System

Abstract

An object of the system according to the embodiment is to improve the efficiency and speed of policy making utilizing data-driven decision making and generation AI.SOLUTION: A system according to an embodiment includes a AI collector, a generated data analyzer, an information sharer, an opinion exchanger, and a collaborative editor. The data collection unit collects data. The generated data analysis unit analyzes the AI collected by the data collection unit. The information share unit shares the information analyzed by the generated AI analysis unit. The opinion exchange unit exchanges opinions based on the information shared by the information sharing unit. The collaborative editing unit collaboratively edits the document based on the opinions exchanged by the opinion exchanging unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not efficiently collect and analyze data, share information, exchange opinions, or collaborate on policymaking, and there is room for improvement.

[0005] The system of the embodiment aims to improve the efficiency and speed of policymaking by utilizing data-driven decision-making and generative AI. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, a generative AI analysis unit, an information sharing unit, an opinion exchange unit, and a collaborative editing unit. The data collection unit collects data. The generative AI analysis unit analyzes the data collected by the data collection unit. The information sharing unit shares the information analyzed by the generative AI analysis unit. The opinion exchange unit exchanges opinions based on the information shared by the information sharing unit. The collaborative editing unit collaboratively edits a document based on the opinions exchanged by the opinion exchange unit. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency and speed of policymaking by utilizing data-driven decision-making and generative AI. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A policymaking system according to an embodiment of the present invention is a system that aims to improve the efficiency and speed of policymaking by utilizing data-driven decision-making and generative AI. As a result, the policymaking system can achieve improved efficiency and speed of policymaking by utilizing data-driven decision-making and generative AI.

[0029] A policymaking system according to an embodiment includes a data collection unit, a generation AI analysis unit, an information sharing unit, an opinion exchange unit, and a collaborative editing unit. The data collection unit collects data, such as urban traffic data, environmental data, and economic data. The generation AI analysis unit analyzes the data collected by the data collection unit. For example, the generation AI analyzes urban traffic data to predict traffic congestion and propose solutions. The generation AI can also analyze environmental data to predict air pollution and propose countermeasures. The generation AI can also analyze economic data to evaluate the effectiveness of economic policies and propose necessary modifications. The information sharing unit shares information analyzed by the generation AI analysis unit. For example, policymakers can view the analysis results and share information on the platform. The opinion exchange unit exchanges opinions based on the information shared by the information sharing unit. For example, policymakers can exchange opinions and hold discussions on the platform. The collaborative editing unit collaboratively edits documents based on the opinions exchanged by the opinion exchange unit. For example, policymakers can edit documents using the collaborative editing function on the platform. This will enable policymaking systems to achieve more efficient and faster policymaking through data-driven decision-making and the use of generative AI.

[0030] The data collection unit can collect urban traffic data, environmental data, and economic data in real time. The generative AI analysis unit can analyze the data and make immediate policy proposals. For example, the data collection unit collects urban traffic data in real time, and the generative AI predicts traffic congestion and immediately proposes solutions. For example, it analyzes increases and decreases in traffic volume during specific time periods and proposes optimal traffic regulations. The data collection unit also analyzes environmental data in real time, and the generative AI predicts air pollution and immediately proposes countermeasures. For example, it monitors air pollution levels in specific areas and proposes necessary countermeasures. The data collection unit also collects economic data in real time, and the generative AI immediately evaluates the effectiveness of economic policies and proposes necessary adjustments. For example, it analyzes fluctuations in specific economic indicators and proposes optimal economic policies. This makes it possible to make immediate policy proposals through the analysis of real-time data.

[0031] The generative AI analysis unit can compare past policy data with current data and perform simulations to predict the effects of policies. For example, the generative AI analysis unit compares past transportation policy data with current transportation data, and the generative AI simulates the effects of new transportation policies. For example, it predicts new regulations based on the effects of past traffic regulations. The generative AI analysis unit also compares environmental policy data with current environmental data, and the generative AI simulates the effects of new environmental policies. For example, it predicts new measures based on the effects of past air pollution control measures. The generative AI analysis unit also compares economic policy data with current economic data, and the generative AI simulates the effects of new economic policies. For example, it predicts new stimulus measures based on the effects of past economic stimulus measures. This makes it possible to perform simulations to predict the effects of policies by comparing past data with current data.

[0032] The generative AI analysis unit can compare data from different cities and extract and share best practices. For example, the generative AI analysis unit compares traffic data from different cities, and the generative AI extracts the most effective traffic policies. For example, best practices for alleviating traffic congestion are shared with other cities. The generative AI analysis unit also compares environmental data from different cities, and the generative AI extracts the most effective environmental policies. For example, best practices for combating air pollution are shared with other cities. The generative AI analysis unit also compares economic data from different cities, and the generative AI extracts the most effective economic policies. For example, best practices for promoting economic growth are shared with other cities. This makes it possible to extract and share best practices by comparing data from different cities.

