System

Abstract

An object of a system according to an embodiment is to efficiently provide specific crime prevention measures and crime prevention information to citizens.SOLUTION: A system includes an information collection part, an information analysis part, an answer part, a crime prevention map creation part, and an information provision part. The information collecting unit collects data from various information sources on the Internet. The information analysis unit analyzes the data collected by the information collection unit. The answer part proposes concrete crime prevention measures to citizens on the basis of the result analyzed by the information analysis part. The crime prevention map creation part creates a crime prevention map on the basis of the result analyzed by the information analysis part. The information providing part opens the crime prevention map prepared by the crime prevention map preparing part, government information and local news to other companies.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has faced the challenge of making it difficult to efficiently provide specific crime prevention measures and crime prevention information to help citizens live safely.

[0005] The system according to the embodiment aims to efficiently provide citizens with specific crime prevention measures and crime prevention information. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, an information analysis unit, a response unit, a crime prevention map creation unit, and an information provision unit. The information collection unit collects data from various information sources on the Internet. The information analysis unit analyzes the data collected by the information collection unit. The response unit proposes specific crime prevention measures to citizens based on the results of the analysis by the information analysis unit. The crime prevention map creation unit creates a crime prevention map based on the results of the analysis by the information analysis unit. The information provision unit makes the crime prevention map, government information, and local news created by the crime prevention map creation unit available to other companies. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently provide citizens with specific crime prevention measures and crime prevention information. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The crime prevention system according to an embodiment of the present invention is a system that uses AI technology to create a unique crime prevention generation AI that collects, analyzes, and provides answers to help citizens live safely. This allows the crime prevention system to provide a safe environment for citizens and earn revenue through the provision of information.

[0029] A crime prevention system according to an embodiment includes an information collection unit, an information analysis unit, a response unit, a crime prevention map creation unit, and an information provision unit. The information collection unit collects data from various online sources. For example, it collects crime-related information from news sites, social media, government databases, and the like. The information collection unit can also collect data using scraping technology. For example, it can automatically obtain the latest crime information from news sites. The information analysis unit analyzes the collected data. For example, it can identify crime trends and patterns using statistical analysis and machine learning algorithms. The information analysis unit can also build a crime prediction model and evaluate the future risk of crime. For example, it can predict an increase in the crime rate in a specific area. The response unit proposes specific crime prevention measures to citizens based on the analysis results. For example, it can provide advice such as "avoid going out at night in this area." The response unit can also generate appropriate answers to questions from users. For example, in response to the question "How safe is this area?", a generation AI generates an answer based on the analysis results. The crime prevention map creation unit creates a crime prevention map based on the analysis results. For example, a crime prevention map may include information such as the location, type, and time of crime occurrence. The information provider makes the crime prevention map, government information, and local news available to other companies. For example, companies and local governments can use this information to strengthen their crime prevention measures. This allows the crime prevention system according to the embodiment to provide a safe living environment for citizens and earn revenue through information provision. For example, by collaborating with social media influencers, the crime prevention measures proposed by the generation AI can be widely disseminated, enhancing the crime prevention effect. Furthermore, using the crime prevention map makes it easier for citizens to select safe routes. Furthermore, providing information allows companies and local governments to strengthen their crime prevention measures.

[0030] The information collection unit can add traffic camera footage and drone footage that are updated in real time, and visual information can also be used for analysis. For example, the information collection unit collects traffic camera footage in real time, and the generation AI analyzes that footage to detect suspicious behavior. For example, it analyzes abnormal gatherings of people in a specific area or suspicious vehicle behavior. The information collection unit can also collect drone footage, and the generation AI can analyze that footage to detect signs of crime. For example, it can identify suspicious activity in a specific area from drone footage. This makes it possible to use visual information for analysis to make more detailed crime predictions.

[0031] The information gathering unit can directly reflect the voices of the local community, including anonymous reports from local residents. For example, the information gathering unit collects anonymous reports from local residents, and the generation AI analyzes that information to detect signs of crime. For example, it analyzes reports of suspicious sightings or unusual sounds. The information gathering unit can also conduct questionnaire surveys of local residents and analyze the results. For example, it can identify areas where local residents feel uneasy or are dangerous. This allows the voices of local residents to be directly reflected, making it possible to develop crime prevention measures that are more closely tied to the local community.

[0032] The information collection unit can add audio data and perform audio analysis. For example, the information collection unit collects recorded data of emergency calls, and the generation AI analyzes the audio to detect signs of crime. For example, it identifies suspicious activity or dangerous situations from the content of the call. The information collection unit can also collect interview audio, and the generation AI analyzes the audio to detect signs of crime. For example, it identifies signs of crime from the content of the interview. This makes it possible to collect information from more diverse angles by analyzing audio data.

[0033] The information gathering unit can analyze crime trends from an international perspective, including crime data from other cities and countries. For example, the information gathering unit collects crime data from other cities and countries, and the generation AI analyzes that data to identify international crime trends. For example, it identifies areas where specific crimes are increasing. The information gathering unit can also comparatively analyze international crime data to identify crime trends. For example, it compares it with crime data from other countries to evaluate the risk of specific crimes occurring. This allows for the analysis of crime trends from an international perspective, enabling more extensive crime prevention measures.

