AI uses text generation AI (e.g., LLM) to analyze the petition content and classify it into petitions regarding road repairs or petitions regarding improvements to the educational environment. The generation AI uses natural language processing technology to analyze the petition content and classify it into the appropriate category. Step 2: The petition processing unit processes the petition received by the petition reception unit. For example, if the petition is about road repairs, it will automatically forward it to the relevant department in the local government. If the petition is about improving the educational environment, it will notify the relevant board of education. The petition processing unit uses generation AI to automatically generate specific countermeasures based on the petition content and notify the relevant department. For example, the generation AI can learn from past response data and propose the optimal countermeasure. Step 3: The Financial Recording Unit records financial transactions related to petitions processed by the Petition Processing Unit. For example, it records and organizes details of donations and expenditures for specific projects. The Financial Recording Unit uses generative AI to automatically generate detailed reports on financial transactions to ensure transparency. For example, it records details of donation receipts and expenditures and outputs them as reports. Step 4: The visualization unit visualizes the financial transactions recorded by the financial recording unit on a website. For example, it allows users to check in real time how donations for a specific project are being used and their progress. The visualization unit uses generative AI to automatically generate an interface that visually displays petition details and financial transaction data in an easy-to-understand manner. For example, it visualizes the data using graphs and charts.

[0054] (Example 2) The petition processing system according to an embodiment of the present invention utilizes AI to enable assembly members to efficiently process petitions from citizens and ensure financial transparency. This system automates the entire process, from accepting petitions to processing them, recording financial transactions, and visualizing their contents on a website. As a result, the petition processing system eliminates the need for assembly members to hire numerous secretaries, reducing expenses and allowing them to focus on solving citizen problems.

[0055] A petition processing system according to an embodiment includes a petition reception unit, a petition processing unit, a financial recording unit, and a visualization unit. The petition reception unit receives petitions. For example, petitions from citizens are received via a dedicated web form or email. The petition reception unit analyzes the petition content using a generation AI and classifies and organizes the content. For example, the generation AI analyzes the petition content using a text generation AI (e.g., LLM) and classifies the petition content into categories such as petitions regarding road repairs and petitions regarding improvements to the educational environment. The generation AI analyzes the petition content and classifies it into the appropriate category using natural language processing technology. The petition processing unit processes the petition received by the petition reception unit. For example, a petition regarding road repairs is automatically forwarded to the relevant department of the local government. Furthermore, a petition regarding improvements to the educational environment is notified to the relevant board of education. The petition processing unit uses the generation AI to automatically generate specific countermeasures based on the petition content and notify the relevant departments. For example, the generation AI learns from past response data and proposes optimal countermeasures. The financial recording unit records financial transactions related to petitions processed by the petition processing unit. For example, it records details of donations and expenditures for a specific project and organizes the content. The financial recording unit uses a generation AI to automatically generate detailed reports on financial transactions to ensure transparency. For example, it records details of donation receipts and expenditures and outputs them as reports. The visualization unit visualizes the financial transactions recorded by the financial recording unit on a website. For example, it allows users to check the use and progress of donations for a specific project in real time. The visualization unit uses a generation AI to automatically generate an interface that visually displays data on petition content and financial transactions in an easy-to-understand manner. For example, it visualizes the data using graphs and charts. As a result, the petition processing system according to the embodiment enables assembly members to efficiently process petitions from citizens and ensure financial transparency. For example, assembly members no longer need to hire multiple secretaries, allowing them to focus on solving citizen issues while reducing expenses.

[0056] The petition reception unit uses generation AI to perform sentiment analysis of petition content and can automatically set priorities based on the intensity and type of emotion. For example, the petition reception unit uses generation AI to analyze the text of petition content and perform sentiment analysis. For example, based on the words and phrases contained in the petition content, emotions such as joy, anger, and sadness are identified and their intensity is quantified. The petition reception unit also automatically sets petition priorities based on the results of the sentiment analysis. For example, petitions with strong anger emotions are determined to be of high urgency and are processed with priority. The petition reception unit also accumulates sentiment analysis data and compares it with past data to understand petition trends and patterns and set priorities with greater accuracy. This enables petition priorities to be automatically set based on sentiment analysis, enabling prompt and appropriate responses.