[0033] The generative AI analysis unit can integrate data from different fields and make comprehensive policy proposals. For example, the generative AI analysis unit integrates medical data and transportation data, and the generative AI proposes comprehensive health policies. For example, it could propose measures to improve access to hospitals. The generative AI analysis unit also integrates education data and economic data, and the generative AI proposes comprehensive education policies. For example, it could make proposals to maximize the effectiveness of educational investments. The generative AI analysis unit also integrates environmental data and transportation data, and the generative AI proposes comprehensive environmental policies. For example, it could propose measures to promote the use of public transportation. In this way, by integrating data from different fields, comprehensive policy proposals become possible.

[0034] The generative AI analysis unit can automatically collect data necessary for policy formulation and automatically generate analysis prompts. For example, the generative AI automatically collects publicly available data on the Internet and builds a dataset necessary for policy formulation. For example, it collects government statistical data and research papers. The generative AI analysis unit also automatically generates analysis prompts based on the data automatically collected by the generative AI. For example, it generates analysis prompts for transportation policy based on transportation data. The generative AI analysis unit also automatically collects data necessary for policy formulation and updates the analysis prompts in real time. For example, it updates the prompts every time new data is added. This makes it possible to automatically collect data necessary for policy formulation and automatically generate analysis prompts.

[0035] The generative AI analysis unit can integrate data from different data sources and provide analysis results from multiple perspectives. For example, the generative AI integrates data from different data sources, such as government data, private data, and academic data, to provide comprehensive analysis results. For example, it integrates and analyzes transportation data and environmental data. The generative AI analysis unit also integrates data from different data sources and provides analysis results from multiple perspectives. For example, it integrates economic data and social data to make policy proposals. The generative AI analysis unit also integrates data from different data sources in real time to provide the latest analysis results. For example, it updates the analysis results every time new data is added. This makes it possible to provide analysis results from multiple perspectives by integrating data from different data sources.

[0036] The generative AI analysis unit analyzes data in different languages ​​and can make policy proposals from an international perspective. For example, the generative AI analysis unit automatically translates data in different languages ​​and makes policy proposals from an international perspective. For example, it analyzes data in English, Chinese, French, etc. The generative AI analysis unit also analyzes data in different languages ​​and makes policy proposals from an international perspective. For example, it makes policy proposals based on successful examples from other countries. The generative AI analysis unit also analyzes data in different languages ​​in real time and makes policy proposals from the latest international perspective. For example, it updates proposals every time new data is added. This makes it possible to make policy proposals from an international perspective by analyzing data in different languages.

[0037] The generative AI analysis unit can analyze data from different time periods and make optimal policy proposals for each time period. For example, the generative AI analysis unit analyzes traffic data from different time periods and proposes optimal traffic policies for each time period. For example, it compares traffic volume during morning rush hour and at night and makes proposals. The generative AI analysis unit also analyzes environmental data from different time periods and proposes optimal environmental policies for each time period. For example, it compares air pollution levels during the day and at night and makes proposals. The generative AI analysis unit also analyzes economic data from different time periods and proposes optimal economic policies for each time period. For example, it compares economic activity on weekdays and weekends and makes proposals. In this way, by analyzing data from different time periods, it becomes possible to propose optimal policies for each time period.

[0038] The information sharing unit uses generation AI to automatically organize information and provide the most appropriate information to relevant parties. For example, the information sharing unit uses generation AI to automatically organize policy-related information on the platform and provide the most appropriate information to relevant parties. For example, it automatically organizes the latest policy data and analysis results. The information sharing unit also uses generation AI to filter information based on the role and interests of the relevant parties and provide the most appropriate information. For example, it prioritizes providing transportation data to stakeholders who are interested in transportation policy. The information sharing unit also uses generation AI to evaluate the importance of information on the platform and provide the most appropriate information to relevant parties. For example, it prioritizes providing information with a high level of urgency. This makes it possible to organize information using generation AI and provide the most appropriate information to relevant parties.

[0039] The information sharing unit can use the generation AI to analyze the history of opinion exchanges and extract important discussion points. For example, the information sharing unit uses the generation AI on the platform to analyze the history of opinion exchanges and automatically extract important discussion points. For example, it extracts frequently mentioned keywords and phrases. The information sharing unit also uses the generation AI to analyze the history of opinion exchanges and visualize the progress of the discussion. For example, it displays the progress of the discussion and unresolved issues. The information sharing unit also uses the generation AI on the platform to analyze the history of opinion exchanges and notify relevant parties of important discussion points. For example, it sends an alert when an important discussion takes place. This makes it possible to analyze the history of opinion exchanges and extract important discussion points.

[0040] The information sharing unit promotes information sharing between different organizations, enabling crossover policy proposals. For example, the information sharing unit promotes information sharing between different organizations on the platform, and the generation AI makes crossover policy proposals. For example, it integrates data from local governments and companies to make policy proposals. In addition, to promote information sharing between different organizations, the generation AI automatically generates common data formats. For example, it unifies and analyzes different data formats. In addition, the information sharing unit promotes information sharing between different organizations on the platform, and the generation AI makes comprehensive policy proposals. For example, it integrates data from non-profit organizations and government agencies to make policy proposals. This promotes information sharing between different organizations, enabling crossover policy proposals.