[0034] The information analysis unit can make more detailed crime predictions, including the behavioral patterns and psychological profiles of criminals. For example, the information analysis unit analyzes criminal behavior patterns, and the generation AI uses that data to make crime predictions. For example, it evaluates the risk of crime occurring at specific times or locations. The information analysis unit can also create psychological profiles, and the generation AI can use that data to make crime predictions. For example, it can identify the behavioral patterns of criminals with specific psychological characteristics. This makes it possible to make more detailed crime predictions by including criminal behavioral patterns and psychological profiles.

[0035] The information analysis unit can analyze the correlation between weather and crime occurrence, including past weather data. For example, the information analysis unit collects past weather data, and the generation AI analyzes that data to identify the correlation between weather and crime occurrence. For example, it can identify the types of crime that increase on rainy days. The information analysis unit can also perform correlation analysis between weather data and crime data to assess the risk of crime occurrence. For example, it can assess the risk of crime occurrence under specific weather conditions. By analyzing the correlation between weather and crime occurrence, it becomes possible to implement crime prevention measures according to weather conditions.

[0036] The information analysis unit can analyze the relationship between socioeconomic factors, including local economic conditions and unemployment rates, and crime occurrence. For example, the information analysis unit collects data on local economic conditions, and the generation AI analyzes that data to identify the relationship between economic conditions and crime occurrence. For example, it can evaluate the impact of economic hardship on crime occurrence. The information analysis unit can also collect unemployment rate data, and the generation AI can analyze that data to evaluate the risk of crime occurrence. For example, it can evaluate the risk of crime occurrence in areas with high unemployment rates. This allows for an analysis of the relationship between socioeconomic factors and crime occurrence, making it possible to implement crime prevention measures that are appropriate to the economic situation.

[0037] The information analysis unit can analyze the relationship between the educational environment and crime occurrence, including information on the educational level of the region and school security. For example, the information analysis unit collects data on the educational level of the region, and the generation AI analyzes that data to identify the relationship between educational level and crime occurrence. For example, it can evaluate the risk of crime occurrence in regions with low educational levels. The information analysis unit can also collect information on school security, and the generation AI can analyze that data to evaluate the risk of crime occurrence. For example, it can evaluate the risk of crime occurrence around schools with poor security. By analyzing the relationship between the educational environment and crime occurrence, it becomes possible to implement crime prevention measures according to the educational level.

[0038] The answer section can include specific guidelines for action. For example, the answer section can include evacuation routes in the crime prevention measures provided by the generative AI. For example, it can suggest safe evacuation routes when a crime occurs in a specific area. The answer section can also provide emergency contact information. For example, it can provide contact information for the police or emergency services to contact when a crime occurs. By including specific guidelines for action, citizens can respond quickly and appropriately when a crime occurs.

[0039] The answer section can include the locations of local security cameras and their operational status. For example, the answer section can include the locations of local security cameras in the crime prevention measures provided by the generative AI. For example, it can show the locations of security cameras in a specific area and evaluate the risk of crime. The answer section can also provide the operational status of security cameras. For example, it can show the number and locations of security cameras in operation. This makes it easier for citizens to understand the coverage area of ​​security cameras by including the locations of local security cameras and their operational status.

[0040] The answering unit can strengthen a sense of community by including information on local volunteer activities and community events. For example, the answering unit can include information on local volunteer activities in the crime prevention measures provided by the generation AI. For example, it can provide schedules for crime prevention patrols and monitoring activities. The answering unit can also provide information on community events. For example, it can provide information on local crime prevention events and workshops. In this way, by including information on local volunteer activities and community events, a sense of community can be strengthened.

[0041] The answering unit can strengthen support for crime victims by including information on local medical institutions and counseling services. For example, the answering unit can include information on local medical institutions in crime prevention measures provided by the generative AI. For example, it can provide information on hospitals and clinics that crime victims can use. The answering unit can also provide information on counseling services. For example, it can provide contact information for counseling services that crime victims can use. This can strengthen support for crime victims by including information on local medical institutions and counseling services.

[0042] The crime prevention map creation unit can include not only crime locations, but also locations of attempted crimes and information on suspicious person sightings. The crime prevention map creation unit, for example, displays crime locations on a crime prevention map created by the generation AI. For example, it shows crime locations in a specific area on the map. The crime prevention map creation unit can also display locations of attempted crimes. For example, it shows locations where attempted crimes occurred on the map. The crime prevention map creation unit can also display information on suspicious person sightings. For example, it shows locations where suspicious people have been sighted on the map. This allows for the creation of more detailed crime prevention maps by including locations of attempted crimes and information on suspicious person sightings.

[0043] The crime prevention map creation unit can visually display trends in crime occurrence by time of day and day of the week. The crime prevention map creation unit, for example, displays the time of day when crimes occur on a crime prevention map created by the generation AI. For example, crimes that occur frequently during specific time periods are shown on the map. The crime prevention map creation unit can also display crime occurrence trends by day of the week. For example, crimes that occur frequently on specific days of the week are shown on the map. In this way, by visually displaying trends in crime occurrence by time of day and day of the week, users can intuitively understand crime risk.