[0057] The petition reception unit can compare the petition content with a database of similar petitions from the past and automatically generate an initial response plan that references past response results. The petition reception unit, for example, analyzes the petition content and compares it with a database of similar petitions from the past. For example, it identifies past petitions with high similarity using keyword matching or natural language processing technology. The petition reception unit also automatically generates an initial response plan based on past response results. For example, it generates specific response procedures and proposals by reference to response measures that have been successful in the past. The petition reception unit also takes into account evaluation data of past response results when generating an initial response plan. For example, if a past response was highly rated by citizens, it will prioritize the adoption of that response measure. In this way, the accuracy of the initial response plan is improved by referring to past response results.

[0058] The petition reception unit is equipped with an emotion estimation function, and can estimate the emotion of the user when entering data in real time and provide appropriate feedback. The petition reception unit, for example, installs the emotion estimation function in a petition reception form and analyzes the facial expressions and voice of the user when entering data. For example, it estimates the user's emotion in real time using a camera or microphone. The petition reception unit also provides appropriate feedback to the user based on the emotion estimation results. For example, if a negative emotion is detected, it displays an encouraging message. The petition reception unit also accumulates emotion estimation data and analyzes the user's emotional tendencies to provide more accurate feedback. For example, it provides personalized feedback based on past data. This improves the user experience by providing feedback according to the user's emotion.

[0059] The petition reception unit can accept petitions via voice input or a chatbot, and automatically classify the petition content by analyzing the voice data or dialogue data. The petition reception unit, for example, builds a system that accepts petitions using voice input. For example, a user inputs the petition content by voice and converts it into text using voice recognition technology. The petition reception unit also builds a system that accepts petitions through a chatbot. For example, a user inputs the petition content while interacting with a chatbot, and the dialogue data is analyzed and automatically classified. The petition reception unit also analyzes the voice data or dialogue data to develop an algorithm that automatically classifies the petition content. For example, natural language processing technology is used to classify the petition content by category. This makes the acceptance of petitions more efficient by using voice input or a chatbot.

[0060] The petition reception unit is provided as a smartphone app and can automatically classify petitions by region based on location information. The petition reception unit, for example, develops a smartphone app and provides a petition reception system. For example, a user inputs petition content through the app and location information is automatically obtained. The petition reception unit also develops an algorithm that automatically classifies petitions by region based on location information. For example, it analyzes GPS data and classifies petition content by region. The petition reception unit also analyzes petition data collected through the smartphone app in real time to understand petition trends by region. For example, it can identify problems that occur frequently in specific regions and respond to them as a priority. This makes it possible to efficiently classify petitions by region using a smartphone app.

[0061] The petition reception unit can use the emotion estimation function to analyze the user's emotions when receiving a petition and provide an interface for eliciting positive emotions. The petition reception unit, for example, uses the emotion estimation function to analyze the user's emotions in real time when receiving a petition. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The petition reception unit also provides an interface for eliciting positive emotions based on the results of analyzing the user's emotions. For example, it displays encouraging messages and positive feedback. The petition reception unit also accumulates emotion estimation data and analyzes the user's emotional tendencies to provide a more effective interface. For example, it provides personalized feedback based on past data. This improves the user experience by providing an interface that corresponds to the user's emotions.

[0062] The petition processing unit can use generation AI to automatically generate specific countermeasures based on the petition content and notify relevant departments. For example, the petition processing unit uses generation AI to analyze the petition content and automatically generate specific countermeasures. For example, if the petition is about road repairs, it will generate a repair plan and budget proposal. The petition processing unit also builds a system to notify relevant departments based on the automatically generated countermeasures. For example, it sends the generated countermeasures to the relevant department via email or a notification system. The petition processing unit also uses generation AI to learn from past response data and automatically generate more effective countermeasures. For example, it will propose the optimal countermeasure based on past success stories. In this way, generation AI can be used to quickly generate specific countermeasures and notify relevant departments.

