System

Abstract

An object of the system according to the embodiment is to analyze a legal document and map information so that a legally acceptable place can be visually understood.SOLUTION: A system according to an embodiment includes a law analysis unit, a map information analysis unit, and a visualization unit. The law analysis unit analyzes a legal sentence. The map information analysis unit analyzes map information. The visualization unit generates a law visualization map on the basis of analysis results of the law analysis unit and the map information analysis 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] With conventional technology, it was difficult to integrate legal texts and map information for visual understanding, and it took time and effort to identify legally acceptable locations.

[0005] The system according to the embodiment aims to analyze legal documents and map information to enable visual understanding of legally acceptable locations. [Means for solving the problem]

[0006] The system according to the embodiment includes a legal analysis unit, a map information analysis unit, and a visualization unit. The legal analysis unit analyzes legal documents. The map information analysis unit analyzes map information. The visualization unit generates a legal visualization map based on the analysis results of the legal analysis unit and the map information analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze legal texts and map information to provide a visual understanding of legally acceptable locations. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The legal visualization map generation system according to an embodiment of the present invention is a system that analyzes legal documents and map information to visualize legally safe and unsafe locations. This allows users to intuitively understand legally safe locations.

[0029] A legal visualization map generation system according to an embodiment includes a legal analysis unit, a map information analysis unit, and a visualization unit. The legal analysis unit analyzes legal texts. For example, the legal analysis unit analyzes legal texts such as the Road Traffic Act and the Building Act to understand their contents. The legal analysis unit can also analyze the context of legal texts and automatically interpret ambiguous expressions and exceptions. The map information analysis unit analyzes map information. For example, the map information analysis unit analyzes GIS data and geographic coordinate information to extract information on a map. The map information analysis unit can also analyze the legal situation of a specific location based on the map information. The visualization unit generates a legal visualization map based on the analysis results of the legal analysis unit and the map information analysis unit. For example, the visualization unit displays legally safe and unsafe locations on a map using different colors based on the analysis results. The visualization unit can also display the legal visualization map using a 3D model or augmented reality (AR). This allows the legal visualization map generation system according to an embodiment to intuitively understand legally safe locations. For example, users can use the Law Visualization Map to check where they can park and drop off their electric scooters. Users can also use the Law Visualization Map to simulate the impact of legal changes.

[0030] The legal analysis unit understands the context of legal texts and can automatically interpret ambiguous expressions and exceptions. For example, the legal analysis unit adds a function to the generation AI to analyze the context of legal texts and automatically interpret ambiguous expressions and exceptions. For example, it converts ambiguous expressions such as "parking is permitted under certain conditions" into specific conditions. In addition, to understand the context of legal texts, the legal analysis unit adds a function to the generation AI to refer to a database of past precedents and legal interpretations and automatically interpret ambiguous expressions and exceptions. For example, the generation AI analyzes the context of legal texts and strengthens natural language processing technology to automatically interpret ambiguous expressions and exceptions. This improves the accuracy of the analysis results by automatically interpreting ambiguous expressions and exceptions in legal texts.

[0031] The visualization unit can display the results of legal text analysis using 3D models or AR (augmented reality). For example, the visualization unit displays the results of legal text analysis using 3D models to enable users to intuitively understand the results. For example, building height restrictions based on building laws are visualized using 3D models. The visualization unit also displays the results of legal text analysis using AR (augmented reality), allowing users to check them by overlaying them on real-world scenery. For example, no-parking zones can be displayed using AR, allowing users to check them on-site. The visualization unit also develops an interface for displaying the results of legal text analysis using 3D models or AR, in addition to map information. For example, the visualization unit enables users to view 3D models or AR displays using a smartphone or tablet. This allows users to intuitively understand the results of legal text analysis by displaying them using 3D models or AR.

[0032] The legal analysis unit and the map information analysis unit can integrate traffic flow data and environmental data to perform comprehensive analysis. For example, the legal analysis unit and the map information analysis unit integrate the results of the analysis of legal texts and map information with traffic flow data to analyze the impact of no-parking zones. For example, they identify areas where traffic congestion is likely to occur and set up no-parking zones. The legal analysis unit and the map information analysis unit also integrate the results of the analysis of legal texts and map information with environmental data to identify environmentally protected areas. For example, they set up no-parking zones around nature reserves and parks. The legal analysis unit and the map information analysis unit also build a database to integrate the results of the analysis of legal texts and map information with traffic flow data and environmental data to perform comprehensive analysis. For example, they integrate different data sources to provide comprehensive analysis results. This enables more comprehensive analysis by integrating with traffic flow data and environmental data.

[0033] The legal analysis unit can analyze legal documents from different countries and regions and perform comparative analysis from an international perspective. For example, the legal analysis unit analyzes legal documents from different countries and regions and performs comparative analysis from an international perspective. For example, it compares parking regulations in each country and identifies similarities and differences. The legal analysis unit also develops multilingual natural language processing technology to analyze legal documents from different countries and regions and performs comparative analysis from an international perspective. For example, it analyzes legal documents in English, French, Chinese, etc. The legal analysis unit also builds legal databases from different countries and regions and integrates the analysis results to perform comparative analysis from an international perspective. For example, it collects legal data from each country and analyzes it in a unified format. This makes it possible to analyze legal documents from different countries and regions and perform comparative analysis from an international perspective.