[0044] The security map creation unit can display the locations of security cameras in the area and their operation status. The security map creation unit, for example, displays the locations of security cameras in the area on a security map created by the generation AI. For example, it shows the locations of security cameras in a specific area on a map. The security map creation unit can also display the operation status of security cameras. For example, it shows the number and locations of security cameras in operation. This makes it easier for users to understand the coverage area of ​​security cameras by displaying the locations of security cameras in the area and their operation status.

[0045] The crime prevention map creation unit can display information about local evacuation shelters and emergency contacts. For example, the crime prevention map creation unit displays information about local evacuation shelters on a crime prevention map created by the generation AI. For example, it shows the locations of evacuation shelters in a specific area on the map. The crime prevention map creation unit can also display emergency contact information. For example, it shows the contact information for the police and emergency services to contact in the event of a crime. In this way, by displaying information about local evacuation shelters and emergency contacts, users can intuitively understand the information they need in an emergency.

[0046] The information provision department can raise awareness of local safety by including the results of local crime prevention activities and police efforts. For example, the information provision department can include the results of local crime prevention activities in the information provided by the generation AI. For example, it can report on the implementation status and results of crime prevention patrols. The information provision department can also provide the results of police efforts. For example, it can report on declines in crime rates and the number of arrests. In this way, by including the results of local crime prevention activities and police efforts, it can raise awareness of local safety.

[0047] The information provision unit can include information on educational programs and workshops for crime prevention. For example, the information provision unit can include information on educational programs for crime prevention in the information provided by the generation AI. For example, it can provide a crime prevention education curriculum and participation methods. The information provision unit can also provide information on workshops. For example, it can provide a crime prevention workshop schedule and participation methods. In this way, by including information on educational programs and workshops for crime prevention, it is possible to raise crime prevention awareness among local residents.

[0048] The information provision unit can strengthen a sense of community by including information on local crime prevention volunteer activities and community activities. For example, the information provision unit can include information on local crime prevention volunteer activities in the information provided by the generation AI. For example, it can provide schedules for crime prevention patrols and monitoring activities. The information provision unit can also provide information on community activities. For example, it can provide information on local crime prevention events and workshops. In this way, by including information on local crime prevention volunteer activities and community activities, a sense of community can be strengthened.

[0049] The information provision unit can strengthen support for crime victims by including information on local medical institutions and counseling services. For example, the information provision unit includes information on local medical institutions in the information provided by the generation AI. For example, it provides information on hospitals and clinics that crime victims can use. The information provision unit can also provide information on counseling services. For example, it provides contact information for counseling services that crime victims can use. In this way, by including information on local medical institutions and counseling services, support for crime victims can be strengthened.

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

[0051] The information gathering unit can collect usage data on local commercial and public facilities and assess the risk of crime. For example, it can analyze the flow of people at a particular commercial facility and identify times when crimes are likely to occur. It can also collect usage data on public facilities and assess the risk of crime. For example, it can analyze usage data on libraries and parks and identify areas where crimes are likely to occur. This allows for more detailed crime predictions by analyzing usage data on commercial and public facilities.

[0052] The information gathering unit can collect local weather data and analyze the relationship between weather and crime occurrence. For example, it can identify a tendency for crime to increase on rainy or snowy days. It can also evaluate the relationship between changes in temperature and humidity and crime occurrence. For example, it can identify a tendency for certain crimes to increase on hot or cold days. By analyzing weather data, it becomes possible to implement crime prevention measures according to weather conditions.

[0053] The information gathering unit can collect local traffic data and analyze the relationship between traffic conditions and crime occurrence. For example, it can identify a tendency for crime to increase during times when traffic congestion is likely to occur. It can also evaluate the relationship between public transportation usage and crime occurrence. For example, it can identify a tendency for certain crimes to increase during times when bus and train usage is most common. By analyzing traffic data, it becomes possible to implement crime prevention measures that are tailored to traffic conditions.

[0054] The information gathering unit can collect data from local medical institutions and assess the risk of crime. For example, if an increase in emergency patients or an increase in specific injuries is a sign of crime, the data can be analyzed to assess the crime risk. It can also assess the risk of crime in a specific area based on data from medical institutions. For example, if an increase in emergency patients at a specific hospital is a sign of crime, the crime risk in that area can be assessed. This makes it possible to analyze data from medical institutions to make more detailed crime predictions.

[0055] The Information Analysis Department can collect regional economic data and analyze the relationship between economic conditions and crime occurrence. For example, it can evaluate the impact of rising unemployment rates on crime occurrence. It can also evaluate the relationship between regional economic growth rates and crime occurrence. For example, it can evaluate the risk of crime occurrence in areas where economic growth is stagnant. By analyzing economic data, it becomes possible to implement crime prevention measures that are appropriate to the economic situation.