[0041] The information sharing unit can use the generative AI to automatically match experts from different fields and promote collaborative editing. For example, the information sharing unit uses the generative AI to automatically match experts from different fields on the platform to promote collaborative editing. For example, it matches experts in transportation policy with experts in environmental policy. The information sharing unit also uses the generative AI to analyze the experts' profiles and make the optimal match. For example, it makes a match based on expertise and experience. The information sharing unit also uses the generative AI to match experts from different fields on the platform and monitor the progress of the collaborative editing. For example, it displays the progress of the collaborative editing in real time. This makes it possible to automatically match experts from different fields and promote collaborative editing.

[0042] The opinion exchange unit can use the generation AI to analyze the content of the opinion exchange in real time and automatically summarize the important points. For example, the generation AI in the opinion exchange unit analyzes the content of the opinion exchange in real time and automatically summarizes the important points. For example, it summarizes the main points of the discussion in short sentences. The opinion exchange unit also analyzes the content of the opinion exchange, and the generation AI extracts and summarizes the important points. For example, it includes frequently mentioned keywords and phrases in the summary. The opinion exchange unit also analyzes the content of the opinion exchange in real time and notifies the relevant parties of the important points. For example, it summarizes and displays the progress of the discussion. This makes it possible to analyze the content of the opinion exchange in real time and automatically summarize the important points.

[0043] The collaborative editing department can use generative AI to analyze documents being co-edited and automatically point out inconsistencies and areas for improvement. For example, the collaborative editing department uses generative AI to analyze documents being co-edited in real time and automatically point out inconsistencies and areas for improvement. For example, it points out logical inconsistencies within the document. The collaborative editing department also analyzes documents being co-edited, and the generative AI suggests areas for improvement. For example, it makes suggestions to improve the structure and expression of the document. The collaborative editing department also uses generative AI to analyze documents being co-edited in real time and notify relevant parties of inconsistencies and areas for improvement. For example, it displays points to be corrected in the document. This makes it possible to analyze documents being co-edited and automatically point out inconsistencies and areas for improvement.

[0044] The opinion exchange unit uses the generation AI to translate exchanges of opinions in different languages ​​in real time, thereby promoting international discussion. For example, the opinion exchange unit uses the generation AI to translate exchanges of opinions in different languages ​​in real time, thereby promoting international discussion. For example, it translates opinions in English, Chinese, French, etc. The opinion exchange unit also translates exchanges of opinions in different languages ​​in real time, and the generation AI supports international discussions. For example, it facilitates exchanges of opinions with experts from other countries. The opinion exchange unit also uses the generation AI to translate exchanges of opinions in different languages ​​in real time, and provides the translated results to those involved. For example, it translates and displays the progress of the discussion. This makes it possible to translate exchanges of opinions in different languages ​​in real time, thereby promoting international discussion.

[0045] The collaborative editing department can use generative AI to automatically match experts from different fields and promote collaborative editing. For example, the collaborative editing department uses generative AI to automatically match experts from different fields and promote collaborative editing. For example, it matches an expert in transportation policy with an expert in environmental policy. In addition, the collaborative editing department uses generative AI to analyze the expert's profile and make the optimal match. For example, it makes a match based on expertise and experience. In addition, the collaborative editing department uses generative AI to match experts from different fields and monitor the progress of collaborative editing. For example, it displays the progress of collaborative editing in real time. This makes it possible to automatically match experts from different fields and promote collaborative editing.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The policymaking system can also be equipped with a data visualization unit. The data visualization unit visually displays collected data, allowing policymakers to intuitively understand it. For example, traffic data can be displayed on a map and congestion areas color-coded. Environmental data can also be displayed in graphs and charts to visually show fluctuations in air pollution. Furthermore, economic data can be displayed on an interactive dashboard, allowing fluctuations in economic indicators to be confirmed in real time. Data visualization can thus help policymakers make more effective use of data.

[0048] The policy-making system can further include a simulation unit. The simulation unit creates a virtual environment based on the collected data and simulates the impact of policies. For example, it can simulate the impact of a change in transportation policy on traffic flow throughout a city. It can also simulate the impact of a change in environmental policy on air pollution levels. It can also simulate the impact of a change in economic policy on the local economy. This allows policymakers to evaluate the impact of policies in advance and select the optimal policy.

[0049] The policymaking system can further include a feedback collection unit. The feedback collection unit collects feedback from citizens and stakeholders to help improve policies. For example, it can collect citizen opinions through online surveys and social media. It can also hold public debates and workshops on policies to collect direct feedback. The feedback collection unit can then analyze the collected opinions and propose improvements to the policy. This makes it possible to formulate policies that reflect the opinions of citizens and stakeholders.

[0050] The policy-making system can also compare data from different cities to extract and share best practices. For example, it can compare transportation data from different cities to extract the most effective transportation policy. It can also compare environmental data from different cities to extract the most effective environmental policy. It can also compare economic data from different cities to extract the most effective economic policy. This makes it possible to extract and share best practices by comparing data from different cities.

[0051] The policy-making system can further integrate data from different fields to make comprehensive policy proposals. For example, medical data and transportation data can be integrated to propose comprehensive health policies. Education data and economic data can also be integrated to propose comprehensive education policies. Furthermore, environmental data and transportation data can be integrated to propose comprehensive environmental policies. In this way, by integrating data from different fields, comprehensive policy proposals become possible.