[0034] The legal analysis unit can predict and simulate the impact of future legal reforms based on the results of analyzing legal texts. For example, the legal analysis unit adds a function that allows the generation AI to predict and simulate the impact of future legal reforms based on the results of analyzing legal texts. For example, it simulates the impact of new parking regulations being introduced. In addition, the legal analysis unit adds a function that allows the generation AI to learn data on past legal reforms and simulate future impacts in order to predict the impact of legal reforms. For example, it predicts impacts based on past data. In addition, the legal analysis unit develops an algorithm that allows the generation AI to simulate the impact of future legal reforms based on the results of analyzing legal texts. For example, it visualizes the simulation results and provides them to users. This allows the impact of future legal reforms to be predicted and simulated, allowing measures to be taken in advance.

[0035] The legal analysis unit can update the analysis results of legal texts in real time, always reflecting the latest legal information. For example, the legal analysis unit builds a system in which the generation AI updates the analysis results of legal texts in real time, always reflecting the latest legal information. For example, it immediately updates the analysis results when a new law is enacted. The legal analysis unit also builds a database for updating the analysis results of legal texts in real time, always reflecting the latest legal information. For example, it regularly updates the legal database and reflects this in the analysis results. The legal analysis unit also develops an interface that allows the generation AI to update the analysis results of legal texts in real time, always reflecting the latest legal information. For example, it makes it easy for users to check the latest legal information. This allows the latest legal information to be always provided by updating the legal information in real time.

[0036] The legal analysis unit can integrate the results of the analysis of legal texts with urban planning data and infrastructure data to build a comprehensive city management system. For example, the legal analysis unit integrates the results of the analysis of legal texts with other urban planning data to build a comprehensive city management system. For example, it sets no-parking zones based on urban planning data. The legal analysis unit also integrates the results of the analysis of legal texts with infrastructure data to build a comprehensive city management system. For example, it sets no-parking zones based on road infrastructure data. The legal analysis unit also builds a database for integrating the results of the analysis of legal texts with other urban planning data and infrastructure data to provide a comprehensive city management system. For example, it integrates different data sources to provide comprehensive analysis results. This makes it possible to integrate the results of the analysis of legal texts with other urban planning data and infrastructure data to provide comprehensive city management.

[0037] The legal analysis unit can apply the results of the analysis of legal text to different industries to meet industry-specific needs. For example, the legal analysis unit applies the results of the analysis of legal text to the real estate industry to meet industry-specific needs. For example, the legal analysis unit analyzes legal constraints in real estate development and identifies land that can be developed. The legal analysis unit also applies the results of the analysis of legal text to the logistics industry to meet industry-specific needs. For example, the legal constraints in establishing a logistics base are analyzed and the optimal base is identified. The legal analysis unit also develops an interface for applying the results of the analysis of legal text to different industries to meet industry-specific needs. For example, it makes it easy to check the legal constraints for each industry. This makes it possible to apply the results of the analysis of legal text to different industries to meet industry-specific needs.

[0038] The visualization unit can integrate traffic information and weather information into the law visualization map to generate a dynamic map. In the visualization unit, for example, the generation AI integrates real-time traffic information into the law visualization map to generate a dynamic map. For example, traffic congestion and accident information is reflected and available parking locations are dynamically displayed. In addition, the visualization unit integrates real-time weather information into the law visualization map to generate a dynamic map. For example, the effects of heavy rain and snow are reflected and available parking locations are dynamically displayed. In addition, the visualization unit builds a database for integrating real-time traffic information and weather information into the law visualization map to provide a dynamic map. For example, different data sources are integrated and comprehensive analysis results are provided. In this way, a dynamic map can be generated by integrating real-time traffic information and weather information.

[0039] The visualization unit can add a customizable filter function to the law visualization map and generate a map based on specific conditions. The visualization unit, for example, adds a customizable filter function to the law visualization map and generates a map based on specific conditions. For example, it filters available parking locations by time of day or day of the week. The visualization unit also generates a law visualization map based on specific conditions using a user-customizable filter function. For example, it displays available parking locations for specific vehicle types or uses. The visualization unit also develops an interface for adding a customizable filter function to the law visualization map, allowing the user to easily set conditions. For example, the filter conditions can be set by drag and drop. In this way, adding a customizable filter function makes it possible to generate a map based on specific conditions.

[0040] The visualization unit can optimize the law visualization map for mobile devices, allowing users to use it anywhere. For example, the visualization unit optimizes the law visualization map for smartphones and tablets, allowing users to use it anywhere. For example, the visualization unit develops an interface compatible with mobile devices. The visualization unit also provides the law visualization map optimized for mobile devices, allowing users to easily use it on the go. For example, the visualization unit utilizes a GPS function to display information around the current location. The visualization unit also develops an application to optimize the law visualization map for smartphones and tablets, allowing users to use it anywhere. For example, the visualization unit adds a function that allows it to be used offline. In this way, by optimizing the law visualization map for mobile devices, users can use it anywhere.