[0056] The information analysis unit can collect local educational data and analyze the relationship between educational level and crime occurrence. For example, it can evaluate the risk of crime occurring in areas with low educational levels. It can also collect school security information and evaluate the risk of crime occurring. For example, it can evaluate the risk of crime occurring around schools with poor security. By analyzing educational data, it becomes possible to implement crime prevention measures according to educational level.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The information gathering unit collects data from various sources on the internet. For example, it collects crime-related information from news sites, social media, government databases, etc. The information gathering unit can also collect data using scraping technology. For example, it can automatically obtain the latest crime information from news sites. Step 2: The Intelligence Analysis Department analyzes the collected data. For example, statistical analysis and machine learning algorithms are used to identify crime trends and patterns. The Intelligence Analysis Department can also build crime prediction models to assess future crime risks. For example, predicting an increase in crime rates in a particular area. Step 3: The answering unit proposes specific crime prevention measures to citizens based on the analysis results. For example, it provides advice such as "Avoid going out at night in this area." The answering unit can also generate appropriate answers to questions from users. For example, in response to the question "How safe is this area?", the generation AI generates an answer based on the analysis results. Step 4: The crime prevention map creation unit creates a crime prevention map based on the analysis results. For example, the crime prevention map includes information such as the location of crimes, the type of crime, and the time of day they occurred. Step 5: The information provider makes the crime prevention map, government information, and local news available to other companies. For example, companies and local governments can use this information to strengthen their crime prevention measures. This allows the crime prevention system according to the embodiment to provide a safe environment for citizens and earn revenue through information provision. For example, by collaborating with social media influencers, the crime prevention measures proposed by the generation AI can be widely disseminated, increasing the crime prevention effect. Furthermore, using the crime prevention map makes it easier for citizens to select safe routes. Furthermore, providing information allows companies and local governments to strengthen their crime prevention measures.

[0059] (Example 2) The crime prevention system according to an embodiment of the present invention is a system that uses AI technology to create a unique crime prevention generation AI that collects, analyzes, and provides answers to help citizens live safely. This allows the crime prevention system to provide a safe environment for citizens and earn revenue through the provision of information.

[0060] A crime prevention system according to an embodiment includes an information collection unit, an information analysis unit, a response unit, a crime prevention map creation unit, and an information provision unit. The information collection unit collects data from various online sources. For example, it collects crime-related information from news sites, social media, government databases, and the like. The information collection unit can also collect data using scraping technology. For example, it can automatically obtain the latest crime information from news sites. The information analysis unit analyzes the collected data. For example, it can identify crime trends and patterns using statistical analysis and machine learning algorithms. The information analysis unit can also build a crime prediction model and evaluate the future risk of crime. For example, it can predict an increase in the crime rate in a specific area. The response unit proposes specific crime prevention measures to citizens based on the analysis results. For example, it can provide advice such as "avoid going out at night in this area." The response unit can also generate appropriate answers to questions from users. For example, in response to the question "How safe is this area?", a generation AI generates an answer based on the analysis results. The crime prevention map creation unit creates a crime prevention map based on the analysis results. For example, a crime prevention map may include information such as the location, type, and time of crime occurrence. The information provider makes the crime prevention map, government information, and local news available to other companies. For example, companies and local governments can use this information to strengthen their crime prevention measures. This allows the crime prevention system according to the embodiment to provide a safe living environment for citizens and earn revenue through information provision. For example, by collaborating with social media influencers, the crime prevention measures proposed by the generation AI can be widely disseminated, enhancing the crime prevention effect. Furthermore, using the crime prevention map makes it easier for citizens to select safe routes. Furthermore, providing information allows companies and local governments to strengthen their crime prevention measures.

[0061] The information collection unit can add traffic camera footage and drone footage that are updated in real time, and visual information can also be used for analysis. For example, the information collection unit collects traffic camera footage in real time, and the generation AI analyzes that footage to detect suspicious behavior. For example, it analyzes abnormal gatherings of people in a specific area or suspicious vehicle behavior. The information collection unit can also collect drone footage, and the generation AI can analyze that footage to detect signs of crime. For example, it can identify suspicious activity in a specific area from drone footage. This makes it possible to use visual information for analysis to make more detailed crime predictions.

[0062] The information gathering unit can directly reflect the voices of the local community, including anonymous reports from local residents. For example, the information gathering unit collects anonymous reports from local residents, and the generation AI analyzes that information to detect signs of crime. For example, it analyzes reports of suspicious sightings or unusual sounds. The information gathering unit can also conduct questionnaire surveys of local residents and analyze the results. For example, it can identify areas where local residents feel uneasy or are dangerous. This allows the voices of local residents to be directly reflected, making it possible to develop crime prevention measures that are more closely tied to the local community.

[0063] The information collection unit can use the emotion estimation function to analyze user emotions from posts on social media and prioritize collecting posts that express anxiety or fear. For example, the information collection unit collects posts on social media, and the generation AI uses the emotion estimation function to identify posts that express anxiety or fear. For example, if there is an increase in anxious posts in a specific area, the crime risk in that area can be evaluated. The information collection unit can also use the emotion estimation function to monitor user emotions from posts on social media in real time. For example, it can prioritize collecting posts that include specific keywords. This allows for faster response by prioritizing the collection of posts that express anxiety or fear.

[0064] The information collection unit can add audio data and perform audio analysis. For example, the information collection unit collects recorded data of emergency calls, and the generation AI analyzes the audio to detect signs of crime. For example, it identifies suspicious activity or dangerous situations from the content of the call. The information collection unit can also collect interview audio, and the generation AI analyzes the audio to detect signs of crime. For example, it identifies signs of crime from the content of the interview. This makes it possible to collect information from more diverse angles by analyzing audio data.