[0063] The petition processing unit can provide a dashboard that tracks the petition processing process in real time and automatically updates the progress. The petition processing unit, for example, builds a system that tracks the petition processing process in real time. For example, the progress of each step is automatically updated and displayed on a dashboard. The petition processing unit also visually displays the progress of the petition through the dashboard. For example, graphs and charts can be used to allow the processing progress to be understood at a glance. The petition processing unit also provides a dashboard that is updated in real time, allowing relevant parties to always check the latest information. For example, delays and problems in processing can be immediately identified and countermeasures can be taken. This improves the efficiency of petition processing by allowing progress to be understood in real time.

[0064] The petition processing unit can use the emotion estimation function to analyze the emotions of the petitioner and propose countermeasures according to the emotions. The petition processing unit, for example, uses the emotion estimation function to analyze the emotions of the petitioner in real time. For example, it analyzes the text or audio of the petition content and calculates an emotion score. The petition processing unit also builds a system that proposes countermeasures according to the emotions of the petitioner. For example, if the emotion is strong, it responds quickly or provides additional explanation. The petition processing unit also compares the emotion analysis data with past response cases and proposes the optimal countermeasure. For example, it refers to past successful cases with petitioners who have similar emotions. This makes it possible to propose countermeasures according to the emotions of the petitioner, enabling a more appropriate response.

[0065] The petition processing unit can build a platform for sharing petition processing data between different local governments and organizations and learning from best practices. The petition processing unit, for example, builds a platform for sharing petition processing data between different local governments and organizations. For example, it may integrate databases and make them accessible to each organization. The petition processing unit also develops a system for learning best practices based on the shared data. For example, it may extract success stories and effective countermeasures and provide them to other organizations. The petition processing unit also allows different local governments and organizations to exchange information on the platform, thereby improving the efficiency of petition processing. For example, it may hold regular online meetings and forums. This allows data sharing between different local governments and organizations, thereby improving the efficiency of petition processing.

[0066] The petition processing unit can use blockchain technology to increase the transparency of petition processing and ensure the reliability of processing. The petition processing unit, for example, introduces blockchain technology into a petition processing system to ensure the transparency of processing. For example, each processing step is recorded on the blockchain to prevent tampering. The petition processing unit also uses blockchain technology to improve the reliability of petition processing. For example, the processing history is made public so that citizens can check it. The petition processing unit also builds a system on the blockchain to track the progress of petition processing in real time. For example, the completion of each step is recorded on the blockchain to visualize the progress. In this way, the use of blockchain technology improves the transparency and reliability of petition processing.

[0067] The petition processing unit can use the emotion estimation function to monitor the user's emotions at each step of petition processing and provide feedback according to the emotions. The petition processing unit, for example, uses the emotion estimation function to monitor the user's emotions at each step of petition processing in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The petition processing unit also builds a system that provides feedback according to the user's emotions. For example, if a negative emotion is detected, it provides additional explanation or support. The petition processing unit also compares the emotion monitoring data with past response cases and provides optimal feedback. For example, it refers to past successful cases for users with similar emotions. In this way, satisfaction with petition processing is improved by providing feedback according to the user's emotions.

[0068] The Financial Recording Unit uses generation AI to automatically generate detailed reports on financial transactions, ensuring transparency. For example, the Financial Recording Unit uses generation AI to automatically generate detailed reports on financial transactions. For example, it records details of donation receipts and expenditures and outputs them as reports. The Financial Recording Unit also builds a system based on the automatically generated reports to make the use of money transparent. For example, it publishes the reports on a website so that citizens can check them. The Financial Recording Unit also uses generation AI to learn from past financial data and automatically generate more detailed and accurate reports. For example, it performs predictions and analysis based on past transaction history. In this way, generation AI can be used to automatically generate detailed reports on financial transactions, ensuring transparency.