[0041] The visualization unit can apply the legal visualization map to different cities and regions to provide legal information from a global perspective. For example, the visualization unit applies the legal visualization map to different cities and regions to provide legal information from a global perspective. For example, the visualization unit compares parking regulations in each city and identifies similarities and differences. The visualization unit also builds a system that collects legal information from different cities and regions and integrates it into the legal visualization map. For example, the visualization unit collects legal data from each city and analyzes it in a unified format. The visualization unit also develops a multilingual interface to provide the legal visualization map from a global perspective, allowing users to easily check legal information from different cities and regions. For example, legal information can be provided in English, French, Chinese, and other languages. In this way, the legal visualization map can be applied to different cities and regions to provide legal information from a global perspective.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The legal analysis unit can predict and simulate the impact of future legal reforms based on the results of analyzing legal texts. For example, the generation AI adds a function to predict and simulate the impact of future legal reforms based on the results of analyzing legal texts. For example, it can simulate the impact of new parking regulations being introduced. In addition, the legal analysis unit adds a function to predict the impact of legal reforms by having the generation AI learn data on past legal reforms and simulate future impacts. For example, it can predict impacts based on past data. The legal analysis unit also develops algorithms that allow the generation AI to simulate the impact of future legal reforms based on the results of analyzing legal texts. For example, it can visualize the simulation results and provide them to users. This allows the impact of future legal reforms to be predicted and simulated, allowing measures to be taken in advance.

[0044] The visualization unit can integrate traffic information and weather information into the law visualization map to generate a dynamic map. For example, the generation AI integrates real-time traffic information into the law visualization map to generate a dynamic map. For example, it reflects traffic congestion and accident information and dynamically displays available parking locations. The visualization unit also integrates real-time weather information into the law visualization map to generate a dynamic map. For example, it reflects the effects of heavy rain and snow and dynamically displays available parking locations. The visualization unit also builds a database for integrating real-time traffic information and weather information into the law visualization map to provide a dynamic map. For example, it integrates different data sources and provides comprehensive analysis results. This makes it possible to generate a dynamic map by integrating real-time traffic information and weather information.

[0045] The legal analysis unit can analyze legal documents from different countries and regions and conduct comparative analysis from an international perspective. For example, it can analyze legal documents from different countries and regions and conduct comparative analysis from an international perspective. For example, it can compare parking regulations in each country and identify similarities and differences. The legal analysis unit also develops multilingual natural language processing technology to analyze legal documents from different countries and regions and conduct comparative analysis from an international perspective. For example, it can analyze legal documents in English, French, Chinese, and other languages. The legal analysis unit also builds legal databases from different countries and regions and integrates the analysis results to conduct comparative analysis from an international perspective. For example, it collects legal data from each country and analyzes it in a unified format. This makes it possible to analyze legal documents from different countries and regions and conduct comparative analysis from an international perspective.

[0046] The legal analysis unit and map information analysis unit can integrate traffic flow data and environmental data to perform comprehensive analysis. For example, the results of the analysis of legal texts and map information can be integrated with traffic flow data to analyze the impact of no-parking zones. For example, areas prone to traffic congestion can be identified and no-parking zones can be set up. The legal analysis unit and map information analysis unit can also integrate the results of the analysis of legal texts and map information with environmental data to identify environmentally protected areas. For example, no-parking zones can be set up around nature reserves and parks. The legal analysis unit and map information analysis unit can also build a database to integrate the results of the analysis of legal texts and map information with traffic flow data and environmental data to perform comprehensive analysis. For example, different data sources can be integrated to provide comprehensive analysis results. This allows for more comprehensive analysis by integrating with traffic flow data and environmental data.

[0047] The legal analysis unit can update the results of legal text analysis in real time, always reflecting the latest legal information. For example, a system can be constructed in which the generation AI updates the results of legal text analysis in real time, always reflecting the latest legal information. For example, the analysis results can be updated immediately when a new law is enacted. The legal analysis unit can also build a database for updating the results of legal text analysis in real time, reflecting the latest legal information. For example, the legal database can be updated regularly to reflect this in the analysis results. The legal analysis unit can also develop an interface that allows the generation AI to update the results of legal text analysis in real time, always reflecting the latest legal information. For example, it can make it easy for users to check the latest legal information. This allows legal information to be updated in real time, always providing the latest information.

[0048] The visualization unit can add a customizable filter function to the law visualization map and generate a map based on specific conditions. For example, a customizable filter function can be added to the law visualization map to generate a map based on specific conditions. For example, available parking locations can be filtered by time of day or day of the week. The visualization unit also generates a law visualization map based on specific conditions using a user-customizable filter function. For example, available parking locations for specific vehicle types or uses can be displayed. The visualization unit also develops an interface for adding a customizable filter function to the law visualization map, allowing users to easily set conditions. For example, filter conditions can be set by drag and drop. In this way, adding a customizable filter function makes it possible to generate a map based on specific conditions.