[0065] The information gathering unit can analyze crime trends from an international perspective, including crime data from other cities and countries. For example, the information gathering unit collects crime data from other cities and countries, and the generation AI analyzes that data to identify international crime trends. For example, it identifies areas where specific crimes are increasing. The information gathering unit can also comparatively analyze international crime data to identify crime trends. For example, it compares it with crime data from other countries to evaluate the risk of specific crimes occurring. This allows for the analysis of crime trends from an international perspective, enabling more extensive crime prevention measures.

[0066] The information analysis unit can make more detailed crime predictions, including the behavioral patterns and psychological profiles of criminals. For example, the information analysis unit analyzes criminal behavior patterns, and the generation AI uses that data to make crime predictions. For example, it evaluates the risk of crime occurring at specific times or locations. The information analysis unit can also create psychological profiles, and the generation AI can use that data to make crime predictions. For example, it can identify the behavioral patterns of criminals with specific psychological characteristics. This makes it possible to make more detailed crime predictions by including criminal behavioral patterns and psychological profiles.

[0067] The information analysis unit can analyze the correlation between weather and crime occurrence, including past weather data. For example, the information analysis unit collects past weather data, and the generation AI analyzes that data to identify the correlation between weather and crime occurrence. For example, it can identify the types of crime that increase on rainy days. The information analysis unit can also perform correlation analysis between weather data and crime data to assess the risk of crime occurrence. For example, it can assess the risk of crime occurrence under specific weather conditions. By analyzing the correlation between weather and crime occurrence, it becomes possible to implement crime prevention measures according to weather conditions.

[0068] The information analysis unit can use the emotion estimation function to analyze emotion data of crime victims and propose crime prevention measures that take into account the psychological impact on the victim. For example, the information analysis unit collects emotion data of crime victims, and the generation AI analyzes the data to identify the psychological impact on the victim. For example, it evaluates the degree of fear and anxiety felt by the victim. The information analysis unit can also use the emotion estimation function to propose crime prevention measures that take into account the psychological impact on the victim. For example, it can propose measures to increase the victim's sense of security. In this way, by proposing crime prevention measures that take into account the psychological impact on the victim, it is possible to increase the victim's sense of security.

[0069] The information analysis unit can analyze the relationship between socioeconomic factors, including local economic conditions and unemployment rates, and crime occurrence. For example, the information analysis unit collects data on local economic conditions, and the generation AI analyzes that data to identify the relationship between economic conditions and crime occurrence. For example, it can evaluate the impact of economic hardship on crime occurrence. The information analysis unit can also collect unemployment rate data, and the generation AI can analyze that data to evaluate the risk of crime occurrence. For example, it can evaluate the risk of crime occurrence in areas with high unemployment rates. This allows for an analysis of the relationship between socioeconomic factors and crime occurrence, making it possible to implement crime prevention measures that are appropriate to the economic situation.

[0070] The information analysis unit can analyze the relationship between the educational environment and crime occurrence, including information on the educational level of the region and school security. For example, the information analysis unit collects data on the educational level of the region, and the generation AI analyzes that data to identify the relationship between educational level and crime occurrence. For example, it can evaluate the risk of crime occurrence in regions with low educational levels. The information analysis unit can also collect information on school security, and the generation AI can analyze that data to evaluate the risk of crime occurrence. For example, it can evaluate the risk of crime occurrence around schools with poor security. By analyzing the relationship between the educational environment and crime occurrence, it becomes possible to implement crime prevention measures according to the educational level.

[0071] The information analysis unit can use the emotion estimation function to measure the crime prevention awareness and sense of security of local residents and adjust crime prevention measures based on that. For example, the information analysis unit collects crime prevention awareness data from local residents, and the generation AI analyzes that data to evaluate the level of crime prevention awareness. For example, it evaluates the risk of crime in areas with high crime prevention awareness. The information analysis unit can also use the emotion estimation function to measure the sense of security of local residents and adjust crime prevention measures based on that. For example, it can strengthen crime prevention measures in areas with a low sense of security. In this way, by measuring the crime prevention awareness and sense of security of local residents and adjusting crime prevention measures based on that, it is possible to increase the sense of security of local residents.

[0072] The answer section can include specific guidelines for action. For example, the answer section can include evacuation routes in the crime prevention measures provided by the generative AI. For example, it can suggest safe evacuation routes when a crime occurs in a specific area. The answer section can also provide emergency contact information. For example, it can provide contact information for the police or emergency services to contact when a crime occurs. By including specific guidelines for action, citizens can respond quickly and appropriately when a crime occurs.

[0073] The answer section can include the locations of local security cameras and their operational status. For example, the answer section can include the locations of local security cameras in the crime prevention measures provided by the generative AI. For example, it can show the locations of security cameras in a specific area and evaluate the risk of crime. The answer section can also provide the operational status of security cameras. For example, it can show the number and locations of security cameras in operation. This makes it easier for citizens to understand the coverage area of ​​security cameras by including the locations of local security cameras and their operational status.