[0069] The financial recording department can implement a system that tracks financial transactions in real time and automatically detects fraudulent transactions. For example, the financial recording department builds a system that tracks financial transactions in real time. For example, it monitors transaction data in real time and implements an algorithm to detect fraudulent transactions. The financial recording department also develops a system that automatically detects fraudulent transactions. For example, it detects abnormal transaction patterns or signs of fraud and issues an alert. The financial recording department also takes measures to prevent fraudulent transactions based on the transaction data tracked in real time. For example, it suspends suspicious transactions and conducts detailed checks. In this way, the security of transactions is improved by tracking financial transactions in real time and automatically detecting fraudulent transactions.

[0070] The money recording unit can use the emotion estimation function to analyze the user's emotions regarding monetary transactions and provide feedback according to the emotions. For example, the money recording unit uses the emotion estimation function to analyze the user's emotions regarding monetary transactions in real time. For example, it calculates the user's emotion score for the transaction content. The money recording unit also builds a system that provides feedback according to the user's emotions. For example, if negative emotions are detected, it provides additional explanations or support. The money recording unit also compares the emotion analysis data with past transaction cases and provides optimal feedback. For example, it refers to past success stories for users with similar emotions. In this way, by providing feedback according to the user's emotions, the transparency and reliability of transactions are improved.

[0071] The Financial Recording Department can use smart contracts to automate monetary transactions and improve the transparency and reliability of transactions. For example, the Financial Recording Department may use smart contracts to build a system that automates monetary transactions. For example, it may make payments automatically when transaction conditions are met. The Financial Recording Department may also introduce smart contracts to improve the transparency and reliability of transactions. For example, it may record transaction history on a blockchain to prevent tampering. The Financial Recording Department may also use smart contracts to develop a system that automates transactions and simultaneously monitors transaction content in real time. For example, it may issue an alert if an abnormal transaction is detected. In this way, the use of smart contracts can automate monetary transactions and improve the transparency and reliability of transactions.

[0072] The financial recording unit can safely manage data related to financial transactions on the cloud and make it accessible as needed. The financial recording unit, for example, builds a system that manages data related to financial transactions on the cloud. For example, it encrypts transaction data and stores it in the cloud to ensure security. The financial recording unit also develops a system that allows access to data managed on the cloud as needed. For example, it introduces an authentication system to ensure that only authorized users have access. The financial recording unit also updates the data managed on the cloud in real time, allowing the latest transaction information to be constantly checked. For example, it automatically updates the database every time a transaction is made. This allows data to be safely managed on the cloud and made accessible as needed, thereby improving the transparency and reliability of transactions.

[0073] The money recording unit can use the emotion estimation function to monitor a user's emotions regarding monetary transactions in real time and propose countermeasures based on those emotions. For example, the money recording unit develops a system that uses the emotion estimation function to monitor a user's emotions regarding monetary transactions in real time. For example, it calculates a user's emotion score for the transaction content. The money recording unit also builds a system that proposes countermeasures based on the user's emotions. For example, if negative emotions are detected, it provides additional explanations and support. The money recording unit also compares the emotion monitoring data with past transaction cases and proposes optimal countermeasures. For example, it refers to past successful cases for users with similar emotions. In this way, by proposing countermeasures based on the user's emotions, the transparency and reliability of transactions are improved.

[0074] The visualization unit can use a generation AI to automatically generate an interface that visually displays data on petition content and monetary transactions in an easy-to-understand manner. The visualization unit, for example, uses a generation AI to automatically generate an interface that visually displays data on petition content and monetary transactions. For example, it visualizes data using graphs and charts. The visualization unit also enables users to intuitively understand the data based on the automatically generated interface. For example, it provides an interactive dashboard. The visualization unit also uses a generation AI to learn from past data and automatically generate more effective visualization methods. For example, it improves the interface based on user feedback. In this way, by using a generation AI, it is possible to automatically generate an interface that visually displays data in an easy-to-understand manner.