[0049] The legal analysis unit can apply the results of legal text analysis to different industries to meet industry-specific needs. For example, the results of legal text analysis can be applied to the real estate industry to meet industry-specific needs. For example, legal constraints in real estate development can be analyzed to identify land that can be developed. The legal analysis unit can also apply the results of legal text analysis to the logistics industry to meet industry-specific needs. For example, legal constraints in establishing a logistics base can be analyzed to identify the optimal base. The legal analysis unit can also develop an interface for applying the results of legal text analysis to different industries to meet industry-specific needs. For example, it can make it easy to check legal constraints for each industry. This allows the results of legal text analysis to be applied to different industries to meet industry-specific needs.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The legal analysis unit analyzes legal documents. For example, the legal analysis unit analyzes legal documents such as road traffic laws and building laws to understand their content. The legal analysis unit can also analyze the context of legal documents and automatically interpret ambiguous expressions and exceptions. Step 2: The map information analysis unit analyzes the map information. For example, the map information analysis unit analyzes GIS data and geographic coordinate information to extract information from the map. The map information analysis unit can also analyze the legal status of a specific location based on the map information. Step 3: The visualization unit generates a legal visualization map based on the analysis results of the legal analysis unit and the map information analysis unit. For example, based on the analysis results, the visualization unit displays legally problem-free and illegal locations on a map in different colors. The visualization unit can also display the legal visualization map using a 3D model or AR (augmented reality). This allows users to intuitively understand where legally problem-free locations are.

[0052] (Example 2) The legal visualization map generation system according to an embodiment of the present invention is a system that analyzes legal documents and map information to visualize legally safe and unsafe locations. This allows users to intuitively understand legally safe locations.

[0053] A legal visualization map generation system according to an embodiment includes a legal analysis unit, a map information analysis unit, and a visualization unit. The legal analysis unit analyzes legal texts. For example, the legal analysis unit analyzes legal texts such as the Road Traffic Act and the Building Act to understand their contents. The legal analysis unit can also analyze the context of legal texts and automatically interpret ambiguous expressions and exceptions. The map information analysis unit analyzes map information. For example, the map information analysis unit analyzes GIS data and geographic coordinate information to extract information on a map. The map information analysis unit can also analyze the legal situation of a specific location based on the map information. The visualization unit generates a legal visualization map based on the analysis results of the legal analysis unit and the map information analysis unit. For example, the visualization unit displays legally safe and unsafe locations on a map using different colors based on the analysis results. The visualization unit can also display the legal visualization map using a 3D model or augmented reality (AR). This allows the legal visualization map generation system according to an embodiment to intuitively understand legally safe locations. For example, users can use the Law Visualization Map to check where they can park and drop off their electric scooters. Users can also use the Law Visualization Map to simulate the impact of legal changes.

[0054] The legal analysis unit understands the context of legal texts and can automatically interpret ambiguous expressions and exceptions. For example, the legal analysis unit adds a function to the generation AI to analyze the context of legal texts and automatically interpret ambiguous expressions and exceptions. For example, it converts ambiguous expressions such as "parking is permitted under certain conditions" into specific conditions. In addition, to understand the context of legal texts, the legal analysis unit adds a function to the generation AI to refer to a database of past precedents and legal interpretations and automatically interpret ambiguous expressions and exceptions. For example, the generation AI analyzes the context of legal texts and strengthens natural language processing technology to automatically interpret ambiguous expressions and exceptions. This improves the accuracy of the analysis results by automatically interpreting ambiguous expressions and exceptions in legal texts.

[0055] The visualization unit can display the results of legal text analysis using 3D models or AR (augmented reality). For example, the visualization unit displays the results of legal text analysis using 3D models to enable users to intuitively understand the results. For example, building height restrictions based on building laws are visualized using 3D models. The visualization unit also displays the results of legal text analysis using AR (augmented reality), allowing users to check them by overlaying them on real-world scenery. For example, no-parking zones can be displayed using AR, allowing users to check them on-site. The visualization unit also develops an interface for displaying the results of legal text analysis using 3D models or AR, in addition to map information. For example, the visualization unit enables users to view 3D models or AR displays using a smartphone or tablet. This allows users to intuitively understand the results of legal text analysis by displaying them using 3D models or AR.

[0056] The visualization unit can use the emotion estimation function to adjust the display format of the analysis results of legal texts according to the user's emotions. For example, the visualization unit uses the emotion estimation function to adjust the display format of the analysis results of legal texts according to the user's emotions. For example, if the user is feeling stressed, information is provided in a concise and easy-to-understand format. The visualization unit also analyzes the user's emotions in real time and provides the analysis results of legal texts in a format that is easy for the user to understand. For example, if the user is feeling anxious, detailed explanations and supplementary information are added. The visualization unit also uses the emotion estimation function to develop a system that customizes the analysis results of legal texts according to the user's emotions. For example, detailed information is provided if the user is feeling positive, and concise information is provided if the user is feeling negative. In this way, by adjusting the display format according to the user's emotions, it is possible to provide information that is easy for the user to understand.