[0074] The answering unit can use the emotion estimation function to suggest crime prevention measures according to the user's emotional state, thereby increasing the sense of security. For example, the answering unit uses the emotion estimation function to analyze the user's emotional state, and the generation AI suggests crime prevention measures based on that data. For example, it suggests measures to increase the sense of security for a user who is feeling anxious. The answering unit can also provide feedback according to the user's emotional state. For example, it provides specific advice to increase the sense of security. This makes it possible to suggest crime prevention measures according to the user's emotional state, thereby increasing the sense of security.

[0075] The answering unit can strengthen a sense of community by including information on local volunteer activities and community events. For example, the answering unit can include information on local volunteer activities in the crime prevention measures provided by the generation AI. For example, it can provide schedules for crime prevention patrols and monitoring activities. The answering unit can also provide information on community events. For example, it can provide information on local crime prevention events and workshops. In this way, by including information on local volunteer activities and community events, a sense of community can be strengthened.

[0076] The answering unit can strengthen support for crime victims by including information on local medical institutions and counseling services. For example, the answering unit can include information on local medical institutions in crime prevention measures provided by the generative AI. For example, it can provide information on hospitals and clinics that crime victims can use. The answering unit can also provide information on counseling services. For example, it can provide contact information for counseling services that crime victims can use. This can strengthen support for crime victims by including information on local medical institutions and counseling services.

[0077] The answering unit can use the emotion estimation function to collect emotional reactions when a user implements a crime prevention measure and evaluate the effectiveness of the measure. The answering unit, for example, uses the emotion estimation function to collect emotional reactions when a user implements a crime prevention measure. For example, it evaluates the sense of security a user feels after installing a security camera. The answering unit can also evaluate the effectiveness of the crime prevention measure based on the collected emotional reactions. For example, it identifies areas for improvement in the crime prevention measure based on user feedback. In this way, by collecting emotional reactions when a user implements a crime prevention measure and evaluating the effectiveness of the measure, more effective crime prevention measures become possible.

[0078] The crime prevention map creation unit can include not only crime locations, but also locations of attempted crimes and information on suspicious person sightings. The crime prevention map creation unit, for example, displays crime locations on a crime prevention map created by the generation AI. For example, it shows crime locations in a specific area on the map. The crime prevention map creation unit can also display locations of attempted crimes. For example, it shows locations where attempted crimes occurred on the map. The crime prevention map creation unit can also display information on suspicious person sightings. For example, it shows locations where suspicious people have been sighted on the map. This allows for the creation of more detailed crime prevention maps by including locations of attempted crimes and information on suspicious person sightings.

[0079] The crime prevention map creation unit can visually display trends in crime occurrence by time of day and day of the week. The crime prevention map creation unit, for example, displays the time of day when crimes occur on a crime prevention map created by the generation AI. For example, crimes that occur frequently during specific time periods are shown on the map. The crime prevention map creation unit can also display crime occurrence trends by day of the week. For example, crimes that occur frequently on specific days of the week are shown on the map. In this way, by visually displaying trends in crime occurrence by time of day and day of the week, users can intuitively understand crime risk.

[0080] The crime prevention map creation unit can use the emotion estimation function to reflect the emotion data of local residents and color-code areas with a high sense of security and areas with a high sense of anxiety. The crime prevention map creation unit, for example, uses the emotion estimation function to collect emotion data of local residents and reflect it on the crime prevention map. For example, areas with a high sense of security can be displayed in green. The crime prevention map creation unit can also display areas with a high sense of anxiety in red. For example, areas with a high sense of anxiety can be shown on the map. In this way, by reflecting the emotion data of local residents and color-coding areas with a high sense of security and areas with a high sense of anxiety, users can intuitively understand the safety of an area.

[0081] The security map creation unit can display the locations of security cameras in the area and their operation status. The security map creation unit, for example, displays the locations of security cameras in the area on a security map created by the generation AI. For example, it shows the locations of security cameras in a specific area on a map. The security map creation unit can also display the operation status of security cameras. For example, it shows the number and locations of security cameras in operation. This makes it easier for users to understand the coverage area of ​​security cameras by displaying the locations of security cameras in the area and their operation status.

[0082] The crime prevention map creation unit can display information about local evacuation shelters and emergency contacts. For example, the crime prevention map creation unit displays information about local evacuation shelters on a crime prevention map created by the generation AI. For example, it shows the locations of evacuation shelters in a specific area on the map. The crime prevention map creation unit can also display emergency contact information. For example, it shows the contact information for the police and emergency services to contact in the event of a crime. In this way, by displaying information about local evacuation shelters and emergency contacts, users can intuitively understand the information they need in an emergency.

[0083] The crime prevention map creation unit can use the emotion estimation function to collect emotional responses from users of the crime prevention map and reflect them in improving the map. The crime prevention map creation unit, for example, uses the emotion estimation function to collect emotional responses from users of the crime prevention map. For example, it evaluates the sense of security or anxiety felt when using the map. The crime prevention map creation unit can also improve the map based on the collected emotional responses. For example, it improves the display content of the crime prevention map based on user feedback. In this way, by collecting the emotional responses of users of the crime prevention map and reflecting them in improving the map, a crime prevention map that is easier to use can be created.