[0052] The policymaking system can further analyze data in different languages ​​and make policy proposals from an international perspective. For example, it can automatically translate data in different languages ​​and make policy proposals from an international perspective. It can also analyze data in different languages ​​and make policy proposals based on success stories from other countries. Furthermore, it can analyze data in different languages ​​in real time and make policy proposals from the latest international perspective. This makes it possible to make policy proposals from an international perspective by analyzing data in different languages.

[0053] The policy planning system can further analyze data from different time periods and propose optimal policies for each time period. For example, it can analyze traffic data from different time periods and propose optimal traffic policies for each time period. It can also analyze environmental data from different time periods and propose optimal environmental policies for each time period. It can also analyze economic data from different time periods and propose optimal economic policies for each time period. In this way, analyzing data from different time periods makes it possible to propose optimal policies for each time period.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The data collection unit collects data, such as urban traffic data, environmental data, and economic data. Step 2: The Generative AI Analysis Unit analyzes the data collected by the Data Collection Unit. For example, the Generative AI may analyze urban traffic data to predict traffic congestion and propose solutions. The Generative AI may also analyze environmental data to predict air pollution and propose solutions. The Generative AI may also analyze economic data to evaluate the effectiveness of economic policies and propose necessary revisions. Step 3: The information sharing unit shares the information analyzed by the generative AI analysis unit. For example, policymakers can view the analysis results on the platform and share the information. Step 4: The Opinion Exchange Unit exchanges opinions based on the information shared by the Information Sharing Unit. For example, policymakers can exchange opinions and hold discussions on the platform. Step 5: The collaborative editing section will collaboratively edit the document based on the opinions exchanged by the opinion exchange section. For example, policymakers can use the collaborative editing function on the platform to edit the document.

[0056] (Example 2) A policymaking system according to an embodiment of the present invention is a system that aims to improve the efficiency and speed of policymaking by utilizing data-driven decision-making and generative AI. As a result, the policymaking system can achieve improved efficiency and speed of policymaking by utilizing data-driven decision-making and generative AI.

[0057] A policymaking system according to an embodiment includes a data collection unit, a generation AI analysis unit, an information sharing unit, an opinion exchange unit, and a collaborative editing unit. The data collection unit collects data, such as urban traffic data, environmental data, and economic data. The generation AI analysis unit analyzes the data collected by the data collection unit. For example, the generation AI analyzes urban traffic data to predict traffic congestion and propose solutions. The generation AI can also analyze environmental data to predict air pollution and propose countermeasures. The generation AI can also analyze economic data to evaluate the effectiveness of economic policies and propose necessary modifications. The information sharing unit shares information analyzed by the generation AI analysis unit. For example, policymakers can view the analysis results and share information on the platform. The opinion exchange unit exchanges opinions based on the information shared by the information sharing unit. For example, policymakers can exchange opinions and hold discussions on the platform. The collaborative editing unit collaboratively edits documents based on the opinions exchanged by the opinion exchange unit. For example, policymakers can edit documents using the collaborative editing function on the platform. This will enable policymaking systems to achieve more efficient and faster policymaking through data-driven decision-making and the use of generative AI.

[0058] The data collection unit can collect urban traffic data, environmental data, and economic data in real time. The generative AI analysis unit can analyze the data and make immediate policy proposals. For example, the data collection unit collects urban traffic data in real time, and the generative AI predicts traffic congestion and immediately proposes solutions. For example, it analyzes increases and decreases in traffic volume during specific time periods and proposes optimal traffic regulations. The data collection unit also analyzes environmental data in real time, and the generative AI predicts air pollution and immediately proposes countermeasures. For example, it monitors air pollution levels in specific areas and proposes necessary countermeasures. The data collection unit also collects economic data in real time, and the generative AI immediately evaluates the effectiveness of economic policies and proposes necessary adjustments. For example, it analyzes fluctuations in specific economic indicators and proposes optimal economic policies. This makes it possible to make immediate policy proposals through the analysis of real-time data.

[0059] The generative AI analysis unit can compare past policy data with current data and perform simulations to predict the effects of policies. For example, the generative AI analysis unit compares past transportation policy data with current transportation data, and the generative AI simulates the effects of new transportation policies. For example, it predicts new regulations based on the effects of past traffic regulations. The generative AI analysis unit also compares environmental policy data with current environmental data, and the generative AI simulates the effects of new environmental policies. For example, it predicts new measures based on the effects of past air pollution control measures. The generative AI analysis unit also compares economic policy data with current economic data, and the generative AI simulates the effects of new economic policies. For example, it predicts new stimulus measures based on the effects of past economic stimulus measures. This makes it possible to perform simulations to predict the effects of policies by comparing past data with current data.

[0060] The generative AI analysis unit uses the emotion estimation function to analyze citizen emotional data and evaluate the acceptability of policies. For example, the generative AI analysis unit analyzes citizens' social media posts and survey results, and the generative AI collects emotional data about policies. For example, it prioritizes proposals for policies that have a high level of positive emotion. The generative AI analysis unit also evaluates the acceptability of policies based on citizen emotional data and proposes necessary modifications. For example, it makes proposals to improve policies that have a high level of negative emotion. The generative AI analysis unit also analyzes citizen emotional data in real time, and the generative AI immediately evaluates the acceptability of policies. For example, it analyzes citizen reactions after a policy is announced and proposes necessary responses. This makes it possible to evaluate the acceptability of policies by analyzing citizen emotional data.