[0075] The visualization unit can ensure transparency by updating the petition progress and the use of funds on the homepage in real time. The visualization unit, for example, builds a system that updates the petition progress and the use of funds on the homepage in real time. For example, the progress of each step is automatically updated and displayed on the homepage. The visualization unit also enables users to always check the latest information based on the data updated in real time. For example, the progress and the use of funds are displayed in graphs and charts. The visualization unit also automates data updates on the homepage to ensure transparency. For example, the database is automatically updated every time a transaction is made and reflected on the homepage. In this way, transparency is ensured by updating the progress and the use of funds in real time.

[0076] The visualization unit can use the emotion estimation function to analyze the emotions of homepage viewers and provide content that corresponds to those emotions. For example, the visualization unit uses the emotion estimation function to build a system that analyzes the emotions of homepage viewers in real time. For example, it analyzes the viewer's facial expressions and voice and calculates an emotion score. The visualization unit also develops a system that provides content that corresponds to the viewer's emotions. For example, if a negative emotion is detected, it displays an encouraging message or positive content. The visualization unit also compares the emotion analysis data with past browsing history and provides optimal content. For example, it refers to past success stories for viewers with similar emotions. This improves the user experience by providing content that corresponds to the viewer's emotions.

[0077] The Visualization Department can link the visualized data on the website with social media, allowing information to be shared widely with citizens. For example, the Visualization Department could build a system that links the visualized data on the website with social media. For example, the data on the website could be automatically posted to social media. The Visualization Department could also share information widely with citizens through social media. For example, the progress of petitions and the use of funds could be published on social media in real time. The Visualization Department could also collect citizen reactions on social media and reflect this in the visualized data on the website. For example, the data could be updated based on citizen comments and feedback. In this way, linking with social media could increase information sharing and raise citizen interest.

[0078] The visualization unit automatically translates the visualized data of the homepage into different languages, thereby obtaining feedback from an international perspective. The visualization unit, for example, builds a system that automatically translates the visualized data of the homepage into different languages. For example, it translates into multiple languages ​​such as English, French, and Chinese. The visualization unit also collects feedback from an international perspective based on the translated data. For example, it collects comments and opinions from users in different languages. The visualization unit also reflects improvement suggestions from an international perspective based on the automatically translated data. For example, it reflects feedback that takes cultural and market differences into account on the homepage. In this way, by automatically translating into different languages, feedback from an international perspective can be obtained.

[0079] The visualization unit can use the emotion estimation function to monitor the emotions of homepage viewers in real time and provide an interface that corresponds to the emotions. For example, the visualization unit uses the emotion estimation function to build a system that monitors the emotions of homepage viewers in real time. For example, it analyzes the viewer's facial expressions and voice and calculates an emotion score. The visualization unit also develops a system that provides an interface that corresponds to the viewer's emotions. For example, if a negative emotion is detected, it displays an encouraging message or positive content. The visualization unit also compares the emotion monitoring data with past browsing history and provides an optimal interface. For example, it refers to past success stories for viewers with similar emotions. This improves the user experience by providing an interface that corresponds to the viewer's emotions.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] When accepting petition content, the petition reception unit translates the user's input in real time, enabling multilingual support. For example, it supports multiple languages, including English, French, and Chinese. The petition reception unit also develops algorithms to classify the translated content into appropriate categories. For example, it uses natural language processing technology to analyze the translated text and classify it into categories such as road repairs or improvements to the educational environment. The petition reception unit also accumulates translation data and compares it with past data to improve translation accuracy. This enables multilingual support, making it possible to efficiently process petitions from foreign citizens.

[0082] The petition reception unit uses an emotion estimation function to perform an emotion analysis of the petition content and can automatically set priorities based on the intensity and type of emotion. For example, emotions such as joy, anger, and sadness can be identified based on the words and phrases contained in the petition content and their intensity can be quantified. The petition reception unit also automatically sets the priority of petitions based on the results of the emotion analysis. For example, petitions with strong emotions of anger are judged to be of high urgency and are processed with priority. The petition reception unit also accumulates emotion analysis data and compares it with past data to understand the trends and patterns of petitions and set priorities with greater accuracy. This enables petitions to be automatically prioritized based on emotion analysis, enabling prompt and appropriate responses.