[0084] The information provision department can raise awareness of local safety by including the results of local crime prevention activities and police efforts. For example, the information provision department can include the results of local crime prevention activities in the information provided by the generation AI. For example, it can report on the implementation status and results of crime prevention patrols. The information provision department can also provide the results of police efforts. For example, it can report on declines in crime rates and the number of arrests. In this way, by including the results of local crime prevention activities and police efforts, it can raise awareness of local safety.

[0085] The information provision unit can include information on educational programs and workshops for crime prevention. For example, the information provision unit can include information on educational programs for crime prevention in the information provided by the generation AI. For example, it can provide a crime prevention education curriculum and participation methods. The information provision unit can also provide information on workshops. For example, it can provide a crime prevention workshop schedule and participation methods. In this way, by including information on educational programs and workshops for crime prevention, it is possible to raise crime prevention awareness among local residents.

[0086] The information provision unit can use the emotion estimation function to analyze the emotional reactions of companies and local governments that receive information and optimize the content provided. For example, the information provision unit can use the emotion estimation function to collect the emotional reactions of companies and local governments that receive information, and the generation AI can analyze the data to optimize the content provided. For example, it can prioritize the provision of information that receives a large number of positive reactions. The information provision unit can also use the emotion estimation function to monitor the emotional reactions of companies and local governments in real time. For example, it can collect emotion scores for the provided information. This allows the emotional reactions of companies and local governments that receive information to be analyzed and the content provided to be optimized, enabling more effective information provision.

[0087] The information provision unit can strengthen a sense of community by including information on local crime prevention volunteer activities and community activities. For example, the information provision unit can include information on local crime prevention volunteer activities in the information provided by the generation AI. For example, it can provide schedules for crime prevention patrols and monitoring activities. The information provision unit can also provide information on community activities. For example, it can provide information on local crime prevention events and workshops. In this way, by including information on local crime prevention volunteer activities and community activities, a sense of community can be strengthened.

[0088] The information provision unit can strengthen support for crime victims by including information on local medical institutions and counseling services. For example, the information provision unit includes information on local medical institutions in the information provided by the generation AI. For example, it provides information on hospitals and clinics that crime victims can use. The information provision unit can also provide information on counseling services. For example, it provides contact information for counseling services that crime victims can use. In this way, by including information on local medical institutions and counseling services, support for crime victims can be strengthened.

[0089] The information provision unit can use the emotion estimation function to monitor the emotional reactions of companies and local governments that receive the information in real time and continuously improve the content of the information provided. The information provision unit, for example, uses the emotion estimation function to monitor the emotional reactions of companies and local governments that receive the information in real time. For example, it collects an emotion score for the provided information. The information provision unit can also continuously improve the content of the information provided based on the collected emotional reactions. For example, it can prioritize providing information that receives a large number of positive reactions. This makes it possible to provide more effective information by monitoring the emotional reactions of companies and local governments that receive the information in real time and continuously improving the content of the information provided.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The information gathering unit can collect usage data on local commercial and public facilities and assess the risk of crime. For example, it can analyze the flow of people at a particular commercial facility and identify times when crimes are likely to occur. It can also collect usage data on public facilities and assess the risk of crime. For example, it can analyze usage data on libraries and parks and identify areas where crimes are likely to occur. This allows for more detailed crime predictions by analyzing usage data on commercial and public facilities.

[0092] The information gathering unit can collect local weather data and analyze the relationship between weather and crime occurrence. For example, it can identify a tendency for crime to increase on rainy or snowy days. It can also evaluate the relationship between changes in temperature and humidity and crime occurrence. For example, it can identify a tendency for certain crimes to increase on hot or cold days. By analyzing weather data, it becomes possible to implement crime prevention measures according to weather conditions.

[0093] The information gathering unit can collect local traffic data and analyze the relationship between traffic conditions and crime occurrence. For example, it can identify a tendency for crime to increase during times when traffic congestion is likely to occur. It can also evaluate the relationship between public transportation usage and crime occurrence. For example, it can identify a tendency for certain crimes to increase during times when bus and train usage is most common. By analyzing traffic data, it becomes possible to implement crime prevention measures that are tailored to traffic conditions.

[0094] The information collection unit can use the emotion estimation function to collect emotional data from social media posts by local residents and assess the risk of crime. For example, if there is an increase in posts expressing anxiety or fear in a particular area, the crime risk in that area can be assessed. The emotion estimation function can also be used to monitor the emotional data of local residents in real time. For example, posts containing specific keywords can be collected preferentially. This allows for faster response by analyzing the emotional data.

[0095] The information gathering unit can collect data from local medical institutions and assess the risk of crime. For example, if an increase in emergency patients or an increase in specific injuries is a sign of crime, the data can be analyzed to assess the crime risk. It can also assess the risk of crime in a specific area based on data from medical institutions. For example, if an increase in emergency patients at a specific hospital is a sign of crime, the crime risk in that area can be assessed. This makes it possible to analyze data from medical institutions to make more detailed crime predictions.