[0061] The generative AI analysis unit can compare data from different cities and extract and share best practices. For example, the generative AI analysis unit compares traffic data from different cities, and the generative AI extracts the most effective traffic policies. For example, best practices for alleviating traffic congestion are shared with other cities. The generative AI analysis unit also compares environmental data from different cities, and the generative AI extracts the most effective environmental policies. For example, best practices for combating air pollution are shared with other cities. The generative AI analysis unit also compares economic data from different cities, and the generative AI extracts the most effective economic policies. For example, best practices for promoting economic growth are shared with other cities. This makes it possible to extract and share best practices by comparing data from different cities.

[0062] The generative AI analysis unit can integrate data from different fields and make comprehensive policy proposals. For example, the generative AI analysis unit integrates medical data and transportation data, and the generative AI proposes comprehensive health policies. For example, it could propose measures to improve access to hospitals. The generative AI analysis unit also integrates education data and economic data, and the generative AI proposes comprehensive education policies. For example, it could make proposals to maximize the effectiveness of educational investments. The generative AI analysis unit also integrates environmental data and transportation data, and the generative AI proposes comprehensive environmental policies. For example, it could propose measures to promote the use of public transportation. In this way, by integrating data from different fields, comprehensive policy proposals become possible.

[0063] The generative AI analysis unit can analyze the emotional state of policymakers and support optimal decision-making. For example, the generative AI analysis unit collects emotional data from policymakers, and the generative AI analyzes their emotional state. For example, it makes suggestions for relaxing when stress levels are high. The generative AI analysis unit also supports optimal decision-making based on the policymaker's emotional state. For example, it suggests making important decisions when positive emotions are strong. The generative AI analysis unit also monitors the policymaker's emotional state in real time based on emotion estimation data and provides necessary support. For example, it provides advice based on the emotional state. This makes it possible to support optimal decision-making by analyzing the policymaker's emotional state.

[0064] The generative AI analysis unit can automatically collect data necessary for policy formulation and automatically generate analysis prompts. For example, the generative AI automatically collects publicly available data on the Internet and builds a dataset necessary for policy formulation. For example, it collects government statistical data and research papers. The generative AI analysis unit also automatically generates analysis prompts based on the data automatically collected by the generative AI. For example, it generates analysis prompts for transportation policy based on transportation data. The generative AI analysis unit also automatically collects data necessary for policy formulation and updates the analysis prompts in real time. For example, it updates the prompts every time new data is added. This makes it possible to automatically collect data necessary for policy formulation and automatically generate analysis prompts.

[0065] The generative AI analysis unit can integrate data from different data sources and provide analysis results from multiple perspectives. For example, the generative AI integrates data from different data sources, such as government data, private data, and academic data, to provide comprehensive analysis results. For example, it integrates and analyzes transportation data and environmental data. The generative AI analysis unit also integrates data from different data sources and provides analysis results from multiple perspectives. For example, it integrates economic data and social data to make policy proposals. The generative AI analysis unit also integrates data from different data sources in real time to provide the latest analysis results. For example, it updates the analysis results every time new data is added. This makes it possible to provide analysis results from multiple perspectives by integrating data from different data sources.

[0066] The generative AI analysis unit can use the emotion estimation function to analyze the intentions of policymakers and output optimal analysis results based on that. For example, the generative AI analysis unit collects emotional data from policymakers and the generative AI analyzes their intentions. For example, if positive emotions are strong, it will make proactive policy proposals. The generative AI analysis unit also uses the emotion estimation function to analyze the intentions of policymakers and output optimal analysis results based on that. For example, if negative emotions are strong, it will make risk avoidance proposals. The generative AI analysis unit also analyzes the emotional data of policymakers in real time, and the generative AI immediately outputs optimal analysis results. For example, it provides analysis results according to emotional state. This makes it possible to output optimal analysis results by analyzing the intentions of policymakers.

[0067] The generative AI analysis unit analyzes data in different languages ​​and can make policy proposals from an international perspective. For example, the generative AI analysis unit automatically translates data in different languages ​​and makes policy proposals from an international perspective. For example, it analyzes data in English, Chinese, French, etc. The generative AI analysis unit also analyzes data in different languages ​​and makes policy proposals from an international perspective. For example, it makes policy proposals based on successful examples from other countries. The generative AI analysis unit also analyzes data in different languages ​​in real time and makes policy proposals from the latest international perspective. For example, it updates proposals every time new data is added. This makes it possible to make policy proposals from an international perspective by analyzing data in different languages.

[0068] The generative AI analysis unit can analyze data from different time periods and make optimal policy proposals for each time period. For example, the generative AI analysis unit analyzes traffic data from different time periods and proposes optimal traffic policies for each time period. For example, it compares traffic volume during morning rush hour and at night and makes proposals. The generative AI analysis unit also analyzes environmental data from different time periods and proposes optimal environmental policies for each time period. For example, it compares air pollution levels during the day and at night and makes proposals. The generative AI analysis unit also analyzes economic data from different time periods and proposes optimal economic policies for each time period. For example, it compares economic activity on weekdays and weekends and makes proposals. In this way, by analyzing data from different time periods, it becomes possible to propose optimal policies for each time period.