[0057] The legal analysis unit and the map information analysis unit can integrate traffic flow data and environmental data to perform comprehensive analysis. For example, the legal analysis unit and the map information analysis unit integrate the results of the analysis of legal texts and map information with traffic flow data to analyze the impact of no-parking zones. For example, they identify areas where traffic congestion is likely to occur and set up no-parking zones. The legal analysis unit and the map information analysis unit also integrate the results of the analysis of legal texts and map information with environmental data to identify environmentally protected areas. For example, they set up no-parking zones around nature reserves and parks. The legal analysis unit and the map information analysis unit also build a database to integrate the results of the analysis of legal texts and map information with traffic flow data and environmental data to perform comprehensive analysis. For example, they integrate different data sources to provide comprehensive analysis results. This enables more comprehensive analysis by integrating with traffic flow data and environmental data.

[0058] The legal analysis unit can analyze legal documents from different countries and regions and perform comparative analysis from an international perspective. For example, the legal analysis unit analyzes legal documents from different countries and regions and performs comparative analysis from an international perspective. For example, it compares parking regulations in each country and identifies similarities and differences. The legal analysis unit also develops multilingual natural language processing technology to analyze legal documents from different countries and regions and performs comparative analysis from an international perspective. For example, it analyzes legal documents in English, French, Chinese, etc. The legal analysis unit also builds legal databases from different countries and regions and integrates the analysis results to perform comparative analysis from an international perspective. For example, it collects legal data from each country and analyzes it in a unified format. This makes it possible to analyze legal documents from different countries and regions and perform comparative analysis from an international perspective.

[0059] The visualization unit can use the emotion estimation function to collect users' emotional reactions to the analysis results of legal texts and use the collected data to improve the analysis results. For example, the visualization unit uses the emotion estimation function to collect users' emotional reactions to the analysis results of legal texts and improves the analysis results based on the collected data. For example, it identifies and improves parts that are difficult for users to understand. The visualization unit also builds a system for improving the analysis results of legal texts based on the user's emotional reaction data. For example, it reanalyzes and improves parts that have a high number of negative emotional reactions. The visualization unit also uses the emotion estimation function to collect users' emotional reactions to the analysis results of legal texts in real time and immediately improves them. For example, it adjusts the analysis results based on user feedback. In this way, collecting users' emotional reactions and using them to improve the analysis results improves the user experience.

[0060] The legal analysis unit can predict and simulate the impact of future legal reforms based on the results of analyzing legal texts. For example, the legal analysis unit adds a function that allows the generation AI to predict and simulate the impact of future legal reforms based on the results of analyzing legal texts. For example, it simulates the impact of new parking regulations being introduced. In addition, the legal analysis unit adds a function that allows the generation AI to learn data on past legal reforms and simulate future impacts in order to predict the impact of legal reforms. For example, it predicts impacts based on past data. In addition, the legal analysis unit develops an algorithm that allows the generation AI to simulate the impact of future legal reforms based on the results of analyzing legal texts. For example, it visualizes the simulation results and provides them to users. This allows the impact of future legal reforms to be predicted and simulated, allowing measures to be taken in advance.

[0061] The legal analysis unit can update the analysis results of legal texts in real time, always reflecting the latest legal information. For example, the legal analysis unit builds a system in which the generation AI updates the analysis results of legal texts in real time, always reflecting the latest legal information. For example, it immediately updates the analysis results when a new law is enacted. The legal analysis unit also builds a database for updating the analysis results of legal texts in real time, always reflecting the latest legal information. For example, it regularly updates the legal database and reflects this in the analysis results. The legal analysis unit also develops an interface that allows the generation AI to update the analysis results of legal texts in real time, always reflecting the latest legal information. For example, it makes it easy for users to check the latest legal information. This allows the latest legal information to be always provided by updating the legal information in real time.

[0062] The legal analysis unit can use the emotion estimation function to provide feedback on the user's emotions regarding the legal text analysis results and improve the analysis algorithm. For example, the legal analysis unit can use the emotion estimation function to provide feedback on the user's emotions regarding the legal text analysis results and improve the analysis algorithm based on that data. For example, the legal analysis unit can provide information in a format that is easy for the user to understand. The legal analysis unit can also build a system that improves the legal text analysis algorithm based on the user's emotional response data. For example, it can reanalyze and improve parts that have a high number of negative emotional responses. The legal analysis unit can also use the emotion estimation function to provide feedback on the user's emotions regarding the legal text analysis results in real time and instantly improve the analysis algorithm. For example, it can adjust the analysis results based on the user's feedback. In this way, feedback on the user's emotions and improvement of the analysis algorithm can improve the accuracy of the analysis results.