[0096] The information analysis unit can use the emotion estimation function to analyze the emotion data of crime victims and propose crime prevention measures that take into account the psychological impact on the victim. For example, emotion data on crime victims is collected, and the generation AI analyzes that data to identify the psychological impact on the victim. For example, it evaluates the degree of fear and anxiety felt by the victim. The emotion estimation function can also be used to propose crime prevention measures that take into account the psychological impact on the victim. For example, measures to increase the victim's sense of security can be proposed. This makes it possible to propose crime prevention measures that take into account the psychological impact on the victim, thereby increasing the victim's sense of security.

[0097] The Information Analysis Department can collect regional economic data and analyze the relationship between economic conditions and crime occurrence. For example, it can evaluate the impact of rising unemployment rates on crime occurrence. It can also evaluate the relationship between regional economic growth rates and crime occurrence. For example, it can evaluate the risk of crime occurrence in areas where economic growth is stagnant. By analyzing economic data, it becomes possible to implement crime prevention measures that are appropriate to the economic situation.

[0098] The information analysis unit can use the emotion estimation function to measure the crime prevention awareness and sense of security of local residents and adjust crime prevention measures based on that. For example, data on crime prevention awareness of local residents can be collected, and the generation AI can analyze that data to evaluate the level of crime prevention awareness. For example, it can evaluate the risk of crime in areas with high crime prevention awareness. The emotion estimation function can also be used to measure the sense of security of local residents and adjust crime prevention measures based on that. For example, crime prevention measures can be strengthened in areas with a low sense of security. In this way, by measuring the crime prevention awareness and sense of security of local residents and adjusting crime prevention measures based on that, it is possible to increase the sense of security of local residents.

[0099] The information analysis unit can collect local educational data and analyze the relationship between educational level and crime occurrence. For example, it can evaluate the risk of crime occurring in areas with low educational levels. It can also collect school security information and evaluate the risk of crime occurring. For example, it can evaluate the risk of crime occurring around schools with poor security. By analyzing educational data, it becomes possible to implement crime prevention measures according to educational level.

[0100] The information analysis unit can use the emotion estimation function to analyze the emotional data of local residents and propose crime prevention measures to increase the sense of security in the area. For example, the emotion data of local residents is collected, and the generation AI analyzes that data to identify areas with a low sense of security. For example, if anxiety is high in a specific area, crime prevention measures in that area can be strengthened. The emotion estimation function can also be used to propose specific measures to increase the sense of security of local residents. For example, it can propose installing security cameras and increasing patrols to increase the sense of security. In this way, analyzing the emotional data of local residents and proposing crime prevention measures to increase the sense of security in the area can increase the sense of security of local residents.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The information gathering unit collects data from various sources on the internet. For example, it collects crime-related information from news sites, social media, government databases, etc. The information gathering unit can also collect data using scraping technology. For example, it can automatically obtain the latest crime information from news sites. Step 2: The Intelligence Analysis Department analyzes the collected data. For example, statistical analysis and machine learning algorithms are used to identify crime trends and patterns. The Intelligence Analysis Department can also build crime prediction models to assess future crime risks. For example, predicting an increase in crime rates in a particular area. Step 3: The answering unit proposes specific crime prevention measures to citizens based on the analysis results. For example, it provides advice such as "Avoid going out at night in this area." The answering unit can also generate appropriate answers to questions from users. For example, in response to the question "How safe is this area?", the generation AI generates an answer based on the analysis results. Step 4: The crime prevention map creation unit creates a crime prevention map based on the analysis results. For example, the crime prevention map includes information such as the location of crimes, the type of crime, and the time of day they occurred. Step 5: The information provider makes the crime prevention map, government information, and local news available to other companies. For example, companies and local governments can use this information to strengthen their crime prevention measures. This allows the crime prevention system according to the embodiment to provide a safe environment for citizens and earn revenue through information provision. For example, by collaborating with social media influencers, the crime prevention measures proposed by the generation AI can be widely disseminated, increasing the crime prevention effect. Furthermore, using the crime prevention map makes it easier for citizens to select safe routes. Furthermore, providing information allows companies and local governments to strengthen their crime prevention measures.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, a 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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]

[0170] 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. an information gathering unit that gathers data from various sources on the Internet; an information analysis unit that analyzes the data collected by the information collection unit; an answering section that proposes specific crime prevention measures to citizens based on the results of the analysis by the information analysis section; a crime prevention map creation unit that creates a crime prevention map based on the results of the analysis by the information analysis unit; an information providing unit that makes the crime prevention map created by the crime prevention map creating unit, government information, and local news available to other companies. A system characterized by:

2. The information collecting unit Add real-time traffic camera and drone footage to analyze visual information.

2. The system of claim 1.

3. The information collecting unit Adding audio data and performing audio analysis 2. The system of claim 1.

4. The answering section Include specific guidelines for action 2. The system of claim 1.

5. The crime prevention map creation unit It includes not only the locations of crimes, but also locations of attempted crimes and sightings of suspicious people.

2. The system of claim 1.

6. The information providing unit Raising awareness of local safety, including the results of local crime prevention activities and police efforts 2. The system of claim 1.

7. The information collecting unit Analyze user emotions from posts on the SNS and prioritize collecting posts that express anxiety or fear.

2. The system of claim 1.

8. The information analysis unit Analyzing the emotional data of crime victims and proposing crime prevention measures that take into account the psychological impact on victims 2. The system of claim 1.

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