[0069] The generative AI analysis unit can use the emotion estimation function to set priorities for data analysis based on the policymaker's emotions. For example, the generative AI analysis unit collects emotional data from policymakers, and the generative AI sets priorities for data analysis based on emotions. For example, if positive emotions are strong, proactive data analysis is prioritized. The generative AI analysis unit also uses the emotion estimation function to set priorities for data analysis based on the policymaker's emotions. For example, if negative emotions are strong, risk-avoidance data analysis is prioritized. The generative AI analysis unit also analyzes the policymaker's emotional data in real time, and the generative AI immediately sets priorities for data analysis based on emotions. For example, data analysis according to emotional state is prioritized. This makes it possible to set priorities for data analysis based on the policymaker's emotions.

[0070] The information sharing unit uses generation AI to automatically organize information and provide the most appropriate information to relevant parties. For example, the information sharing unit uses generation AI to automatically organize policy-related information on the platform and provide the most appropriate information to relevant parties. For example, it automatically organizes the latest policy data and analysis results. The information sharing unit also uses generation AI to filter information based on the role and interests of the relevant parties and provide the most appropriate information. For example, it prioritizes providing transportation data to stakeholders who are interested in transportation policy. The information sharing unit also uses generation AI to evaluate the importance of information on the platform and provide the most appropriate information to relevant parties. For example, it prioritizes providing information with a high level of urgency. This makes it possible to organize information using generation AI and provide the most appropriate information to relevant parties.

[0071] The information sharing unit can use the generation AI to analyze the history of opinion exchanges and extract important discussion points. For example, the information sharing unit uses the generation AI on the platform to analyze the history of opinion exchanges and automatically extract important discussion points. For example, it extracts frequently mentioned keywords and phrases. The information sharing unit also uses the generation AI to analyze the history of opinion exchanges and visualize the progress of the discussion. For example, it displays the progress of the discussion and unresolved issues. The information sharing unit also uses the generation AI on the platform to analyze the history of opinion exchanges and notify relevant parties of important discussion points. For example, it sends an alert when an important discussion takes place. This makes it possible to analyze the history of opinion exchanges and extract important discussion points.

[0072] The information sharing unit can use the emotion estimation function to analyze changes in emotions during an exchange of opinions and support the progress of the discussion. The information sharing unit, for example, uses the emotion estimation function on the platform to analyze changes in emotions during an exchange of opinions in real time. For example, if the discussion becomes heated, it may suggest that participants calm down. The information sharing unit also uses the emotion estimation function to analyze changes in emotions during an exchange of opinions and support the progress of the discussion. For example, it may suggest that the discussion be stopped if negative emotions are strong. The information sharing unit also uses the emotion estimation function on the platform to analyze changes in emotions during an exchange of opinions and provide feedback to the parties involved. For example, it may provide advice according to the changes in emotions. This makes it possible to analyze changes in emotions during an exchange of opinions and support the progress of the discussion.

[0073] The information sharing unit promotes information sharing between different organizations, enabling crossover policy proposals. For example, the information sharing unit promotes information sharing between different organizations on the platform, and the generation AI makes crossover policy proposals. For example, it integrates data from local governments and companies to make policy proposals. In addition, to promote information sharing between different organizations, the generation AI automatically generates common data formats. For example, it unifies and analyzes different data formats. In addition, the information sharing unit promotes information sharing between different organizations on the platform, and the generation AI makes comprehensive policy proposals. For example, it integrates data from non-profit organizations and government agencies to make policy proposals. This promotes information sharing between different organizations, enabling crossover policy proposals.

[0074] The information sharing unit can use the generative AI to automatically match experts from different fields and promote collaborative editing. For example, the information sharing unit uses the generative AI to automatically match experts from different fields on the platform to promote collaborative editing. For example, it matches experts in transportation policy with experts in environmental policy. The information sharing unit also uses the generative AI to analyze the experts' profiles and make the optimal match. For example, it makes a match based on expertise and experience. The information sharing unit also uses the generative AI to match experts from different fields on the platform and monitor the progress of the collaborative editing. For example, it displays the progress of the collaborative editing in real time. This makes it possible to automatically match experts from different fields and promote collaborative editing.

[0075] The information sharing unit can use the emotion estimation function to analyze changes in emotions while information is being shared and suggest the most appropriate communication method. The information sharing unit, for example, uses the emotion estimation function on the platform to analyze changes in emotions while information is being shared in real time. For example, it suggests that the person stay calm if emotions are high. The information sharing unit also uses the emotion estimation function to analyze changes in emotions while information is being shared and suggest the most appropriate communication method. For example, it suggests that the person stop talking if negative emotions are strong. The information sharing unit also uses the emotion estimation function on the platform to analyze changes in emotions while information is being shared and provide feedback to the parties involved. For example, it provides advice according to the changes in emotions. This makes it possible to analyze changes in emotions while information is being shared and suggest the most appropriate communication method.