[0083] The petition reception unit can compare petitions with a database of similar petitions from the past and automatically generate an initial response plan that references past response results. For example, it can use keyword matching or natural language processing technology to identify past petitions with high similarity. The petition reception unit also automatically generates an initial response plan based on past response results. For example, it can reference responses that have been successful in the past to generate specific response procedures and proposals. The petition reception unit also takes into account evaluation data of past response results when generating an initial response plan. For example, if a past response received high praise from citizens, it will prioritize the adoption of that response plan. In this way, the accuracy of the initial response plan can be improved by referring to past response results.

[0084] The petition reception unit is equipped with an emotion estimation function, which can estimate the user's emotion in real time when entering data and provide appropriate feedback. For example, the emotion estimation function can be installed in a petition reception form, and the user's facial expressions and voice can be analyzed when entering data. For example, a camera or microphone can be used to estimate the user's emotion in real time. The petition reception unit also provides appropriate feedback to the user based on the emotion estimation results. For example, if a negative emotion is detected, an encouraging message can be displayed. The petition reception unit also accumulates emotion estimation data and analyzes the user's emotional tendencies to provide more accurate feedback. For example, it can provide personalized feedback based on past data. This improves the user experience by providing feedback that corresponds to the user's emotion.

[0085] The petition reception unit can accept petitions via voice input or a chatbot, and automatically classify the petition content by analyzing the voice data or dialogue data. For example, a system can be built that accepts petitions using voice input. For example, a user can input the petition content by voice and convert it into text using voice recognition technology. The petition reception unit can also build a system that accepts petitions through a chatbot. For example, a user can input the petition content while interacting with a chatbot, and the dialogue data can be analyzed and automatically classified. The petition reception unit can also analyze the voice data or dialogue data and develop an algorithm that automatically classifies the petition content. For example, natural language processing technology can be used to classify the petition content by category. This makes the acceptance of petitions more efficient by using voice input or a chatbot.

[0086] The petition reception unit is provided as a smartphone app and can automatically classify petitions by region based on location information. For example, a smartphone app is developed to provide a petition reception system. For example, a user enters petition details through the app and location information is automatically obtained. The petition reception unit also develops an algorithm to automatically classify petitions by region based on location information. For example, it analyzes GPS data and classifies petition details by region. The petition reception unit also analyzes petition data collected through the smartphone app in real time to understand petition trends by region. For example, it can identify problems that occur frequently in specific regions and respond to them as a priority. This makes it possible to efficiently classify petitions by region using a smartphone app.

[0087] The petition reception unit can use the emotion estimation function to analyze the user's emotions when receiving a petition and provide an interface for eliciting positive emotions. For example, the emotion estimation function is used to analyze the user's emotions in real time when receiving a petition. For example, the emotion estimation function may be used to analyze the user's facial expressions and voice and calculate an emotion score. The petition reception unit also provides an interface for eliciting positive emotions based on the results of analyzing the user's emotions. For example, it may display an encouraging message or positive feedback. The petition reception unit also accumulates emotion estimation data and analyzes the user's emotional tendencies to provide a more effective interface. For example, it may provide personalized feedback based on past data. This improves the user experience by providing an interface that corresponds to the user's emotions.

[0088] The petition processing unit can use generation AI to automatically generate specific countermeasures based on the petition content and notify relevant departments. For example, generation AI can be used to analyze the petition content and automatically generate specific countermeasures. For example, if the petition is about road repairs, a repair plan and budget proposal can be generated. The petition processing unit also builds a system to notify relevant departments based on the automatically generated countermeasures. For example, the generated countermeasures can be sent to the relevant department via email or a notification system. The petition processing unit also uses generation AI to learn from past response data and automatically generate more effective countermeasures. For example, it can propose optimal countermeasures based on past success stories. In this way, generation AI can be used to quickly generate specific countermeasures and notify relevant departments.