[0063] The legal analysis unit can integrate the results of the analysis of legal texts with urban planning data and infrastructure data to build a comprehensive city management system. For example, the legal analysis unit integrates the results of the analysis of legal texts with other urban planning data to build a comprehensive city management system. For example, it sets no-parking zones based on urban planning data. The legal analysis unit also integrates the results of the analysis of legal texts with infrastructure data to build a comprehensive city management system. For example, it sets no-parking zones based on road infrastructure data. The legal analysis unit also builds a database for integrating the results of the analysis of legal texts with other urban planning data and infrastructure data to provide a comprehensive city management system. For example, it integrates different data sources to provide comprehensive analysis results. This makes it possible to integrate the results of the analysis of legal texts with other urban planning data and infrastructure data to provide comprehensive city management.

[0064] The legal analysis unit can apply the results of the analysis of legal text to different industries to meet industry-specific needs. For example, the legal analysis unit applies the results of the analysis of legal text to the real estate industry to meet industry-specific needs. For example, the legal analysis unit analyzes legal constraints in real estate development and identifies land that can be developed. The legal analysis unit also applies the results of the analysis of legal text to the logistics industry to meet industry-specific needs. For example, the legal constraints in establishing a logistics base are analyzed and the optimal base is identified. The legal analysis unit also develops an interface for applying the results of the analysis of legal text to different industries to meet industry-specific needs. For example, it makes it easy to check the legal constraints for each industry. This makes it possible to apply the results of the analysis of legal text to different industries to meet industry-specific needs.

[0065] The legal analysis unit can use the emotion estimation function to analyze the user's emotional response to the analysis results of the legal text, thereby improving the user experience. For example, the legal analysis unit can use the emotion estimation function to analyze the user's emotional response to the analysis results of the legal text, and improve the user experience based on the data. For example, the legal analysis unit can provide information in a format that is easy for the user to understand. The legal analysis unit can also improve the analysis results of the legal text based on the user's emotional response data, and build a system that improves the user experience. For example, it can reanalyze and improve parts that have a high number of negative emotional responses. The legal analysis unit can also use the emotion estimation function to analyze the user's emotional response to the analysis results of the legal text in real time, thereby instantly improving the user experience. For example, it can adjust the analysis results based on user feedback. In this way, the user's emotional response can be analyzed and the user experience can be improved, thereby providing information that is easier for the user to understand.

[0066] The visualization unit can integrate traffic information and weather information into the law visualization map to generate a dynamic map. In the visualization unit, for example, the generation AI integrates real-time traffic information into the law visualization map to generate a dynamic map. For example, traffic congestion and accident information is reflected and available parking locations are dynamically displayed. In addition, the visualization unit integrates real-time weather information into the law visualization map to generate a dynamic map. For example, the effects of heavy rain and snow are reflected and available parking locations are dynamically displayed. In addition, the visualization unit builds a database for integrating real-time traffic information and weather information into the law visualization map to provide a dynamic map. For example, different data sources are integrated and comprehensive analysis results are provided. In this way, a dynamic map can be generated by integrating real-time traffic information and weather information.

[0067] The visualization unit can add a customizable filter function to the law visualization map and generate a map based on specific conditions. The visualization unit, for example, adds a customizable filter function to the law visualization map and generates a map based on specific conditions. For example, it filters available parking locations by time of day or day of the week. The visualization unit also generates a law visualization map based on specific conditions using a user-customizable filter function. For example, it displays available parking locations for specific vehicle types or uses. The visualization unit also develops an interface for adding a customizable filter function to the law visualization map, allowing the user to easily set conditions. For example, the filter conditions can be set by drag and drop. In this way, adding a customizable filter function makes it possible to generate a map based on specific conditions.

[0068] The visualization unit can use the emotion estimation function to optimize the display format of the legal visualization map according to the user's emotions. The visualization unit, for example, uses the emotion estimation function to optimize the display format of the legal visualization map according to the user's emotions. For example, if the user is feeling stressed, information is provided in a concise and easy-to-understand format. The visualization unit also analyzes the user's emotions in real time and provides the display format of the legal visualization map in a format that is easy for the user to understand. For example, if the user is feeling anxious, detailed explanations and supplementary information are added. The visualization unit also uses the emotion estimation function to develop a system that customizes the display format of the legal visualization map according to the user's emotions. For example, detailed information is provided if the user has positive emotions, and concise information is provided if the user has negative emotions. In this way, by optimizing the display format according to the user's emotions, it is possible to provide information that is easy for the user to understand.

[0069] The visualization unit can optimize the law visualization map for mobile devices, allowing users to use it anywhere. For example, the visualization unit optimizes the law visualization map for smartphones and tablets, allowing users to use it anywhere. For example, the visualization unit develops an interface compatible with mobile devices. The visualization unit also provides the law visualization map optimized for mobile devices, allowing users to easily use it on the go. For example, the visualization unit utilizes a GPS function to display information around the current location. The visualization unit also develops an application to optimize the law visualization map for smartphones and tablets, allowing users to use it anywhere. For example, the visualization unit adds a function that allows it to be used offline. In this way, by optimizing the law visualization map for mobile devices, users can use it anywhere.