[0076] The opinion exchange unit can use the generation AI to analyze the content of the opinion exchange in real time and automatically summarize the important points. For example, the generation AI in the opinion exchange unit analyzes the content of the opinion exchange in real time and automatically summarizes the important points. For example, it summarizes the main points of the discussion in short sentences. The opinion exchange unit also analyzes the content of the opinion exchange, and the generation AI extracts and summarizes the important points. For example, it includes frequently mentioned keywords and phrases in the summary. The opinion exchange unit also analyzes the content of the opinion exchange in real time and notifies the relevant parties of the important points. For example, it summarizes and displays the progress of the discussion. This makes it possible to analyze the content of the opinion exchange in real time and automatically summarize the important points.

[0077] The collaborative editing department can use generative AI to analyze documents being co-edited and automatically point out inconsistencies and areas for improvement. For example, the collaborative editing department uses generative AI to analyze documents being co-edited in real time and automatically point out inconsistencies and areas for improvement. For example, it points out logical inconsistencies within the document. The collaborative editing department also analyzes documents being co-edited, and the generative AI suggests areas for improvement. For example, it makes suggestions to improve the structure and expression of the document. The collaborative editing department also uses generative AI to analyze documents being co-edited in real time and notify relevant parties of inconsistencies and areas for improvement. For example, it displays points to be corrected in the document. This makes it possible to analyze documents being co-edited and automatically point out inconsistencies and areas for improvement.

[0078] The opinion exchange unit can use the emotion estimation function to analyze changes in emotions during the exchange of opinions and support the progress of the discussion. The opinion exchange unit, for example, uses the emotion estimation function to analyze changes in emotions during the exchange of opinions in real time. For example, if the discussion becomes heated, it suggests that participants calm down. The opinion exchange unit also uses the emotion estimation function to analyze changes in emotions during the exchange of opinions and support the progress of the discussion. For example, it suggests that the discussion be stopped if negative emotions are strong. The opinion exchange unit also uses the emotion estimation function to analyze changes in emotions during the exchange of opinions and provide feedback to the parties involved. For example, it provides advice according to the changes in emotions. This makes it possible to analyze changes in emotions during the exchange of opinions and support the progress of the discussion.

[0079] The opinion exchange unit uses the generation AI to translate exchanges of opinions in different languages ​​in real time, thereby promoting international discussion. For example, the opinion exchange unit uses the generation AI to translate exchanges of opinions in different languages ​​in real time, thereby promoting international discussion. For example, it translates opinions in English, Chinese, French, etc. The opinion exchange unit also translates exchanges of opinions in different languages ​​in real time, and the generation AI supports international discussions. For example, it facilitates exchanges of opinions with experts from other countries. The opinion exchange unit also uses the generation AI to translate exchanges of opinions in different languages ​​in real time, and provides the translated results to those involved. For example, it translates and displays the progress of the discussion. This makes it possible to translate exchanges of opinions in different languages ​​in real time, thereby promoting international discussion.

[0080] The collaborative editing department can use generative AI to automatically match experts from different fields and promote collaborative editing. For example, the collaborative editing department uses generative AI to automatically match experts from different fields and promote collaborative editing. For example, it matches an expert in transportation policy with an expert in environmental policy. In addition, the collaborative editing department uses generative AI to analyze the expert's profile and make the optimal match. For example, it makes a match based on expertise and experience. In addition, the collaborative editing department uses generative AI to match experts from different fields and monitor the progress of collaborative editing. For example, it displays the progress of collaborative editing in real time. This makes it possible to automatically match experts from different fields and promote collaborative editing.

[0081] The opinion exchange unit can use the emotion estimation function to analyze changes in emotions during the exchange of opinions and suggest the optimal communication method. The opinion exchange unit, for example, uses the emotion estimation function to analyze changes in emotions during the exchange of opinions in real time. For example, if emotions are high, it suggests that the person calm down. The opinion exchange unit also uses the emotion estimation function to analyze changes in emotions during the exchange of opinions and suggest the optimal communication method. For example, it suggests that the person stop the conversation if negative emotions are strong. The opinion exchange unit also uses the emotion estimation function to analyze changes in emotions during the exchange of opinions and provide feedback to the parties involved. For example, it provides advice according to the changes in emotions. This makes it possible to analyze changes in emotions during the exchange of opinions and suggest the optimal communication method.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The policymaking system can also be equipped with a data visualization unit. The data visualization unit visually displays collected data, allowing policymakers to intuitively understand it. For example, traffic data can be displayed on a map and congestion areas color-coded. Environmental data can also be displayed in graphs and charts to visually show fluctuations in air pollution. Furthermore, economic data can be displayed on an interactive dashboard, allowing fluctuations in economic indicators to be confirmed in real time. Data visualization can thus help policymakers make more effective use of data.

[0084] The policy-making system can further include a simulation unit. The simulation unit creates a virtual environment based on the collected data and simulates the impact of policies. For example, it can simulate the impact of a change in transportation policy on traffic flow throughout a city. It can also simulate the impact of a change in environmental policy on air pollution levels. It can also simulate the impact of a change in economic policy on the local economy. This allows policymakers to evaluate the impact of policies in advance and select the optimal policy.

[0085] The policymaking system can further include a feedback collection unit. The feedback collection unit collects feedback from citizens and stakeholders to help improve policies. For example, it can collect citizen opinions through online surveys and social media. It can also hold public debates and workshops on policies to collect direct feedback. The feedback collection unit can then analyze the collected opinions and propose improvements to the policy. This makes it possible to formulate policies that reflect the opinions of citizens and stakeholders.