[0089] The petition processing unit can provide a dashboard that tracks the petition processing process in real time and automatically updates the progress. For example, a system can be built that tracks the petition processing process in real time. For example, the progress of each step can be automatically updated and displayed on the dashboard. The petition processing unit can also visually display the progress of the petition through the dashboard. For example, graphs and charts can be used to allow the processing progress to be understood at a glance. The petition processing unit can also provide a dashboard that is updated in real time, allowing relevant parties to always check the latest information. For example, delays and problems in processing can be immediately identified and countermeasures can be taken. This improves the efficiency of petition processing by allowing progress to be understood in real time.

[0090] The petition processing unit can use an emotion estimation function to analyze the emotions of the petitioner and propose countermeasures according to those emotions. For example, the emotion estimation function is used to analyze the emotions of the petitioner in real time. For example, the text and audio of the petition content are analyzed and an emotion score is calculated. The petition processing unit also builds a system that proposes countermeasures according to the petitioner's emotions. For example, if the emotion is strong, a prompt response or additional explanation is provided. The petition processing unit also compares the emotion analysis data with past response cases and proposes the optimal countermeasure. For example, it refers to past successful cases with petitioners who have similar emotions. This makes it possible to propose countermeasures according to the petitioner's emotions, enabling a more appropriate response.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The petition reception unit accepts petitions. For example, petitions from citizens are accepted via a dedicated web form or email. The petition reception unit also uses generation AI to analyze the petition content and classify and organize it. For example, the generation AI uses text generation AI (e.g., LLM) to analyze the petition content and classify it into petitions regarding road repairs or petitions regarding improvements to the educational environment. The generation AI uses natural language processing technology to analyze the petition content and classify it into the appropriate category. Step 2: The petition processing unit processes the petition received by the petition reception unit. For example, if the petition is about road repairs, it will automatically forward it to the relevant department in the local government. If the petition is about improving the educational environment, it will notify the relevant board of education. The petition processing unit uses generation AI to automatically generate specific countermeasures based on the petition content and notify the relevant department. For example, the generation AI can learn from past response data and propose the optimal countermeasure. Step 3: The Financial Recording Unit records financial transactions related to petitions processed by the Petition Processing Unit. For example, it records and organizes details of donations and expenditures for specific projects. The Financial Recording Unit uses generative AI to automatically generate detailed reports on financial transactions to ensure transparency. For example, it records details of donation receipts and expenditures and outputs them as reports. Step 4: The visualization unit visualizes the financial transactions recorded by the financial recording unit on a website. For example, it allows users to check in real time how donations for a specific project are being used and their progress. The visualization unit uses generative AI to automatically generate an interface that visually displays petition details and financial transaction data in an easy-to-understand manner. For example, it visualizes the data using graphs and charts.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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, in order to avoid confusion and to 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.

[0159] 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]

[0160] 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 petition reception section for receiving petitions; a petition processing unit that processes petitions accepted by the petition acceptance unit; a money recording unit that records monetary transactions related to petitions processed by the petition processing unit; a visualization unit that visualizes the monetary transactions recorded by the monetary recording unit on a homepage. A system characterized by:

2. The petition reception unit Using generative AI to analyze the sentiment of petition content and automatically set priorities based on the intensity and type of sentiment 2. The system of claim 1.

3. The petition reception unit Accepts petitions via voice input or chatbot, analyzes voice data or dialogue data, and automatically classifies petition content 2. The system of claim 1.

4. The petition processing unit: Using generative AI, specific countermeasures based on the petition content are automatically generated and notified to the relevant departments.

2. The system of claim 1.

5. The money recording unit Generative AI automatically generates detailed reports on financial transactions to ensure transparency 2. The system of claim 1.

6. The visualization unit Using generative AI, we automatically generate an interface that visually displays petition content and financial transaction data in an easy-to-understand manner.

2. The system of claim 1.

7. The petition reception unit Equipped with an emotion estimation function, it estimates the user's emotions in real time as they input and provides appropriate feedback.

2. The system of claim 1.

8. The petition processing unit: Analyze the emotions of the petitioner and propose appropriate measures to deal with them 2. The system of claim 1.

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