[0070] The visualization unit can apply the legal visualization map to different cities and regions to provide legal information from a global perspective. For example, the visualization unit applies the legal visualization map to different cities and regions to provide legal information from a global perspective. For example, the visualization unit compares parking regulations in each city and identifies similarities and differences. The visualization unit also builds a system that collects legal information from different cities and regions and integrates it into the legal visualization map. For example, the visualization unit collects legal data from each city and analyzes it in a unified format. The visualization unit also develops a multilingual interface to provide the legal visualization map from a global perspective, allowing users to easily check legal information from different cities and regions. For example, legal information can be provided in English, French, Chinese, and other languages. In this way, the legal visualization map can be applied to different cities and regions to provide legal information from a global perspective.

[0071] The visualization unit can use the emotion estimation function to collect users' emotional reactions to the legal visualization map and improve the accuracy and usability of the map. For example, the visualization unit uses the emotion estimation function to collect users' emotional reactions to the legal visualization map and improve the accuracy and usability of the map based on that data. For example, the visualization unit provides information in a format that is easy for users to understand. The visualization unit also builds a system that improves the legal visualization map and improves usability based on the user's emotional reaction data. For example, it reanalyzes and improves parts with a high number of negative emotional reactions. The visualization unit also uses the emotion estimation function to collect users' emotional reactions to the legal visualization map in real time and instantly improves the accuracy and usability of the map. For example, it adjusts the map based on user feedback. In this way, the user experience is improved by collecting users' emotional reactions and improving the accuracy and usability of the map.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The legal analysis unit can predict and simulate the impact of future legal reforms based on the results of analyzing legal texts. For example, the generation AI adds a function to predict and simulate the impact of future legal reforms based on the results of analyzing legal texts. For example, it can simulate the impact of new parking regulations being introduced. In addition, the legal analysis unit adds a function to predict the impact of legal reforms by having the generation AI learn data on past legal reforms and simulate future impacts. For example, it can predict impacts based on past data. The legal analysis unit also develops algorithms that allow the generation AI to simulate the impact of future legal reforms based on the results of analyzing legal texts. For example, it can visualize the simulation results and provide them to users. This allows the impact of future legal reforms to be predicted and simulated, allowing measures to be taken in advance.

[0074] The visualization unit can integrate traffic information and weather information into the law visualization map to generate a dynamic map. For example, the generation AI integrates real-time traffic information into the law visualization map to generate a dynamic map. For example, it reflects traffic congestion and accident information and dynamically displays available parking locations. The visualization unit also integrates real-time weather information into the law visualization map to generate a dynamic map. For example, it reflects the effects of heavy rain and snow and dynamically displays available parking locations. The visualization unit also builds a database for integrating real-time traffic information and weather information into the law visualization map to provide a dynamic map. For example, it integrates different data sources and provides comprehensive analysis results. This makes it possible to generate a dynamic map by integrating real-time traffic information and weather information.

[0075] The legal analysis unit can analyze legal documents from different countries and regions and conduct comparative analysis from an international perspective. For example, it can analyze legal documents from different countries and regions and conduct comparative analysis from an international perspective. For example, it can compare parking regulations in each country and identify similarities and differences. The legal analysis unit also develops multilingual natural language processing technology to analyze legal documents from different countries and regions and conduct comparative analysis from an international perspective. For example, it can analyze legal documents in English, French, Chinese, and other languages. The legal analysis unit also builds legal databases from different countries and regions and integrates the analysis results to conduct comparative analysis from an international perspective. For example, it collects legal data from each country and analyzes it in a unified format. This makes it possible to analyze legal documents from different countries and regions and conduct comparative analysis from an international perspective.

[0076] The visualization unit can use the emotion estimation function to adjust the display format of the analysis results of legal text according to the user's emotion. For example, the emotion estimation function is used to adjust the display format of the analysis results of legal text according to the user's emotion. For example, if the user is feeling stressed, information is provided in a concise and easy-to-understand format. The visualization unit also analyzes the user's emotion in real time and provides the analysis results of legal text in a format that is easy for the user to understand. For example, if the user is feeling anxious, detailed explanations and supplementary information are added. The visualization unit also uses the emotion estimation function to develop a system that customizes the analysis results of legal text according to the user's emotion. For example, if the user is feeling positive, detailed information is provided, and if the user is feeling negative, concise information is provided. In this way, by adjusting the display format according to the user's emotion, it is possible to provide information that is easy for the user to understand.

[0077] The legal analysis unit and map information analysis unit can integrate traffic flow data and environmental data to perform comprehensive analysis. For example, the results of the analysis of legal texts and map information can be integrated with traffic flow data to analyze the impact of no-parking zones. For example, areas prone to traffic congestion can be identified and no-parking zones can be set up. The legal analysis unit and map information analysis unit can also integrate the results of the analysis of legal texts and map information with environmental data to identify environmentally protected areas. For example, no-parking zones can be set up around nature reserves and parks. The legal analysis unit and map information analysis unit can also build a database to integrate the results of the analysis of legal texts and map information with traffic flow data and environmental data to perform comprehensive analysis. For example, different data sources can be integrated to provide comprehensive analysis results. This allows for more comprehensive analysis by integrating with traffic flow data and environmental data.