[0086] The policymaking system can also use emotion estimation functions to analyze citizen emotional data and evaluate the acceptability of policies. For example, it can analyze citizens' social media posts and survey results and prioritize policies that have a high level of positive emotion. It can also evaluate policy acceptability and propose necessary revisions based on citizen emotional data. Furthermore, it can analyze citizen emotional data in real time, analyze citizen reactions after policy announcements, and propose necessary responses. This makes it possible to evaluate policy acceptability by analyzing citizen emotional data.

[0087] The policy-making system can also compare data from different cities to extract and share best practices. For example, it can compare transportation data from different cities to extract the most effective transportation policy. It can also compare environmental data from different cities to extract the most effective environmental policy. It can also compare economic data from different cities to extract the most effective economic policy. This makes it possible to extract and share best practices by comparing data from different cities.

[0088] The policy-making system can further integrate data from different fields to make comprehensive policy proposals. For example, medical data and transportation data can be integrated to propose comprehensive health policies. Education data and economic data can also be integrated to propose comprehensive education policies. Furthermore, environmental data and transportation data can be integrated to propose comprehensive environmental policies. In this way, by integrating data from different fields, comprehensive policy proposals become possible.

[0089] The policymaking system can further use emotion estimation functions to analyze the emotional state of policymakers and support optimal decision-making. For example, it can collect emotional data from policymakers and make suggestions for relaxation when stress levels are high. It can also support optimal decision-making based on the policymaker's emotional state. Furthermore, it can monitor the policymaker's emotional state in real time based on emotion estimation data and provide necessary support. This makes it possible to support optimal decision-making by analyzing the policymaker's emotional state.

[0090] The policymaking system can further analyze data in different languages ​​and make policy proposals from an international perspective. For example, it can automatically translate data in different languages ​​and make policy proposals from an international perspective. It can also analyze data in different languages ​​and make policy proposals based on success stories from other countries. Furthermore, it can analyze data in different languages ​​in real time and make policy proposals from the latest international perspective. This makes it possible to make policy proposals from an international perspective by analyzing data in different languages.

[0091] The policymaking system can further use emotion estimation functions to analyze the intentions of policymakers and output optimal analysis results based on that. For example, it can collect emotional data on policymakers and make proactive policy proposals when positive emotions are strong. It can also use emotion estimation functions to analyze the intentions of policymakers and output optimal analysis results based on that. Furthermore, it can analyze the emotional data of policymakers in real time and output optimal analysis results instantly. This makes it possible to output optimal analysis results by analyzing the intentions of policymakers.

[0092] The policy planning system can further analyze data from different time periods and propose optimal policies for each time period. For example, it can analyze traffic data from different time periods and propose optimal traffic policies for each time period. It can also analyze environmental data from different time periods and propose optimal environmental policies for each time period. It can also analyze economic data from different time periods and propose optimal economic policies for each time period. In this way, analyzing data from different time periods makes it possible to propose optimal policies for each time period.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The data collection unit collects data, such as urban traffic data, environmental data, and economic data. Step 2: The Generative AI Analysis Unit analyzes the data collected by the Data Collection Unit. For example, the Generative AI may analyze urban traffic data to predict traffic congestion and propose solutions. The Generative AI may also analyze environmental data to predict air pollution and propose solutions. The Generative AI may also analyze economic data to evaluate the effectiveness of economic policies and propose necessary revisions. Step 3: The information sharing unit shares the information analyzed by the generative AI analysis unit. For example, policymakers can view the analysis results on the platform and share the information. Step 4: The Opinion Exchange Unit exchanges opinions based on the information shared by the Information Sharing Unit. For example, policymakers can exchange opinions and hold discussions on the platform. Step 5: The collaborative editing section will collaboratively edit the document based on the opinions exchanged by the opinion exchange section. For example, policymakers can use the collaborative editing function on the platform to edit the document.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0108] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0109] 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.

[0110] 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.

[0111] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0112] 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.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0124] 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.

[0125] 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.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] 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.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0139] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] 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.

[0141] 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.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0161] 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]

[0162] 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 data collection unit that collects data; a generation AI analysis unit that analyzes the data collected by the data collection unit; an information sharing unit that shares the information analyzed by the generation AI analysis unit; an opinion exchange unit that exchanges opinions based on the information shared by the information sharing unit; a collaborative editing unit that collaboratively edits a document based on the opinions exchanged by the opinion exchange unit. A system characterized by:

2. The data collection unit Collecting urban traffic, environmental, and economic data in real time, The generation AI analysis unit Analyze the data and make real-time policy recommendations 2. The system of claim 1.

3. The generation AI analysis unit Comparing past policy data with current data and conducting simulations to predict the effects of policies 2. The system of claim 1.

4. The generation AI analysis unit Analyzing citizen sentiment data to assess policy acceptability 2. The system of claim 1.

5. The generation AI analysis unit Compare data from different cities, extract and share best practices 2. The system of claim 1.

6. The generation AI analysis unit Integrating data from different fields to make comprehensive policy proposals 2. The system of claim 1.

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