[0078] The visualization unit can use the emotion estimation function to collect users' emotional reactions to the analysis results of legal texts and use the collected data to improve the analysis results. For example, the emotion estimation function can be used to collect users' emotional reactions to the analysis results of legal texts and improve the analysis results based on that data. For example, parts that are difficult for users to understand can be identified and improved. The visualization unit also builds a system to improve the analysis results of legal texts based on the user's emotional reaction data. For example, parts with a high number of negative emotional reactions can be reanalyzed and improved. The visualization unit also uses the emotion estimation function to collect users' emotional reactions to the analysis results of legal texts in real time and immediately improve them. For example, the analysis results can be adjusted based on user feedback. In this way, collecting users' emotional reactions and using them to improve the analysis results improves the user experience.

[0079] The legal analysis unit can update the results of legal text analysis in real time, always reflecting the latest legal information. For example, a system can be constructed in which the generation AI updates the results of legal text analysis in real time, always reflecting the latest legal information. For example, the analysis results can be updated immediately when a new law is enacted. The legal analysis unit can also build a database for updating the results of legal text analysis in real time, reflecting the latest legal information. For example, the legal database can be updated regularly to reflect this in the analysis results. The legal analysis unit can also develop an interface that allows the generation AI to update the results of legal text analysis in real time, always reflecting the latest legal information. For example, it can make it easy for users to check the latest legal information. This allows legal information to be updated in real time, always providing the latest information.

[0080] The visualization unit can add a customizable filter function to the law visualization map and generate a map based on specific conditions. For example, a customizable filter function can be added to the law visualization map to generate a map based on specific conditions. For example, available parking locations can be filtered by time of day or day of the week. The visualization unit also generates a law visualization map based on specific conditions using a user-customizable filter function. For example, available parking locations for specific vehicle types or uses can be displayed. The visualization unit also develops an interface for adding a customizable filter function to the law visualization map, allowing users to easily set conditions. For example, filter conditions can be set by drag and drop. In this way, adding a customizable filter function makes it possible to generate a map based on specific conditions.

[0081] The visualization unit can use the emotion estimation function to optimize the display format of the legal visualization map according to the user's emotions. For example, the emotion estimation function is used to optimize the display format of the legal visualization map according to the user's emotions. For example, if the user is feeling stressed, information is provided in a concise and easy-to-understand format. The visualization unit also analyzes the user's emotions in real time and provides the display format of the legal visualization map in a format that is easy for the user to understand. For example, if the user is feeling anxious, detailed explanations and supplementary information are added. The visualization unit also uses the emotion estimation function to develop a system that customizes the display format of the legal visualization map according to the user's emotions. For example, detailed information is provided if the user is feeling positive, and concise information is provided if the user is feeling negative. In this way, by optimizing the display format according to the user's emotions, it is possible to provide information that is easy for the user to understand.

[0082] The legal analysis unit can apply the results of legal text analysis to different industries to meet industry-specific needs. For example, the results of legal text analysis can be applied to the real estate industry to meet industry-specific needs. For example, legal constraints in real estate development can be analyzed to identify land that can be developed. The legal analysis unit can also apply the results of legal text analysis to the logistics industry to meet industry-specific needs. For example, legal constraints in establishing a logistics base can be analyzed to identify the optimal base. The legal analysis unit can also develop an interface for applying the results of legal text analysis to different industries to meet industry-specific needs. For example, it can make it easy to check legal constraints for each industry. This allows the results of legal text analysis to be applied to different industries to meet industry-specific needs.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The legal analysis unit analyzes legal documents. For example, the legal analysis unit analyzes legal documents such as road traffic laws and building laws to understand their content. The legal analysis unit can also analyze the context of legal documents and automatically interpret ambiguous expressions and exceptions. Step 2: The map information analysis unit analyzes the map information. For example, the map information analysis unit analyzes GIS data and geographic coordinate information to extract information from the map. The map information analysis unit can also analyze the legal status of a specific location based on the map information. Step 3: The visualization unit generates a legal visualization map based on the analysis results of the legal analysis unit and the map information analysis unit. For example, based on the analysis results, the visualization unit displays legally problem-free and illegal locations on a map in different colors. The visualization unit can also display the legal visualization map using a 3D model or AR (augmented reality). This allows users to intuitively understand where legally problem-free locations are.

[0085] 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.

[0086] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0129] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] The data processing system 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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]

[0152] 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 legal analysis unit that analyzes legal documents; a map information analysis unit that analyzes map information; a visualization unit that generates a legal visualization map based on the analysis results of the legal analysis unit and the map information analysis unit. A system characterized by:

2. The legal analysis unit Understand the context of the legal text and automatically interpret ambiguous expressions and exceptions 2. The system of claim 1.

3. The visualization unit The results of the analysis of the legal text are displayed as 3D models and augmented reality (AR).

2. The system of claim 1.

4. The visualization unit The display format of the analysis result of the legal text is adjusted according to the user's feelings.

2. The system of claim 1.

5. The legal analysis unit and the map information analysis unit Integrate traffic flow data and environmental data for comprehensive analysis 2. The system of claim 1.

Images